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Faculteit Bio-ingenieurswetenschappenVakgroep Bos- en waterbeheer
Academiejaar 2010–2011
Retrospective time series analysis of temporal NDVI and
EVI profiles extracted from MODIS images in order to
support APB (Aerial Prescribed Burning) activities in the
Northern Territory, Australia.
Gijs Bracke
Promotor: Prof. dr. ir. R. De Wulf
Tutor: Dr. ir. F. Van Coillie
Dr. G. Allan
Scriptie voorgedragen tot het behalen van de graad van
Master in de Bio-ingenieurswetenschappen: Bos- en natuurbeheer
De auteur en de promotoren geven de toelating deze masterproef voor consultatie beschikbaar
te stellen en delen ervan te kopieren voor persoonlijk gebruik. Elk ander gebruik valt onder
de beperkingen van het auteursrecht, in het bijzonder met betrekking tot de verplichting
uitdrukkelijk de bron te vermelden bij het aanhalen van resultaten uit deze masterproef.
The author and the promoters give the authorization to consult and copy parts of this work for
personal use only. Any other use is limited by the laws of copyright, particularly concerning
the obligation to mention the source when reproducing parts of this work.
Gent, 10 juni 2011
De promotor De tutor De auteur
Prof. dr. ir. R. De Wulf Dr. ir. F. Van Coillie Gijs Bracke
Acknowledgements
This first page is dedicated to several people I am pleased to express gratitude to for their
assistance and support to complete this thesis.
First of all, I would like to thank prof. dr. ir. Robert De Wulf and dr. ir. Frieke Van Coillie
for their guidance and expertise. I am also pleased to thank dr. Grant Allan for his assistance
on the Australian related topics.
Furthermore, I would like to thank all people for enduring me in my diverse moods and my
thesis-state-of-being. Especially my parents and family, helping me through the toughest
periods with their infinite support and confidence. Also my dad in particular, for revising the
many pages I wrote and his well-appreciated ’how-to-write-a-thesis’ -leads.
Also many thanks go to my friends, sharing with me the wonderful time I spent in Ghent and
at the ’Boerekot’, making the student life even more exhilarating than I expected it to be six
year ago. Particularly to Ellemie, my companion in times of adversity and prosperity. Also to
Maarten, for his patience, care and recreation in times when needed. And last but not least,
to my fellow housemates, for enduring me even through the hardest episodes of this adventure,
for the recreation, the background violin music, the driving-me-crazy drum’nbass-sounds and
much more.
Gijs Bracke
Gent, 10 juni 2011
ii
Summary
The vegetation in the Northern Territory (NT), a vast territory in central north Australia,
undergoes an annual of biannual cycle of desiccation and burning in the dry season followed by
a period of rejuvenation in the wet season. The variability of the areal extent, the frequency
and the severity of the bushfires is mainly caused by the annual precipitation and its temporal
and spatial distribution. In the tropical north, strongly influenced by the monsoon, enough
biomass is accumulated to maintain annual, extensive bushfires, while in the arid south, far
less influenced by the precipitation the monsoon brings, biomass accumulates several years
before large, intensive fire events arise.
Numerous approaches are developed to control those wildfires, including Aerial Prescribed
Burning (APB) programs, which comprehend the creation of a burned sector in the early
dry season by dropping incendiaries from an airplane or helicopter in order to establish a fire
break. The prosperity of this program relies on good timing and planning based on adequate
information and knowledge. This is facilitated by remote sensing, which plays an important
role in the detection and characterization of change caused by, for instance, fire events, and
is due the correlation between the greenness of vegetation and the fuel load, a good utility to
monitor the vegetation and its curing status.
Various change detection techniques have been described to detect and monitor those changes
and to assess information about its causes and consequences. Amongst all techniques, the
approach identified to be most suitable to meet the objectives of this thesis is the temporal
trajectory analysis. The high quality multi-temporal data this technique requires are pro-
vided by the MODIS sensor, delivering 16-day composites of numerous spectral bands and
vegetation indices with a spatial resolution of 250m. All provided composites are subjected to
a profound atmospheric calibration and geometric and radiometric correction, making further
pre-processing unnecessary.
In combination with additional data concerning the vegetation cover and the fire history of
the study period (2001-2008), a dataset of temporal profiles is generated in 3 different study
areas in the NT. The different aspects of the variation in the temporal profiles is attained
iii
iv
through the consideration of four different vegetation indices: the NDVI, the EVI, the NDWI
and the mSAVI2, each having their specific advantages and disadvantages. To characterize
the temporal trajectories, a curve fitting technique is applied. In this technique, a sixth grade
polynomial curve is fitted to the trajectory, from which the equation is used to calculate ten
specific metrics. The metrics used in this thesis are the maximum and minimum reflectance
value, their corresponding timing in the year, the amplitude of the reflectance value and the
time span to go from the maximum to the minimum reflectance value, the maximum rate of
decay and its corresponding moment of occurrence and reflectance value, and the integrated
value. The variability in the NT is assessed by comparing the metrics of the temporal profiles
of different study areas, vegetation types and burning statuses on an inter- and intra-annual
basis.
The analysis of the spatial variation is performed by comparing three different study areas
across the north-south axis in the NT. A clear variability, strongly correlated with the climatic
regions, is observed in the reflectance values of the temporal trajectories. The trajectories in
the north, with a pronounced seasonality, differ significantly from the trajectories in the arid
south, showing almost no seasonal influences.
The comparison of the different vegetation classes revealed that, regardless of the correlation
to the seasons, each vegetation class is characterized by its own annual cycle and particular
features. Furthermore, two general classes, the forest and the woodland class, have been
subdivided into more detailed, species specific classes. In the comparison of both types of
classes some significant differences appeared. Nevertheless, as the surfaces of the detailed
classes are relatively small regarding the study area, the surplus value of the information
gained is too small to compete with the additional processing work that needs to be done.
For the study of the fire history of the different vegetation types, the burned (B) vegetation
is compared to its unburned (UB) and never burned (NB) equivalent. In this analysis, a
substantial variability both between the vegetation classes and within the same classes in
different study areas is observed. The B trajectories are characterized by a higher amplitude
in reflectance value and a higher maximum rate of decay than the UB or NB trajectories. The
high amplitude is a consequence of the greater likelihood of vegetation with a high cover to
burn, resulting in a significant lower minimum reflectance afterwards. The significant higher
maximum rate of decay is due the fast burning of biomass in a short period of time.
The temporal variability is verified by comparing trajectories from the reference year 2004 to
equivalent trajectories of the other years in the study period (2001 - 2008). Most years differ
significantly from one another, however, in years with comparable fire activity, a similar trend
in the metric values is observed. For example, years with a high biomass production tend to
v
be susceptible for a severe fire season, while years with a low vegetation productivity have a
tendency to undergo a rather mild fire season.
To study the trade-off between data size and detail in obtained information, MODIS-based re-
sults (spatial resolution of 250m) are compared to the results acquired with SPOT-Vegetation
data, with a lower spatial resolution of 1km. In spite of the analogous analysis, most detail is
attained for the results based on MODIS. However, the results achieved with SPOT-imagery
were generally of enough detail to observe similar trends. In a second comparison, the per-
formances of a classification, in which new test-trajectories are classified into B or UB classes
by means of 95% prediction intervals, are compared. Also in this test, MODIS is strongly
favored due its remarkable higher performance. Consequently, the additional amount of data
to be processed improves the level of detail in the prediction of future fire events significantly.
The results perceived in this thesis can assist the planning of APB-activities as they emphasize
several points of interest in the study of temporal trajectories. Depending on the objectives of
a future study, a well-considered selection of VI and metrics needs to be applied. Furthermore,
different vegetation types need to be analyzed separately and, subordinate to the spatial extent
of the study area, a further subdivision of the vegetation classes could be advised. In studies
over large areas covering several climatic regions, the pronounced north-south variation needs
to be considered. And when the means and computing capacity is available, the use of high
spatial resolution imagery is recommended, as more detailed results are achieved. Finally,
the results in this thesis suggest to apply a combination of several metrics and VI in the
prediction of the likelihood of future fire events.
Samenvatting
De vegetatie in de Northern Territory (NT), gelegen in centraal noord-Australie, ondergaat
een jaarlijkse of tweejaarlijkse cyclus van uitdrogen en branden in het droge seizoen, gevolgd
door een periode van heropleving en verjonging in het regenseizoen. De variabiliteit van de
oppervlakte, de frequentie en de hevigheid van de bosbranden is grotendeels bepaald door de
jaarlijkse neerslagshoeveelheid en de temporele distributie ervan. In het tropische noorden,
sterk beınvloed door de moesson, wordt er voldoende biomassa geaccumuleerd om jaarlijkse,
extensieve branden te onderhouden, terwijl in het aride zuiden, slechts weinig beınvloed door
de moesson, het enkele jaren kan duren eer er ernstige, intensieve branden ontstaan.
In de literatuur zijn vele methodes terug te vinden om dergelijke ongecontroleerde bosbranden
te beheersen. Een van die methodes is het Aerial Prescribed Burning (APB) programma. Hi-
erin wordt met behulp van brandbommen, gedropt uit een vliegtuig of helikopter, een sector
afgebrand om een brandbuffer te creeren. Het welslagen van dit programma hangt sterk af
van de goede voorbereiding en het tijdsstip waarop het wordt uitgevoerd. Teledetectie speelt
hierin een grote rol. Via teledetectie wordt het detecteren en karakteriseren van veranderin-
gen, zoals bosbranden, vergemakkelijkt en is het, door de uitgesproken correlatie tussen de
groenheid en de brandbaarheid van de vegetatie, een uitstekend middel om de vegetatiestatus
te controleren.
Uit de verschillende detectietechnieken voor plotse en graduele veranderingen beschreven in de
literatuur, is de temporele trajectorie analyse gekozen voor de verdere ontwikkeling van de ob-
jectieven in deze thesis. De multi-temporele data die deze analyse vereist, worden aangeleverd
door de MODIS-sensor. Per 16 dagen wordt een composietbeeld, met een spatiale resolutie
van 250m, van verschillende spectrale banden en vegetatie indices beschikbaar gesteld. Op
alle composietbeelden werd een grondige atmosferische kalibratie en een geometrische en ra-
diometrische correctie toegepast, wat verdere beeldvoorbewerkingen overbodig maakt.
In combinatie met metadata betreffende de floristische bedekking en de brandgeschiedenis
van de studieperiode (2001-2008), wordt voor drie verschillende studiegebieden in de NT een
dataset met temporele trajecten aangemaakt. Om verschillende facetten van de waarnemin-
vi
vii
gen te kunnen bestuderen, wordt dit gedaan voor 4 verschillende vegetatie indices: namelijk
de NDVI, de EVI, de NDWI en de mSAVI2, elk met hun eigen voor- en nadelen. De karakter-
isatie van de temporele profielen wordt gedaan aan de hand van de ’curve fitting technique’,
een techniek waarbij een polynoom van de zesde graad wordt gepast aan de trajecten. De
vergelijking van die polynoom wordt dan gebruikt om tien metrieken te berekenen. De me-
trieken toegepast in deze thesis zijn: de maximum en minimum reflectantiewaarde, samen
met hun corresponderende timing in het jaar, de amplitude in de reflectantiewaarden en de
tijd om van de maximum naar de minimum reflectantiewaarde te gaan, het maximale verval,
de timing ervan en de reflectantiewaarde op dat moment, en tenslotte de integraal van de
curve. De waarden van die metrieken worden gebruikt om profielen binnen een zelfde jaar en
tussen verschillende jaren te vergelijken.
Voor een eerste onderzoek naar de spatiale variabiliteit binnen de NT, worden trajecten van
3 studiegebieden, gelegen op de Noord-Zuid-as, vergeleken met elkaar. Hieruit blijkt dat er
een duidelijke gradient, gaande van een uitgesproken seizoengebonden profiel in het noorden,
tot een profiel met weinig seizoenale invloeden in het zuiden, aanwezig is.
De verscheidenheid tussen verschillende vegetatietypes wordt nagegaan door de vegetatie op te
delen in verschillende klassen en die dan onderling te vergelijken. De resultaten tonen aan dat,
ondanks de sterke relatie met de seizoenen, elk vegetatietype gekarakteriseerd wordt door een
eigen jaarlijkse cyclus met specifieke eigenschappen. Daarnaast worden twee vegetatieklassen,
de bos- en woodland -klasse, verder opgesplitst in gedetailleerde, species-specifieke subklasses.
Uit de onderlinge vergelijking van beide blijkt dat enkele subklasses significant verschillen van
de originele klasses. Ondanks deze waarnemingen wordt er, gezien de erg kleine oppervlaktes
van die specifieke subklasses, niet verder gewerkt met die gedetailleerde onderverdeling. De
toegevoegde waarde van de extra informatie die hieruit wordt verkregen, weegt niet op tegen
de extra verwerkingstijd die dit met zich mee brengt.
Om de invloed van bosbranden tussen en binnen de verschillende vegetatietypes te bestud-
eren worden gebrande (B) profielen vergeleken met hun ongebrande (UB) en nooit gebrande
(NB) equivalent. Uit deze analyse blijkt dat er een substantiele variabiliteit zowel tussen veg-
etatietypes als binnen eenzelfde vegetatietype in de verschillende studiegebieden, aanwezig
is. De gebrande profielen worden gekarakteriseerd door een grotere amplitude en een hogere
afstervingsgraad dan de ongebrandde en nooit gebrande profielen. De hoge amplitude bij
brandprofielen is een gevolg van een hoge maximum reflectantiewaarde, gevolgd door een sig-
nificant lagere minimumwaarde. Het verbranden van de biomassa op een korte tijdspanne
wordt dan weer gereflecteerd in de hoge afstervingsgraad.
Om de temporele variatie in kaart te brengen worden de profielen van 2004, het referentiejaar,
viii
vergeleken met de equivalente profielen in de andere studiejaren (2001-2008). De meeste jaren
verschillen van elkaar, hoewel er bij de terugkoppeling naar de ruwheid van het brandseizoen
per jaar een duidelijke trend waarneembaar is. Zo worden jaren met een hoge biomassapro-
ductie gelinkt aan jaren met een hevig brandseizoen, terwijl de jaren met een lage productie
een eerder mild brandseizoen ondergaan.
In de studie naar de afweging tussen de hoeveelheid data en het detail in de resultaten van
de analyses, worden de resultaten van twee sensoren met een verschillende spatiale resolu-
tie met elkaar vergeleken. De MODIS sensor heeft een spatiale resolutie van 250m en de
SPOT-Vegetation sensor een resolutie van ongeveer 1km. In een eerste methode worden de
resultaten van de analyses van MODIS vergeleken met die van SPOT, terwijl een tweede
methode de prestaties van een classificatie, waarbij nieuwe test-profielen ingedeeld worden in
de B of UB klasse op basis van een 95% voorspellingsinterval, vergelijkt. In het eerste geval
zijn de MODIS-gebaseerde resultaten gedetailleerder dan die van SPOT, maar toch is het
mogelijk om in beide gevallen dezelfde conclusies te trekken. In de tweede methode worden
de classificatieresultaten gebaseerd op MODIS als beste vooruitgeschoven. Bijgevolg is het
aangeraden om data met een hogere spatiale resolutie te gebruiken, gezien de gewonnen infor-
matie significant bijdraagt tot gedetailleerdere resultaten bij een voorspelling van toekomstige
bosbranden.
De conclusies getrokken in deze masterthesis leveren een bijdrage aan de studie van temporele
profielen, nodig voor de planning van APB-activiteiten. Afhankelijk van de objectieven van
toekomstig onderzoek, moet een doordachte selectie van metrieken en vegetatie indices wor-
den toegepast. Zeker in het geval wanneer er voorspellingen van toekomstige bosbranden
gemaakt worden, wordt een combinatie van de voorgenoemde aangeraden. Verder moet bij
het analyseren van verschillende vegetatietypes een duidelijke onderverdeling gemaakt worden,
gezien de soms sterke verschillen tussen de types. Daarnaast wordt geadviseerd om, afhanke-
lijk van de bestudeerde oppervlakte, een gedetailleerdere onderverdeling in de vegetatietypes
te overwegen. In studies van grote regio’s waarin meerdere klimaatzones voorkomen, moet
rekening worden gehouden met een sterke spatiale variabiliteit. Tenslotte wordt, in de mate
dat de beschikbare middelen dit toelaten, beeldmateriaal met een hoge temporele en spatiale
resolutie aangeraden om tot betere en gedetailleerdere resultaten te leiden.
Contents
1 Introduction 1
2 Literature Study 4
2.1 Land change detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.1.1 Change detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 Detection techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.1.3 Image acquisition for change detection . . . . . . . . . . . . . . . . . . 7
2.1.4 Data pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 Fire detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 The curve fitting technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 The utility of metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Remote sensing and its contribution to change detection . . . . . . . . . . . . 13
2.5.1 MODIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.5.2 SPOT-Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3 Materials and methods 17
3.1 Study area: Northern Territory . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.2 Climate and soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.1.3 Vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.1.4 Fire in the NT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.5 Sampled areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Remote sensing data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2.2 Vegetation Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 The used metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4 Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.5 Methodology for temporal trajectory analysis . . . . . . . . . . . . . . . . . . 31
ix
Contents x
3.5.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5.2 Temporal profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.5.3 Characterization of the temporal trajectories . . . . . . . . . . . . . . 33
3.5.4 The comparison of metrics . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5.5 Accuracy assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4 Results and discussion 37
4.1 Analysis of the temporal profiles: introduction . . . . . . . . . . . . . . . . . 37
4.2 Variability along the north-south axis . . . . . . . . . . . . . . . . . . . . . . 38
4.2.1 Preliminary visual interpretation . . . . . . . . . . . . . . . . . . . . . 38
4.2.2 Analysis of variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.2.3 Discussion of the metrics . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.3 Variability of vegetation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3.1 Different vegetation types . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.3.2 Significance of detailed subdivision of vegetation classes . . . . . . . . 47
4.4 Variability caused by fire events . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.2 Analysis of the variance . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.5 Comparison with the reference year (2004) . . . . . . . . . . . . . . . . . . . . 57
4.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.5.2 Analysis of variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.5.3 Discussion of the metrics . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
4.6 The comparison of SPOT- versus MODIS-imagery . . . . . . . . . . . . . . . 60
4.6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.6.2 Comparison of the ability to cope with variance . . . . . . . . . . . . . 60
4.6.3 The classification method . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5 Conclusion 65
A Used floristic classes 68
B Different vegetation types 70
B.1 Figures for EVI and mSAVI2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
B.2 Tables with significant differences in SA2 for EVI and mSAVI2 . . . . . . . . 71
B.3 Table with mean values and standard deviation for SA2 . . . . . . . . . . . . 72
Contents xi
B.4 Tables for SA1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
B.5 Tables for SA3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
C Significance of further subdivision in vegetation classes 79
C.1 Figures for EVI and mSAVI2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
C.2 Tables with significant differences for EVI and mSAVI2 . . . . . . . . . . . . 80
D Variability caused by fire events 81
D.1 Tables for FOREST class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
D.2 Tables for WOODLAND class . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
D.3 Tables for SHRUB class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
D.4 Tables for TUSSOCK GRASSLAND class . . . . . . . . . . . . . . . . . . . . 87
D.5 Tables for HUMMOCK GRASSLAND class . . . . . . . . . . . . . . . . . . . 89
E Comparison with the reference year (2004) 91
E.1 Results multiple comparison tests . . . . . . . . . . . . . . . . . . . . . . . . . 91
E.2 Tables with mean values and standard deviation . . . . . . . . . . . . . . . . 91
F MODIS versus SPOT 96
F.1 The results of the analysis based on SPOT-imagery . . . . . . . . . . . . . . . 96
F.2 Results of the classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Bibliography 103
List of abbreviations
AVHRR Advanced Very High Resolution Radiometer
APB Aerial Prescribed Burning
ANOVA Analysis of variance
BRDF Bidirection Reflectance Distribution Function
bFo BR FOREST
bGr BR GRASSLAND
bSh BR SHRUB
BR Broad vegetation class
CV-MVC Constrained View angle Maximum Value Composite
EDS Early Dry Season
EOS Earth Observing System
ESE Earth Science Enterprise
EVI Enhanced Vegetation Index
Fo FOREST
FMC Fuel Moisture Content
GIS Geagraphical Information System
Hu HUMMOCK GRASSLAND
IR Infrared
LP DAAC Land Processes Distributed Active Archive Center
LDS Late Dry Season
MVC Maximum Value Composite
MIR Mid-infrared
MODIS Moderate-resolution Imaging Spectro-radiometer
NASA National Aeronautics and Sapce Administration
NIR Near-infrared
NDVI Normalized Difference Vegetation Index
NDWI Normalized Difference Water Index
xii
Contents xiii
NT Northern Territory
RVI Ratio Vegetation Index
mSAVI2 Second modified Soil-Adjusted Vegetation Index
Sh SHRUB
sFA SM FOREST ACACIA
sFE SM FOREST EUCALYPT
sFO SM FOREST OTHER
sWA SM WOODLAND ACACIA
sWE SM WOODLAND EUCALYPT
sWO SM WOODLAND OTHER
SM Small vegetation class
SAVI Soil-Adjusted Vegetation Index
SA Study Area
SPOT Systeme Pour l’Observation de la Terre
TreeE Tree eucalypt
Tu TUSSOCK GRASSLAND
UV Ultra-violet
VI Vegetation Index
Wo WOODLAND
Chapter 1
Introduction
The Northern Territory (NT) is an enormous territory containing much of the centre and
the central northern regions of Australia. The sparse population is concentrated in Darwin,
Alice Springs and Katherine, the main cities in the NT, also some aboriginal tribes settled
in different reserves spread over the country. The majority of the land is used for pastoral
activities, and furthermore Aboriginal Land trusts or conservation and recreation reserves
can also be found.
In the NT, three general climate zones can be distinguished: a humid, a semi-arid and an arid
zone (Wilson et al., 1990). These climate zones have a great influence on the spatial distribu-
tion on the different vegetation types appearing in the NT. In general, according to Woinarski
et al. (1996), the environment is dominated by hummock grassland (38%), Eucalyptus forests
and woodlands with a tussock grass understory (17%), Eucalyptus woodland with hummock
grass understory (14%), Acacia woodlands and shrub lands (13%), Eucalyptus low woodland
with tussock grass understory (7%) and tussock grasslands (6%).
Bushfires are an essential part of the Australian ecosystems and can have both positive or
negative effects on the environment (Edwards et al., 2008; Turner et al., 2008). In the humid
and the semi-arid zones, both strongly influenced by a monsoonal regime, the grasslands
and shrubs undergo a yearly or biannual cycle of desiccation and burning in the dry season
followed by a period of rejuvenation during the wet season. The trees commonly survive these
low intensity fires. In the arid zone, fires occur less frequent than in the other zones, this
due the less significant fuel amounts produced during a season, as rainfall rarely happens.
Bushfires occurring in the arid zone are often more severely and happening on a huge scale,
and this sometimes implies a shift in vegetation cover (Wilson et al., 1990).
Their areal extent, frequency and severity are very variable. A main cause of this variability is
the annual rainfall and its temporal distribution. In higher rainfall areas, the grassy vegetation
1
Chapter 1. Introduction 2
produces sufficient fuel to maintain fires on a annual basis, in areas with a much dryer climate,
only once a few years (Allan et al., 2003).
Allan et al. (2003) arbitrarily defines two types of fires, dependent on the moment of occur-
rence; so there are early dry season (EDS) fires and late dry season (LDS) fires. Generally,
EDS fires are believed to be management fires, which are supposed to have positive con-
sequences, whereas LDS fires usually are wildfires, with a negative, undesired impact on
its surroundings. The management fires aim for fuel reduction, biodiversity management,
protection of assets and pasture maintenance, as the burning makes the ground vegetation
rejuvenate (Allan et al., 2003). In order to control LDS fires, the EDS fires have to be planned
carefully. If the management fires are started too early, the impact will be unsatisfactory and
the objectives won’t be accomplished, if the they are started too late, then the fires could get
out of control and cause a too large burned area. That is why timing of the EDS fires is so
critical (Allan et al., 2003).
An approach to control wildfires is to use permanent firebreaks, like streams, roads, cliffs,
and combine them with imposed breaks from aerial prescribed burning (APB) programs
(Price et al., 2007). In an APB program a burned sector is created in the EDS by dropping
incendiaries from an airplane or helicopter in order to impose finer-scale fire patchiness and
reduce the area of destructive LDS fires.
Effective and efficient application of APB programs requires good timing and planning, based
on adequate resource information and knowledge of fire history (Edwards and Allan, 2009).
This can be achieved by implying records of natural and prescribed burnings into a geographic
information systems (GIS). In GIS, remote sensing plays an important role in identifying and
characterizing bushfires and is able to give information on the curing state of fuel loads (Allan
et al., 2003). As the relation between the timing of the APB program and the greenness
or the curing state of the vegetation is crucial to the prosperity of the program, remote
sensing plays a major part in the planning of APB activities. Many studies proved that the
fuel moisture content (FMC) and the Normalized Difference Vegetation Index (NDVI) are
strongly correlated (Allan et al., 2003; Chuvieco et al., 2004; Verbesselt et al., 2006b). Thus,
NDVI-profile analysis will provide information needed for planning APB on a cost efficient
way.
Fire events and many other alter the vegetation cover abruptly or more gradually. In order to
detect these changes, the area of interest is observed at different times. So, change detection
is about the capability to quantify temporal effects with a temporal trajectory analysis, using
multi-temporal data, commonly acquired by remote sensing. The Moderate-resolution Imag-
ing Spectro-radiometer (MODIS) on the Terra and Aqua satellites, provides 16-daily NDVI
Chapter 1. Introduction 3
and Enhanced Vegetation Index (EVI) composites with a spatial resolution of 250m. Also a
blue, red, near-infrared (NIR), and mid-infrared (MIR) band are available each 16 days, and
these can be used to provide extra information and can be combined to other indices, like the
Ratio Vegetation Index (RVI), the Soil Adjusted Vegetation Index (SAVI), the Normalized
Difference Water Index (NDWI) (Verbesselt et al., 2006b).
Objectives of the thesis
The main purpose of this thesis is using these products and additional information about fire
history and vegetation cover, to study the possibilities to attain essential information from
temporal profiles.
A first objective is to employ remote sensing in order to characterize land cover changes on
a regional scale. This requires the development and the characterization of the temporal
profiles, which will be achieved by the employment of different metrics. Furthermore, four
different indices, respectively NDVI, EVI, NDWI, mSAVI2, each having their specific advan-
tages and disadvantages, will be used to describe temporal profiles and their possibilities will
be compared to each other.
Because of the different climate along the north-south axis, a north-south variation should
be possible to observe. Because of that, temporal profiles are developed for three different
study areas, chosen on the north-south axis. In this second objective, those differences will
be examined.
The third objective is the investigation of significant differences between vegetation types.
Different types will be compared to each other and the required level of detail will be deter-
mined in order to obtain the best results possible.
In a forth objective the fire history and its influence on the vegetation will be scrutinized. The
burned profiles of various vegetation types will be compared to their unburned equivalents. To
study the temporal variability of the fire history, the temporal trajectories of a reference year,
in this case 2004, will be paralleled to those of other years and the anomalies and deviations
will be studied and explained.
Finally, the fifth objective is to study whether the spatial resolution is essential to obtain
all information acquired in the former objectives. The results from MODIS images will be
compared to those from Systeme Pour l’Observation de la Terre (SPOT) and scrutiny will
determine if there is a significant difference. This objective will be accomplished in cooperation
with Ellemie Comeyne, who provided and analyzed the SPOT-based data.
Chapter 2
Literature Study
In this chapter, a brief disccusion about the relevant literature for research is given. First,
change detection and its application on fire monitoring is summarized. Next, the impor-
tance of metrics is discussed. And finally, the contribution of remotely sensed data is briefly
described.
2.1 Land change detection
All around the world, ecosystems are in a state of permanent flux at a broad range of spatial
and temporal scales. They can be induced naturally, for example, by flooding and disease
epidemics, as well as anthropogenic, exemplified by tree removal for agricultural expansion,
or by a combination of both. Change can be interpreted in many ways, for example as ’an
alteration in the surface components of the vegetation cover’ (Milne (1988), cited in Coppin
et al. (2004), p.1566) or as ’a spectral/spatial movement of a vegetation entity over time’
(Lund (1983), cited in Coppin et al. (2004), p.1566). The rate of change can be dramatic
and/or abrupt, for example fire, which is categorized as land-cover conversion; or can be
more subtle and/or gradual, such as biomass accumulation, generally denoted as land-cover
modification. The first deals with changes of land-cover where whole classes are replaced by
others, while the latter defines changes that affect the character of the land-cover without
changing its overall classification. Land-cover modifications are more common than land-cover
conversions (Coppin et al., 2004).
Lately, ecosystem change monitoring has become a popular subject, which results in the
continuous need of accurate and updated resource data. Where large-area processes are
concerned, accurate monitoring of land surface attributes over at least a few years is required
as a basis to understand the changes thoroughly. Monitoring at such regional scales imposes
numerous other methodological challenges. Due to the lack of quantitative, spatially explicit
4
Chapter 2. Literature Study 5
and statistically representative data on ecosystem change, simplistic representations are made
(Coppin et al., 2004).
2.1.1 Change detection
In the literature, change detection is described as a process to identify differences in a state
of an object or a phenomenon by observing it at different times. It is the first step toward
identifying the driver of the change and understanding the change mechanism (Verbesselt
et al., 2010). Essentially, change detection is about the capability to quantify temporal effects
using multi-temporal datasets, commonly acquired by satellite-based multi-spectral sensors,
as the changes in land-cover result in changes in radiance values (Coppin et al., 2004; Singh,
1989). It has to be taken into account that changes in radiance values, next to land-cover
change, also can be caused by other factors, such as differences in atmospheric conditions,
differences in sun angle and differences in soil moisture (Lu et al., 2004; Verbesselt et al.,
2010). Here, the repetitive coverage at short time intervals and the consistent image quality
from the remotely sensed data, is of great importance (Lu et al., 2004; Singh, 1989). More
sophisticated than the detection of the change event itself, is the proper comprehension of the
nature of the change and the underlying principles. According to Coppin et al. (2004), the key
challenges facing ecosystem change monitoring are induced by the requirement to (1) detect
land-cover modifications and conversions; (2) monitor rapid/abrupt changes next to trends;
(3) separate inter-annual variability from secular trends; (4) correct for the scale dependence
of statistical estimates of change derived from data at different spatial resolutions; and (5)
match the temporal sampling rates of observations of processes to their intrinsic scales.
Coppin et al. (2004), Hobbs (1990) and Verbesselt et al. (2010) state that, next to the capa-
bility to deal adequately with the initial static situation, the aptitude of a system to detect
and monitor change in ecosystems depends on its capacity to account for variability at one
particular scale, for example, seasonal, while interpreting changes at another, e.g. directional.
Furthermore, when performing a change detection method, not all detected changes will be
equally important and some changes of interest will only be acquired very little or not at all.
Digital methods, roughly characterized by data transformation procedures and analysis tech-
niques to delineate areas of change, offer consistent and repeatable procedures (Coppin et al.,
2004; Lu et al., 2004). And furthermore, they also facilitate including features from the non-
optical parts of the electromagnetic spectrum more efficiently. According to the scientific
literature, digital change detection is a difficult task to perform. Interpreting analyzed aerial
photography will almost always achieve more accurate and precise results. However, just
because of the visual interpretation, this way of performing change detection is difficult to
replicate and requires furthermore a substantial data acquisition cost (Coppin et al., 2004).
Chapter 2. Literature Study 6
In summary, the change detection process involves three major steps (Lu et al., 2004): (1)
image preprocessing, meaning to perform a geometrical rectification and image registration,
radiometric and atmospheric correction, and, if the study area is in mountainous regions,
a topographic correction; (2) selection of the best suitable detection techniques; and (3) an
overall accuracy assessment.
Timely and accurate change detection offers a basis for understanding relationships and in-
teractions between human and natural phenomena which can result in a better management
and usage of resources (Lu et al., 2004).
2.1.2 Detection techniques
Many different applications based on change detection are described, they vary from land
use change analysis, assessment of deforestation, urban change, crop monitoring, diverse
environmental changes, to disaster monitoring, such as bush- or forest fires (Lu et al., 2004;
Singh, 1989). Identifying a suitable change detection technique becomes of great importance
in producing good quality results (Lu et al., 2004).
Many change detection techniques have been developed. In the past, most of the method-
ologies developed were for bi-temporal change detection, but recently change detection based
on temporal trajectory analysis became more popular. As the latter technique is used in this
study, it will be discussed in more detail. The first, in this study of less importance, have
been summarized and reviewed many times. More information can be found in various review
articles (Coppin et al., 2004; Coppin and Bauer, 1996; Lu et al., 2004; Singh, 1989).
The temporal trajectory technique
When performing a temporal trajectory analysis, time profiles of a certain relevant indicator,
made for different successive years or growing seasons, are being compared. Due to high
temporal frequency in data acquisition, detection of ecosystem modifications and the charac-
terization of the phenological variations in the ecosystem status are facilitated. When a time
profile of a certain indicator of interest for a particular pixel departs from the standard profile,
a change event or process is detected. This standard profile can be the average, optimal or
normal profile, depending on the chosen objectives of the study (Coppin et al., 2004).
Various wide field-of-view, high temporal resolution sensors and different indicators have
been used for temporal trajectory analysis. This technique has proven sensitive for subtle
and abrupt changes in different ecosystems, often more than classical bi-temporal techniques.
The latter technique often suffers from grave under-sampling at the time-scale, especially
for abrupt and relative short ecological events, such as fire, flooding and vegetation stress.
Chapter 2. Literature Study 7
However, validation with independent datasets remains a major challenge for ecosystem mon-
itoring due to the coarse to moderate spatial resolution of the wide field-of-view sensors and
the large area coverage (Coppin et al., 2004).
2.1.3 Image acquisition for change detection
When performing change detection for ecosystem monitoring, the data achieved have to be
comparable, be it for a bi-temporal change detection or for a temporal trajectory analysis.
The timescale of the first is a two-point timescale, while the latter operates on a continuous
timescale. In order to achieve good results with a temporal trajectory analysis, optimal data
needs to be selected. Here the selection of optimal imagery acquisition dates is very important,
as is the choice of the sensor(s) and change detection techniques. To avoid the problem of the
selection of optimal imagery acquisition dates, researchers approach the ecosystem monitoring
by comparing seasonal development curves or profiles, also called time series. These time
series of remotely sensed indicators of certain land surface attributes, depending on the topic
of interest, e.g. NDVI for vegetation monitoring, are constructed from images, produced on
daily or short intervallic basis. Sensors such as Advanced Very High Resolution Radiometer
(AVHRR), SPOT and MODIS provide material suitable for that specific purpose.
An advantage of profile-based techniques is that, because the data collection happens through-
out the whole growing season, the influence of phenology on change detection performance
is resolved. This results in being able to separate the seasonal variation from other changes
(Coppin et al., 2004). A serious disadvantage however, is the fact that presently, the only
sensors providing high temporal frequency observations, have a coarse to moderate spatial
resolution, which limits the ability to detect and monitor changes at certain scales.
2.1.4 Data pre-processing
As noise will inherently influence the outcome of the change detection, the signal-to-noise ratio
must be maximized. Noise is caused by differences in atmospheric absorption, scattering due
to variations in water vapor, temporal variations in the solar zenith and/or azimuth angles
and sensor failure. So, when working with multi-temporal data, before being able to compare
the images, they must be atmospheric and geographic corrected and radiometric calibrated.
Also errors and noise have to be removed and irrelevant and cloud contaminated areas to
be masked, as they hamper easy comparison between images (Coppin et al., 2004; Lu et al.,
2004; Singh, 1989).
The accurate geometric registration of successive images uses geometric rectification algo-
rithms to register the images to each other or to a standard map projection (Singh, 1989). A
study on this subject showed that for a spatial resolution of 250m and 500m errors of more
Chapter 2. Literature Study 8
than 50% of the actual NDVI differences were caused by a misregistration of 1 pixel (Coppin
and Bauer, 1994). Roy (2000) showed that if incorrect registered images were composited,
the high contrast boundaries might be shifted, which results in a incorrect change observa-
tions. Further degradation of the areal assessment of change events is caused by so called
residual misregistration, at below-pixel level, which is inherent to any digital change detection
technique (Coppin and Bauer, 1996).
The radiometric calibration is important as only then observed spatial or temporal changes
can be considered as real differences, and not as errors, induced by differences in sensor
calibration, atmosphere and/ or sun-angle (Coppin and Bauer, 1996). Clouds and other
atmospheric effects can be removed simply by a temporal compositing process, where the
information of a series of successive images is put together, in order to only include useful
data.
Because the present-day high-temporal-frequency sensors have a wide field of view, a correc-
tion for directionality effects becomes necessary. This can be achieved with a bidirectional
reflectance distribution function (BRDF). The angle-corrected vegetation index resulted in a
more consistent displaying of the surface properties than monthly maximum value compos-
ites would. Schaaf et al. (2002) applied this to generate nadir BRDF-adjusted reflectances of
MODIS data, which resulted in 16days period composites, free from view angle effects and
cloud and aerosol contamination. A more detailed description about these considerations
before implementing change detection can be found in (Coppin and Bauer, 1996).
2.2 Fire detection
Applying change detection, many researchers have been studying methods for mapping and
monitoring fire activity on a continental scale. Graig et al. (2002) stated that remotely sensed
data can be employed in three stages of the fire management: before, during and after burning,
all leading toward information in their own specific field of application. Information obtained
from pre-fire observations is important in the prevention of fire and the design of controlled
burns. During the fire, remote sensing is used to detect and monitor fire events, and after the
fire the fire scar can be mapped and the burnt area assessed.
Robinson (1991) suggested that fire forms four for space observable appearances: (1) direct
radiation from active fires, (2) the smoke developed by the fire, (3) the post fire scar, and (4)
the altered vegetation structure. The direct radiation of fire can be captured with mid-infrared
(MIR) detecting sensors, because, in the MIR, fires radiate intensely against a low-energetic
background. Therefore, even when only occupying a fraction of the pixel, fire can easily be
detected. So, in theory, fire size and temperature can be calculated from multi-channel IR
Chapter 2. Literature Study 9
measurements. The fire scar is relative easy to distinguish; the area generally appears darker
than the surrounding vegetation as the fire destroys most of the surface vegetation leaving
a cover of surface charcoal. It gets harder when there is a significant tree canopy, whereby
sub-canopy fires may go undetected. As fire also alters the vegetation structure, observing
vegetation with the various vegetation indices available makes it possible to detect burned
areas (Graig et al., 2002).
All former methods handle about fire monitoring during or after the fire events, but the latter
method, vegetation monitoring in order to assess the burnt area characteristics, could also
be used to predict or control fire, when applied for fire risk monitoring instead of monitoring
fire itself. This is due to the fact that fire activity mainly depends on, besides fire source
location, the evolution of the vegetation biomass (fuel) and water content during the fire
season (Verbesselt et al., 2006b). The moisture content of fuel is one of the most important
variables in fire ignition and behavior modeling and is therefore generally included in most
fire risk models. The relationship of the fuel moisture content (FMC), the quantity of water
per dry mass, with several vegetation indices was studied with temporal trajectory analysis
by Verbesselt et al. (2007, 2006a); Yebra et al. (2008). Verbesselt et al. (2007) declared
that the NDVI, related to the chlorophyll content in the leaves, showed a good correlation
with the FMC only for some vegetation types, such as grasslands and herbaceous species. The
NDWI, more related to the water content in the biomass, showed good correlations in general,
less depending from type of vegetation studied, and thus proved to be more appropriate for
monitoring live FMC (Verbesselt et al., 2006a,b). Furthermore, Verbesselt et al. (2006a)
showed that NDWI and NDVI can be used to predict the start of the fire season by studying
the time-lag between their temporal profile and that of fire activity data.
The estimation of FMC from satellite data has been attempted with as well high as low spatial
resolution sensors. The former achieved better result due to its higher spatial accuracy, but,
since fire managers require frequent updates of the FMC, the latter, providing results with a
higher temporal resolution, was more likely to be used (Verbesselt et al., 2007). Therefore,
high temporal resolution remote sensed data are essential to monitor the inter- and intra-
annual fire risk evolution.
2.3 The curve fitting technique
The analysis of time series of various indices provides a significant insight into the response of
vegetation to short- and long-term environmental forcing effects emanated from both natural
and anthropogenic activities. The nature of fluctuations in the intra-annual and interannual
behavior of time series provides important information for identifying and discriminating
Chapter 2. Literature Study 10
among vegetation communities and the changes occurring in those communities. To extract
information about the intra-annual details and the interannual variability of the phenology
of vegetation from a time series, several methods are proposed in the literature, e.g. the
wavelet analysis or the Savitzky-Golay filter (Jonsson and Eklundh, 2004, 2002; Bradley
et al., 2007; Hermance et al., 2007; Pettorelli et al., 2005; Maignan et al., 2008; Pus and
Ducheyne, 2006; Martınez and Gilabert, 2009). One of them, the curve fitting method, is
often used to extract that information by fitting a polynomial or Fourier function to NDVI or
other time series. For instance Bradley et al. (2007) and Hermance et al. (2007) use a fourth
and a sixth order polynomial to fit to the time series in order to estimate the annual average
curve. The single curve fitting procedure is flexible enough to accommodate various ranges
of phenological amplitudes and the interannual variability, while it remains stable through
periods of anomalously low data values and data gaps (Hermance et al., 2007). Therefore, this
method facilitates the identification of different metrics and smoothly describes their course
on seasonal and interannual bases (Pettorelli et al., 2005; Hermance et al., 2007).
The main advantages are the easy appliance, the possibility of predicting the trajectory
and also the time series can by summarized by several metrics adopted to the trajectory.
However, several disadvantages are accompanied when using the curve fitting techniques.
One disadvantage is that high-order polynomials require too much computation time. A
second drawback is that medium-order polynomials can be too inflexible to reproduce an
entire season or generate spurious oscillations, especially at both tail ends and when data are
not well conditioned or significant data gaps occur, resulting in a loss of valuable information
(Pettorelli et al., 2005; Hermance et al., 2007). Therefore, one needs to be wary when using
this technique to represent actual data and, in order to obtain an optimal curve fit, one
must account for missing data and discount negative and anomalously low NDVI values.
According to Bradley et al. (2007), this can be acquired by spatial (lower spatial resolutions)
or temporal (compositing) averaging during the preprocessing of the data, which is briefly
described in the previous chapters. Another important element to pay attention to is that
certain plant communities tend to have a strong persistent periodic seasonal component, while
other vegetation types have a more variable phenology.
During the curve fitting procedure, some assumptions are made. First, ecosystems have an
inherent annual cyclicity, which is approximated by an average annual curve. Hence, the
interannual variability can be seen as a second order effect, overprinted on the average annual
curve. As a result, the average annual curve can be a good first order approximation for
anomalously low or missing data and, furthermore, provides a good baseline for determining
interannual fluctuations. Second, in order to avoid artifacts resulting from atmospheric effects
or snow cover, the upper envelope of the data values should be up weighted to obtain the
Chapter 2. Literature Study 11
best approximation of phenological pattern (Bradley et al., 2007).
In conclusion, it is very important to only enclose pixel values that have a clearly recognizable
seasonal curve in the curve fitting procedure. This allows any deviations from the baseline,
annual average curve, to be detected. These deviations indicate a response of vegetation
triggered by short-term environmental forcing effects induced by natural and anthropogenic
activities, such as fire or flooding. Furthermore, metrics can be calculated from those curves
in order to compare them to others in a search for abnormalities (Pettorelli et al., 2005;
Jonsson and Eklundh, 2004).
2.4 The utility of metrics
As remotely sensed satellite data is becoming an increasingly attractive source for deriving
land cover datasets due to its consistency, reproducibility and high temporal coverage, the
need for new methods and techniques to separate changes driven by climatic variability or
land-use change is great (DeFries et al., 1995; Lupo et al., 2007). Many have been developed
and improved for a general or more specific application, depending on the objectives of the
research. One methodology to obtain such information is the use of metrics. Metrics can
be derived from temporal profiles of single spectral bands or vegetation indices, such as
NDVI (DeFries et al., 1995). In scientific literature, a wide assortment of metrics have been
proposed and investigated (DeFries et al., 1995; Lupo et al., 2007; Reed et al., 1994; Verbesselt
et al., 2009; Borak et al., 2000). For example, in a method to categorize land-cover change
patterns, Lupo et al. (2007) characterized the EVI profiles by three temporal metrics and
two greenness metrics: the maximum EVI, the range, the growing season length, the gross
primary production of the year and the start of the growing season, as shown in Fig. 2.1.
In order to detect change, the relative value of a metric for one year is compared to that of
another year. It is therefore less important to have a definition of the perfect phenological
variable, validated in the field for all possible vegetation covers, than having metrics that
can be compared consistently from one year to another. Accordingly, Borak et al. (2000)
subtracted two fine spatial resolution maximum NDVI composites to estimate land cover
changes in the area. In order to define change and find pixels where land cover change
occurred, a threshold was set (Fig. 2.2(a)). In a second part of the study, Borak et al. (2000)
computed coarse spatial resolution temporal change metrics, for instance the annual mean,
the annual minimum, the annual maximum and the annual range (difference of maximum
and minimum). In the last step, the inter-annual land cover change metrics were calculated
as the difference in the values of two given annual metrics calculated for two different years
of interest, as showed in Fig. 2.2(b).
Chapter 2. Literature Study 12
Figure 2.1: Theoretical phenological indicators describing a vegetation index profile (adapted from
Lupo et al. (2007)): (1) start growing season; (2) maximum EVI range; (3) growing
season lenght; (4) integrated area below curve; (5) date of maximm EVI value
(a) Computation of fine spatial resolution
metrics
(b) Computation of coarse spatial resolution metrics
Figure 2.2: Borak et al. (2000)
Chapter 2. Literature Study 13
In conclusion, DeFries et al. (1995) found that global classification of broadly defined cover
types are more accurate using metrics derived from temporal profiles rather than using
monthly composited NDVI values alone. Furthermore, his study showed that using a com-
bination of metrics increased the accuracy of the classification on a significant level. Hence,
metrics are well suited to characterize temporal profiles and make easy inter- and intra-annual
comparison between time profiles possible. Borak et al. (2000) found that fine-resolution
and coarse-resolution change metrics measure different processes and that different coarse-
resolution land cover indicators can respond to different types of land cover change. So the
obtained results from a change detection technique using metrics will depend on the spatial
resolution of the used imagery.
2.5 Remote sensing and its contribution to change detection
Since the launch of the first earth observation satellite, remote sensing from space plays a ma-
jor role in ecosystem monitoring. It brought a new dimension to understanding processes and
their impacts on earth, as the remote sensing systems provide data and images, facilitating
change detection. Remote sensing from space is a rapidly changing subject, numerous coun-
tries and corporations are developing and launching new systems on a regular basis. In order
to improve the sensors, they plan various studies about understanding the characteristics and
their suitability for given applications. Currently, a wide range of satellite systems and their
diverse purposes are circling Earth, each with specific spatial and temporal resolutions and
sensors sensitive to particular spectral bands. The satellite systems generally operate within
the optical spectrum, which extends from approximately 0.3 to 14µm, including UV, visible,
near-infrared (NIR), mid-infrared (MIR) and thermal infrared wavelengths (Lillesand, 2004).
The increasing level of spatial and spectral detail and the high temporal coverage of the more
recent satellites augments the development and improvement of change detection techniques,
thereby enabling more accurate estimates of change and improved results. The products
delivered by the MODIS and the SPOT-Vegetation sensors are very suitable for temporal
trajectory analysis and change detection in general, as they were specifically designed for veg-
etation monitoring and include better navigation, atmosperic correction, reduced geometric
distortions and improved radiometric sensitvity (Fensholt et al., 2009). Hereunder they will
be discussed briefly.
2.5.1 MODIS
History
The first global monitoring systems acquiring moderate resolution data launched were the
U.S. Landsat and French SPOT satellites. In the late 1990’s, National Aeronautics and Space
Chapter 2. Literature Study 14
Administration (NASA) started an international earth science program called Earth Science
Enterprise (ESE), in order to provide the observations, understanding and modeling capabil-
ities to assess the impacts of natural or human-induced activities on the environment. The
program has three main components: (1) a coordinated series of Earth-observing satellites,
(2) an advanced data system designed to support the production, archival and dissemination
of satellite derived data products, and (3) teams of scientists developing algorithms to create
these data products (Justice et al., 2002a). The development of the Earth Observing System
(EOS), the first component, included the launching of the Terra and Aqua platform, in 1999
and 2002 respectively (Lillesand, 2004). They both have multiple remote sensing instruments
on board, including MODIS, which is relevant for this thesis and will be discussed in more
detail.
The MODIS sensor
The MODIS sensor provides comprehensive data about land, ocean and atmospheric processes
with its 36 spectral bands, each having a radiometric sensitivity of 12 bits, on a 2-day repeat
global coverage. This is realized with a spatial resolution of 250, 500 or 1000m, depending
on the particular wavelength (Table 2.1) (Lillesand, 2004; Justice et al., 2002a). All gathered
data are characterized by improved geometric rectification and noise is removed through
enhanced radiometric calibration, atmospheric correction, cloud and shadow removal, and
a standardization of sun-surface-sensor geometries with bidirectional reflectance distribution
function (BRDF) models (Huete et al., 2002). So is the band-to-band registration for all 36
channels specified to be 0.1 pixel or better (Lillesand, 2004). Comparison of the continuous
series of observations on a long term basis requires these stringent calibration standards, as
they aim for documenting very subtle changes. As the dataset should not be dependent on
the sensor providing it, emphasis is put on the sensor calibration.
Table 2.1: General charachteristics of the Terra MODIS sensor (Justice et al. (2002a), p.4: Table 1).
Orbit 705km, sun-synchronous, near-polar nominal descending,
equatorial crossing: 10:30 local time
Swath 2330km ±55◦ cross-track
Spectral bands 36 bands, between 0.405 and 14.385µm, with onboard cali-
bration subsystems
Spectral calibration band 1 -4, 2% for reflectance, band 5-7 under investigation
Data rate 11 Mbps (peak daytime)
Radiometric resolution 12 bits
Spatial resolutions at nadir 250m (bands 1-2), 500m (bands 3-7), 1000m (bands 8-36)
Duty cycle 100%
Repeat coverage daily, north of 30◦latitude, every 2 days for < 30◦latitude
Gridded land products geolocation accuracy within 150 m (1 sigma) at nadir
Band- to band registration within 50 m in the along scan direction
for band 1-7 within 100 m in the along track direction
Chapter 2. Literature Study 15
One of the primary interests of the EOS program is to study the role of terrestrial vegetation
in large-scale global processes and their contribution to ecosystem functioning. For this pur-
pose, good understanding of the global vegetation distribution, as well as their properties and
spatial/temporal variations is required. Therefore, MODIS Vegetation Indices (VI) products
were developed in order to simplify this task. Two of those products designed to provide
consistent, spatial and temporal comparisons of global vegetation conditions to monitor flora
activity on Earth’s surface are the NDVI and EVI. The products delivered are 16-day compos-
ites with a spatial resolution of 250m. The goal of compositing methods is to select the best
observation on a per pixel basis. In 16 days, a maximum of 64 observations for compositing
is collected. In order to obtain only high quality products at the end, only the higher quality,
cloud free, filtered data are retained for compositing. Furthermore, off-nadir pixels are also
filtered, as they are less reliable and accurate corrected for atmospheric distortions and have
a less fine spatial resolution than nadir reflectances (Justice et al., 2002b). Finally, the num-
ber of acceptable pixels over a 16-day compositing period is further reduced to typically less
than 10 or often less than 5 pixels. The MODIS VI compositing algorithm itself consists of
three components, depending on the number and quality of the useable observations, one of
them is applied: (1) BRDF-composite, (2) CV-MVC: constrained-view angle-maximum value
composite, and (3) MVC: maximum value composite (Fig. 2.3) (Huete et al., 2002).
Figure 2.3: Diagram of MODIS VI compositing methodology (Huete et al., 2002).
To facilitate the ease of handling, the composites are accessible in tiles of approximately
1200km by 1200km geocoded area, which are projected on a sinusoidal grid (Huete et al.,
2002; Justice et al., 2002a).
Chapter 2. Literature Study 16
2.5.2 SPOT-Vegetation
In early 1978, France, in cooperation with Sweden and Belgium, started the development of
the SPOT-program. The program has been designed to provide long term continuity of data
collection. The first satellite launched in 1986 was SPOT-1, a major breakthrough in space
remote sensing as it was the first earth resource satellite system to include a linear array
sensor and to employ the pushbroom scanning techniques. Later, also its improved successors
SPOT-2, SPOT-3, SPOT-4 and SPOT-5 were launched. The two last listed systems had a
major addition: the Vegetation instrument. Primary developed for vegetation monitoring,
this instrument is useful in a wide range of applications where frequent, large-area coverage
data are required as well (Fensholt et al., 2009; Lillesand, 2004).
SPOT-Vegetation covers the globe on a daily basis, providing images with a spatial resolution
of approximately 1km at nadir and a swath width of 2250km. These are used to derive 10-day
NDVI maximum value composites. All products are corrected for system errors (misregistra-
tion of different channels, calibration along the line-array detectors for each spectral band),
endured a thorough atmospheric correction and were resampled to a Plate-Carree geographic
correction. Each composite product is accompanied with detailed per-pixel cloud-cover infor-
mation. Attention has to be paid as the spectral response function of the bands of SPOT-4
Vegetation (VGT1) and the SPOT-5 Vegetation (VGT2) are not identical and induce re-
flectance variations. So increases the observed NDVI with 3.5% due to the reflectance bias of
6.3% and 2.1% for the NIR and red band respectively (Fensholt et al., 2009).
2.6 Conclusion
In conclusion, temporal trajectory analysis of vegetation indices has proven to be a suitable
method for detecting change such as fire disturbance. This is due to the relationship between
vegetation and fire, as vegetation provides fuel and fire alters the vegetation status. Good
quality data with a high temporal resolution are required in order to characterize the pro-
files properly with metrics, which ultimately leads to information used to compare temporal
vegetation profiles on inter- and intra-annual basis.
Chapter 3
Materials and methods
3.1 Study area: Northern Territory
3.1.1 General
The Northern Territory (NT) is a federal territory of Australia, extending 14 degrees of
latitude from the tropical north to the arid centre (Fig. 3.1(a)). With an area of 1 349 200km2,
the NT is the third largest province in Australia, occupying approximately one-sixth of the
total land area. Despite its large area, the territory is sparsely populated. The majority
of the 227 000 inhabitants of the NT live urbanized areas; more than the half in Darwin,
the territory’s capital, and the other part in less densely populated cities e.g. Palmerston,
Alice Springs, Katherine and Nhulunbuy. Approximately one-third of the population are
indigenous Australians, or so called Aboriginals, owning approximately half of the territory
(Wilson et al., 1990).
The majority of the land is held under pastoral lease, Aboriginal Land trusts and conservation
and recreation reserves. The pastoral activity generally signifies to extensive cattle grazing
with low stocking rates. Large-scale cropping is mainly restricted to zones around Darwin
and the Daly Basin. The Aboriginal lands support a variety of uses in order to maintain
their traditional way of living. The NT contains 95 Protected Areas with a total extent of
53 500km2.
Topographic variation is generally limited, although some sandstone ranges in the north and
the south offer a little topographic complexity. Furthermore there is an extensive series of
river systems and the two large deserts, the Tanami Desert in the north and the Simpson
Desert in the south.
17
Chapter 3. Materials and methods 18
3.1.2 Climate and soil
Climate
According to Wilson et al. (1990), the Northern Territory is divided in three distinctive climate
zones based on the median annual rainfall. The north is subjected to a seasonal wet tropical
climate while the south is arid, and amid a semi-arid zone influenced by both adjacent climates
is situated (Fig. 3.1(b)). The northern part is strongly influenced by the north-west monsoon,
with a wet summer from November till April and a dry winter from May till October. During
the wet season, often associated with tropical cyclones and monsoon rains, almost 95% of
the annual precipitation rains down. Some locations have a mean annual precipitation over
2000mm. The semi-arid zone, less strongly influenced by the monsoon, is characterized by a
lower mean annual rainfall (ca 500-1000mm) and a higher temperature range than the humid
zone. In the southern arid zone, precipitation is less than 350mm and strong seasonal and
diurnal temperature fluctuations are common (Wilson et al., 1990; Woinarski et al., 1996).
(a) The Northern Territory (b) Three climatic zones in the NT
Figure 3.1
Soil, geology and geomorphology
The soil in the humid and semi-arid part of the NT consists mainly out of red earths with
sandy or loamy textures, commonly intermixed with yellow earth or shallow gravelly podzolics.
Around the rivers, the seasonally flooded alluvial zones are predominantly associated with
grey and brown cracking clays. The arid zone mostly comprises sand covered plains, dune
fields or rugged mountain ranges. The sand plains and dune fields generally consist of red
sands and red clayey sands, while the material found in the rugged mountainous regions are
red loamy or red sandy clays, with little areas covered in yellow earths (Wilson et al., 1990).
Chapter 3. Materials and methods 19
3.1.3 Vegetation
General
Generally, analogous to the climate zones, the Australian Bureau of Meteorology delineates 3
major zones in vegetation: a tropical zone, a grassland zone and a desert zone. A more detailed
description of the vegetation types was illustrated by Wilson et al. (1990). He determined 112
vegetation types, grouped into 13 broad categories (Table 3.1). A brief description and further
explanation of the terms used in Table 3.1 is given in Table 3.2 and Table 3.3. Each category
has a consistent mutual floristic group in the dominant stratum. However, some floristic
variation is possible in the other strata. In the north, the vegetation is typically tropical
savanna which largely consists of eucalypt woodland and eucalypt open woodland with a
grassy understory (Fig. 3.2(a)). From north to south, the dominating eucalypt woodlands are
gradually substituted into areas of Melaleuca and Acacia forests and woodlands (Fig. 3.2(d)),
which are more southwardly replaced by hummock and tussock grasslands (Fig. 3.2(c) and
3.2(b)) and Acacia wood- and shrublands (Fig. 3.2(e) and 3.2(f)).
In the NT, 3632 native formally named vascular plant species are recorded and 10% among
them have a range restricted to the territory. The five most species rich families appearing
in the NT are the Poaceae (454 species), Fabaceae (301), Cyperareae (236), Mimosaceae
(181) and the Asteraceae (180). Furthermore, the five most occurring genera are Acacia (150
species), Fimbristylis (81), Cyperus (76), Eucalyptus (60) and Calogyne (51).
Table 3.1: Vegetation types in the Northern Territory (Woinarski et al., 1996).
Vegetation category Area
Total area in NT (km2) % Area reserved
Closed forest 1029 26.2
Eucalypt forest or woodland with tussock grass understory 235 478 11.0
Eucalypt low woodland with tussock grass understory 91 831 2.2
Eucalypt woodland with hummock grass understory 186 669 6.6
Mixed species low open woodland 6903 10.0
Miscellaneous shrubland 1 151 0.01
Melaleuca forest or woodland 13 236 7.1
Floodplain 10 334 24.9
Acacia woodland 173 725 0.6
Hummock grassland 507 840 0.9
Tussock grassland 83 436 0.3
Littoral complex 11 090 5.1
Chenopod shrubland 19 059 0.1
Chapter 3. Materials and methods 20
Tab
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Chapter 3. Materials and methods 21
(a) Eucalypt woodland (b) Tussock grassland
(c) Hummock grassland (d) Acacia forest
(e) Acacia woodland (f) Acacia shrubland
Figure 3.2: Different vegetation types in the NT
Temporal variability of the vegetation
In the humid and semi-arid zone, climate and fire have a profound influence on the temporal
behavior of the vegetation. A yearly cycle of desiccation and burning in the dry season and
rejuvenation in the wet season are typical for many of the graminoids and low shrubs. As
Chapter 3. Materials and methods 22
Table 3.3: Definitions of used vegetational terms (Wilson et al., 1990)
Tree Woody plant with a single stem within 2 metres of ground
Shrub Woody perennial plant with multiple stems arising within 2m of the base
Mallee (shrub) Woody plant with multiple stems each stem arising at or near the base
and usually less then 8m tall and 10cm in diameter. From the genus
Eucalyptus
Chenopod Shrub or forb from the halophyte family Chenopodaceae, exhibiting
drought and salt tolerance
Hummock grass Coarse, xeromorphic grass with a mound like habit. From the genus
Triodia, Plentrachne or Zygochloa
Tussock grass Tussock grass with open habit, distinct individual shoots or not in hum-
mocks
Forb Herbaceous or slightly woody, annual or sometimes perennial plant; not
a grass
well lightening at the beginning of the wet season as the low intensity frequent fires lit by
local inhabitants are a ignition source of wild fire. The fire frequency is strongly related to
the climate, as this determines the biomass quantity produced by the vegetation. Hence, in
the high productive humid zone, fire occurs annually or biannually, whereas fires occur less
frequently in the lower productive semi-arid zone (Edwards and Russell-Smith, 2009; Russell-
Smith and Edwards, 2006; Wilson et al., 1990). The bushfires, typically of low intensity,
rarely hit the tree layer as they spread fast and burning the dry biomass in the lower strata.
Depending on the frequency of the fires, the structure of the shrub layer might be altered, but
generally little change occurs in the floristic composition. Also fluctuating precipitation in
the semi-arid zone might cause a variation in the structure and composition of the grassland
communities. So can cattle affect the composition of the flora and the structure of the ground
layer, however, the grazing intensity is generally too low to notice an effect.
In the arid region fires are less frequent but more intensive and large-scaled than in the
more humid areas (Wilson et al., 1990; Yates and Russell-Smith, 2003). Hummock grass-
lands (Spinifex grasslands) are more susceptible for fire, however the occuring fires are often
more extensive (Wilson et al., 1990; Greenville et al., 2009). Most of the plants in this com-
munity will persist and resprout through frequent fires, although the species abundance is
also strongly related to the amount of rainfall since the last occurring fire event. Prevent-
ing ground fuel to build up to fire carrying levels through maintaining a dense canopy cover
and a high grazing pressure might help to withstand grave, devastating fires (Wilson et al.,
1990; Edwards et al., 2008). In contrast to the other climate regions, precipitation plays
Chapter 3. Materials and methods 23
an important role in plant species composition. Early winter precipitation will promote the
growth of herbs and forbs, while summer rains will tend to promote grasses. Additionally,
grazing, mostly concentrated in the Acacia and chenopod shrublands, alters the vegetation
composition as well, but this effect is inferior to the effect of seasonal climatic variability or
to that of fires (Wilson et al., 1990)
Hence, fire activity is unevenly distributed (1) spatially, as the fires occur mostly in higher-
rainfall areas (producing more biomass and thus fuel), and (2) temporally, because they occur
mostly in the latter half of the dry season (Edwards and Russell-Smith, 2009; Russell-Smith
et al., 2007). So in conclusion, the variability of the areal extent, frequency and severity of
wildfires is mainly determined by the variation in annual rainfall quantity and its temporal
distribution, and the fuel type, mainly typified by the vegetation structure and the floristic
composition (Wilson et al., 1990; Russell-Smith et al., 2007).
3.1.4 Fire in the NT
Bushfires have been an essential part of the Australian ecosystems for millennia. Before,
Aboriginals used it as a hunting tool, for farming and to signal their presence, but the last
130 years the demography and land use patterns changed drastically and the management
changed to fire suppression. And more recently, fire began to be used as a tool, aiming for
fuel reduction, biodiversity management, protection of assets and pasture maintenance, as the
burning makes the ground vegetation rejuvenate (Allan et al., 2003; Burrows, 2008; Edwards
et al., 2008; Turner et al., 2008).
Allan et al. (2003) defines two types of fires, dependent on the moment of occurrence there are
early dry season (EDS) fires and late dry season (LDS) fires. Generally, EDS fires are believed
to be management fires, which are supposed to have positive consequences, whereas LDS fires
usually are wildfires, with a negative, undesired impact on its surroundings and needs to be
suppressed. There are two complementary approaches to do so (Price et al., 2007; Russell-
Smith et al., 2007). The first approach is to apply an active fire management by lighting
and suppressing fires, similar to the traditional indigenous practice. A second approach is
to control wildfires using permanent firebreaks, like streams, roads, cliffs, and combine them
with imposed breaks from aerial prescribed burning (APB) programs. An APB program
implies the creation of a burned sector in the EDS by dropping incendiaries from an airplane
or helicopter in order to impose finer-scale fire patchiness and reduce the severity (scale and
intensity) of destructive LDS fires (Price et al., 2007; Burrows, 2008). Effective and efficient
application of APB programs requires good timing and planning, based on adequate resource
information and knowledge of fire history (Edwards and Allan, 2009). If the management
fires are started too early, the impact will be unsatisfactory and the objectives won’t be
Chapter 3. Materials and methods 24
accomplished, if the they are started too late, then the fires could get out of control and cause
a too large burned area (Allan et al., 2003). As the relation between the timing of the APB
program and the greenness or the curing state of the vegetation is crucial to the prosperity
of the program, remote sensing plays a major part in the planning of APB activities.
3.1.5 Sampled areas
As the NT has an enormous area, analyzing it completely would be too difficult and time-
consuming. Therefore several study areas are picked in a way they would account for the
whole region. In order to do so, the previous section about the NT is of great importance.
First of all, as mentioned before, three climatic zones can be distinguished in the NT: a
tropical humid north, a semi-arid centre and an arid southern region. As vegetation biomass
production is strongly influenced by the precipitation, a difference in the temporal profiles of
vegetation indices should be possible to observe across the north-south axis. Furthermore, as
a consequence of the variation in the precipitation and thus the vegetation, other fire regimes
occur in each different climatic zone.
With the purpose of capturing those variabilities, three study areas are selected starting with
study area (SA) 1 in the north, to SA2 in the centre and finally SA3 in the south of the NT,
as showed in Fig. 3.3. Each SA has an approximate area of 86 000km2 and was chosen in a
way to enclose as much as the same vegetation types as possible, however this was hard to
obtain as the northern vegetation differs a lot from the vegetation growing in the south.
Figure 3.3: The location of SA1, SA2 and SA3 in the NT.
Chapter 3. Materials and methods 25
Accounting for vegetation
As concluded in the previous section, fire activity is partly stipulated by the vegetation
type, so is hummock grassland much more sensitive for fire occurrence than other types of
vegetation. Therefore, based on a shapefile, delivered by the NT Bushfire Council, which
enclosed a map of the NT vegetation cover, showed in Fig. 3.4(a), the SA are subdivided
into different vegetation categories. First, a general subdivision is made, containing 3 floristic
classes: (1) ’forest’, containing closed and open forests and woodlands; (2) ’shrub’, which
contains all varieties of shrublands; and (3) ’grassland’, existing out of all types of grassland,
mainly containing tussock and hummock grasslands. Further in this thesis, these classes will
be referred to as the ’broad vegetation classes’, receiving ’BR ’ (broad) as a preposition, which
makes the three classes in this subdivision BR FOREST, BR SHRUB and BR GRASSLAND.
A second classification divides the BR GRASSLAND and BR FOREST class from the
previous paragraph, into two smaller classes. So is BR FOREST parted into WOOD-
LAND and FOREST and is BR GRASSLAND divided in TUSSOCK GRASSLAND and
HUMMOCK GRASSLAND. This gives 5 different classes, FOREST, WOODLAND, SHRUB,
TUSSOCK GRASSLAND and HUMMOCK GRASSLAND.
As last, a third classification is made, characterized by the prefix ’SM ’, referring to small.
Herein the WOODLAND and FOREST are subdivided into smaller classes, resulting in
SM FOREST ACACIA, SM FOREST EUCALYPT, SM FOREST OTHER, SM WOODLA-
ND ACACIA, SM WOODLAND EUCALYPT, and SM WOODLAND OTHER. SHRUB and
both GRASSLAND classes are not further divided and thus not included in the third classi-
fication as this would create too much information to process. An overview of the different
classifications is given in Table 3.4.
All three classifications are summarized in Table 3.6, where also more information is given
about their coverage of the terrain and their abbreviation, which is used in tables and figures
in the discussion. Furthermore, information about the exact vegetation types included in
each class can be found in Table A.1 in the Appendix.
Accounting for fire events
In order to asses information in burned and unburned areas, the fire history needs to be
mapped. The Australian Northern Territory Bush Fire Council provided data of annual
fire occurrence in the NT from 1998 till 2008 in Arcview shapefiles (Fig. 3.4(b)). With this
information a mask is created for each studied year. As the fire history is mapped with satellite
remote sensing, similar to Goetz et al. (2006), an interior buffer of five pixels, this corresponds
to 1.25km from the edge, is created for each burned patch to exclude unburned vegetation
Chapter 3. Materials and methods 26
Table 3.4: Overview of the subdivision of the different vegetation classes.
Classification
I (broad) II III (small)
Prefix: BR No prefix Prefix: SM
FOREST FOREST FOREST ACACIA
FOREST EUCALYPT
FOREST OTHER
WOODLAND WOODLAND ACACIA
WOODLAND EUCALYPT
WOODLAND OTHER
GRASSLAND TUSSOCK GRASSLAND TUSSOCK GRASSLAND
HUMMOCK GRASSLAND HUMMOCK GRASSLAND
SHRUB SHRUB SHRUB
patches at the fire boundaries. Information about the spatial and temporal distribution of
the fire history in the different SA can be accessed in Table 3.5 and the percentage of pixels
burned per vegetation class can be consulted in Table 3.6.
(a) The vegetation in the NT (b) The red patches represent the burned areas
in 2004
Figure 3.4
Chapter 3. Materials and methods 27
Table 3.5: The annual percentage of the burned area per SA
Year SA1 (B%) SA2 (B%) SA3 (B%)
2001 43.3% 37.9% 13.4%
2002 46.0% 17.3% 11.4%
2003 29.5% 1.2% 0.3%
2004 35.9% 27.9% 0.1%
2005 28.2% 0.1% 0.0%
2006 40.3% 11.1% 0.0%
2007 41.1% 39.7% 0.3%
2008 39.7% 1.0% 0.0%
3.2 Remote sensing data
3.2.1 Data
The MODIS derived products used to create the temporal profiles were obtained from the
website of the Land Processes Distributed Active Archive Center (LP DAAC) of U.S. gov-
ernmental program for Geological Survey: https://lpdaac.usgs.gov/lpdaac/.
The MOD 13Q1.5 database contained 250m spatial resolution 16-day composites of the
MODIS VI from the launching of the sensor MODIS Terra platform in 2000 up till now.
The downloaded files contained the NDVI and EVI vegetation images and included further-
more the corresponding red, blue, NIR and MIR spectral bands. Like all MODIS products,
a profound atmospheric calibration and geometric and radiometric correction was performed,
making further preprocessing unnecessary. To cover the Northern Territory, the H30V10 and
the H30V11 tiles had to be downloaded. An example of the tiles is shown in Fig.3.5.
(a) (b)
Figure 3.5: Two examples of MODIS NDVI images in sinusoidal projection: (a) the northern part
of the NT, captured by tile H30V10, and (b) the southern part of the NT, captured by
tile H30V11
Chapter 3. Materials and methods 28
Table 3.6: The different vegetation classes used for analysis, their abbreviation, their abundance and
the percentage burned per SA.
Vegetation class Abbreviation Area covered (%) Burned area (%)
SA1 SA2 SA3 SA1 SA2 SA3
First classification
BR FOREST bFo 97.3 42.3 27.1 36.7 36.9 0.0
BR GRASSLAND bGr 1.9 53.0 38.0 7.4 17.8 0.3
BR SHRUB bSh 0.8 3.5 25.9 12.7 76.3 0.0
Second classification
FOREST Fo 86.1 37.3 2.0 37.4 37.1 0.0
SHRUB Sh 0.8 3.5 25.9 12.7 76.3 0.0
WOODLAND Wo 11.2 5.0 25.2 30.9 35.5 0.0
TUSSOCK GRASSLAND Tu 1.9 18.9 0.2 7.4 12.1 0.0
HUMMOCK GRASSLAND Hu 0.0 34.2 37.8 0.0 21.0 0.3
Third classification
SM WOODLAND ACACIA sWA 0.1 4.6 0.0 0.2 36.8 0.0
SM WOODLAND EUCAYPT sWE 8.4 0.0 0.0 24.7 0.0 0.0
SM WOODLAND OTHER sWO 0.3 0.0 0.0 2.2 0.0 0.0
SM FOREST ACACIA sFA 0.0 0.0 25.1 0.0 0.0 0.0
SM FOREST EUCALYPT sFE 76.3 24.7 1.9 38.8 43.5 0.0
SM FOREST OTHER sFO 12.1 13.0 0.0 32.9 24.6 0.0
3.2.2 Vegetation Indices
Information contained in a single spectral band is usually insufficient to characterize the
vegetation status. Therefore various vegetation indices were developed by combining two or
more spectral bands, which enhance vegetation signals from remote sensing measurements.
They allow us to make reliable spatial and temporal comparisons between various vegetation
parameters and to monitor seasonal, inter-annual and long-term trends (Huete et al., 2002;
Qi et al., 1994).
Normalized Difference Vegetation Index
The NDVI is a vegetation index based on the fact that chlorophyll absorbs red light whereas
the mesophyll leaf structure scatters NIR. Combining both reflections, the formula for NDVI
becomes:
NDVI =ρNIR − ρred
ρNIR + ρred
Chapter 3. Materials and methods 29
with ρNIR and ρred respectively the amounts of reflected NIR and red light captured by the
sensor (Pettorelli et al., 2005). It shows a consistent correlation with the vegetation biomass
and dynamics in various ecosystems all over the world. This relationship is well established
and has been successfully used in research on temporal and spatial trends and variations
in vegetation distribution, productivity and dynamics, to monitor habitat degradation and
fragmentation, and the ecological effects of climatic disasters like flooding, drought and fire
(Bajocco et al., 2010; Fensholt et al., 2009; Fraser et al., 2000; Lhermitte et al., 2008; Pettorelli
et al., 2005; Verbesselt et al., 2010, 2006a).
Enhanced Vegetation Index
Where NDVI is sensitive for RED variations, and thus responsive to chlorophyll, is EVI is
more sensitive to NIR, and so, more responsive to canopy structural variations, including the
leaf area index (LAI), canopy type and architecture
EVI = 2.5ρNIR − ρred
ρNIR + 6ρred − 7.5ρblue + 1
It is because of the blue band, who corrects the red band for atmospheric influences, that
many of the atmospheric contaminations NDVI has to deal with, such as residual aerosol
influences, are minimized (Huete et al., 2002; Pettorelli et al., 2005).
Second modified Soil-Adjusted Vegetation Index
The information extracted from remote sensed data are often contaminated with noise, such
as soil background variations. Therefore, Huete (1988) introduced a soil-adjustment factor
L, in combination with the NDVI-equation this resulted in the soil-adjusted vegetation index
(SAVI):
SAVI =ρNIR − ρred
ρNIR + ρred + L(1 + L)
The soil-adjustment factor L was empirically set to 0.5, unless prior knowledge of vegeta-
tion amounts was available (Huete, 1988). But, as the constant L buffers the reflectance
variations, information for change detection is lost. Therefore, Qi et al. (1994) developed the
modified SAVI (mSAVI), wherein the soil adjustment factor L is self-adjustable, which results
in a higher signal-to-noise ratio without the necessity of prior vegetation cover knowledge.
Employing an inductive method to derive L, mSAVI2 becomes:
Chapter 3. Materials and methods 30
mSAVI2 =2ρNIR + 1 −
√(2ρNIR + 1)2 − 8(ρNIR − ρred)
2
which proved to be satisfactory with respect to the vegetation sensitivity and soil noise re-
duction. Although the signal-to-noise ratio was higher than that of other vegetation indices,
NDVI remains recommended when working with high vegetation density data (Qi et al.,
1994).
Normalized Difference Water Index
The NDWI uses 2 near-IR bands, in contrast with the other vegetation indices described
above. It is an indicator sensitive to the total amounts of liquid water in the leaves and is
therefore more directly related to the vegetation water status than the NDVI (Gao, 1996;
Verbesselt et al., 2006a). The equation of the NDWI is:
NDWI =ρNIR − ρSWIR
ρNIR + ρSWIR
Although it is less sensitive to atmospheric scattering effects than NDVI, similar to NDVI, it
does not remove the soil background effects completely (Gao, 1996). Verbesselt et al. (2006a)
found that the NDWI has a high capacity to monitor fire activity dynamics and is very
suitable to predict the start of the fire season.
Conclusion
Each vegetation index has its own advantages and disadvantages, and therefore all vegetation
indices will be compared to each other, in order to discover the most suitable VI given a
particular situation.
3.3 The used metrics
The data extraction from the temporal trajectories is performed using metrics, derived from
the temporal profiles of the different vegetation indices (DeFries et al., 1995; Cridland,
2000b,a; Lupo et al., 2007; Reed et al., 1994; Verbesselt et al., 2009). The metrics applied in
this study, their abbreviation and the corresponding biophysical interpretation are summa-
rized in Table 3.7.
The maximum VI value, VImax, corresponds with the reflectance at the time of maximum
vegetation cover, from here on symbolized by DOYmax. They are related to the amount of
Chapter 3. Materials and methods 31
vegetation biomass at the end of the growing season. In contrast with the minimum VI value
(VImin) at the time of minimum vegetation cover (DOYmin), which represent the amount of
biomass left at the end of the dry season or after severe damage to the vegetation, e.g. fire
events. Both maximum and minimum values are subtracted from each other, resulting in the
amplitude of the VI during the year, further denoted as VIrange, and the period of senescence
(DOYrange). The first informs about the quantity of vegetation diminished during the dry
season, while the latter notifies the duration of the dry season.
The slope of the trajectory is a measure for the rate of vegetation change. As fire events cause
a sudden obliteration of the vegetation, a steep slope should be observed, while the seasonal
decay is much more gradual. Therefore, the maximum rate of decay (Smax) is calculated as
it assess information about possible sudden change events. Also the corresponding moment
(DOYS) and VI value (VIS) are contemplated in the further study. Finally, the integrated VI
profile (I) from the maximum to the minimum VI value is calculated. It provides an estimate
of the accumulated biomass in the period of senescence.
Table 3.7: The used metrics for analysis and their biophysical interpretation.
Metric Abbreviation Biophysical interpretation
Maximum VI in the year VImax Greenness of vegetation at peak of growing season
Minimum VI in the year VImin Greenness of vegetation at lowest point of season
VI range/amplitude VIrange Range in greenness of vegetation in year
Time of VImax DOYmax Day of the year at maximum VI
Time of VImin DOYmin Day of the year at minimum VI
Difference of VImax and VImin DOYrange Period of senescence
Maximum derivation after DOYmax Smax Maximum rate of senescense
VI value on Smax VIS Greenness of vegetation at maximum rate of senescence
Time of occurence Smax DOYS Day of the year at maximum rate of senescence
Integrated VI (VImax to VImin) I Accumulated biomass in period of decay
3.4 Software
The operations concerning the processing and the extracting of information from the MODIS
images were done in Idrisi Andes Edition. For the creation of the fire history and vegetation
masks a combination of ArcView and Idrisi was used. And in MATLAB the temporal
profiling, further data analysis and the statistical framework were performed.
3.5 Methodology for temporal trajectory analysis
The general methodology adopted was similar to the one adopted by (Goetz et al., 2006).
This method can be divided into three major steps. First, all temporal profiles for every VI
Chapter 3. Materials and methods 32
are constructed. So, per VI, per SA and per vegetation class, an annual unburned and, if
the vegetation type burned during the year, a burned temporal profile is made (Fig. 3.6).
Secondly, a polynomial curve is fitted to the temporal profiles in order to calculate the different
metrics summed. The third major step is the comparison of the metrics calculated and this
information is used to set thresholds for a classification method which classifies pixels in a
burned or unburned class. The values of the thresholds are validated by applying this method
for new random pixels.
Figure 3.6: The steps to obtain information about vegetation and fire history from subsequent com-
posites. (based on Goetz et al. (2006))
3.5.1 Preprocessing
The remotely sensed data
As the composites at delivery already are geographical and atmospherically corrected, and
radiometric calibrated, no further corrections are required. Although, before the extraction
of information from the MODIS composites can begin, the geographic projection needs to be
changed from a sinusoidal projection, standard in MODIS composites, to the latitude/longi-
tude projection, or short latlong projection. Hence the same projection is used for the images
and the masks.
Masking
To extract the required information from the 16-day composites, several masks are made. The
fire history masks are combined with the vegetation cover masks and this resulted in a series
of binary masks. Each mask having ’ones’ for a specific vegetation class and its according
fire history (burned or unburned) for a particular year. Furthermore, all pixels classified as
unburned for all years are reclassified in a new class: never burned. Finally a burned, an
Chapter 3. Materials and methods 33
unburned and a never burned mask per vegetation class, per year, per SA and per VI is
created.
3.5.2 Temporal profiling
The used method for detecting change is via a temporal trajectory analysis as described by
Coppin et al. (2004). Therefore, the temporal profiles are set up with the extracted data from
the MODIS composites.
From all 16-day composites, for each VI 23 composites per year, data is extracted using the
masks in which the fire history and vegetation class are combined. This results in a data
matrix per processed composite. Each matrix contains the values of all pixels prescribed
by the mask. Thereafter all the matrices enclosing information about the fire history of a
particular vegetation class per SA is put together in one single matrix per year. So briefly,
one matrix contains all burned or unburned pixel values from one vegetation class situated in
one SA and for one year. The matrix counts 23 rows, one for each composite, and a variable
quantity of columns, each describing the pixel value of a particular pixel during the year or
otherwise described as a temporal trajectory.
For the missing composites, the mean value is calculated from both neighboring images in
order to circumvent voids in the data. So malfunctioned the MODIS instrument from June
15, 2001 till July 2, 2001, omitting 3 subsequent composites. Furthermore, because of instable
system configurations and try-outs of the sensor, all data obtained before November 1, 2000
were not consistently calibrated and validated. Therefore, the year 2000 is not taken into
consideration during the analysis (Justice et al., 2002a).
3.5.3 Characterization of the temporal trajectories
The characterization of the temporal profiles is acquired by the calculation of several metrics.
As metrics have to be calculated from the temporal profiles, e.g. the integral, a mathematical
function of the profile is required. Therefore, in MATLAB, a polynomial curve of the sixth
grade is fitted to the temporal profile (DeFries et al., 1995; Hermance et al., 2007; Bradley
et al., 2007).
The temporal profiles whereupon the polynomials are fitted are not based on single pixel
values for the reason that if only one pixel was considered, the perceived variability could
be significantly influenced by noise. If in contrary too much pixels are contemplated, the
variability sought for could be leveled out. In the literature, based on empirical or theoretical
knowledge, often 10 to 50 pixels are used for the construction of temporal profiles (Verbesselt
et al., 2009; Graetz et al., 2003; DeFries et al., 1995). To determine the ideal pixel count
Chapter 3. Materials and methods 34
for further calculations, a simple test is performed. In this test, two random samples of a
specific pixel count are tested whether they have equal means and thus represent the same
population or not. This test is performed 500 times for each pixel count, starting from random
1 pixel up to the median value of 250 random pixels. The percentage of comparisons by which
the equality was rejected is compared in Fig. 3.8. It clearly shows that contemplating only
one pixel is insufficient as a high percentage is rejected, while a decrease of rejected cases
is noticed as the count of contemplated pixels rises. Around 25 pixels, the percentage of
rejection becomes nearly constant. Therefore, the temporal profiles are based on the median
value of 25 randomly chosen pixel values. The median value is used instead of the mean value
as the mean value is more susceptible for outliers potentially caused by undetected data errors
during the preprocessing (Verbesselt et al., 2009, 2006a). On this temporal profile, the sixth
grade polynomial is fitted. A plotted temporal trajectory and fitted polynomial is showed in
Fig. 3.7.
Figure 3.7: An example of a temporal trajectory with a fitted polynomial curve.
Next, the polynomial equation is used to calculate the different metrics, discussed in section
3.3, in order to characterize the temporal profile. When calculating the metrics, attention has
to be paid as the values at both tail ends of the polynomial curve might deviate significantly
from the temporal profile. Therefore, as the temporal resolution of the composites is 16 days,
the values of the metrics prior to the first 16 days or later than the last 16 days of the year
were precluded from the further study.
3.5.4 The comparison of metrics
All metrics are calculated for each vegetation class, per year, per SA and for burned, unburned
and never burned pixels. In order to compare the metrics, a reference year has to be picked.
Chapter 3. Materials and methods 35
Figure 3.8: The percentage of cases by which the equality is rejected per number of pixels contem-
plated.
In this case the year 2004 is the reference, as this is a year, according to the Northern Territory
Bushfire Council, in which the fire season is considered as approximately average.
The metrics from the burned and the unburned pixels are compared intra-annually to each
other in order to obtain information about the temporal differences in profiles and whether
those differences are considered significant. When comparing those differences, all seasonal
effects are diminished and the other variability left is proper studied.
Climate related variation
To assess the variability between fire behavior, strongly dependent on the precipitation and
thus the climate, on the north-south axis, all five vegetation classes from the second classifi-
cation are put next to each other in each SA, and finally a comparison between all three SA’s
is made to distinct the north-south trend and thus the spatial variation.
Vegetation class related variation
Also the surplus value of dividing the vegetation classes into smaller subclasses is
examined. For the reference year, the BR FOREST class from the first floris-
tic classification, the FOREST and WOODLAND class from the second classification
and the SM FOREST EUCALYPT, SM FOREST OTHER, SM WOODLAND ACACIA,
SM WOODLAND EUCALYPT and SM WOODLAND OTHER is subjected for comparison.
Here the significance of the level of detail in the floristic classes is exposed.
Furthermore, all classes of the second classification are weighed against each other for ex-
Chapter 3. Materials and methods 36
ploring the variation between forest, woodland, shrubland and grassland (both tussock and
hummock grassland).
3.5.5 Accuracy assessment
All intra-annual results are put together and the mean value with the standard deviation is
calculated per VI and per sensor. In order to validate the results, new randomly chosen pixel
values are picked and classified according to the results obtained from all above described
outcomes. The rate of correctly classified pixels is used as a benchmark for the acquired
accuracy.
Finally, the dependence of the spatial resolution on the accuracy is investigated by comparing
the values obtained for MODIS composites, with a spatial resolution of 250m, to those attained
with SPOT-Vegetation imagery, having a spatial resolution of 1km.
Chapter 4
Results and discussion
4.1 Analysis of the temporal profiles: introduction
In this chapter the variability of the vegetation in the NT is scrutinized. The metrics ob-
tained from the temporal trajectories are compared to each other via several grouping vari-
ables leading to more information about the variation of those metrics. Comparison of the
metrics grouped by study area assesses more information about the north-south variability.
Also different vegetation types and associations between different years are evaluated. Fur-
thermore, when the fire history is comprised, the impact of fire events on the vegetation is
investigated. Likewise, the VI most suitable for each specific case and scenario is discussed.
Finally, two different sensors are compared in order to acquire more information about the
possible advantages of a higher spatial resolution in the analysis.
Once a preliminary visual interpretation of the trajectories is performed, the metrics are
compared by a one-way analysis of variance (ANOVA). This test compares the means of two
or more groups, and therefore generalizes the t-test to more than two groups. The variability
of the data is divided into two parts: the variability between groups and the variability within
groups. The null hypothesis in the ANOVA postulates that the means of the compared groups
are equal and thus part of the same population. When, at a confidence level of 95%, the p-
value calculated in the comparison is smaller than 0.05, the null hypothesis is rejected and
the means of the compared groups are proved to differ significant.
An AVONA provides only a statistical test whether the means of several groups are all equal
or not. These test results are to general to determine which pairs of means differ significant.
To allocate these differences, a multiple comparison test based on Tukey’s honestly significant
difference criterion is performed.
Furthermore, a table with the means (µ) and the standard deviation (σ) is calculated for the
37
Chapter 4. Results and discussion 38
various metrics, clustered by a specific grouping variable. This is the basis on which further
assumptions and conclusions are made regarding the variability in the NT.
4.2 Variability along the north-south axis
4.2.1 Preliminary visual interpretation
The NT has a profound variation in vegetation cover and density along its north-south axis.
This variation is mainly caused by a difference in climate, ranging from tropical in the very
north to arid in the south. In the north, the seasonal response is clearly visible in the
vegetation cover. The wet season facilitates a high vegetation density to develope and is
followed by a period of pronounced decay in the dry season. In the arid regions of the NT,
vegetation cover is much more sparse and withers almost completely due the absence of water
in the often prolonged periods of drought. This results in a hardly noticeable seasonality in
the vegetation. The effects of the climate on the vegetation growth are clearly visible in Fig.
4.1. The study area in the north, represented by SA1, shows a higher amplitude than the
study area (SA3) in the south, with an intermediate SA2, being a transition from the tropical
north to the arid south. Likewise, the maxima of the trajectories show a similar behavior,
ranging from high vegetation cover in SA1 to low vegetation cover in SA3.
4.2.2 Analysis of variance
In order to analyze the variability of the vegetation along the north-south axis in more detail,
the grouping variable of the dataset is set to the different study areas. The northern study
area is SA1, SA3 is localized in the far south of the NT and SA2 lies in between. The dataset
used for this analysis contains never burned and unburned trajectory metrics of 2004, the
reference year, and only those of the vegetation types from the second classification. The
reference year 2004 is chosen because if multiple years were combined, the variance of the
seasonality would be lost as the seasonal timing not always is the same over different years.
Also certain variability would be lost when burned pixels are considered, as their trajectories
deviate from the normal unburned vegetation trajectories, which are nearly solely influenced
by seasonal parameters.
When the pairwise combinations are tested with an ANOVA, nearly all study areas show a
significant difference among each other (Table 4.1). The calculations based on the EVI and
the NDWI indicate there is even a complete significant difference between the study areas.
As the NDWI was developed to be more susceptible to water content in the leaves of the
vegetation, which is directly related to the precipitation and thus the climate, it proves to
distinguish very good between different climates. Also EVI, which is enhanced to discriminate
Chapter 4. Results and discussion 39
(a) EVI (b) NDVI
(c) NDWI (d) mSAVI2
Figure 4.1: The reference trajectories of the study areas for different VI
better amongst structures in vegetation cover, appears to separate the climatic regions well.
On those constructed with the NDVI and the mSAVI2, some cases appear to have no signifi-
cant difference. So is the difference for the integrated trajectory and the timing of maximum
decay between SA1 and SA2 too small when using the NDVI. As the maximum decay is
related to the set in of the dry season, the similarity proves the dry season and its effects in
vegetation struck SA1 and SA2 around the same time in the year. Also the integral shows
that the accumulated biomass production in that growing season was analogous. However,
as the calculations with the other VI prove, this might be caused by the fact that the NDVI
easily saturates and therefore indicates a resemblance between the two study areas. For the
mSAVI2, the timing of reaching the maximum VI value for SA1 and SA3 is about the same,
just like the timing of maximum decay is. Also the amplitude of the trajectory as well the
accumulated biomass production for that growing season resemble for both SA2 and SA3.
As this is unlikely to happen, the mSAVI2 is not considered to be a good VI to discriminate
Chapter 4. Results and discussion 40
amongst different climates. As mSAVI2 is designed to reduce the soil background scattering,
it is less sensitive when it is applied to high vegetation density data than for example NDVI
is. Therefore, the calculations based on mSAVI2 will be excluded for further analysis in this
specific section.
Table 4.1: The statistical output of the pairwise comparisons of the temporal trajectory metrics for
the different study areas. Significant differences are indicated with ’X’. In this table P1
stands for SA1, P2 for SA2 and P3 for SA3.
VI NDVI EVI mSAVI2 NDWI
Metrics p P1-P2 P1-P3 P2-P3 p P1-P2 P1-P3 P2-P3 p P1-P2 P1-P3 P2-P3 p P1-P2 P1-P3 P2-P3
VImax 0.00 X X X 0.00 X X X 2.16E-84 X X X 0.00 X X X
VImin 0.00 X X X 0.00 X X X 0.00 X X X 1.7E-302 X X X
VIrange 0.00 X X X 0.00 X X X 0.00 X X 0.00 X X X
DOYmax 0.00 X X X 0.00 X X X 4.18E-95 X X 0.00 X X X
DOYmin 3.63E-145 X X X 7.34E-168 X X X 7.41E-19 X X X 6.51E-91 X X X
DOYrange 0.00 X X X 0.00 X X X 2.52E-14 X X X 6.5E-298 X X X
Smax 1.8E-259 X X X 4.46E-150 X X X 1.3E-188 X X X 0.00 X X X
VIS 0.00 X X X 0.00 X X X 8.1E-279 X X X 0.00 X X X
DOYS 1.46E-21 X X 1.80E-39 X X X 6.71E-20 X X 3.29E-88 X X X
I 0.00 X X 0.00 X X X 2.48E-07 X X 0.00 X X X
4.2.3 Discussion of the metrics
The means and standard deviation of the metrics per SA is displayed in Table 4.2 to study
the differences between the different study areas in more detail.
The maximum values of all VI are highest in the north and drop significant the more south the
SA is located. In SA1, most influenced by the monsoon, the maximum of the VI trajectories
is reached at the end of the rain season. According to the NDVI this is in the beginning of
March, the results of EVI show the vegetation stops growing halfway March and the NDWI
proves this is much earlier; around mid-February. The difference between the NDWI and
the EVI and NDVI is caused by the sensitivity of the NDWI to the water content in the
foliage. As young leaves contain more water than more mature leaves, the NDWI reaches its
maximum before the other VI, reaching their maximum when the leaves are fully developed.
This trend is similar in SA2, only for the NDWI the maximum is attained at the end of
March. In SA3, the maxima are attained much later in the year; in the first half of July. This
confirms that the seasonality is considerably less significant in the southern parts of the NT
and thus causing more variability.
For the minima the same trend as for the maxima can be concluded: higher values in the
north and lower values in the south. However, the differences are less pronounced. The timing
of minimum vegetation cover in SA1 ranges from latter half of August for NDVI and EVI
till mid-September for NDWI, at the end of the dry season. In SA2, the low point of the
trajectory falls at the end of November, whereas the minimum in SA3 ranges from the end of
Chapter 4. Results and discussion 41
July (NDVI and EVI) till mid-August (NDWI). The differences between the NDWI and the
EVI and NDVI are a result of the better capability of the NDWI to discriminate amongst low
vegetation cover and no vegetation cover, as it is more sensitive to water than the other VI.
The difference between the maximum and minimum value of the VI, the range, is highest in the
north and lowest in the south. The high precipitation during the wet season, particularly in
SA1 and also in SA2, allows the vegetation to grow enormously and leads to a large difference
with the dry season as there is more vegetation to wilt. Therefore, a huge difference in
vegetation cover is observed caused by the seasonality of the northern parts of the NT. In
SA3 situated far more inland, the seasonality is of less importance and thus the amplitude of
the VI is rather small. The period between the maximum and the minimum value is similarly
linked to the seasons.
The slope of the VI trajectory attains its maximum where the decline in vegetation cover
is maximal and is thus strongly related to the climate. Therefore, as the largest difference
between maximum and minimum vegetation cover is situated in the north, the largest decay
can be found in SA1 and decreases the more south the vegetation is located. Also the moment
on which it occurs is related to the season, as the aridity has a larger impact when it strikes in
a densely vegetated area. Therefore maximum rate of decay occurs first in the north, around
May when the dry season fully has begun. This happens in the latter half of June in SA2
and in mid-July in SA3.
The integrated trajectories represent the biomass production in the growing season. The
biomass productivity is highest in the northern regions, SA1 and SA2, whom are strongly
influenced by the monsoon. In the arid south, only small amounts of biomass are produced.
This indicates that precipitation is directly proportional to the productivity of the vegetation.
4.2.4 Conclusion
The analysis demonstrates that the variability of the trajectory metrics along the north-south
gradient differ significantly. Nearly all metrics from one SA show significant differences when
compared to another. The north is heavily influenced by the seasonality, while farther south
this influence becomes less pronounced. Especially the maximum and the difference of the
maximum with the minimum VI values prove to be good indicators for climatic variability,
as they reach high values in the north and consistently lessen towards the south. Also the
integrated trajectory shows the similar behavior and indicates properly whether the biomass
production is low or high, which is directly related to the seasonality. Therefore, this gradient
needs to be considered in the further inquiries of this thesis.
The different VI react dissimilar on the occurring climatic events, as each VI has its own
Chapter 4. Results and discussion 42
Table 4.2: The mean (µ) and standard deviation (σ) of the metrics per SA for NDVI, EVI and NDWI
of the unburned trajectories in 2004
VI NDVI EVI NDWI
Metric Study area µ σ µ σ µ σ
VImax SA1 0.6658 0.0026 0.3717 0.0015 0.5458 0.0033
SA2 0.4762 0.0023 0.2642 0.0014 0.2305 0.0030
SA3 0.2547 0.0024 0.1369 0.0015 -0.0936 0.0031
VImin SA1 0.3564 0.0020 0.1924 0.0010 0.0456 0.0034
SA2 0.2374 0.0018 0.1395 0.0009 -0.0210 0.0030
SA3 0.1826 0.0019 0.1017 0.0010 -0.1748 0.0032
VIrange SA1 0.3094 0.0026 0.1794 0.0016 0.5002 0.0044
SA2 0.2389 0.0023 0.1247 0.0014 0.2515 0.0039
SA3 0.0721 0.0024 0.0352 0.0015 0.0812 0.0041
DOYmax SA1 64 1.0 72 1.7 43 1.5
SA2 56 0.9 53 1.5 92 1.3
SA3 191 1.0 194 1.6 186 1.4
DOYmin SA1 244 3.5 221 3.6 260 3.5
SA2 333 3.1 334 3.2 331 3.1
SA3 203 3.3 194 3.4 234 3.3
DOYrange SA1 181 1.3 164 1.3 217 2.0
SA2 278 1.2 281 1.2 242 1.8
SA3 121 1.2 121 1.3 121 1.9
Smax SA1 -0.002720 0.000043 -0.001486 0.000037 -0.003789 0.000045
SA2 -0.001664 0.000038 -0.000948 0.000033 -0.002032 0.000041
SA3 -0.000173 0.000040 0.000007 0.000035 -0.000482 0.000043
VIS SA1 0.5023 0.0026 0.2896 0.0016 0.3003 0.0039
SA2 0.3739 0.0023 0.2109 0.0014 0.0906 0.0035
SA3 0.2182 0.0025 0.1190 0.0015 -0.1352 0.0037
DOYS SA1 162 2.7 135 3.2 139 3.4
SA2 170 2.4 167 2.8 234 3.0
SA3 196 2.5 194 3.0 211 3.2
I SA1 93.25 0.673 45.40 0.351 61.66 0.631
SA2 94.61 0.602 54.14 0.314 28.75 0.565
SA3 26.37 0.635 14.45 0.330 -16.26 0.595
advantages and disadvantages, but in general, they all show the same seasonal variability
during the year. However, the differences are most pronounced using the NDWI, as this
index is most sensitive to water content in the leaves of the vegetation. In this analysis, the
mSAVI2 appears to be less useful.
Chapter 4. Results and discussion 43
4.3 Variability of vegetation
4.3.1 Different vegetation types
Introduction
Each vegetation type, e.g. grassland or forest, has its own specific characteristics and growing
pattern. Therefore, a separation in vegetation classes is necessary. In this section, different
vegetation types from the second classification are weighed against each other in order to
analyze this variation. This classification, constructed on general vegetation features, divides
the vegetation in the NT in 5 different classes: a forest class, a shrub class, a woodland class
and two grassland classes. The abundance and abbreviations of these classes is shown in
Table 3.6, p28.
The analysis is performed on the data acquired in the reference year 2004, and only for
the unburned vegetation, as fire interference would hamper the study of the variation in
vegetation. Furthermore, as concluded in the previous section, the spatial variability in
vegetation patterns cannot be neglected and thus a separation in the three SA along the
north-south axis is requisite. All five vegetation types only occur together in SA2, and
therefore only SA2 is discussed in detail. The results of SA1 and SA3 can be found in the
Appendix (Section B.4 and B.5).
Preliminary visual interpretation
The trajectories of the five different vegetation classes for the NDVI and the NDWI can be
found in Fig. 4.2(a) and Fig. 4.2(b). On first notice, tussock grassland has a completely
different shape compared to the other four vegetation types. Furthermore, for FOREST and
SHRUB, a same trend can be detected; however the VI of the latter is generally higher.
Hummock grassland is situated at a similar height as the FOREST class, but has a slightly
different curvature. The WOODLAND class then again reaches a VI similar to that of
SHRUB, nevertheless both trajectories display a dissimilar curvature. On the figure based
on the EVI similar conclusions are drawn, while the differences on the figure based on the
mSAVI2 are less pronounced. Both figures can be found in Appendix, Fig. B.1(a) and Fig.
B.1(b).
Analysis of variance
The results of the multiple comparison test performed on all five vegetation classes is given
in Table 4.3 for the NDVI and the NDWI, and those based on the EVI and mSAVI2 can
be found in the Appendix (Table B.1). Similar to the table in the previous section, a row is
added to show the quantity of significantly different metrics per pair of vegetation types (T1).
Chapter 4. Results and discussion 44
(a) NDVI (b) NDWI
Figure 4.2: The trajectories of the five vegetation classes in SA2 for the NDVI and NDWI
Furthermore, in order to find the metrics most capable to discriminate amongst vegetation
classes, an extra column representing the count of significant differences between vegetation
classes per metric is added (T2).
A clear distinction is noticeable between all vegetation types. Especially HUMMOCK GRASS-
LAND shows significant differences with almost all vegetation classes for all metrics. The T2
column indicates that all vegetation classes generally show significant differences for the VI
value based metrics, the Smax and the VIS and finally also the integrated value I. Because
all vegetation types are subjected to the same seasonality, the specific timing in the year of
the former named metrics is less indicative for differences among vegetation classes. As well
as the NDVI, NDWI and EVI appear to be good discriminators amongst different types of
vegetation, only the mSAVI2 performs less, but still sufficient.
Chapter 4. Results and discussion 45
Table 4.3: The statistical output of the pairwise comparisons between the temporal trajectory metrics
of Group A and Group B for the NDVI and the NDWI. Signifcant differences are indicated
with ’x’.
NDVI
Group A Fo Fo Fo Fo Hu Hu Hu Sh Sh Tu
Group B Hu Sh Tu Wo Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 3.2E-196 x x x x x x x x x x 10
VImin 4.75E-96 x x x x x x x x 8
VIrange 8.6E-106 x x x x x x x x x x 10
DOYmax 1.56E-11 x x x x x x 6
DOYmin 5.06E-07 x x x x 4
DOYrange 2.31E-13 x x x x 4
Smax 2.5E-126 x x x x x x x x x 9
VIS 1.6E-108 x x x x x x x x x 9
DOYS 9.51E-39 x x x x x x x x 8
I 1.27E-76 x x x x x x x x x 9
T1 9 7 6 7 8 10 10 6 7 7
NDWI
Group A Fo Fo Fo Fo Hu Hu Hu Sh Sh Tu
Group B Hu Sh Tu Wo Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 1.4E-145 x x x x x x x x x x 10
VImin 2.01E-53 x x x x x x x x x x 10
VIrange 5.25E-41 x x x x x x x x 8
DOYmax 8.85E-66 x x x x x x x x x 9
DOYmin 8.6E-11 x x x x 4
DOYrange 1.98E-37 x x x x x x x 7
Smax 3.29E-30 x x x x x x x x 8
VIS 1.03E-44 x x x x x x x x 8
DOYS 2.76E-16 x x x x x x 6
I 7.05E-96 x x x x x x x x x x 10
T1 7 7 9 5 9 9 7 9 10 8
Discussion of the metrics
The mean value and standard deviation for all vegetation types per metric for each VI are
given in the Appendix (Table B.2).
Chapter 4. Results and discussion 46
The VImax value differs for all vegetation classes. According to the NDWI, the TUSSOCK -
GRASSLAND, SHRUB and WOODLAND trajectories peak highest, while the FOREST and
the HUMMOCK GRASSLAND classes peak far less high. The same trend can be observed
with the VImin, except for TUSSOCK GRASSLAND, which has the smallest amount of veg-
etation reflection in its minimum. This results in a very high amplitude of 0.30 units for
the tussock grasslands compared to WOODLAND and FOREST according to the NDVI and
NDWI. However, the VIrange for SHRUB and HUMMOCK GRASSLAND is strongly depen-
dent on the VI used. The NDWI demonstrates SHRUB has a relative small amplitude of 0.13
units, while HUMMOCK GRASSLAND fluctuates over 0.20 units, while the NDVI proves
this to be the exact opposite.
The DOYmax, DOYmin and DOYrange turn out to be far less indicative than the other metrics.
This is mainly because the seasonality is influencing all vegetation types at the same moment.
Some significant differences are observed as each vegetation types responds a little different
to e.g. drought or the inset of the wet season, but they are of little value to discriminate
among vegetation. Only when the NDWI is used, the DOYmax becomes a valuable metric to
detect significant differences after all.
Both Smax and the corresponding VI value, VIS, differ greatly among vegetation types for
all VI. The timing on which the maximum rate of decay is achieved (DOYS) is only useful
when the NDVI or the EVI is applied. Of all vegetation classes, the tussock class obtains the
highest rate of decay. This is because tussock grasslands are known for a fast regrowth from
a large persist seed bank in the wet season, followed by heavy decay in the dry season. Other
vegetation types are less productive or contain a significant amount of evergreen species,
spreading and diminishing the effect of decay. HUMMOCK GRASSLAND shows the lowest
rate of decay for most VI, confirming its low productivity and is persistence to drought.
The integrated value of the trajectories is also a valued metric for the comparison of different
vegetation types; however, not in case the mSAVI2 is used. As well as EVI, NDVI and NDWI
indicate the hummock grasslands as least productive vegetation type.
The analysis in the other study areas
For both SA1 and SA3, trends similar as for SA2 are observed. However, in the mean values
a clear north-south variation is noticeable: higher amplitudes and vegetation cover in the
north, less pronounced amplitude and vegetation cover in the south. Nonetheless, all VI are
suitable to distinguish the classes from each other. Also here, the best suited metrics are the
VI values, the maximum slope and the integrated value. However, the metrics based upon
the DOY also perform well in the south, as the seasonality in less pronounced and thus the
Chapter 4. Results and discussion 47
timing of certain proceedings is more directly related to the vegetation type.
Also remarkable is that the mSAVI2 is the best performing VI for the analysis in SA3. This
VI is less sensitive to the background scattering of the soil, which significantly increases in
the more sparsely vegetated arid south, and is therefore slightly better in discriminating
vegetation types than the other VI.
Conclusion
All vegetation classes differ significantly when compared to each other, hence a subdivision
of the vegetation types is required in further analysis. As stated before, a clear north-south
variability is observed; a high vegetation cover and amplitude, both gravely influenced by
the seasonality, in the north and less pronounced cover, amplitude and seasonality in the
south. Nonetheless, a similar trend within the same vegetation class is perceived across the
NT. Tussock grassland is the most productive class and shows the largest amplitude over a
growing season. In contrary to hummock grassland, which is the least productive vegetation
type.
The NDWI, EVI and NDVI are the best VI to implement this analysis in general, however
mSAVI2 performs slightly better in sparsely vegetated areas because of its ability to cope
better with the background scatter of the bare soil.
The VImax, VImin, VIrange, Smax, VIS, and I are the metrics showing most discriminative
power for vegetation types.
4.3.2 Significance of detailed subdivision of vegetation classes
Introduction
An assessment has to be made of which level of detail in vegetation classes is required to
obtain the best results for the study. The smaller the classes, the fewer pixels left for proper
study of variability, jeopardizing the trustworthiness of the result, as it becomes more sensitive
to various errors. On the other hand, the information is less influenced by the variation in
vegetation, which is reduced to a minimum. However, the more the vegetation is subdivided,
the more classes one needs to process.
To investigate the significance of detailed subdivision of vegetation types, 8 vegetation classes
are selected in the northern SA1 of the year 2004. As showed in Table 3.4, the broad vegetation
class BR FOREST from the first classification is divided in FOREST and WOODLAND in a
second classification. A third classification divides those two vegetation classes in respectively
SM WOODLAND ACACIA, SM WOODLAND EUCALYPT and SM WOODLAND OTHER,
Chapter 4. Results and discussion 48
and SM FOREST EUCALYPT and SM FOREST OTHER. The abbreviations used in the
figures and tables are given in Table 3.6, p28. The northern SA is selected as it is the only
SA which contains all the previous named vegetation classes without having influence from
seasonal parameters. Furthermore, only the unburned pixels are sorted for this particular sec-
tion as fire interference is undesirable when comparing different levels of detail in vegetation
classes. In order to compare the different VI, all four are used for this study.
Preliminary visual interpretation
A first visual interpretation of the different plotted trajectories of the broad and most detailed
vegetation classes for the NDWI and the NDVI, given in Fig. 4.3(a) and Fig. 4.3(b), show that
SM WOODLAND ACACIA, SM WOODLAND OTHER and SM FOREST OTHER differ
gravely from the BR FOREST class, while SM WOODLAND EUCALYPT only differs slightly.
On the other hand, SM FOREST EUCALYPT is nearly similar to the broad class. This is a
logical consequence as the BR FOREST is a mean value of all forests and woodlands, which
are dominated by eucalypt tree species (Table 3.6). This applies to all four VI, as can be seen
on the figures in Appendix (Fig. C.1).
(a) NDVI (b) NDWI
Figure 4.3: The trajectories of the broad and most detailed vegetation classes for the NDVI and
NDWI
Analysis of variance
In Table 4.4, the broad BR FOREST class is compared to its subdivisions for the NDVI and
the NDWI, in a search for significant differences in metrics. The results of the EVI and the
mSAVI2 can be found in the Appendix (Table C.1). The last row of each table represents the
quantity of metrics found significantly different. When compared to the second classification,
the general BR FOREST class is almost completely similar to the FOREST class for all VI.
Chapter 4. Results and discussion 49
Table 4.4: The statistical output of the pairwise comparisons between the temporal trajectory metrics
of Group A and Group B for the NDVI and the NDWI. Signifcant differences are indicated
with ’x’.
NDVI
Group A bFo bFo bFo bFo bFo bFo bFo Wo Wo Wo Fo Fo
Group B Wo sWA sWE sWO Fo sFE sFO sWA sWE sWO sFE sFO
Metric p-value
VImax 5.2E-217 x x x x x x x x
VImin 1.1E-227 x x x x x x x
VIrange 2.3E-102 x x x x x x x
DOYmax 1.13E-94 x x x x
DOYmin 7.87E-35 x x x x x x
DOYrange 1E-130 x x x x x x x x
Smax 1.8E-42 x x
VIS 1.5E-115 x x x x x x x
DOYS 1.65E-52 x x x x x
I 5.55E-46 x x x x
Total differences 4 9 5 9 1 0 7 10 0 8 0 5
NDWI
Group A bFo bFo bFo bFo bFo bFo bFo Wo Wo Wo Fo Fo
Group B Wo sWA sWE sWO Fo sFE sFO sWA sWE sWO sFE sFO
Metric p-value
VImax 1.3E-204 x x x x x x x x x
VImin 6.4E-194 x x x x x x x x x
VIrange 3.29E-26 x x x x x x
DOYmax 0.003335 x
DOYmin 6.55E-70 x x x x x x
DOYrange 2.51E-35 x x x x x
Smax 7.42E-32 x x x x
VIS 1.7E-99 x x x x x x x
DOYS 3.4E-117 x x x x x x
I 1.2E-164 x x x x x x x x
Total differences 6 6 6 7 0 1 9 5 1 9 2 9
According to the mSAVI2, the WOODLAND class also shows no significant differences with
BR FOREST, however, when the NDWI, EVI or NDVI is used, significant differences are
found for the VImax and the VImin. In this specific case, the NDWI is the VI best capable
to distinguish between woodland based classes and forest based classes, as it indicates six
metrics which differ significantly.
When BR FOREST is compared to the most detailed classification, more significant differ-
ences appear. The deductions from the visual interpretation are proved to be correct, as the
Chapter 4. Results and discussion 50
BR FOREST class has almost all metrics dissimilar to those of SM WOODLAND ACACIA,
SM WOODLAND OTHER and SM FOREST OTHER. Also SM WOODLAND EUCALYPT
appears to be different in most cases, especially in the VI values and their corresponding tim-
ing. Due the dominating presence of eucalypt forest, nearly no difference is found between
BR FOREST and SM FOREST EUCALYPT; only the NDWI is able to discriminate a sig-
nificant difference in VIrange.
To complete the study whether further subdivision is required, the second classification is
compared to their specific subdivision from the third classification, respectively; FOREST is
compared to SM FOREST EUCALYPT and SM FOREST OTHER, and WOODLAND to
SM WOODLAND ACACIA, SM WOODLAND EUCALYPT and SM WOODLAND OTHER.
The results for the NDVI andd the NDWI are given in the right columns of Table 4.4
and for the EVI and mSAVI2 in the Appendix (Table C.1). The first case proves the
SM FOREST OTHER class strongly deviates from FOREST, while nearly no significant dif-
ferences between metrics are found in the comparison of FOREST with SM FOREST EUCAL-
YPT, as most of the forests consist of eucalypt tree species. Here, NDWI proves to be the
best index for discriminating vegetation as it acquires most significant different metrics when
comparing the second with the third classification. In the second case, where WOODLAND
is compared to its subdivisions, nearly no significant differences are found between WOOD-
LAND and SM WOODLAND EUCALYPT. This also is explained by the dominating pro-
portion of eucalypt tree species in the vegetation cover. Nevertheless, WOODLAND differs
strongly from SM WOODLAND OTHER and SM WOODLAND ACACIA. Depending on
the VI, all or almost all metrics are significantly different, indicating a certain need to split
the vegetation in its most detailed classes possible. When the profiles based on mSAVI2 and
NDWI are used, the differences found are less pronounced for SM WOODLAND OTHER
and SM WOODLAND ACACIA respectively.
Conclusion
When the general vegetation types are compared to the species specific subdivisions, sig-
nificant differences appear in most cases. Especially the NDWI is a good discriminator
as it generally acquires more significant different metrics in comparison to the other VI.
The specific information gained in the study of the most detailed classes is of little value
due to the minute surfaces they represent in the NT, e.g. 0.1% and 0.3% for respectively
SM WOODLAND ACACIA and SM WOODLAND OTHER. Hence, the detailed vegetation
types are not further used in this thesis and only the classes generated in the second clas-
sification are applied. However, when a study on a relative small spatial extend would be
performed, a further subdivision of the vegetation classes would be useful.
Chapter 4. Results and discussion 51
4.4 Variability caused by fire events
4.4.1 Introduction
Fire events have a certain impact on the vegetation. In this section, the severity of that
impact on the vegetation cover is assessed. The prior knowledge of the fire history in the NT
enables a classification of the vegetation cover into burned (B) and unburned (UB) classes.
When vegetation did not burn over the whole period of study, from 2001 up to 2008, it is
assigned to a third class: the never burned vegetation (NB). To obtain information whether
significant differences are observed among burned, unburned or never burned vegetation and
to describe differences in burning behavior between vegetation types, a pairwise comparison
is performed per the vegetation class from the second classification in the year 2004 for all VI
and SA.
4.4.2 Analysis of the variance
For particular vegetation classes some SA will not be discussed because the vegetation type
does not occur or did not burn in that specific SA.
Vegetation class: Forest
The FOREST class did not burn in SA3, therefore this SA was not considered in this analysis.
The results of the multiple comparison test is returned in Table D.1 and the mean values in
Table D.2 in the Appendix. In order to facilitate the interpretation of the results, the NDWI
curves of the burned, unburned and never burned forest cover in SA2 is returned in Fig.
4.4(a). For all VI, the differences of the trajectories in SA1 are less pronounced than in SA2
and therefore they are discussed separately.
In SA1, only a few differences are observed comparing B with UB and even no differences
at all are found using the NDVI. Bushfires generally do not affect the top tree layer in
forests and therefore, the reflectance values only show minute differences between burned
and unburned vegetation. More significantly different metrics are found when comparing
the B and NB vegetation, leading to the assumption that the forest vegetation which has
not burned the entire study period is solely influenced by the seasonal variation, while the
vegetation which has burned recently, still needs to recover and is therefore more closely
related to the vegetation that burns in the studied year.
For all VI, the maximum rate of decay (Smax) is considered significantly different between B
and NB, as fire induces a sudden change in vegetation cover. Also the VImin is a characteristic
metric for burned vegetation. Because fires burn a significant amount of biomass, the VImin
Chapter 4. Results and discussion 52
is proved lowest for B, according to all VI. Furthermore, the amplitude VIrange, strongly
related to VImin and VImax, is in most cases largest for B, because a high vegetation cover
provides more fuel for bushfires to burn down to a minimum cover. Except for the mSAVI2,
the DOYmin, DOYmax, DOYrange and I are found significantly different for B versus NB.
So briefly; the forest cover that reaches its VImax early in the year is more likely to burn,
which results through the highest amplitude in the lowest VImin at the end of the season. For
significant differences between B and UB, the comparison of fire history in FOREST appears
to be inadequate.
In SA2 more significant differences are observed, especially where B is compared to UB. Also,
similar to SA1, UB differs considerably from NB. All VI are able to discriminate B, UB and
NB very well, but mSAVI2 excels. In the multiple comparison test, almost all metrics show
significant differences in all cases. Particularly VImax, VImin, VIrange, Smax and VIS perform
outstanding. The DOY-based metrics are less important, except when using the mSAVI2,
where they perform excellent as well. The NDWI returns the most logic values in Table D.2.
The burned pixels have the fastest rate of decay at the end of the dry season (DOY 266,
or around mid-September), resulting in the lowest VImin value and thus highest amplitude.
These are all characteristics associated to fire activity.
In general, the analysis of the fire history in the FOREST class is a difficult task to perform,
mainly because bushfires mostly occur in the lower strata of the vegetation, which are often
covered by the commonly evergreen tree layer. The analysis in SA2 gives better results
compared to SA1 as tree cover is a little less dense in the non-tropical zone. However,
significant differences are found for several metrics, particularly for Smax and the three VI
metrics. The NDWI and EVI perform well in both SA and mSAVI2 performs outstanding in
SA2.
Vegetation class: Woodland
The WOODLAND class has only 21 burned pixels in SA3. The risk for a significant error is
very high, so SA3 is excluded from this analysis. The results of the multiple comparison test
and the mean values are given in respectively Table D.3 and Table D.4 in the Appendix. A
figure to facilitate the interpretation of the results is shown in Fig. 4.4(b).
Both EVI and NDWI perform very well in discriminating B, UB and NB in both SA. The
mSAVI2 performs best in the comparison B versus UB and B versus NB, while NDVI generally
performs less, particularly in SA2, but it is still able to find significant differences.
Also in the WOODLAND class the VImax, VImin, VIrange and Smax are outstanding metrics.
For the northern SA, DOYmin and DOYrange are also metrics found significantly different
Chapter 4. Results and discussion 53
in most cases. Overall, the burned trajectory reaches a rather similar peak height as its
unburned and never burned opponent, but has a significant lower VImin. Therefore, the
VIrange is remarkable higher for B. The VImin is reached much later for B than for UB and
NB, and subsequently, the period of decay is significantly larger, as evidenced by DOYrange.
The maximum decline Smax for the burned vegetation curve is generally less steep than for
UB or NB. This is probably caused by the moderating effect of the tree layer. In the south,
the corresponding DOYS is mostly situated later in the year, as the fire commonly hits late
in the dry season.
Vegetation class: Shrub
Similar to WOODLAND, only 78 pixels burned in SA3. For the same reason the results of
the analysis in this SA are discarded. In the Appendix Table D.5 the results of the multiple
comparison test are shown and in Fig. 4.4(c) the trajectories of SA2 are plotted to assist
the interpretation. The mean values and the standard deviation is given in Table D.6 in the
Appendix
The VI are able to discriminate very well amongst fire history. Only the mSAVI2 encounters
some difficulties distinguishing B from UB in SA2, as only three metrics are found significantly
different. Also the NDVI performs less well, compared to the other VI. Similar to previously
discussed vegetation classes, the characteristic metrics to discriminate B, UB and NB are the
VI-based metrics, the integrated value I and the maximum rate of decay Smax.
The discussion of the metrics is based on the best performing VI: the NDWI and the EVI. In
general, the burned vegetation reaches the highest VImax in comparison to the other groups.
In addition it also acquires the lowest VImin, which consequently results in the largest VIrange.
The moments on which these specific VI values occur are of less importance. The maximum
rate of vegetation decay for B is by far the highest according to all VI, except for the NDVI.
The accumulated biomass over the year, I, is often highest for the burned curve.
Vegetation class: Tussock grassland
None of the tussock grassland in SA3 has burned in the studied year and is subsequently not
enclosed in the analysis. The results are given in Table D.7 and Table D.8 in the Appendix
and an illustrating figure of the temporal trajectories is shown in Fig. 4.4(d).
To cover the discrimination between B versus UB and NB, all VI at least show 6 significant
different metrics, but except for NDVI and EVI in SA2, 8 or more differences are discovered.
The dissimilarities between UB and NB are less pronounced, in particular when using the
mSAVI2. Since these grasslands quickly recover from the frequent burnings, the unburned
Chapter 4. Results and discussion 54
vegetation is much more similar to the never burned vegetation compared to the same situa-
tion in more woody vegetation, which needs several years to recover, as discussed before.
For tussock grasslands, VImax is lower for B than compared to the other two groups in SA1,
while in SA2 it is the other way around. According to the corresponding DOYmax, the VImax
of B is reached much earlier in SA1 and much later in SA2, which might be a result of APB-
activities. The minimum VI value of the burned vegetation is clearly the lowest and reached
at the end of the dry season. For Smax and DOYS, a clear distinction is made between SA1
and SA2. In the north, DOYS for B is situated before NB and UB, while in SA2 the period of
maximum decay is later than NB and UB. The difference between SA1 and SA2 is most likely
a result of the fire management in the north, where the grasslands are burned down early in
the dry season in order to control the late dry season fires. The controlled fires rapidly burn
down most of the grassland, preventing the vegetation to reach a high cover and consequently
circumvent a high VImax or I. Furthermore, both I and Smax are much bigger for the burned
vegetation than for UB and NB in SA2 and are situated at the end of the dry season, when
uncontrolled devastating bushfires destroy the cover.
Vegetation class: Hummock grassland
Hummock grasslands do not occur in SA1, therefore only SA2 and SA3 are discussed. The
results of the multiple comparison test is returned in the Appendix (Table D.9 and Fig. 4.4(e)
and Fig. 4.4(f) represents the temporal trajectories of the burned, unburned and never burned
hummock in SA2, to ease the interpretation. The mean values and standard deviations can
be found in Table D.10 in the Appendix.
According to all VI, as well B as NB and UB are considered significantly different for the
majority of the metrics. The EVI and mSAVI2 show a poor performance in SA2 compared
to the NDWI and NDVI. In SA3, all VI perform well. Also for the hummock grasslands,
the characterizing metrics to discover differences in the fire history are VI-based or related to
the maximum derivative. For NDWI and EVI, the integrated trajectory also is significantly
different for all groups.
In contrast with the tussock grasslands, the variability between UB and NB is strongly
marked. Hummock grassland is known to have a smaller productivity than tussock grass-
land and thus a slower recovery from severe bush fires, typically occurring in the arid south.
Therefore, the impact of a fire event is still noticeable in the succeeding years.
The hummock grasslands which burned are characterized by the highest VIrange, as they
possess a slightly larger VImax and a slightly lower VImin compared to grasslands that will
not burn that year. For nearly all burned curves, the VImax is reached earliest and the VImin
Chapter 4. Results and discussion 55
last in the year, however there is a great variability between the different VI. Furthermore,
Smax is unambitiously the highest for the burned trajectory, which symbolizes the rapid
withering of the vegetation by a fire event. The DOYS indicates the bushfire was situated
around mid-October (± DOY 290). According to the NDVI and the EVI, the I is largest for
the B. Hence, the biomass production in hummock grasslands shows a strong correlation with
the fire-sensitivity.
4.4.3 Conclusion
The variation caused by the fire history is well perceptible in the temporal trajectories. The
quantity of the significant differences found strongly depends on which VI is used. Overall,
the NDWI is the best performing index to distinguish between B, UB or NB. However, it is
recommended to use different VI to analyze the fire history.
Also a substantial variability is noticed between the different vegetation types and within
vegetation types in different SA. For most vegetation types the characterizing metrics are
VImax, VImin, VIrange and Smax, however, for some also DOYS, I, DOYmin and DOYrange are
good differentiating metrics.
In general, the B trajectories possess a high VImax and, more distinct, a low VImin compared
to those of the UB and NB. Combining both metrics results for nearly each case in the highest
VIrange. The physiological interpretation of those metrics shows that vegetation with a high
maximum vegetation cover is more likely to burn, and consequently results in a very low cover
after a burning event. Thus the high amplitude of the vegetation cover is a characteristic for
vegetation that has burned during the studied year. Another characteristic metric for B
is Smax. This metric represents the maximum rate of decay, which is high for events like
fire, as a lot of biomass evanesces in a short period of time. The corresponding DOYS and
VIS repeatedly discriminate B from UB and NB as well. During the dry season all vegetation
gradually dries out and when enough biomass is left at the end, bushfires take place. Therefore
for B, the moment of maximum decay is situated much later than for UB and NB, at the end
of the dry season. The VIS is much higher at that specific time of the year (DOYS) for B than
it is for UB or NB, expressing the relative high biomass at the end of the dry season. The
integrated value I, related to the primary production, is in general highest for B. However,
this strongly depends on the used VI. A high primary production of vegetation is associated
with a high amount of fuel and is consequently more likely to burn.
Chapter 4. Results and discussion 56
(a) Forest (b) Woodland
(c) Shrub (d) Tussock grassland
(e) Hummock grassland: SA2 (f) Hummock grassland : SA3
Figure 4.4: The B, NB and UB trajectories for the different vegetation types, based on the NDWI.
Chapter 4. Results and discussion 57
4.5 Comparison with the reference year (2004)
4.5.1 Introduction
The previous analyses and the literature prove the strong dependence of vegetation on the
climate and the seasonality. In this section, an assessment is made of whether the temporal
variability of the seasons is reflected in the temporal trajectories. For this analysis, multiple
years, from 2001 till 2008, are compared to each other in order to find significant differences.
Furthermore, the mean values of all metrics are studied and the values deviating from those
of the reference year 2004 are discussed. Also the link between the deviations and the severity
of the bushfire occurrence each year is discoursed (Table 3.5, p27).
In total, two vegetation types are investigated. To cover the differences between grassy
and woody vegetation types, respectively the class TUSSOCK GRASSLAND and FOREST
are selected. Furthermore, as fire events have a severe impact on the vegetation cover, the
burned and unburned vegetation in each class is handled separately. In order to omit the
spatial variability, only the data from SA2 are included in this inquiry.
4.5.2 Analysis of variance
The results of the multiple comparison test for FOREST are displayed in Table E.1 and the
results for TUSSOCK GRASSLAND in Table E.2, both can be found in the Appendix. In
these tables, a row is added to show the quantity of significantly different metrics per pair of
years (T1). Furthermore, in order to find the most discriminative metrics, an extra column
representing the count of significant differences amongst years per metric is added (T2).
All vegetation in 2004 differs strongly from the other years, as well for the unburned as the
burned vegetation. Both NDVI and EVI perform markedly better than NDWI and mSAVI2.
The NDWI has difficulties finding differences in the burned vegetation, while mSAVI2 strug-
gles with the unburned vegetation. For that reason, only the NDVI and EVI are discussed in
this analysis.
According the calculations based on the NDVI, most significant differences for the unburned
forest are found for VImin, DOYmin and VIS and least for DOYmax. The burned vegetation
differs most for I and, similar to the unburned forest, least for DOYmax. For tussock grass-
lands, analogous to forest, least dissimilarities are found for DOYmax. However, for all other
metrics, generally more significant differences are found in each specific case, especially for
the burned vegetation.
The results based on the EVI resemble to those established with the NDVI. Nevertheless,
the reduced performance of the DOYmax is less pronounced for the forests, and furthermore,
Chapter 4. Results and discussion 58
only 3 significant different Smax are found for the unburned forest. For unburned tussock
grassland, similar to the NDVI, the DOYmax performs poorly. However, for burned tussock
grassland DOYmax establishes outstanding results.
4.5.3 Discussion of the metrics
In Table E.3 and Table E.4 in the Appendix, the mean NDVI and EVI values of all metrics
and their standard deviation can be found for respectively forest and tussock grassland. The
NDVI and EVI are discussed together, unless a remarkable difference amid both is noticed.
Forest
The VImax of the unburned forest is generally lower in a year subsequent to a pronounced fire
year. This is the case for 2005 and 2008, when in 2004 and 2007 respectively 27% and 39%
of SA2 burned down. Only for 2002, after 37% of SA2 was affected by bushfires in 2001, no
lower VImax is perceived. For the burned forest, no such distinct trend is observed, except in
2005, with a slightly lower VImax. The corresponding DOYmax is not an adequate metric to
discriminate the fire history, as only few significant differences are detected.
The minimum reflectance values are overall a little lower for the burned forest vegetation
compared to the forest that did not burn. In 2001 and 2006, the VImin is relative high, while
the VImin in the other years have approximately the same magnitude as in 2004. No immediate
relation with the fire history is discovered. This is probably caused by the evergreen tree layer,
the covering the lower strata, which are more sensitive to fire. The moment on which the
vegetation reaches its minimum reflectance, DOYmin, is generally later for B than for UB, as
fire hits at the end of the dry season.
The VIrange is always strikingly higher for the burned forests. The unburned forest vegetation
in 2005 and 2008 attained a remarkable low VIrange, denoting a low influence of seasonal
factors. In 2007, the severest burning year, the VIrange is highest compared to the other
years. The high VIrange implements a lot of vegetation, and accordingly fuel, was produced,
which was burned down by harsh fire events. The DOYrange is spectacularly lower in 2001
and 2005 and combined with a rather small VIrange, it proves that those years experienced a
rather mild dry season.
The period of maximum decay (DOYS), the corresponding VI value (VIS) and the maximum
rate of decay (Smax) strongly depend on the VI used for the calculations. Generally, a much
steeper slope is attained for the burning vegetation. The moment on which the maximum
vegetation decay occurs is annually defined, for B is this mostly at the end of the dry season.
However, in some cases it occurs halfway or even in the beginning of the year season, e.g. for
Chapter 4. Results and discussion 59
the NDVI in 2002. For the unburned vegetation, Smax is located in mid dry season. In 2007
and 2002, the maximum rate of decay for the unburned vegetation is situated early in the
year, with a same trend for DOYmax, which indicates an early start of the dry season. In those
cases, DOYS for the burned vegetation stipulates also an early start of the fire season, only a
little later than for UB, but much earlier compared to the other years. The VIS is remarkably
low in 2005, a year with an abnormally low burning frequency. Years with substantial fire
activity usually have a high VIS for the burned vegetation.
The integrated value I, related to the primary production in a year, is higher for the vegetation
that burned in the year than for the unburned vegetation that same year. However, in 2005
this is exactly the opposite. In 2001, 2008 and more extreme in 2005 a much lower I is
achieved.
Tussock grassland
In tussock grasslands, the differences in the maximum reflectance between severe burning
years and less severe burning years is much more pronounced. For the first, respectively
2001, 2004 and 2007, the VImax peaks significantly higher than for the other years, while the
subsequent years, respectively 2002, 2005 and 2008, characterized with a very mild fire season,
the VImax is far lower than for average years. Similar to the FOREST class, the corresponding
DOYmax is not considered to be a suitable metric to discriminate the fire history.
The values for VImin are all close to the average value, however in 2001, 2003 and 2006 they
are slightly higher. The VImin is unanimously reached at the end of the dry season, only in
2005, it tends to be reached a little sooner.
For VIrange, a similar trend is observed as for forests, however, the differences are far less
pronounced. The trend of DOYrange differs a little from that one of the forest class, as only
2005 attains a remarkable smaller value.
Also the trends for Smax, VIS and I are similar to those observed for forest. However, DOYS
is situated almost 2 months sooner.
4.5.4 Conclusion
The temporal variability is strongly pronounced in SA2. All years in the study period, from
2001 until 2008, have multiple metrics significantly different from the reference year 2004.
Particularly the EVI and the NDVI are able to differentiate the different study years. Some
dissimilarities are perceived amongst different vegetation types, however, the overall trends
are noticeable in each type. Furthermore, the severity of a particular fire season is well
Chapter 4. Results and discussion 60
detected, especially in the VI-value based metrics and the integrated trajectory. A trajectory
with a high VImax and I value tends to be susceptible for fire. This is clearly visible in the
severe burning years: 2001, 2004 and 2007; while in 2005, with an overall low productivity,
nearly no fire events were registered.
4.6 The comparison of SPOT- versus MODIS-imagery
4.6.1 Introduction
The reflectance data used in the previous analyses is provided by the MODIS sensor. With
a spatial resolution of 250m, an enormous amount of data and information needs to be pro-
cessed. Therefore a study is performed to analyze the surplus value of that extra information
and whether imagery with a lower spatial resolution can provide sufficient information to
achieve similar results and conclusions. To accomplish this study, the information based on
the MODIS sensor is compared to that of the SPOT-Vegetation sensor. The spatial resolution
of the latter is approximately 1km, which comes down to one SPOT-pixel for four MODIS-
pixels. This reduces the data size with factor 4, but also the information on the same area is
reduced with the same factor. Therefore, a trade-off needs to be made between the data size
and the quantity of information.
The SPOT-data are provided and analyzed by Ellemie Comeyne. She performed a similar
study as expounded in the previous chapters, based on SPOT-Vegetation imagery. In a first
comparison, the ability to distinguish burned, unburned and never burned vegetation for
several SA, VI and vegetation types for both data types is weighed against each other. A
second comparison includes a classification test. Based upon the results from the previous
analyses, burned metric values are used to set a prediction interval typical for a specific
vegetation type which has burned, while unburned metric values are picked as a condition for
the unburned equivalent. These intervals are used for a classification of new randomly selected
pixels. The percentage of the pixels classified correctly in the group ’burned’ or ’unburned’
is compared and discussed for both MODIS and SPOT.
4.6.2 Comparison of the ability to cope with variance
The vegetation classes used for the SPOT-data are the same as used for the MODIS-data,
except the TreeE class, which is the abbreviation for eucalypt forests. This class is compared
to the FOREST class, as most of the forests in the NT are eucalypt forests (see Table 3.6,
p28). Apart from that, the study performed on the SPOT-data is completely analogous to
that on the MODIS-data.
The MODIS-based data are found in Table D.1, D.5, D.7 and D.9 in the Appendix and the
Chapter 4. Results and discussion 61
SPOT-based data, provided by Ellemie Comeyne, are returned in Table F.1, F.2, F.3 and F.4
in the Appendix.
In the FOREST class, MODIS performs generally better than SPOT-Vegetation, as in nearly
all cases more significant different metrics are observed. However, in SA1, the distinction
between burned and unburned vegetation is more pronounced for SPOT-based NDVI data.
Where the analysis based on MODIS-data is not able to find any significant differences, the
calculations with SPOT-imagery discloses 3 differences. Also for the discrimination between
NB and UB with the NDWI, SPOT finds 6 significant different metrics, while MODIS is able
to find only 4. Remarkable is the immense difference in performance amid the mSAVI2 for
SPOT and the mSAVI2 for MODIS.
When the SHRUB class is compared, similar conclusions are drawn. The general performance
of the MODIS-based analysis is superior to that based on the SPOT-data, except for the
discrimination between B and UB in SA2, using the mSAVI2. Noticing 5 significantly different
metrics, SPOT outranges MODIS with 2 significant differences more. For all other cases,
MODIS-imagery clearly stands out.
For tussock grasslands, the MODIS-based analysis achieves the best results for each case.
Similar to the former results, the outcome of the analysis of hummock grasslands based
on MODIS-imagery is in general more distinct compared to those based on SPOT-imagery.
Especially for the NDWI and the mSAVI2, MODIS finds more significant differences than
SPOT is able to find. However, for the NDVI this is less obvious. For both SA2 and SA3,
the variation between B and NB is perceived better using SPOT-data, as more significant
different metrics are observed. An analogous finding is found for the appraisal between UB
and NB in SA1.
Briefly can be concluded that the level of detail in the results considerably improves with
a higher spatial resolution. For MODIS, with a spatial resolution of 250m, generally more
significant different metrics are found in approximately all cases, when compared to the same
cases using SPOT-data (spatial resolution 1km).
4.6.3 The classification method
Method and results
The classification method classifies new random trajectories into a burned or unburned class.
Based upon the metric values from the former studies, a 95% prediction interval is calculated
for a burned and an unburned vegetation status for each metric per vegetation class per SA.
Furthermore, a new dataset is created which contains metrics from test-trajectories calculated
Chapter 4. Results and discussion 62
with new random chosen pixels. For each vegetation class discussed above, 50 burned and 50
unburned NDVI profiles in each SA in 2004 are extracted. Those profiles are put together per
vegetation class and per SA and based upon the prediction interval of one or several metrics,
all 100 test-trajectories are allocated whether their metric values are located in the specific
interval or not.
As not all metrics appeared to be equally well discriminators, only those performing good
in the analysis of the variability caused by fire events are selected. Both VImax and Smax
performed outstanding and are therefore employed for the two classifications based on the
prediction interval of one single metric. Furthermore, 4 classifications are based upon com-
binations of several metrics. Two classifications use a combination of 2 metrics, respectively
VImax-Smax and VImax-DOYmax, while the 2 other classifications are based on 3 metrics;
namely VImax-Smax-I and VImax-VImin-VIrange.
In order to compare SPOT- and MODIS-imagery, all classifications are performed for both
MODIS- and SPOT-based data.
The results are returned in Table F.5 (VImax), F.6 (Smax), F.7 (VImax-Smax), F.8 (VImax-
DOYmax), F.9 (VImax-Smax-I) and F.10 (VImax-VImin-VIrange) in the Appendix. In each
table per vegetation class and per SA, the burning status of the trajectories used for the
calculation of the prediction interval is given. Furthermore, one column with the amount
of correct assigned test-trajectories and one column with the number of incorrect assigned
test-trajectories are returned. An assignment is correct if, for example, an actually burned
test-trajectory is located within a prediction interval based on burned trajectories, however,
when this occurs to an unburned test-trajectory, the assignment is considered incorrect. Both
columns have a maximum of 50, as exact 50 out of 100 trajectories are correct for each case.
A third column contains the ’performance’. This is a percentage calculated by the subtraction
of incorrect assignments from the correct assignments, divided by 50. The last row of each
table contains two average performances, which are summarized in Table 4.5. This table is
used to compare SPOT- versus MODIS-imagery.
An example to ease the interpretation: In Table F.7, a combination of two prediction intervals
is considered for each case, respectively one interval for VImax and one for Smax. When
both corresponding metric values of a test-trajectory are located within those prediction
intervals, it is considered to be in the same class as the class used for the calculation of
the prediction interval. The case SHRUB in SA1 for MODIS has two of those VImax-Smax
prediction intervals; one based on UB metrics, which will analyze if the test-trajectory fulfills
the requirements to be classified as UB or not, and one based on B metrics, which analyzes
if the test-trajectory is suited to be classified as B or not. According to the intervals based
Chapter 4. Results and discussion 63
on B, 51 of the 100 trajectories are considered B, from which 47 actually burned (correct)
and 4 did not (incorrect). Then again, according to the intervals based on UB, 50 of the 100
test-trajectories are suited for UB, from which 48 actually did not burn (correct) and 2 did
burn (incorrect). The highest performance is achieved by UB, as it found nearly all actual
UB test-trajectories, with almost no miscalculations.
Table 4.5: The average performances of the classification test, sorted per metric used for the calcu-
lation of the prediction interval
Used Metric(s) MODIS SPOT
VImax 61.6% 7.8%
Smax 38.9% 16.3%
VImax-Smax 70.8% 18.6%
VImax-DOYmax 66.3% 14.5%
VImax-Smax-I 70.6% 18.9%
VImax-VImin-VIrange 71.1% 23.5%
Comparison of the performances: MODIS versus SPOT
The average performances in Table 4.5 show the ability and the accuracy of the prediction
intervals, based on single metrics or a combination of metrics, to correctly classify new trajec-
tories and thus to form representative classification thresholds. It is clear that in all cases, the
performance of the analyses designed with MODIS-imagery scores higher than those based
on SPOT-data. These tests leave no doubt that higher spatial resolution imagery gravely
facilitates the precision to analyze variation caused by various events.
Other remarks
When the results of the classification are studied more closely, a general trend is observed
regarding the amount of metrics involved in the demarcation of the classification thresholds
and the accuracy of the classification. When a test-trajectory needs to meet the conditions
of several prediction intervals, a slight decrease of the correct classified test-trajectories is
perceived, and more importantly, the amount of incorrect classified trajectories decreases far
more, which results in a better performance. This shows the advantages of the contemplation
of several metrics.
This method can be used in the first step of the prediction of fire events. The VImax, based on
MODIS-imagery, appears to be able to discriminate and classify rather well, in particular for
both grassland classes. Consequently, the maximum VI values of the temporal trajectories,
usually achieved at the end of the wet season and thus prior to the burning season, can be used
Chapter 4. Results and discussion 64
to determine the pixels with a great likelihood to burn later in the season. This information
can be consulted to plan and assist APB-activities.
4.6.4 Conclusion
Both methodologies to analyze the differences in performance between SPOT- and MODIS-
imagery lead to the similar conclusion: the surplus value in the performance and accuracy
of the results based on data with a higher spatial resolution is worth the extra amount of
data it brings with it. Consequently, the use of MODIS-imagery is preferred above the use
of SPOT-data in the analyses performed for the characterization of the temporal profiles and
the prediction of fire events.
Chapter 5
Conclusion
The major objectives in this thesis are the assessment of how remote sensed MODIS-data
can be employed in order to detect and characterize land cover change in the NT, and a
subsequent detailed analysis of the variance in the MODIS-data induced by several factors,
e.g. fire events, temporal and spatial variation and differences in vegetation types.
Characterization of the profiles
The change detection technique employed in this thesis is the temporal trajectory analysis,
which requires the development and the characterization of a temporal profile. The temporal
profiles are constructed based on a median value of 25 pixels. To assess the variability in the
NT, the profiles of different SA, vegetation types and burning statuses are compared on an
inter- and intra-annual basis. To compare the different temporal trajectories, a sixth grade
polynomial curve is fitted to the trajectory, which allows the calculation of ten different char-
acteristic metrics. The metrics used in this thesis are the maximum and minimum reflectance
value, their corresponding timing in the year, the amplitude of the reflectance value and the
time span to go from the maximum to the minimum reflectance value, the maximum rate
of decay, the moment of the maximum rate of decay and its corresponding reflectance value
and finally the integrated value. All are strongly related to the status and the phenology of
the vegetation in the NT. To cover the different facets of the variation, four different VI are
used: the NDVI, the EVI, the NDWI and the mSAVI2, each having its specific advantages
and disadvantages.
Analysis of the profiles
The spatial variation is well described in the reflectance values. This is mainly because
the different climatic conditions; the north is heavily influenced by the monsoon, while this
influence diminishes more to the arid south. Especially the metrics directly related to the
65
Chapter 5. Conclusion 66
vegetation cover and biomass show a typical north-south variation.
Comparing the different vegetation classes revealed the significant differences amongst vegeta-
tion types. Each vegetation class is typified by its own annual cycle and specific characteristics,
despite the intense correlation to the season. When the vegetation classes are subdivided into
more detailed classes, the species specific classes show some significant differences with the
general vegetation classes. However, as the surfaces of the detailed classes are rather small
regarding the total area of the NT, the surplus value of the information gained is too small
to compete with the additional processing work that needs to be done. Nevertheless, when a
similar study is performed in relative small regions, a subdivision is recommended.
When the fire history of different vegetation types is studied, a substantial variability between
the vegetation classes and within these classes in different SA is observed. The B trajectories
distinguish themselves from UB and NB trajectories by a relative high maximum reflectance
and a more distinct low minimum reflectance, leading to a typical high amplitude. The typical
high amplitude is a result of the greater likelihood of vegetation with a high cover to burn,
resulting in an extra low vegetation cover afterwards. Another representative metric for B is
the maximum rate of decay, due the rapid evanesces of biomass in a short period of time.
The temporal variability is verified by comparing trajectories from the reference year 2004 to
equivalent trajectories of the other years in the study period (2001 - 2008). This temporal
variability is strongly pronounced in nearly all cases. Also the severity of the fire season
is captured in the reflectance values. Years with a high biomass production tend to be
susceptible for a severe fire season, while years with an overall low vegetation productivity
have a tendency to undergo a rather mild fire season.
A final objective is the comparison of low spatial resolution data (SPOT-Vegetation, 1km)
with higher spatial resolution data (MODIS, 250m). This is accomplished with two differ-
ent methodologies. A first methodology is the comparison of the results obtained from an
analogous analysis, in which the more detailed results obtained with the MODIS-imagery are
favored. However, the results achieved with SPOT-imagery were generally of enough detail
to observe similar trends. The second method is the comparison of the performance of a clas-
sification whereby new test-trajectories are classified by means of 95% prediction intervals.
In this test, MODIS is strongly favored due its remarkable higher performances. The surplus
value of the information obtained with MODIS-imagery is worth the extended data size to
be processed.
The different VI have their specific advantages and disadvantages in each case. So performs
the mSAVI2 rather well in the arid regions, typified by an increased soil backscatter. The
Chapter 5. Conclusion 67
NDVI and EVI are both sensitive for the vegetation cover, but is the EVI less easy saturated
when employed for dense covers with multiple vegetation strata. The NDWI on the other hand
is sensitive for the water content in the leaves, which is more directly relatable to fire activity.
Therefore it is recommended to use multiple VI next to each other. A same conclusion
is applicable to the use of metrics. The advantages of combining several metrics are also
demonstrated in the classification test, as the number of incorrect classified test-trajectories
declines when a combination of several metrics is used.
The findings in this thesis can assist the planning of APB-activities as they prescribe several
necessities for the study of temporal trajectories. Depending on the objectives of a future
study, a well-considered selection of VI and metrics needs to be applied. Also a clear dis-
tinction needs to be made between different vegetation types and, subordinate to the spatial
extent of the study area, a further subdivision of the vegetation classes could facilitate the
interpretation of obtained information. Furthermore, one has to consider the explicit north-
south variation in studies over large areas covering several climatic regions. Next, apart from
the spatial variability, also the temporal variability over different years is of great importance
and needs to be taken into consideration. Depending on the means and the computing ca-
pacity available, high spatial resolution imagery is preferred as it improves the quality of the
analyses. Finally, it is recommended to use a combination of metrics and VI when predicting
the likelihood of future fire events.
Appendix A
Used floristic classes
68
Appendix A. Used floristic classes 69
Table A.1: The floristic classes per vegetation class
Classification I Classification II Classification III Broad floristic class
BR FOREST FOREST SM FOREST ACACIA Acacia (mixed) closed forest
Acacia open forest
SM FOREST EUCALYPT Eucalyptus open forest
SM FOREST OTHER Allosyncarpia closed forest
Melaleuca open forest
Rhizophora low closed forest
WOODLAND SM WOODLAND ACACIA Acacia low open woodland
SM WOODLAND EUCALYPT Eucalyptus low open woodland
Eucalyptus low woodland
Eucalyptus open woodland
Eucalyptus woodland
SM WOODLAND OTHER Casuarina woodland
Corymbia low open woodland
Corymbia low woodland
Corymbia woodland
Lysiphyllum low open woodland
Melaleuca low open woodland
Melaleuca low woodland
Terminalia (mixed) low open woodland
BR GRASSLAND TUSSOCK GRASSLAND TUSSOCK GRASSLAND Aristida (mixed) sparse tussock grassland
Astrebla low tussock grassland
Chrysopogon (mixed) low tussock grassland
Chrysopogon (mixed) tussock grassland
Enneapogon tussock grassland
Eragrostis (mixed) low open tussock grassland
Oryza tall closed tussock grassland
Panicum (mixed) tussock grassland
Vetiveria (mixed) tussock grassland
Xerochloa tussock grassland
HUMMOCK GRASSLAND HUMMOCK GRASSLAND Triodia hummock grassland
Triodia low hummock grassland
Triodia low open hummock grassland
Triodia open hummock grassland
BR SHRUB SHRUB SHRUB Acacia open shrubland
Acacia sparse shrubland
Acacia tall open shrubland
Acacia tall sparse shrubland
Atriplex low sparse chenopod shrubland
Chenopodium open chenopod shrubland
Halosarcia low open samphire shrubland
Halosarcia low sparse samphire shrubland
Livistona (mixed) tall open shrubland
Macropteranthes tall shrubland
Maireana low open chenopod shrubland
Maireana open chenopod shrubland
Melaleuca open shrubland
Senna open shrubland
Appendix B
Different vegetation types
B.1 Figures for EVI and mSAVI2
(a) EVI (b) mSAVI2
Figure B.1: The trajectories of the five vegetation classes in SA2 for the EVI and mSAVI2
70
Appendix B. Different vegetation types 71
B.2 Tables with significant differences in SA2 for EVI and
mSAVI2
Table B.1: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the EVI and the mSAVI2. Signifcant differences are
indicated with ’x’.
MSAVI2
Group A Fo Fo Fo Fo Hu Hu Hu Sh Sh Tu
Group B Hu Sh Tu Wo Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 7.3E-126 x x x x x x x x x x 10
VImin 4.2E-134 x x x x x x x x x x 10
VIrange 1.52E-16 x x x x x x x 7
DOYmax 2.52E-07 x x x x x 5
DOYmin 9.31E-38 x x x x 4
DOYrange 0.000364 x x x x 4
Smax 8.96E-33 x x x x x x x x 8
VIS 2.2E-124 x x x x x x x x x x 10
DOYS 1.56E-14 x x x x x 5
I 1.27E-09 x x x x x 5
Totaal 7 7 7 6 8 7 8 6 7 5
EVI
Group A Fo Fo Fo Fo Hu Hu Hu Sh Sh Tu
Group B Hu Sh Tu Wo Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 4.9E-158 x x x x x x x x x x 10
VImin 2.4E-99 x x x x x x x x 8
VIrange 2.8E-109 x x x x x x x x x 9
DOYmax 3.47E-10 x x x x 4
DOYmin 0.329738 0
DOYrange 0.003367 x x x x 4
Smax 2.7E-117 x x x x x x x x x 9
VIS 2.82E-98 x x x x x x x x x x 10
DOYS 6.06E-60 x x x x x x x x 8
I 9.3E-52 x x x x x x x x x 9
Totaal 8 7 6 7 8 9 8 7 5 6
Appendix B. Different vegetation types 72
B.3 Table with mean values and standard deviation for SA2
Table B.2: The mean (µ) and standard deviation (σ) of the metrics per vegetation class for NDVI,
NDWI, mSAVI2 and EVI in SA2
VI NDVI NDWI mSAVI2 EVIMetric Class µ σ µ σ µ σ µ σ
VImax Fo 0.3704 0.0019 0.0974 0.0029 -0.2523 0.0009 0.2091 0.0013Hu 0.3331 0.0019 0.1111 0.0029 -0.2344 0.0009 0.1919 0.0013Sh 0.5181 0.0019 0.2334 0.0029 -0.2679 0.0009 0.2630 0.0013Tu 0.5045 0.0019 0.2897 0.0029 -0.2198 0.0009 0.2949 0.0013Wo 0.5328 0.0019 0.2526 0.0029 -0.2754 0.0009 0.2683 0.0013
VImin Fo 0.2155 0.0033 -0.0485 0.0075 -0.2749 0.0009 0.1272 0.0014Hu 0.2088 0.0033 -0.1131 0.0075 -0.2638 0.0009 0.1098 0.0014Sh 0.3118 0.0033 0.0955 0.0075 -0.2969 0.0009 0.1660 0.0014Tu 0.1833 0.0033 -0.0138 0.0075 -0.2487 0.0009 0.1247 0.0014Wo 0.3071 0.0033 0.0306 0.0075 -0.3083 0.0009 0.1637 0.0014
VIrange Fo 0.1549 0.0038 0.1459 0.0079 0.0226 0.0008 0.0819 0.0018Hu 0.1243 0.0038 0.2242 0.0079 0.0295 0.0008 0.0821 0.0018Sh 0.2062 0.0038 0.1380 0.0079 0.0290 0.0008 0.0970 0.0018Tu 0.3212 0.0038 0.3035 0.0079 0.0289 0.0008 0.1702 0.0018Wo 0.2257 0.0038 0.2220 0.0079 0.0330 0.0008 0.1046 0.0018
DOYmax Fo 59 2.8 81 3.9 178 10.4 48 2.5Hu 74 2.8 182 3.9 201 10.4 71 2.5Sh 44 2.8 116 3.9 236 10.4 47 2.5Tu 53 2.8 55 3.9 254 10.4 52 2.5Wo 61 2.8 84 3.9 247 10.4 55 2.5
DOYmin Fo 340 5.8 317 8.5 143 9.4 331 4.4Hu 298 5.8 331 8.5 313 9.4 335 4.4Sh 330 5.8 263 8.5 137 9.4 329 4.4Tu 340 5.8 333 8.5 160 9.4 336 4.4Wo 337 5.8 347 8.5 137 9.4 341 4.4
DOYrange Fo 281 5.4 239 7.1 122 6.4 284 4.5Hu 231 5.4 149 7.1 120 6.4 264 4.5Sh 286 5.4 170 7.1 154 6.4 282 4.5Tu 287 5.4 278 7.1 119 6.4 284 4.5Wo 275 5.4 263 7.1 121 6.4 286 4.5
Smax Fo -0.000786 3.33E-05 -0.001333 8.03E-05 5.43E-06 4.43E-05 -0.000458 1.92E-05Hu -0.001023 3.33E-05 -0.002496 7.95E-05 -0.000461 4.43E-05 -0.000701 1.92E-05Sh -0.0011 3.33E-05 -0.001123 7.95E-05 6.37E-05 4.43E-05 -0.000565 1.92E-05Tu -0.002847 3.33E-05 -0.002076 7.95E-05 0.000277 4.43E-05 -0.001542 1.92E-05Wo -0.001254 3.33E-05 -0.001632 7.95E-05 0.000386 4.43E-05 -0.000663 1.92E-05
VIS Fo 0.2875 0.0038 0.0032 0.0088 -0.2630 0.0010 0.1707 0.0023Hu 0.2554 0.0038 -0.0219 0.0087 -0.2522 0.0010 0.1378 0.0023Sh 0.3844 0.0038 0.1465 0.0087 -0.2846 0.0010 0.1882 0.0023Tu 0.3821 0.0038 0.1508 0.0087 -0.2336 0.0010 0.2302 0.0023Wo 0.4489 0.0038 0.0870 0.0087 -0.2910 0.0010 0.2408 0.0023
DOYS Fo 214 6.4 263 9.8 163 9.3 189 6.9Hu 240 6.4 275 9.7 271 9.3 276 6.9Sh 233 6.4 227 9.7 175 9.3 271 6.9Tu 118 6.4 176 9.7 208 9.3 115 6.9Wo 158 6.4 296 9.7 197 9.3 116 6.9
I Fo 83.0952 1.5967 11.1490 0.9653 -32.0885 1.7314 47.9502 0.7680Hu 66.0535 1.5967 2.0860 0.9653 -29.5002 1.7314 43.4783 0.7680Sh 120.4548 1.5967 29.1882 0.9653 -43.6016 1.7314 61.7996 0.7680Tu 82.9839 1.5967 36.5296 0.9653 -27.8176 1.7314 50.9150 0.7680Wo 112.9244 1.5967 43.0088 0.9653 -35.4656 1.7314 59.9631 0.7680
Appendix B. Different vegetation types 73
B.4 Tables for SA1
Table B.3: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the NDVI and the NDWI. Signifcant differences are
indicated with ’x’.
NDVI
Group A Fo Fo Fo Sh Sh Tu
Group B Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 1.49E-66 x x x x x x 6
VImin 3.65E-27 x x x x x x 6
VIrange 2.97E-36 x x x x 4
DOYmax 9.17E-69 x x x x x 5
DOYmin 1.03E-14 x x x x x 5
DOYrange 1.31E-40 x x x x x 5
Smax 1.13E-56 x x x x x 5
VIS 5.12E-21 x x x x 4
DOYS 1.44E-06 x x x 3
I 5.2E-26 x x x x x 5
T1 8 9 5 8 10 8
NDWI
Group A Fo Fo Fo Sh Sh Tu
Group B Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 1.23E-85 x x x x x x 6
VImin 3.1E-115 x x x x x x 6
VIrange 4.7E-119 x x x x x 5
DOYmax 7.66E-30 x x x x x 5
DOYmin 1.61E-20 x x x x x 5
DOYrange 9.98E-15 x x x x 4
Smax 1.07E-77 x x x x x 5
VIS 6.1E-79 x x x x x x 6
DOYS 6.39E-89 x x x x x 5
I 7.87E-47 x x x x x 5
T1 9 10 6 10 8 9
Appendix B. Different vegetation types 74
Table B.4: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the EVI and the mSAVI2. Signifcant differences are
indicated with ’x’.
mSAVI2
Group A Fo Fo Fo Sh Sh Tu
Group B Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 1E-42 x x x x x x 6
VImin 4.16E-21 x x x x x 5
VIrange 1.29E-25 x x x x x 5
DOYmax 0.051289 0
DOYmin 8.93E-36 x x x x x 5
DOYrange 1.55E-28 x x x x x 5
Smax 7.98E-13 x x x x 4
VIS 1.65E-15 x x x x x 5
DOYS 0.000339 x x x 3
I 8.19E-20 x x x x x 5
T1 8 8 7 4 7 9
EVI
Group A Fo Fo Fo Sh Sh Tu
Group B Sh Tu Wo Tu Wo Wo
Metric p-value T2
VImax 2.51E-42 x x x x x 5
VImin 1.42E-42 x x x x x x 6
VIrange 6.09E-58 x x x x x 5
DOYmax 3.44E-21 x x x x x 5
DOYmin 0.872882 0
DOYrange 1.88E-40 x x x x x 5
Smax 5.33E-10 x x x x 4
VIS 2.79E-19 x x x 3
DOYS 2.86E-29 x x x x x 5
I 3.55E-38 x x x x x x 6
T1 8 8 5 6 9 8
Appendix B. Different vegetation types 75
Table B.5: The mean (µ) and standard deviation (σ) of the metrics per vegetation class for NDVI,
NDWI, mSAVI2 and EVI in SA1
VI NDVI NDWI MSAVI2 EVIMetric Class µ σ µ σ µ σ µ σ
VImax Fo 0.6197 0.0021 0.4584 0.0038 -0.2370 0.0025 0.3367 0.0019Sh 0.6553 0.0021 0.5586 0.0038 -0.1763 0.0025 0.3868 0.0019Tu 0.6994 0.0021 0.6426 0.0038 -0.2110 0.0025 0.3632 0.0019Wo 0.6643 0.0021 0.5154 0.0038 -0.2277 0.0025 0.3613 0.0019
VImin Fo 0.3276 0.0032 0.0654 0.0031 -0.3637 0.0079 0.1817 0.0020Sh 0.3062 0.0032 -0.0903 0.0031 -0.4686 0.0079 0.1580 0.0020Tu 0.3423 0.0032 -0.0329 0.0031 -0.4676 0.0079 0.1681 0.0020Wo 0.3653 0.0032 0.1207 0.0031 -0.4030 0.0079 0.2065 0.0020
VIrange Fo 0.2921 0.0036 0.3930 0.0049 0.1266 0.0102 0.1550 0.0026Sh 0.3491 0.0036 0.6489 0.0049 0.2922 0.0102 0.2288 0.0026Tu 0.3572 0.0036 0.6755 0.0049 0.2566 0.0102 0.1951 0.0026Wo 0.2990 0.0036 0.3947 0.0049 0.1752 0.0102 0.1548 0.0026
DOYmax Fo 44 1.4 43 0.9 88 6.6 48 4.3Sh 62 1.4 32 0.9 84 6.6 78 4.3Tu 93 1.4 51 0.9 108 6.6 103 4.3Wo 43 1.4 40 0.9 89 6.6 41 4.3
DOYmin Fo 254 1.3 277 1.6 187 1.2 235 2.7Sh 238 1.3 255 1.6 208 1.2 233 2.7Tu 248 1.3 264 1.6 209 1.2 234 2.7Wo 245 1.3 256 1.6 193 1.2 233 2.7
DOYrange Fo 210 2.4 234 1.8 113 0.6 187 2.5Sh 176 2.4 223 1.8 124 0.6 165 2.5Tu 155 2.4 213 1.8 114 0.6 135 2.5Wo 202 2.4 216 1.8 117 0.6 191 2.5
Smax Fo -0.002092 5.56E-05 -0.003149 5.5E-05 -0.00165 0.000192 -0.001358 9.77E-05Sh -0.003259 5.56E-05 -0.003966 5.5E-05 -0.003702 0.000192 -0.001927 9.77E-05Tu -0.003637 5.56E-05 -0.005269 5.5E-05 -0.003113 0.000192 -0.002145 9.77E-05Wo -0.002259 5.56E-05 -0.003082 5.5E-05 -0.002172 0.000192 -0.001341 9.77E-05
VIS Fo 0.4536 0.0052 0.3233 0.0051 -0.2983 0.0027 0.2700 0.0024Sh 0.4648 0.0052 0.1583 0.0051 -0.3167 0.0027 0.2745 0.0024Tu 0.5203 0.0052 0.2613 0.0051 -0.3340 0.0027 0.2697 0.0024Wo 0.5134 0.0052 0.3848 0.0051 -0.3125 0.0027 0.3011 0.0024
DOYS Fo 165 3.6 105 1.9 136 3.5 122 3.3Sh 164 3.6 165 1.9 143 3.5 155 3.3Tu 172 3.6 178 1.9 155 3.5 164 3.3Wo 144 3.6 100 1.9 138 3.5 107 3.3
I Fo 100.1138 1.4543 50.5578 0.8405 -34.2281 0.4269 47.5530 0.7411Sh 87.4968 1.4543 52.2306 0.8405 -40.3837 0.4269 44.8387 0.7411Tu 81.0350 1.4543 71.5331 0.8405 -39.0310 0.4269 35.4892 0.7411Wo 104.3240 1.4543 61.4047 0.8405 -37.1495 0.4269 52.7937 0.7411
Appendix B. Different vegetation types 76
B.5 Tables for SA3
Table B.6: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the NDVI and the NDWI. Signifcant differences are
indicated with ’x’.
NDVI
Group A Fo Fo Fo Hu Hu Sh
Group B Hu Sh Wo Sh Wo Wo
Metric p-value T2
VImax 3.85E-83 x x x x x x 6
VImin 1.15E-35 x x x x x 5
VIrange 5.29E-48 x x x x x x 6
DOYmax 1.14E-71 x x x x x x 6
DOYmin 1.08E-09 x x x x 4
DOYrange 1.21E-17 x x x x x x 6
Smax 5.14E-12 x x x x 4
VIS 1.99E-72 x x x x x 5
DOYS 3.08E-10 x x x 3
I 1.35E-52 x x x x x 5
T1 9 10 10 9 5 7
NDWI
Group A Fo Fo Fo Hu Hu Sh
Group B Hu Sh Wo Sh Wo Wo
Metric p-value T2
VImax 1.72E-80 x x x x x x 6
VImin 4.42E-71 x x x x x x 6
VIrange 8.58E-56 x x x x x 5
DOYmax 6.27E-13 x x x x x 5
DOYmin 1.59E-14 x x x 3
DOYrange 1.98E-10 x x x x 4
Smax 3.48E-18 x x x x 4
VIS 3.56E-83 x x x x x x 6
DOYS 4.08E-12 x x x 3
I 2.81E-33 x x x x x x 6
T1 8 9 5 10 7 9
Appendix B. Different vegetation types 77
Table B.7: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the EVI and the mSAVI2. Signifcant differences are
indicated with ’x’.
mSAVI2
Group A Fo Fo Fo Hu Hu Sh
Group B Hu Sh Wo Sh Wo Wo
Metric p-value T2
VImax 4.2E-109 x x x x x x 6
VImin 3.9E-113 x x x x x x 6
VIrange 1.29E-27 x x x x x x 6
DOYmax 5.25E-29 x x x x x x 6
DOYmin 1.35E-18 x x x x 4
DOYrange 1.53E-11 x x x x 4
Smax 5.07E-09 x x x x x 5
VIS 1.1E-107 x x x x x x 6
DOYS 1.14E-29 x x x x x 5
I 1.12E-09 x x x x 4
T1 8 10 8 9 8 9
EVI
Group A Fo Fo Fo Hu Hu Sh
Group B Hu Sh Wo Sh Wo Wo
Metric p-value T2
VImax 3.02E-60 x x x x x 5
VImin 5.3E-05 x x x 3
VIrange 1.41E-35 x x x x x x 6
DOYmax 2.67E-98 x x x x x 5
DOYmin 2.54E-23 x x x x 4
DOYrange 9.77E-20 x x x x x 5
Smax 4.17E-25 x x x x x 5
VIS 6.37E-29 x x x x x x 6
DOYS 1.48E-22 x x x x x 5
I 2.44E-11 x x x x 4
T1 9 9 9 8 5 8
Appendix B. Different vegetation types 78
Table B.8: The mean (µ) and standard deviation (σ) of the metrics per vegetation class for NDVI,
NDWI, mSAVI2 and EVI in SA3
VI NDVI NDWI MSAVI2 EVIMetric Class µ σ µ σ µ σ µ σ
VImax Fo 0.2284 0.0016 -0.1211 0.0018 -0.2014 0.0012 0.1346 0.0007Hu 0.2080 0.0016 -0.1450 0.0018 -0.1905 0.0012 0.1195 0.0007Sh 0.2836 0.0016 -0.0696 0.0018 -0.2669 0.0012 0.1349 0.0007Wo 0.2495 0.0016 -0.0832 0.0018 -0.2070 0.0012 0.1431 0.0007
VImin Fo 0.1629 0.0016 -0.2025 0.0012 -0.2207 0.0014 0.0946 0.0009Hu 0.1724 0.0016 -0.1875 0.0012 -0.2152 0.0014 0.0979 0.0009Sh 0.1982 0.0016 -0.1550 0.0012 -0.3036 0.0014 0.1006 0.0009Wo 0.1729 0.0016 -0.1802 0.0012 -0.2356 0.0014 0.0986 0.0009
VIrange Fo 0.0655 0.0018 0.0814 0.0018 0.0192 0.0009 0.0401 0.0011Hu 0.0356 0.0018 0.0425 0.0018 0.0248 0.0009 0.0216 0.0011Sh 0.0854 0.0018 0.0853 0.0018 0.0367 0.0009 0.0343 0.0011Wo 0.0765 0.0018 0.0971 0.0018 0.0286 0.0009 0.0444 0.0011
DOYmax Fo 197 1.0 190 1.7 112 8.3 198 1.0Hu 226 1.0 202 1.7 197 8.3 241 1.0Sh 187 1.0 183 1.7 41 8.3 193 1.0Wo 193 1.0 190 1.7 76 8.3 193 1.0
DOYmin Fo 82 14.7 142 15.0 298 10.3 92 12.0Hu 168 14.7 174 15.0 279 10.3 132 12.0Sh 224 14.7 311 15.0 158 10.3 284 12.0Wo 187 14.7 161 15.0 254 10.3 155 12.0
DOYrange Fo 114 1.3 116 2.0 196 9.0 116 1.5Hu 132 1.3 136 2.0 121 9.0 135 1.5Sh 127 1.3 128 2.0 117 9.0 127 1.5Wo 120 1.3 122 2.0 182 9.0 118 1.5
Smax Fo 0.000901 0.00011 0.000482 0.00012 -0.00023 2.81E-05 0.000511 4.85E-05Hu 0.000162 0.00011 4.33E-05 0.00012 -0.00037 2.81E-05 0.000193 4.85E-05Sh -0.00032 0.00011 -0.00107 0.00012 -0.0005 2.81E-05 -0.00033 4.85E-05Wo 7.27E-05 0.00011 0.000375 0.00012 -0.00036 2.81E-05 0.000256 4.85E-05
VIS Fo 0.1943 0.0014 -0.1628 0.0013 -0.2135 0.0013 0.1139 0.0006Hu 0.1897 0.0014 -0.1677 0.0013 -0.2051 0.0013 0.1090 0.0006Sh 0.2410 0.0014 -0.1137 0.0013 -0.2822 0.0013 0.1174 0.0006Wo 0.2107 0.0014 -0.1326 0.0013 -0.2213 0.0013 0.1204 0.0006
DOYS Fo 136 7.4 165 8.2 251 10.0 142 6.0Hu 199 7.4 189 8.2 258 10.0 192 6.0Sh 204 7.4 250 8.2 85 10.0 239 6.0Wo 188 7.4 176 8.2 165 10.0 172 6.0
I Fo 22.4471 0.2583 -18.7953 0.4137 -40.8819 1.8873 13.3018 0.1598Hu 25.0770 0.2583 -22.6588 0.4137 -24.3143 1.8873 14.6803 0.1598Sh 30.2881 0.2583 -14.2629 0.4137 -33.6817 1.8873 14.9218 0.1598Wo 25.2724 0.2583 -16.0455 0.4137 -40.2538 1.8873 14.2529 0.1598
Appendix C
Significance of further subdivision
in vegetation classes
C.1 Figures for EVI and mSAVI2
(a) EVI (b) mSAVI2
Figure C.1: The trajectories of the broad and most detailed vegetation classes for EVI and mSAVI2
79
Appendix C. Significance of further subdivision in vegetation classes 80
C.2 Tables with significant differences for EVI and mSAVI2
Table C.1: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of Group A and Group B for the EVI and the mSAVI2. Signifcant differences are
indicated with ’x’.
EVI
Group A bFo bFo bFo bFo bFo bFo bFo Wo Wo Wo Fo Fo
Group B Wo sWA sWE sWO Fo sFE sFO sWA sWE sWO sFE sFO
Metric p-value
VImax 4.6E-147 x x x x x x x x x x
VImin 5.6E-157 x x x x x x
VIrange 1.75E-82 x x x x x x x x
DOYmax 1.4E-146 x x x x
DOYmin 8.56E-29 x x x x
DOYrange 1.11E-63 x x x x x x
Smax 4.4E-106 x x x x x x
VIS 2.1E-109 x x x x x x x x
DOYS 1.8E-127 x x x x
I 1.17E-19 x x x x
Total differences 3 9 4 9 2 0 6 9 1 10 1 6
mSAVI2
Group A bFo bFo bFo bFo bFo bFo bFo Wo Wo Wo Fo Fo
Group B Wo sWA sWE sWO Fo sFE sFO sWA sWE sWO sFE sFO
Metric p-value
VImax 6.68E-32 x x x x x
VImin 9.59E-49 x x x x x x x
VIrange 3.84E-41 x x x x x x x x
DOYmax 1.22E-65 x x
DOYmin 4.44E-26 x x x x x
DOYrange 0.960642
Smax 3.44E-44 x x x x x x
VIS 3.56E-69 x x x x x x x
DOYS 1.14E-87 x x
I 6.11E-11 x x x
Total differences 0 8 5 6 0 0 6 8 1 3 2 6
Appendix D
Variability caused by fire events
D.1 Tables for FOREST class
Table D.1: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for forest. Signifcant differences are indicated with ’x’.
SA1
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.006352 x x 0.448242 4.91E-12 x x x 0.00016 x
VImin 2.97E-07 x x 5.87E-33 x x 5.75E-06 x x 1.85E-05 x x
VIrange 0.014604 x 8.6E-18 x x 1.97E-07 x x 0.031744 x
DOYmax 4.55E-08 x x 8.47E-09 x x 0.178333 5.18E-06 x x x
DOYmax 2.12E-07 x x 3.04E-06 x x 0.077163 3.95E-17 x x x
DOYmax 1.27E-08 x x 2.88E-08 x x 1.06E-05 x x 1.3E-13 x x x
Smax 0.000673 x x 3.03E-10 x x x 8.45E-05 x x 1.84E-05 x x
VIS 0.005566 x 0.262337 0.002231 x x 0.065948
DOYmax 0.128658 0.375612 0.298764 0.002599 x
I 0.002126 x x 0.000222 x x 0.000181 x x 2.87E-07 x x x
T1 8 0 8 7 4 4 5 3 7 7 5 7
SA2
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 3.49E-77 x x x 4.71E-74 x x x 1.71E-62 x x x 1.43E-61 x x x
VImin 0.000292 x x 1.01E-10 x x x 2.55E-39 x x x 4.41E-45 x x x
VIrange 6.99E-48 x x x 2.86E-19 x x 5.81E-18 x x x 3.1E-38 x x
DOYmax 2.43E-06 x x 1.31E-06 x x x 3.7E-11 x x 0.007338 x
DOYmin 0.996757 0.085239 6.36E-25 x x x 0.002357 x x
DOYrange 0.030369 x 9.35E-05 x x 1.23E-16 x x x 0.012867 x
Smax 1.19E-47 x x x 6.28E-18 x x x 2.75E-20 x x x 4.85E-34 x x x
VIS 1.64E-22 x x x 1.05E-38 x x 2.15E-47 x x x 7.07E-47 x x x
DOYS 1.58E-14 x x 6.29E-23 x x 2.61E-18 x x 3.27E-29 x x x
I 8.52E-21 x x 5.68E-59 x x x 3.1E-12 x x 2.19E-18 x x
T1 6 7 8 8 6 9 9 9 9 8 8 7
81
Appendix D. Variability caused by fire events 82
Tab
leD
.2:
Th
em
ean
(µ)
an
dst
an
dard
dev
iati
on
(σ)
of
the
met
rics
per
fire
his
tory
for
fore
st
ND
VI
ND
WI
mSA
VI 2
EV
I
SA
1SA
2SA
1SA
2SA
1SA
2SA
1SA
2
Met
rics
Gro
up
µσ
µσ
µσ
µσ
µσ
µσ
µσ
µσ
VI m
ax
B0.
6208
50.
0026
40.
4595
30.
0024
50.
4635
80.
0038
20.
1982
10.
0039
7-0
.247
390.
0031
1-0
.260
780.
0008
60.3
4305
0.0
0220
0.2
4745
0.0
0191
NB
0.63
071
0.00
264
0.49
989
0.00
245
0.46
481
0.00
382
0.30
112
0.00
397
-0.2
1349
0.00
311
-0.2
2632
0.00
086
0.3
5012
0.0
0220
0.2
8741
0.0
0191
UB
0.61
972
0.00
264
0.37
039
0.00
245
0.45
835
0.00
382
0.09
738
0.00
397
-0.2
3705
0.00
311
-0.2
5230
0.00
086
0.3
3673
0.0
0220
0.2
0909
0.0
0191
VI m
inB
0.31
696
0.00
351
0.21
271
0.00
298
0.00
530
0.00
316
-0.1
1717
0.01
161
-0.3
4544
0.01
036
-0.2
9248
0.00
109
0.1
7612
0.0
0215
0.1
1367
0.0
0136
NB
0.34
532
0.00
351
0.22
903
0.00
298
0.06
887
0.00
316
0.00
318
0.01
161
-0.4
1758
0.01
036
-0.2
6402
0.00
109
0.1
9075
0.0
0215
0.1
5335
0.0
0136
UB
0.32
762
0.00
351
0.21
546
0.00
298
0.06
537
0.00
316
-0.0
4851
0.01
161
-0.3
6366
0.01
036
-0.2
7490
0.00
109
0.1
8172
0.0
0215
0.1
2724
0.0
0136
VI r
an
ge
B0.
3038
90.
0044
90.
2468
20.
0038
80.
4582
80.
0051
20.
3153
80.
0122
50.
0980
50.
0132
40.
0317
00.
0010
50.1
6693
0.0
0321
0.1
3377
0.0
0234
NB
0.28
538
0.00
449
0.27
086
0.00
388
0.39
594
0.00
512
0.29
794
0.01
225
0.20
409
0.01
324
0.03
770
0.00
105
0.1
5936
0.0
0321
0.1
3406
0.0
0234
UB
0.29
210
0.00
449
0.15
493
0.00
388
0.39
298
0.00
512
0.14
588
0.01
225
0.12
662
0.01
324
0.02
260
0.00
105
0.1
5501
0.0
0321
0.0
8185
0.0
0234
DO
Ym
ax
B43
1.6
481.
539
0.6
664.
072
7.2
257
9.4
42
1.7
52
1.4
NB
561.
649
1.5
440.
650
4.0
897.
227
19.
455
1.7
46
1.4
UB
441.
659
1.5
430.
681
4.0
887.
217
89.
448
1.7
48
1.4
DO
Ym
inB
257
1.8
340
4.1
280
3.0
312
8.8
193
1.8
283
10.8
246
1.9
346
4.4
NB
243
1.8
340
4.1
259
3.0
338
8.8
191
1.8
9210
.8219
1.9
325
4.4
UB
254
1.8
340
4.1
277
3.0
317
8.8
187
1.8
143
10.8
235
1.9
331
4.4
DO
Yra
nge
B21
43.
129
43.
424
13.
025
38.
212
41.
598
6.5
204
3.3
294
3.6
NB
188
3.1
291
3.4
215
3.0
289
8.2
119
1.5
185
6.5
164
3.3
279
3.6
UB
210
3.1
281
3.4
234
3.0
239
8.2
113
1.5
122
6.5
187
3.3
284
3.6
Sm
ax
B-0
.002
097.
4E-0
5-0
.001
223.
52E
-05
-0.0
029
16.
01E
-05
-0.0
0322
0.00
0132
-0.0
0127
0.00
0199
-0.0
004
4.79
E-0
5-0
.00133
4.3
5E
-05
-0.0
0086
2.3
5E
-05
NB
-0.0
0245
7.4E
-05
-0.0
0188
3.52
E-0
5-0
.0035
16.
01E
-05
-0.0
0194
0.00
0132
-0.0
025
0.00
0199
0.00
0359
4.79
E-0
5-0
.0016
4.3
5E
-05
-0.0
0098
2.3
5E
-05
UB
-0.0
0209
7.4E
-05
-0.0
0079
3.52
E-0
5-0
.0031
56.
01E
-05
-0.0
0133
0.00
0134
-0.0
0165
0.00
0199
5.43
E-0
64.
79E
-05
-0.0
0136
4.3
5E
-05
-0.0
0046
2.3
5E
-05
VI S
B0.
4631
80.
0056
50.
3182
40.
0078
10.
3183
30.
0057
0-0
.017
040.
0101
2-0
.294
480.
0036
7-0
.278
740.
0010
80.2
7763
0.0
0249
0.1
4327
0.0
0346
NB
0.47
957
0.00
565
0.41
454
0.00
781
0.31
016
0.00
570
0.22
047
0.01
012
-0.3
1211
0.00
367
-0.2
4513
0.00
108
0.2
7666
0.0
0249
0.2
4655
0.0
0346
UB
0.45
365
0.00
565
0.28
752
0.00
781
0.32
328
0.00
570
0.00
324
0.01
022
-0.2
9831
0.00
367
-0.2
6303
0.00
108
0.2
7000
0.0
0249
0.1
7072
0.0
0346
DO
YS
B15
34.
1922
29.
4211
02.
5126
610
.05
129
3.93
282
8.61
115
2.2
9294
9.1
1
NB
158
4.19
115
9.42
109
2.51
115
10.0
513
73.
9318
68.
61127
2.2
9107
9.1
1
UB
165
4.19
214
9.42
105
2.51
263
10.1
513
63.
9316
38.
61122
2.2
9189
9.1
1
IB
100.
381
1.79
399
.713
1.10
049
.878
0.57
424
.014
0.73
3-3
6.74
10.
609
-27.
026
1.70
751.3
63
0.9
25
55.1
09
0.6
28
NB
92.3
811.
793
96.5
311.
100
47.3
110.
574
39.7
690.
733
-37.
818
0.60
9-4
5.67
11.
707
43.8
01
0.9
25
56.6
09
0.6
28
UB
100.
114
1.79
383
.095
1.10
050
.558
0.57
411
.149
0.73
3-3
4.22
80.
609
-32.
089
1.70
747.5
53
0.9
25
47.9
50
0.6
28
Appendix D. Variability caused by fire events 83
D.2 Tables for WOODLAND class
Table D.3: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for woodland. Signifcant differences are indicated with ’x’.
SA1
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 1.42E-08 x x x 1.46E-08 x x 3.63E-12 x x 1.01E-07 x x
VImin 0.135737 6.3E-33 x x x 1.43E-15 x x 0.004399 x x
VIrange 0.015835 x 9.9E-17 x x x 1.03E-14 x x x 1.38E-05 x x
DOYmax 6E-22 x x 0.05373 1.48E-07 x x 0.003808 x x
DOYmin 8.46E-16 x x x 1.88E-17 x x x 0.0002 x x 2.06E-21 x x x
DOYrange 9.7E-21 x x x 2.12E-16 x x x 1.34E-26 x x 8.46E-16 x x x
Smax 5.93E-13 x x x 7.23E-08 x x x 7.52E-05 x x 0.98746
VIS 0.001683 x 3.16E-11 x x x 6.68E-18 x x 0.196038
DOYS 0.041929 1.87E-08 x x 1.61E-11 x x 2.87E-07 x x
I 1.22E-14 x x 0.001426 x x 0.088967 4.64E-07 x x x
T1 6 6 6 7 9 8 9 8 2 7 5 7
SA2
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.089297 0.001926 x x 8.46E-38 x x 1.06E-09 x x x
VImin 8.25E-35 x x x 4.14E-95 x x x 2.46E-49 x x x 6.43E-85 x x x
VIrange 5.15E-26 x x x 1.04E-79 x x x 1.73E-29 x x 6.31E-35 x x
DOYmax 3.23E-08 x x 2.23E-15 x x 0.94739 2.9E-06 x x
DOYmin 0.102904 0.414501 1.32E-45 x x 1.74E-10 x x
DOYrange 1.69E-05 x x 6.53E-09 x x 0.000375 x x 0.042828 x
Smax 0.004068 x 0.077523 8.57E-25 x x 3.27E-45 x x
VIS 2.29E-06 x x 4.89E-74 x x 1.51E-52 x x 9.95E-12 x x x
DOYS 0.132855 5.51E-33 x x 2.62E-50 x x x 7.22E-51 x x
I 0.02891 x 8.68E-29 x x x 0.010212 x x 3.16E-17 x x
T1 2 6 6 5 8 6 9 8 3 9 8 5
Appendix D. Variability caused by fire events 84
Tab
leD
.4:
Th
em
ean
(µ)
and
stan
dard
dev
iati
on
(σ)
of
the
met
rics
per
fire
his
tory
for
wood
lan
d
ND
VI
ND
WI
mSA
VI 2
EV
I
SA
1SA
2SA
1SA
2SA
1SA
2SA
1SA
2
Met
rics
Gro
up
µσ
µσ
µσ
µσ
µσ
µσ
µσ
µσ
VI m
ax
B0.
6398
30.
0027
10.
5281
10.
0015
40.
4799
40.
0042
30.
2614
20.
0020
5-0
.231
040.
0023
8-0
.272
960.
0010
60.3
6633
0.0
0213
0.2
6380
0.0
0117
NB
0.65
352
0.00
271
0.53
131
0.00
154
0.48
524
0.00
423
0.26
187
0.00
205
-0.2
0640
0.00
238
-0.2
5096
0.00
106
0.3
7885
0.0
0213
0.2
7512
0.0
0117
UB
0.66
432
0.00
271
0.53
283
0.00
154
0.51
539
0.00
423
0.25
258
0.00
205
-0.2
2772
0.00
238
-0.2
7539
0.00
106
0.3
6130
0.0
0213
0.2
6826
0.0
0117
VI m
inB
0.35
736
0.00
329
0.26
075
0.00
197
0.06
353
0.00
369
-0.1
4183
0.00
261
-0.3
2171
0.00
846
-0.3
2126
0.00
112
0.1
9734
0.0
0215
0.1
2641
0.0
0073
NB
0.36
562
0.00
329
0.28
921
0.00
197
0.14
486
0.00
369
0.01
585
0.00
261
-0.4
2862
0.00
846
-0.2
8532
0.00
112
0.2
0574
0.0
0215
0.1
6705
0.0
0073
UB
0.36
527
0.00
329
0.30
708
0.00
197
0.12
069
0.00
369
0.03
062
0.00
261
-0.4
0296
0.00
846
-0.3
0834
0.00
112
0.2
0654
0.0
0215
0.1
6367
0.0
0073
VI r
an
ge
B0.
2824
70.
0040
90.
2673
60.
0022
20.
4164
10.
0056
60.
4032
60.
0034
80.
0906
70.
0104
90.
0482
90.
0008
20.1
6899
0.0
0277
0.1
3739
0.0
0152
NB
0.28
790
0.00
409
0.24
211
0.00
222
0.34
038
0.00
566
0.24
602
0.00
348
0.22
222
0.01
049
0.03
436
0.00
082
0.1
7311
0.0
0277
0.1
0807
0.0
0152
UB
0.29
905
0.00
409
0.22
574
0.00
222
0.39
469
0.00
566
0.22
197
0.00
348
0.17
523
0.01
049
0.03
295
0.00
082
0.1
5476
0.0
0277
0.1
0458
0.0
0152
DO
Ym
ax
B39
1.6
551.
140
0.7
711.
146
8.2
244
7.3
36
6.9
60
1.0
NB
641.
652
1.1
420.
772
1.1
113
8.2
244
7.3
67
6.9
53
1.0
UB
431.
661
1.1
400.
784
1.1
898.
224
77.
341
6.9
55
1.0
DO
Ym
inB
254
1.4
341
1.4
270
2.2
348
1.3
184
1.7
323
7.2
245
1.7
344
0.9
NB
235
1.4
337
1.4
239
2.2
346
1.3
194
1.7
130
7.2
217
1.7
335
0.9
UB
245
1.4
337
1.4
256
2.2
347
1.3
193
1.7
137
7.2
233
1.7
341
0.9
DO
Yra
nge
B21
52.
828
61.
622
92.
427
71.
613
91.
398
6.0
209
2.9
284
1.3
NB
171
2.8
284
1.6
197
2.4
274
1.6
117
1.3
132
6.0
170
2.9
282
1.3
UB
202
2.8
275
1.6
216
2.4
263
1.6
117
1.3
121
6.0
191
2.9
286
1.3
Sm
ax
B-0
.001
916.
71E
-05
-0.0
014
2.63
E-0
5-0
.002
615.
47E
-05
-0.0
045.
3E-0
5-0
.001
070.
0002
19-0
.000
864.
13E
-05
-0.0
0133
7.9
8E
-05
-0.0
0085
1.6
9E
-05
NB
-0.0
027
6.71
E-0
5-0
.001
442.
63E
-05
-0.0
0288
5.47
E-0
5-0
.001
675.
3E-0
5-0
.002
350.
0002
190.
0003
674.
13E
-05
-0.0
0135
7.9
8E
-05
-0.0
0074
1.6
9E
-05
UB
-0.0
0226
6.71
E-0
5-0
.001
252.
63E
-05
-0.0
0308
5.47
E-0
5-0
.001
635.
3E-0
5-0
.002
170.
0002
190.
0003
864.
13E
-05
-0.0
0134
7.9
8E
-05
-0.0
0066
1.6
9E
-05
VI S
B0.
4820
50.
0061
60.
4385
50.
0038
20.
3104
90.
0069
5-0
.034
440.
0059
4-0
.272
410.
0032
7-0
.302
110.
0010
60.3
0526
0.0
0289
0.1
6190
0.0
0279
NB
0.50
236
0.00
616
0.44
689
0.00
382
0.35
039
0.00
695
0.07
658
0.00
594
-0.3
1427
0.00
327
-0.2
6759
0.00
106
0.2
9786
0.0
0289
0.2
4438
0.0
0279
UB
0.51
344
0.00
616
0.44
886
0.00
382
0.38
482
0.00
695
0.08
704
0.00
594
-0.3
1248
0.00
327
-0.2
9097
0.00
106
0.3
0105
0.0
0289
0.2
4082
0.0
0279
DO
YS
B15
74.
1214
74.
1812
83.
1531
06.
5610
64.
3729
46.
34101
4.6
0277
6.2
4
NB
157
4.12
138
4.18
110
3.15
289
6.56
152
4.37
192
6.34
136
4.6
0114
6.2
4
UB
144
4.12
158
4.18
100
3.15
296
6.56
138
4.37
197
6.34
107
4.6
0116
6.2
4
IB
107.
223
1.61
911
0.56
20.
630
58.6
850.
790
34.3
690.
436
-38.
562
0.49
6-2
8.95
21.
747
56.5
73
0.8
91
55.9
25
0.3
12
NB
88.1
831.
619
111.
372
0.63
057
.344
0.79
040
.417
0.43
6-3
7.28
80.
496
-35.
601
1.74
749.4
15
0.8
91
59.4
50
0.3
12
UB
104.
324
1.61
911
2.92
40.
630
61.4
050.
790
43.0
090.
436
-37.
150
0.49
6-3
5.46
61.
747
52.7
94
0.8
91
59.9
63
0.3
12
Appendix D. Variability caused by fire events 85
D.3 Tables for SHRUB class
Table D.5: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for shrub. Signifcant differences are indicated with ’x’.
SA1
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 1.92E-29 x x x 6.55E-27 x x x 0.013253 x 9.18E-19 x x x
VImin 5.85E-24 x x 2.19E-36 x x x 1.17E-62 x x x 6.38E-49 x x x
VIrange 1.05E-18 x x x 4.04E-42 x x x 4.44E-38 x x x 1.04E-21 x x x
DOYmax 0.037059 x 1.77E-07 x x 1.08E-05 x x 0.099103
DOYmin 0.112227 3.24E-14 x x 5.86E-52 x x x 9.18E-09 x x x
DOYrange 0.577507 0.07022 7.44E-05 x x 0.006486 x
Smax 0.000764 x x 9.18E-27 x x 2.81E-08 x x 0.006258 x x
VIS 1.49E-22 x x x 2.31E-29 x x x 1.46E-31 x x x 3.07E-29 x x
DOYS 2.37E-05 x x 9.64E-11 x x x 2.83E-05 x x 4.06E-06 x x
I 0.015742 x x 1.57E-08 x x 2.19E-24 x x x 9.04E-10 x x x
T1 6 7 5 8 7 8 8 7 9 8 7 7
SA2
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.113054 1.5E-19 x x 5.92E-67 x x 1.79E-80 x x x
VImin 5.6E-75 x x 3.45E-48 x x x 6.14E-68 x x x 2.6E-99 x x x
VIrange 2.75E-68 x x 2.65E-53 x x x 2.73E-41 x x x 3.48E-86 x x x
DOYmax 2.9E-43 x x x 2.26E-11 x x 1.13E-38 x x 3.02E-57 x x
DOYmin 4.65E-10 x x 2.16E-11 x x 0.135049 7.18E-22 x x
DOYrange 9.07E-05 x x 4.51E-21 x x 0.004369 x x 1.26E-41 x x x
Smax 3.09E-48 x x x 6.77E-74 x x x 0.000265 x 2.69E-78 x x x
VIS 2.24E-65 x x x 6.58E-36 x x x 2.58E-73 x x x 9.99E-92 x x x
DOYS 1.05E-76 x x x 4.03E-11 x x 0.003532 x x 3.79E-81 x x x
I 2.97E-60 x x x 1.34E-10 x x x 9.09E-05 x x 3.13E-47 x x
T1 6 9 8 5 10 10 8 3 9 9 10 8
Appendix D. Variability caused by fire events 86
Tab
leD
.6:
Th
em
ean
(µ)
an
dst
an
dard
dev
iati
on
(σ)
of
the
met
rics
per
fire
his
tory
for
shru
b
ND
VI
ND
WI
mSA
VI 2
EV
I
SA
1SA
2SA
1SA
2SA
1SA
2SA
1SA
2
Met
rics
Gro
up
µσ
µσ
µσ
µσ
µσ
µσ
µσ
µσ
VI m
ax
B0.
6977
70.
0021
90.
5223
50.
0014
60.
609
200.
0026
20.
2620
80.
001
97-0
.172
790.0
008
5-0
.2668
30.
0007
60.4
1145
0.0
0241
0.2
5800
0.0
0084
NB
0.69
015
0.00
219
0.51
960
0.00
146
0.583
780.
0026
20.
2561
40.
001
97-0
.174
820.0
008
5-0
.2376
10.
0007
60.4
2180
0.0
0241
0.3
0213
0.0
0084
UB
0.65
530
0.00
219
0.51
806
0.00
146
0.558
640.
0026
20.
2334
50.
001
97-0
.176
350.0
008
5-0
.2679
10.
0007
60.3
8679
0.0
0241
0.2
6303
0.0
0084
VI m
inB
0.31
800
0.00
386
0.23
492
0.00
172
-0.1
2091
0.00
388
-0.1
4705
0.00
765
-0.4
8091
0.0
007
2-0
.3191
30.
0009
40.1
4789
0.0
0184
0.1
1620
0.0
0064
NB
0.37
047
0.00
386
0.23
321
0.00
172
-0.0
2726
0.00
388
-0.0
2935
0.00
765
-0.4
5078
0.0
007
2-0
.2756
40.
0009
40.2
0360
0.0
0184
0.1
4683
0.0
0064
UB
0.30
618
0.00
386
0.31
183
0.00
172
-0.0
9026
0.00
388
0.09
547
0.007
65-0
.468
580.0
007
2-0
.2969
40.
0009
40.1
5795
0.0
0184
0.1
6602
0.0
0064
VI r
an
ge
B0.
3797
70.
0040
30.
2874
30.
0020
10.
730
110.
0043
50.
4091
30.
007
730.
3081
30.
0012
60.0
5230
0.00
085
0.2
6356
0.0
0287
0.1
4180
0.0
0097
NB
0.31
968
0.00
403
0.28
639
0.00
201
0.611
030.
0043
50.
2854
90.
007
730.
2759
60.
0012
60.0
3803
0.00
085
0.2
1821
0.0
0287
0.1
5530
0.0
0097
UB
0.34
912
0.00
403
0.20
623
0.00
201
0.648
900.
0043
50.
1379
70.
007
730.
2922
40.
0012
60.0
2903
0.00
085
0.2
2883
0.0
0287
0.0
9702
0.0
0097
DO
Ym
ax
B10
111
.548
0.6
310.
585
3.5
858.
9253
7.6
62
6.1
76
0.9
NB
6911
.561
0.6
350.
581
3.5
139
8.9
74
7.6
61
6.1
45
0.9
UB
6211
.544
0.6
320.
511
63.5
848.
9236
7.6
78
6.1
47
0.9
DO
Ym
inB
238
0.6
342
1.6
253
0.6
348
9.3
211
0.2
183
16.5
237
1.0
342
1.0
NB
237
0.6
345
1.6
261
0.6
351
9.3
205
0.2
164
16.5
227
1.0
344
1.0
UB
238
0.6
330
1.6
255
0.6
263
9.3
208
0.2
137
16.5
233
1.0
329
1.0
DO
Yra
nge
B18
13.
729
41.
722
21.
026
36.8
126
0.5
152
9.0
174
2.0
266
1.2
NB
177
3.7
284
1.7
225
1.0
270
6.8
127
0.5
116
9.0
171
2.0
299
1.2
UB
176
3.7
286
1.7
223
1.0
170
6.8
124
0.5
154
9.0
165
2.0
282
1.2
Sm
ax
B-0
.001
620.
0003
03-0
.001
592.
81E
-05
-0.0
0485
5.18
E-0
5-0
.004
717.
3E-0
5-0
.003
840.0
002
33-0
.0001
98.
9E-0
5-0
.00237
0.0
001
09
-0.0
0127
1.3
1E
-05
NB
-0.0
0267
0.00
0303
-0.0
0199
2.81
E-0
5-0
.004
015.
18E
-05
-0.0
0201
7.3E
-05
-0.0
0199
0.0
002
33-0
.0004
68.
9E-0
5-0
.00195
0.0
001
09
-0.0
0106
1.3
1E
-05
UB
-0.0
0326
0.00
0303
-0.0
011
2.81
E-0
5-0
.003
975.
18E
-05
-0.0
0112
7.3E
-05
-0.0
037
0.00
0233
6.3
7E-0
58.
9E-0
5-0
.00193
0.0
001
09
-0.0
0056
1.3
1E
-05
VI S
B0.
4881
60.
0033
90.
2988
80.
0028
60.
125
200.
0076
2-0
.013
400.
00673
-0.3
2023
0.0
005
0-0
.3016
10.
0009
30.2
7921
0.0
0294
0.1
4921
0.0
0155
NB
0.52
213
0.00
339
0.42
158
0.00
286
0.275
160.
0076
20.
0360
00.
006
73-0
.309
460.0
005
0-0
.2552
20.
0009
30.3
2920
0.0
0294
0.2
5545
0.0
0155
UB
0.46
479
0.00
339
0.38
445
0.00
286
0.158
300.
0076
20.
1464
60.
006
73-0
.316
650.0
005
0-0
.2846
20.
0009
30.2
7449
0.0
0294
0.1
8820
0.0
0155
DO
YS
B19
55.
4229
12.
9117
93.
0530
78.3
014
44.
48
181
14.0
3151
3.9
8305
3.6
2
NB
163
5.42
136
2.91
147
3.05
299
8.3
016
94.
48
120
14.0
3128
3.9
8109
3.6
2
UB
164
5.42
233
2.91
165
3.05
227
8.3
014
34.
48
175
14.0
3155
3.9
8271
3.6
2
IB
96.2
132.
320
117.
416
0.57
659
.121
0.98
239
.997
1.056
-41.8
07
0.11
7-4
3.792
2.555
49.0
15
0.7
3554.
352
0.2
70
NB
95.3
132.
320
99.1
410.
576
60.5
060.
982
33.3
881.
056
-39.7
60
0.11
7-2
9.760
2.555
52.0
17
0.7
3561.
424
0.2
70
UB
87.4
972.
320
120.
455
0.57
652
.231
0.98
229
.188
1.056
-40.3
84
0.11
7-4
3.602
2.555
44.8
39
0.7
3561.
800
0.2
70
Appendix D. Variability caused by fire events 87
D.4 Tables for TUSSOCK GRASSLAND class
Table D.7: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for tussock grassland. Signifcant differences are indicated with
’x’.
SA1
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 1E-99 x x x 1.42E-74 x x x 3.24E-75 x x 1.75E-64 x x x
VImin 2.95E-67 x x x 3.17E-48 x x x 5E-80 x x x 5.79E-58 x x x
VIrange 8.17E-30 x x x 1.01E-33 x x x 1.83E-90 x x 1.36E-30 x x x
DOYmax 2.58E-08 x x 1.12E-08 x x x 0.003867 x 3.55E-13 x x
DOYmin 6.92E-08 x x 1.51E-15 x x x 3.34E-76 x x 3.15E-08 x x
DOYrange 2.31E-37 x x x 1.27E-20 x x 9.1E-41 x x 3.28E-23 x x
Smax 1.3E-22 x x x 6.23E-22 x x x 1.07E-08 x x x 1.71E-10 x x x
VIS 5.55E-32 x x x 3.38E-16 x x 2.56E-42 x x x 9.46E-57 x x x
DOYS 0.01202 x x 3.29E-70 x x x 1.68E-05 x x 0.000988 x x
I 1.25E-18 x x x 9.18E-70 x x x 2.05E-59 x x x 0.02654 x
T1 10 10 7 10 9 9 10 9 4 8 9 7
SA2
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 5.85E-26 x x x 7.62E-36 x x x 5.81E-21 x x 3.96E-23 x x
VImin 2.66E-09 x x 3.13E-27 x x 3.86E-18 x x x 0.399875
VIrange 4E-20 x x 2.77E-45 x x x 1.53E-07 x x 1.85E-22 x x
DOYmax 6.65E-12 x x 3.01E-12 x x 0.067358 2.89E-09 x x x
DOYmin 0.097681 0.080013 1.63E-10 x x 0.012476 x
DOYrange 0.24748 0.011663 x 0.000428 x x 0.141991
Smax 9.96E-07 x x 2.9E-55 x x x 2.79E-07 x x 2.03E-19 x x x
VIS 6.27E-21 x x x 4.76E-29 x x x 1.27E-23 x x x 4.54E-21 x x x
DOYS 1.27E-17 x x x 3.51E-25 x x x 2.57E-05 x x 2.86E-12 x x x
I 8.94E-19 x x x 9.09E-05 x x 5.57E-05 x x 8.83E-08 x x
T1 7 6 7 9 8 5 9 8 3 6 7 6
Appendix D. Variability caused by fire events 88
Tab
leD
.8:
Th
em
ean
(µ)
and
stan
dard
dev
iati
on
(σ)
of
the
met
rics
per
fire
his
tory
for
tuss
ock
gra
ssla
nd
ND
VI
ND
WI
mSA
VI 2
EV
I
SA
1SA
2SA
1SA
2SA
1SA
2SA
1SA
2
Met
rics
Gro
up
µσ
µσ
µσ
µσ
µσ
µσ
µσ
µσ
VI m
ax
B0.
576
280.0
019
10.
5547
70.
0027
00.
4560
10.
0043
40.
3813
30.
0037
7-0
.272
920.
0014
0-0
.221
250.
0008
60.2
8892
0.0021
60.
3251
90.0
0190
NB
0.713
000.0
019
10.
5363
90.
0027
00.
6578
60.
0043
40.
3352
10.
0037
7-0
.207
880.
0014
0-0
.208
620.
0008
60.3
7522
0.0021
60.
3208
40.0
0190
UB
0.699
430.0
019
10.
5045
40.
0027
00.
6426
10.
0043
40.
2897
10.
0037
7-0
.210
970.
0014
0-0
.219
760.
0008
60.3
6319
0.0021
60.
2948
70.0
0190
VI m
inB
0.277
530.0
033
30.
1944
10.
0013
8-0
.058
53
0.00
592
-0.1
5266
0.00
827
-0.3
2374
0.00
281
-0.2
6074
0.00
147
0.1
5162
0.0020
00.
1227
80.0
0100
NB
0.428
350.0
033
30.
1827
70.
0013
80.
1154
50.
0059
2-0
.012
040.
0082
7-0
.455
660.
0028
1-0
.239
270.
0014
70.2
2461
0.0020
00.
1233
40.0
0100
UB
0.342
260.0
033
30.
1833
30.
0013
8-0
.032
86
0.00
592
-0.0
1375
0.00
827
-0.4
6760
0.00
281
-0.2
4870
0.00
147
0.1
6810
0.0020
00.
1246
50.0
0100
VI r
an
ge
B0.
298
750.0
036
80.
3603
60.
0026
70.
5145
40.
0074
50.
5339
80.
0081
90.
0508
10.
0034
30.
03949
0.00
135
0.1
3730
0.0028
40.
2024
10.0
0205
NB
0.284
650.0
036
80.
3536
20.
0026
70.
5424
00.
0074
50.
3472
60.
0081
90.
2477
70.
0034
30.
03065
0.00
135
0.1
5061
0.0028
40.
1974
90.0
0205
UB
0.357
170.0
036
80.
3212
10.
0026
70.
6754
70.
0074
50.
3034
60.
0081
90.
2566
30.
0034
30.
02894
0.00
135
0.1
9509
0.0028
40.
1702
20.0
0205
DO
Ym
ax
B50
5.0
550.
943
1.5
650.
995
9.5
215
12.
556
5.5
570.8
NB
805.0
460.
956
1.5
570.
914
09.
522
312.
5120
5.5
500.8
UB
935.0
530.
951
1.5
550.
910
89.
525
412.
5103
5.5
520.8
DO
Ym
inB
255
0.8
345
2.3
285
1.6
336
4.2
184
0.5
252
10.
0236
8.6
344
4.7
NB
249
0.8
338
2.3
272
1.6
346
4.2
207
0.5
166
10.
0170
8.6
324
4.7
UB
248
0.8
340
2.3
264
1.6
333
4.2
209
0.5
160
10.
0234
8.6
336
4.7
DO
Yra
nge
B21
32.4
290
2.4
242
2.0
272
4.2
102
0.5
155
7.2
180
3.4
287
4.7
NB
168
2.4
292
2.4
216
2.0
289
4.2
113
0.5
119
7.2
124
3.4
274
4.7
UB
155
2.4
287
2.4
213
2.0
278
4.2
114
0.5
119
7.2
135
3.4
284
4.7
Sm
ax
B-0
.0017
0.000
114
-0.0
0293
3.07
E-0
5-0
.004
07
7.36
E-0
5-0
.004
87.8
9E-0
5-0
.000
70.
0002
66-0
.000
276.
96E
-05
-0.0
0126
0.0
00188
-0.0
0168
2.51E
-05
NB
-0.0
026
90.
000
114
-0.0
0308
3.07
E-0
5-0
.004
45
7.36
E-0
5-0
.002
637.
89E
-05
-0.0
021
0.00
0266
0.00
013
26.
96E
-05
-0.0
0022
0.0
00188
-0.0
0192
2.51E
-05
UB
-0.0
036
40.
000
114
-0.0
0285
3.07
E-0
5-0
.005
27
7.36
E-0
5-0
.002
087.
89E
-05
-0.0
0311
0.00
0266
0.00
027
76.
96E
-05
-0.0
0215
0.0
00188
-0.0
0154
2.51E
-05
VI S
B0.
426
070.0
063
20.
4193
10.
0023
20.
2740
40.
0061
5-0
.016
200.
0125
8-0
.297
270.
0013
8-0
.244
870.
0012
30.2
2485
0.0019
60.
2532
80.0
0143
NB
0.562
520.0
063
20.
4046
30.
0023
20.
3389
80.
0061
50.
2374
50.
0125
8-0
.326
510.
0013
8-0
.223
280.
0012
30.2
9781
0.0019
60.
2427
50.0
0143
UB
0.520
280.0
063
20.
3821
40.
0023
20.
2612
80.
0061
50.
1507
80.
0125
8-0
.334
020.
0013
8-0
.233
640.
0012
30.2
6974
0.0019
60.
2302
30.0
0143
DO
YS
B15
34.9
912
41.1
110
81.
9328
99.
7613
75.
01
253
9.4
5137
5.6
712
11.0
2
NB
172
4.9
910
91.1
119
51.
9311
19.
7617
15.
01
192
9.4
5138
5.6
710
91.0
2
UB
172
4.9
911
81.1
117
81.
9317
69.
7615
55.
01
208
9.4
5164
5.6
711
51.0
2
IB
90.5
56
1.08
992
.452
0.65
633
.157
1.26
340
.684
0.66
0-3
0.38
50.
237
-36.
951
1.72
639.1
62
0.952
55.9
520.7
43
NB
97.1
37
1.08
985
.509
0.65
692
.376
1.26
338
.447
0.66
0-3
7.80
50.
237
-26.
812
1.72
637.3
06
0.952
50.1
030.7
43
UB
81.0
35
1.08
982
.984
0.65
671
.533
1.26
336
.530
0.66
0-3
9.03
10.
237
-27.
818
1.72
635.4
89
0.952
50.9
150.7
43
Appendix D. Variability caused by fire events 89
D.5 Tables for HUMMOCK GRASSLAND class
Table D.9: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for hummock grassland. Signifcant differences are indicated with
’x’.
SA2
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 8.26E-44 x x x 3.41E-44 x x 1.68E-32 x x x 6.58E-21 x x
VImin 1.25E-05 x x 5.57E-21 x x x 3.45E-37 x x 9.13E-11 x x
VIrange 1.54E-22 x x x 7.47E-37 x x x 1.52E-06 x x 3.17E-33 x x x
DOYmax 7.52E-06 x x 4.75E-15 x x 0.000913 x x 0.3384
DOYmin 0.007248 x x 9.68E-12 x x 0.001536 x 0.533679
DOYrange 4.18E-06 x x 2.87E-15 x x 0.1657 0.189015
Smax 5.3E-22 x x x 5.14E-37 x x x 0.000191 x x 9.09E-37 x x x
VIS 9.46E-07 x x 1.3E-13 x x x 1.02E-35 x x 1.94E-05 x x
DOYS 0.000107 x x 1.01E-32 x x 0.006994 x x 0.000718 x x
I 2.68E-14 x x 2.02E-16 x x 0.345926 5.03E-06 x x
T1 7 9 7 7 7 10 7 4 5 5 6 5
SA3
NDVI NDWI mSAVI2 EVI
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 7.88E-43 x x x 1.8E-22 x x x 7.56E-72 x x x 9.33E-50 x x x
VImin 2.28E-48 x x x 1.1E-113 x x x 2.6E-117 x x x 4.84E-80 x x x
VIrange 2.74E-27 x x 7.39E-87 x x x 1.5E-102 x x 8.13E-09 x x x
DOYmax 1.41E-98 x x x 1.29E-64 x x x 4.87E-14 x x 6E-123 x x x
DOYmin 1.05E-11 x x 4.66E-05 x x 2.03E-18 x x x 1.72E-12 x x
DOYrange 7.95E-19 x x 8.13E-11 x x 0.616463 5.26E-57 x x x
Smax 2.49E-21 x x 9.09E-35 x x 2.64E-89 x x x 1.59E-18 x x
VIS 6.4E-51 x x 2.56E-70 x x x 1.2E-111 x x x 1.12E-76 x x x
DOYS 2.05E-07 x x 0.315514 8.89E-14 x x 0.030123 x
I 0.091508 2.92E-32 x x x 6.95E-15 x x x 2.19E-31 x x x
T1 8 6 7 9 8 7 9 8 7 9 9 8
Appendix D. Variability caused by fire events 90
Tab
leD
.10:
Th
em
ean
(µ)
and
stan
dard
dev
iati
on
(σ)
of
the
met
rics
per
fire
his
tory
for
hu
mm
ock
gra
ssla
nd
ND
VI
ND
WI
mSA
VI 2
EV
I
SA
1SA
2SA
1SA
2SA
1SA
2SA
1SA
2
Met
rics
Gro
up
µσ
µσ
µσ
µσ
µσ
µσ
µσ
µσ
VI m
ax
B0.
4047
30.
003
110.
2198
80.
0012
20.
1713
20.
0022
9-0
.121
930.
0019
7-0
.256
130.
0010
2-0
.229
390.
0012
10.2
214
30.
0024
50.1
066
00.0
0067
NB
0.41
641
0.003
110.
2420
60.
0012
20.
1662
30.
0022
9-0
.112
540.
0019
7-0
.239
270.
0010
2-0
.249
080.
0012
10.2
294
30.
0024
50.1
284
20.0
0067
UB
0.33
308
0.003
110.
2079
90.
0012
20.
1110
90.
0022
9-0
.145
000.
0019
7-0
.234
360.
0010
2-0
.190
490.
0012
10.1
919
40.
0024
50.1
195
30.0
0067
VI m
inB
0.17
460
0.006
040.
1664
20.
0006
4-0
.205
770.
0059
4-0
.282
130.
0011
5-0
.286
940.
0010
7-0
.430
740.
0021
30.0
915
60.
0022
20.0
825
80.0
0035
NB
0.21
318
0.006
040.
1863
30.
0006
4-0
.138
290.
0059
4-0
.179
220.
0011
5-0
.263
720.
0010
7-0
.271
730.
0021
30.1
129
70.
0022
20.1
009
10.0
0035
UB
0.20
878
0.006
040.
1724
10.
0006
4-0
.113
070.
0059
4-0
.187
540.
0011
5-0
.263
840.
0010
7-0
.215
240.
0021
30.1
097
90.
0022
20.0
979
40.0
0035
VI r
an
ge
B0.
2301
30.
006
480.
0534
60.
0011
30.
3770
90.
0061
60.
1602
00.
0019
50.
0308
10.
0008
70.
2013
50.
0024
80.1
298
60.
0021
60.0
240
20.0
0065
NB
0.20
323
0.006
480.
0557
30.
0011
30.
3045
20.
0061
60.
0666
80.
0019
50.
0244
50.
0008
70.
0226
60.
0024
80.1
164
60.
0021
60.0
275
10.0
0065
UB
0.12
430
0.006
480.
0355
80.
0011
30.
2241
60.
0061
60.
0425
40.
0019
50.
0294
80.
0008
70.
0247
50.
0024
80.0
821
50.
0021
60.0
215
90.0
0065
DO
Ym
ax
B48
3.8
144
1.1
192
4.3
126
1.9
235
8.5
332
12.2
62
4.4
76
1.5
NB
653.8
189
1.1
140
4.3
187
1.9
190
8.5
206
12.2
67
4.4
195
1.5
UB
743.8
226
1.1
182
4.3
202
1.9
201
8.5
197
12.2
71
4.4
241
1.5
DO
Ym
inB
338
9.2
277
13.0
338
0.7
268
14.9
278
13.2
207
8.3
339
3.0
250
11.0
NB
330
9.2
142
13.0
338
0.7
200
14.9
244
13.2
157
8.3
339
3.0
152
11.0
UB
298
9.2
168
13.0
331
0.7
174
14.9
313
13.2
279
8.3
335
3.0
132
11.0
DO
Yra
nge
B29
08.1
132
1.1
146
4.5
142
2.2
111
8.5
125
6.7
278
5.1
174
1.6
NB
270
8.1
118
1.1
198
4.5
119
2.2
134
8.5
130
6.7
271
5.1
117
1.6
UB
231
8.1
132
1.1
149
4.5
136
2.2
120
8.5
121
6.7
264
5.1
135
1.6
Sm
ax
B-0
.0019
25.
65E
-05
-0.0
0066
6.58
E-0
5-0
.004
839.
33E
-05
-0.0
0182
8.7E
-05
-0.0
0045
4.43
E-0
50.
0025
284.
69E
-05
-0.0
0133
2.66
E-0
5-0
.0002
33.2
E-0
5
NB
-0.0
012
35.
65E
-05
0.00
0355
6.58
E-0
5-0
.003
489.
62E
-05
-0.0
0014
8.7E
-05
-0.0
0023
4.43
E-0
58.
78E
-05
4.69
E-0
5-0
.000
85
2.66
E-0
50.0
001
65
3.2E
-05
UB
-0.0
010
25.
65E
-05
0.00
0162
6.58
E-0
5-0
.002
59.
33E
-05
4.33
E-0
58.
7E-0
5-0
.000
464.
43E
-05
-0.0
0037
4.69
E-0
5-0
.000
72.6
6E-0
50.0
001
93
3.2E
-05
VI S
B0.
2437
50.
006
620.
1911
50.
0008
0-0
.065
870.
0036
5-0
.208
930.
0013
2-0
.275
070.
0010
8-0
.317
790.
0011
80.1
311
40.
0031
20.0
925
30.0
0042
NB
0.29
319
0.006
620.
2137
10.
0008
0-0
.039
500.
0037
7-0
.146
670.
0013
2-0
.252
620.
0010
8-0
.260
040.
0011
80.1
520
20.
0031
20.1
143
80.0
0042
UB
0.25
537
0.006
620.
1896
90.
0008
0-0
.021
870.
0036
5-0
.167
720.
0013
2-0
.252
150.
0010
8-0
.205
070.
0011
80.1
377
50.
0031
20.1
090
20.0
0042
DO
YS
B28
68.5
921
86.
7029
71.
0520
78.
2726
010
.90
280
8.92
297
5.7
8185
5.61
NB
238
8.5
916
46.
7029
51.
0819
58.
2722
410
.90
177
8.92
265
5.7
8171
5.61
UB
240
8.5
919
96.
7027
51.
0518
98.
2727
110
.90
258
8.92
276
5.7
8192
5.61
IB
95.2
182.4
36
25.7
500.
221
4.29
60.
889
-27.
903
0.47
2-2
9.86
72.
150
-42.
211
1.39
850.
851
1.09
116.
679
0.1
53
NB
88.0
622.4
36
25.2
880.
221
13.5
400.
889
-17.
400
0.47
2-3
3.52
12.
150
-33.
941
1.39
849.
736
1.09
113.
379
0.1
53
UB
66.0
542.4
36
25.0
770.
221
2.08
60.
889
-22.
659
0.47
2-2
9.50
02.
150
-24.
314
1.39
843.
478
1.09
114.
680
0.1
53
Appendix E
Comparison with the reference year
(2004)
E.1 Results multiple comparison tests
E.2 Tables with mean values and standard deviation
91
Appendix E. Comparison with the reference year (2004) 92
Tab
leE
.1:
Th
est
atis
tica
lou
tpu
tof
the
pai
rwis
eco
mp
ari
son
of
the
tem
pora
ltr
aje
ctory
met
rics
for
bu
rned
(B)
an
du
nb
urn
ed(U
B)
fore
stof
2004
wit
hth
ose
ofth
eot
her
stu
die
dyea
rs.
Th
eva
riab
le’Y
ear’
ind
icate
sto
wh
ich
year
the
year
2004
isco
mp
are
d,
Yea
r1
stan
ds
for
2001,
Yea
r2
for
2002
and
soon
till
2008
.S
ign
ifca
nt
diff
eren
ces
are
ind
icate
dw
ith
’x’.
UB
ND
VI
EV
IN
DW
Im
SA
VI 2
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Met
ric
p-v
alu
eT
2p
-val
ue
T2
p-v
alu
eT
2p
-valu
eT
2
VI m
ax
7.11
79E
-172
xx
xx
xx
63.
9393
E-1
58x
xx
xx
x6
2.67
61E
-201
xx
xx
xx
x7
5.3
1033E
-88
xx
xx
x5
VI m
in6.
7299
E-2
23x
xx
xx
xx
74.
5248
E-1
53x
xx
xx
x6
3.78
16E
-108
xx
xx
xx
69.9
873E
-102
xx
xx
xx
6
VI r
an
ge
6.94
28E
-149
xx
xx
x5
4.62
63E
-126
xx
xx
x5
4.27
13E
-99
xx
xx
x5
1.8
7544E
-46
xx
xx
4
DO
Ym
ax
8.26
947E
-43
xx
x3
5.62
435E
-27
xx
xx
41.
5632
7E-4
6x
xx
xx
52.0
0091E
-67
xx
xx
xx
6
DO
Ym
in6.
0339
E-2
11x
xx
xx
xx
74.
9650
6E-5
0x
xx
x4
0.00
1611
329
xx
22.6
32E
-06
x1
DO
Yra
nge
1.32
303E
-97
xx
xx
x5
2.64
149E
-97
xx
xx
xx
61.
8262
1E-1
5x
xx
x4
9.2
0411E
-21
xx
x3
Sm
ax
1.69
17E
-129
xx
xx
xx
62.
6833
2E-9
5x
xx
36.
8713
4E-1
8x
xx
xx
56.1
1974E
-05
xx
2
VI S
4.64
61E
-123
xx
xx
xx
x7
9.84
E-9
9x
xx
xx
xx
77.
3062
E-1
30x
xx
xx
xx
74.1
175E
-102
xx
xx
4
DO
YS
6.08
627E
-59
xx
xx
46.
6365
6E-4
6x
xx
xx
52.
8816
4E-4
2x
xx
x4
2.8
947E
-34
xx
xx
4
I3.
8071
E-1
13x
xx
xx
x6
4.42
62E
-114
xx
xx
xx
66.
1824
E-1
62x
xx
xx
xx
71.3
0793E
-16
xx
x3
T1
96
89
89
77
68
105
88
69
77
96
84
37
46
59
B
ND
VI
EV
IN
DW
Im
SA
VI 2
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Met
ric
p-v
alu
eT
2p
-val
ue
T2
p-v
alu
eT
2p
-valu
eT
2
VI m
ax
5.51
73E
-156
xx
xx
44.
4319
E-1
04x
xx
xx
54.
8109
E-1
88x
xx
x4
1.7
136E
-213
xx
xx
xx
6
VI m
in1.
7476
E-1
29x
xx
xx
x6
4.01
92E
-134
xx
xx
xx
61.
5077
E-9
5x
xx
xx
x6
4.7
5039E
-59
xx
xx
xx
6
VI r
an
ge
1.37
55E
-134
xx
xx
x5
3.31
41E
-118
xx
xx
xx
61.
1166
7E-7
2x
xx
x4
4.0
01E
-119
xx
xx
4
DO
Ym
ax
5.01
06E
-196
xx
x3
6.79
79E
-213
xx
xx
x5
3.26
109E
-23
xx
x3
1.7
8643E
-95
xx
xx
x5
DO
Ym
in9.
197E
-99
xx
xx
45.
2537
E-1
04x
xx
xx
x6
3.55
239E
-91
xx
21.3
8701E
-27
xx
xx
xx
6
DO
Yra
nge
8.49
29E
-160
xx
xx
x5
1.94
75E
-152
xx
xx
xx
66.
073E
-55
xx
xx
42.1
6303E
-38
xx
xx
x5
Sm
ax
1.22
07E
-206
xx
xx
x5
3.98
3E-2
43x
xx
xx
58.
5795
1E-2
6x
xx
xx
51.0
1877E
-38
xx
xx
x5
VI S
9.61
712E
-65
xx
xx
xx
61.
7602
6E-4
1x
xx
xx
x6
1.93
931E
-92
xx
xx
x5
6.9
874E
-147
xx
xx
xx
x7
DO
YS
3.54
21E
-106
xx
xx
xx
61.
9714
E-9
9x
xx
xx
53.
3445
E-1
01x
xx
37.4
1619E
-28
xx
xx
xx
6
I9.
1203
E-1
60x
xx
xx
xx
71.
0383
E-1
76x
xx
xx
x6
4.01
43E
-191
xx
xx
41.1
9104E
-32
xx
xx
x5
T1
68
710
68
610
67
88
89
54
37
74
108
88
98
77
Appendix E. Comparison with the reference year (2004) 93
Tab
leE
.2:
Th
est
atis
tica
lou
tpu
tof
the
pai
rwis
eco
mp
ari
son
of
the
tem
pora
ltr
aje
ctory
met
rics
for
bu
rned
(B)
an
du
nb
urn
ed(U
B)
tuss
ock
gras
slan
dof
2004
wit
hth
ose
ofth
eot
her
stu
die
dye
ars
.T
he
vari
ab
le’Y
ear’
ind
icate
sto
wh
ich
year
the
year
2004
isco
mp
are
d,
Yea
r
1st
and
sfo
r20
01,
Yea
r2
for
2002
and
soon
till
2008.
Sig
nif
cant
diff
eren
ces
are
ind
icate
dw
ith
’x’.
UB
ND
VI
EV
IN
DW
Im
SA
VI 2
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Met
ric
p-v
alu
eT
2p
-val
ue
T2
p-v
alu
eT
2p
-val
ue
T2
VI m
ax
2.3
272
E-3
07x
xx
xx
x6
1.79
71E
-146
xx
xx
xx
69.
3036
E-2
80x
xx
xx
x6
5.5
762E
-113
xx
xx
xx
6
VI m
in4.5
936
E-1
74x
xx
xx
59.
1864
E-1
45x
xx
xx
x6
4.63
65E
-187
xx
xx
xx
x7
7.3
704E
-180
xx
xx
xx
6
VI r
an
ge
1.7
386
E-2
94x
xx
x4
6.26
06E
-134
xx
xx
xx
63.
4273
E-2
50x
xx
xx
x6
2.7
802E
-104
xx
xx
4
DO
Ym
ax
2.1
424
E-1
14x
xx
36.
1698
E-5
8x
x2
1.46
481E
-11
xx
28.4
0988
E-2
3x
1
DO
Ym
in4.5
279
E-2
14x
xx
xx
x6
1.95
31E
-138
xx
xx
x5
1.73
089E
-60
xx
x3
2.2
9969
E-9
6x
xx
x4
DO
Yra
nge
8.1
269
E-1
90x
xx
xx
xx
74.
6667
5E-5
8x
xx
xx
59.
6419
3E-3
0x
xx
xx
51.4
8034
E-2
9x
xx
3
Sm
ax
1.5
157
E-2
55x
xx
xx
xx
73.
2682
E-1
63x
xx
xx
51.
6519
E-1
50x
xx
xx
54.8
8801
E-9
0x
x2
VI S
8.23
4E-2
53x
xx
xx
x6
6.39
94E
-154
xx
xx
xx
x7
3.41
65E
-141
xx
xx
xx
63.2
104E
-155
xx
xx
xx
6
DO
YS
1.8
716
E-1
90x
xx
xx
51.
2947
E-9
9x
xx
x4
8.63
828E
-49
xx
xx
x5
6.2
5116
E-2
7x
xx
3
I3.
385E
-283
xx
xx
xx
67.
5430
6E-7
5x
xx
xx
54.
83E
-282
xx
xx
xx
61.1
3034
E-2
4x
xx
3
T1
99
49
88
810
74
104
88
77
59
77
97
36
63
85
B
ND
VI
EV
IN
DW
Im
SA
VI 2
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Yea
r1
23
56
78
Met
ric
p-v
alu
eT
2p
-val
ue
T2
p-v
alu
eT
2p
-val
ue
T2
VI m
ax
0x
xx
xx
x6
1.29
2E-2
57
xx
xx
xx
x7
6.05
99E
-306
xx
xx
xx
63.0
027E
-133
xx
xx
x5
VI m
in4.0
285
E-1
39x
xx
xx
xx
71.
0532
E-1
62x
xx
xx
x6
1.10
054E
-52
xx
xx
x5
9.1
8164
E-7
7x
xx
xx
xx
7
VI r
an
ge
1.5
033
E-2
58x
xx
xx
xx
71.
3055
E-2
31x
xx
xx
xx
71.
5024
E-2
24x
xx
xx
x6
1.3
1861
E-8
1x
xx
xx
x6
DO
Ym
ax
1.4
849
E-1
99x
xx
x4
0x
xx
xx
xx
72.
1827
5E-1
4x
xx
32.5
761E
-120
xx
xx
4
DO
Ym
in3.3
412
E-1
70x
xx
xx
xx
77.
6928
9E-9
4x
xx
x4
2.40
65E
-110
xx
21.9
1262
E-9
2x
xx
xx
5
DO
Yra
nge
1.9
527
E-2
24x
xx
xx
xx
71.
5035
5E-7
8x
xx
xx
x6
2.17
348E
-50
xx
xx
x5
8.6
9071
E-6
4x
xx
xx
5
Sm
ax
4.9
564
E-2
32x
xx
xx
xx
71.
1917
E-3
02x
xx
xx
xx
71.
1202
1E-6
2x
xx
x4
4.3
288E
-158
xx
xx
xx
6
VI S
1.4
312
E-1
31x
xx
xx
x6
7.46
1E-1
20
xx
xx
xx
61.
0747
3E-7
4x
x2
6.8
345E
-124
xx
xx
xx
6
DO
YS
3.9
683
E-1
38x
xx
x4
1.40
71E
-147
xx
xx
xx
61.
6146
E-1
03x
xx
x4
1.0
614E
-69
xx
xx
4
I1.6
669
E-3
00x
xx
xx
xx
76.
6967
E-1
99x
xx
xx
x6
2.49
97E
-259
xx
xx
xx
67.7
1605
E-6
2x
xx
xx
5
T1
98
810
109
88
99
107
910
17
69
47
99
73
109
87
Appendix E. Comparison with the reference year (2004) 94
Table E.3: The mean (µ) and standard deviation (σ) of the forest metrics for each year per fire
history
VI NDVI EVIBurning status UB B UB BMetric Year µ σ µ σ µ σ µ σ
VImax 2001 0.38953 0.00335 0.45376 0.00271 0.21192 0.00179 0.21899 0.001522002 0.37168 0.00335 0.48798 0.00271 0.20092 0.00179 0.24671 0.001522003 0.40502 0.00335 0.45215 0.00271 0.22800 0.00179 0.24840 0.001522004 0.37039 0.00335 0.45953 0.00271 0.20909 0.00179 0.24745 0.001522005 0.26322 0.00335 0.32677 0.00271 0.15494 0.00179 0.20376 0.001522006 0.43920 0.00335 0.45182 0.00271 0.23793 0.00179 0.25980 0.001522007 0.44192 0.00335 0.43629 0.00271 0.24551 0.00179 0.23497 0.001522008 0.28320 0.00335 0.43131 0.00271 0.16718 0.00179 0.23191 0.00152
VImin 2001 0.28186 0.00111 0.29191 0.00231 0.16086 0.00103 0.15067 0.001162002 0.22808 0.00111 0.19694 0.00231 0.13605 0.00103 0.09693 0.001162003 0.24188 0.00111 0.22260 0.00231 0.14762 0.00103 0.12493 0.001162004 0.21546 0.00111 0.21271 0.00231 0.12724 0.00103 0.11367 0.001162005 0.20262 0.00111 0.19505 0.00231 0.11599 0.00103 0.11277 0.001162006 0.26748 0.00111 0.24519 0.00231 0.16190 0.00103 0.13947 0.001162007 0.22435 0.00111 0.19881 0.00231 0.13125 0.00103 0.10654 0.001162008 0.19476 0.00111 0.19695 0.00231 0.11808 0.00103 0.12458 0.00116
VIrange 2001 0.10767 0.00300 0.16185 0.00331 0.05106 0.00168 0.06832 0.001922002 0.14360 0.00300 0.29103 0.00331 0.06487 0.00168 0.14977 0.001922003 0.16315 0.00300 0.22955 0.00331 0.08038 0.00168 0.12348 0.001922004 0.15493 0.00300 0.24682 0.00331 0.08185 0.00168 0.13377 0.001922005 0.06061 0.00300 0.13172 0.00331 0.03895 0.00168 0.09099 0.001922006 0.17172 0.00300 0.20663 0.00331 0.07603 0.00168 0.12033 0.001922007 0.21757 0.00300 0.23748 0.00331 0.11426 0.00168 0.12843 0.001922008 0.08844 0.00300 0.23437 0.00331 0.04910 0.00168 0.10733 0.00192
DOYmax 2001 109.8 8.5 52.8 4.3 127.4 6.5 131.3 4.02002 40.3 8.5 32.8 4.3 46.3 6.5 45.9 4.02003 83.4 8.5 83.3 4.3 83.9 6.5 81.8 4.02004 58.6 8.5 48.3 4.3 48.4 6.5 52.0 4.02005 201.8 8.5 329.1 4.3 76.9 6.5 342.5 4.02006 105.6 8.5 94.5 4.3 98.9 6.5 73.4 4.02007 46.2 8.5 47.2 4.3 47.3 6.5 47.1 4.02008 43.8 8.5 37.2 4.3 36.7 6.5 30.9 4.0
DOYmin 2001 281.4 1.2 272.7 3.1 264.4 4.7 275.2 3.12002 316.7 1.2 325.9 3.1 328.1 4.7 330.8 3.12003 325.0 1.2 331.9 3.1 320.4 4.7 332.0 3.12004 339.9 1.2 340.1 3.1 331.1 4.7 346.3 3.12005 250.4 1.2 245.0 3.1 247.6 4.7 240.4 3.12006 329.7 1.2 339.0 3.1 335.7 4.7 337.1 3.12007 284.9 1.2 293.4 3.1 285.2 4.7 296.6 3.12008 309.2 1.2 335.5 3.1 295.7 4.7 293.1 3.1
DOYrange 2001 174.9 4.1 224.0 3.5 153.5 3.7 159.5 3.92002 276.5 4.1 293.1 3.5 281.8 3.7 284.9 3.92003 241.6 4.1 248.5 3.5 236.5 3.7 250.2 3.92004 281.3 4.1 293.6 3.5 284.2 3.7 294.3 3.92005 155.3 4.1 103.7 3.5 207.1 3.7 102.1 3.92006 224.1 4.1 244.5 3.5 236.8 3.7 263.7 3.92007 238.7 4.1 246.2 3.5 237.9 3.7 249.5 3.92008 265.4 4.1 298.3 3.5 258.9 3.7 262.3 3.9
Smax 2001 -0.001 4.68E-05 -0.00132 5.31E-05 -0.00045 2.82E-05 -0.00064 2.73E-052002 -0.00115 4.68E-05 -0.00209 5.31E-05 -0.00056 2.82E-05 -0.00094 2.73E-052003 -0.00119 4.68E-05 -0.00154 5.31E-05 -0.0006 2.82E-05 -0.00102 2.73E-052004 -0.00079 4.68E-05 -0.00122 5.31E-05 -0.00046 2.82E-05 -0.00086 2.73E-052005 0.000105 4.68E-05 0.002013 5.31E-05 -0.00019 2.82E-05 0.001472 2.73E-052006 -0.00108 4.68E-05 -0.00129 5.37E-05 -0.00046 2.82E-05 -0.00072 2.73E-052007 -0.00233 4.68E-05 -0.00209 5.31E-05 -0.00127 2.82E-05 -0.00118 2.73E-052008 -0.00059 4.68E-05 -0.00157 5.31E-05 -0.00042 2.82E-05 -0.00082 2.73E-05
VIS 2001 0.33279 0.00383 0.35581 0.00602 0.18688 0.00229 0.18367 0.003692002 0.33299 0.00383 0.42075 0.00602 0.18232 0.00229 0.16807 0.003692003 0.35892 0.00383 0.27567 0.00602 0.20574 0.00229 0.15129 0.003692004 0.28752 0.00383 0.31824 0.00602 0.17072 0.00229 0.14327 0.003692005 0.24297 0.00383 0.27587 0.00602 0.14316 0.00229 0.16844 0.003692006 0.35887 0.00383 0.31516 0.00608 0.20348 0.00229 0.19311 0.003692007 0.39447 0.00383 0.37136 0.00602 0.21912 0.00229 0.20875 0.003692008 0.25647 0.00383 0.36399 0.00602 0.15392 0.00229 0.19840 0.00369
DOYS 2001 204.7 7.6 198.3 7.2 191.1 7.9 209.0 7.72002 89.0 7.6 79.2 7.2 94.2 7.9 195.3 7.72003 140.7 7.6 280.7 7.2 139.8 7.9 295.3 7.72004 214.0 7.6 221.9 7.2 189.5 7.9 293.8 7.72005 208.8 7.6 297.9 7.2 117.1 7.9 308.0 7.72006 216.1 7.6 264.7 7.2 209.9 7.9 232.0 7.72007 75.4 7.6 107.5 7.2 77.1 7.9 82.6 7.72008 118.7 7.6 99.5 7.2 86.6 7.9 90.3 7.7
I 2001 59.398 1.196 86.585 1.266 28.424 0.624 30.023 0.6702002 77.079 1.196 93.012 1.266 44.453 0.624 48.618 0.6702003 75.588 1.196 86.299 1.266 43.336 0.624 49.328 0.6702004 83.095 1.196 99.713 1.266 47.950 0.624 55.109 0.6702005 36.356 1.196 26.047 1.266 27.670 0.624 15.439 0.6702006 79.470 1.196 88.611 1.266 47.320 0.624 55.479 0.6702007 75.404 1.196 77.776 1.266 42.082 0.624 41.685 0.6702008 61.886 1.196 87.918 1.266 35.208 0.624 43.841 0.670
Appendix E. Comparison with the reference year (2004) 95
Table E.4: The mean (µ) and standard deviation (σ) of the tussock grassland metrics for each year
per fire history
VI NDVI EVIBurning status UB B UB BMetric Year µ σ µ σ µ σ µ σ
VImax 2001 0.54598 0.00278 0.56608 0.00274 0.24652 0.00412 0.31414 0.001692002 0.41389 0.00278 0.34768 0.00274 0.25013 0.00412 0.20389 0.001692003 0.50270 0.00278 0.52182 0.00274 0.31518 0.00412 0.27461 0.001692004 0.50454 0.00278 0.55477 0.00274 0.29487 0.00412 0.32519 0.001692005 0.22248 0.00278 0.28188 0.00274 0.14854 0.00412 0.20342 0.001692006 0.53492 0.00278 0.52525 0.00274 0.32527 0.00412 0.29120 0.001692007 0.51874 0.00278 0.59540 0.00274 0.29975 0.00412 0.33670 0.001692008 0.25834 0.00278 0.28538 0.00274 0.16097 0.00412 0.20243 0.00169
VImin 2001 0.23454 0.00109 0.24285 0.00163 0.14442 0.00082 0.13645 0.000662002 0.19136 0.00109 0.17534 0.00163 0.13128 0.00082 0.11306 0.000662003 0.17645 0.00109 0.23266 0.00163 0.12658 0.00082 0.14250 0.000662004 0.18333 0.00109 0.19441 0.00163 0.12465 0.00082 0.12278 0.000662005 0.18609 0.00109 0.18308 0.00163 0.11858 0.00082 0.11713 0.000662006 0.21728 0.00109 0.22310 0.00163 0.14889 0.00082 0.12437 0.000662007 0.17880 0.00109 0.20156 0.00163 0.11091 0.00082 0.10752 0.000662008 0.17305 0.00109 0.18129 0.00163 0.11589 0.00082 0.11278 0.00066
VIrange 2001 0.31144 0.00281 0.32323 0.00335 0.10210 0.00424 0.17769 0.001912002 0.22254 0.00281 0.17234 0.00335 0.11885 0.00424 0.09083 0.001912003 0.32625 0.00281 0.28916 0.00335 0.18861 0.00424 0.13211 0.001912004 0.32121 0.00281 0.36036 0.00335 0.17022 0.00424 0.20241 0.001912005 0.03639 0.00281 0.09881 0.00335 0.02997 0.00424 0.08628 0.001912006 0.31764 0.00281 0.30215 0.00335 0.17638 0.00424 0.16683 0.001912007 0.33994 0.00281 0.39384 0.00335 0.18884 0.00424 0.22918 0.001912008 0.08528 0.00281 0.10409 0.00335 0.04508 0.00424 0.08965 0.00191
DOYmax 2001 16.8 6.2 26.4 4.0 154.8 10.0 31.4 1.02002 40.3 6.2 48.5 4.0 44.0 10.0 68.2 1.02003 64.7 6.2 80.8 4.0 65.3 10.0 82.2 1.02004 53.2 6.2 54.8 4.0 52.3 10.0 56.8 1.02005 272.0 6.2 315.3 4.0 264.1 10.0 345.4 1.02006 88.2 6.2 93.5 4.0 79.0 10.0 77.2 1.02007 45.4 6.2 52.3 4.0 45.3 10.0 52.4 1.02008 58.0 6.2 42.4 4.0 52.8 10.0 32.6 1.0
DOYmin 2001 282.3 1.5 287.2 2.1 309.5 3.7 323.9 4.92002 303.4 1.5 312.8 2.1 237.3 3.7 259.9 4.92003 334.0 1.5 334.3 2.1 331.2 3.7 331.5 4.92004 339.8 1.5 344.7 2.1 336.3 3.7 343.9 4.92005 224.2 1.5 241.8 2.1 210.2 3.7 187.0 4.92006 332.4 1.5 326.2 2.1 337.7 3.7 327.2 4.92007 281.0 1.5 237.3 2.1 275.6 3.7 250.8 4.92008 328.0 1.5 319.0 2.1 201.2 3.7 268.6 4.9
DOYrange 2001 265.5 2.4 260.8 2.2 181.8 7.0 292.6 4.92002 263.0 2.4 264.4 2.2 193.3 7.0 191.7 4.92003 269.3 2.4 253.5 2.2 265.9 7.0 249.3 4.92004 286.6 2.4 289.8 2.2 284.0 7.0 287.0 4.92005 119.3 2.4 107.8 2.2 141.2 7.0 158.4 4.92006 244.2 2.4 232.7 2.2 258.7 7.0 250.0 4.92007 235.6 2.4 185.0 2.2 230.3 7.0 198.4 4.92008 270.0 2.4 276.6 2.2 152.2 7.0 236.0 4.9
Smax 2001 -0.00206 3.71E-05 -0.00187 5.47E-05 -0.00055 4.22E-05 -0.0009 2.41E-052002 -0.00252 3.71E-05 -0.00179 5.47E-05 -0.00138 4.22E-05 -0.0011 2.41E-052003 -0.00321 3.71E-05 -0.0023 5.47E-05 -0.00199 4.22E-05 -0.00099 2.41E-052004 -0.00285 3.71E-05 -0.00293 5.47E-05 -0.00154 4.22E-05 -0.00168 2.41E-052005 0.000385 4.05E-05 0.001343 5.53E-05 0.00019 4.55E-05 0.001427 2.41E-052006 -0.00237 3.71E-05 -0.00184 5.47E-05 -0.00138 4.22E-05 -0.00122 2.41E-052007 -0.00377 3.71E-05 -0.00417 5.47E-05 -0.0022 4.22E-05 -0.00232 2.41E-052008 -0.00087 3.71E-05 -0.00082 5.47E-05 -0.00049 4.22E-05 -0.00101 2.41E-05
VIS 2001 0.32843 0.00244 0.37859 0.00577 0.19217 0.00265 0.22110 0.003052002 0.33670 0.00244 0.27925 0.00577 0.20616 0.00265 0.16354 0.003052003 0.38381 0.00244 0.43149 0.00577 0.24260 0.00265 0.20731 0.003052004 0.38214 0.00244 0.41931 0.00577 0.23023 0.00265 0.25328 0.003052005 0.20389 0.00266 0.24480 0.00583 0.13766 0.00285 0.17025 0.003052006 0.42712 0.00244 0.33484 0.00577 0.26489 0.00265 0.15763 0.003052007 0.42594 0.00244 0.46488 0.00577 0.24542 0.00265 0.26409 0.003052008 0.22413 0.00244 0.25015 0.00577 0.14193 0.00265 0.17654 0.00305
DOYS 2001 211.2 2.9 176.6 4.7 240.5 5.9 184.8 5.42002 84.5 2.9 104.5 4.7 89.9 5.9 123.9 5.42003 119.5 2.9 140.6 4.7 119.4 5.9 209.2 5.42004 117.8 2.9 124.4 4.7 114.7 5.9 120.9 5.42005 284.0 3.2 297.1 4.8 270.3 6.3 312.1 5.42006 155.2 2.9 246.1 4.7 143.0 5.9 288.3 5.42007 80.6 2.9 96.8 4.7 80.6 5.9 96.8 5.42008 118.0 2.9 105.4 4.7 108.2 5.9 69.5 5.4
I 2001 106.809 0.613 105.394 0.591 39.639 1.489 65.959 0.6572002 65.098 0.613 59.156 0.591 32.762 1.489 27.840 0.6572003 77.176 0.613 89.566 0.591 49.624 1.489 51.397 0.6572004 82.984 0.613 92.452 0.591 50.915 1.489 55.952 0.6572005 23.986 0.613 24.323 0.591 18.363 1.489 22.675 0.6572006 84.401 0.613 89.573 0.591 55.438 1.489 54.826 0.6572007 68.415 0.613 63.441 0.591 39.091 1.489 37.352 0.6572008 54.218 0.613 60.227 0.591 20.641 1.489 33.220 0.657
Appendix F
MODIS versus SPOT
F.1 The results of the analysis based on SPOT-imagery
Table F.1: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for forest based on SPOT-imagery. Signifcant differences are
indicated with ’x’.
SA1
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.042588 x 0.733344 0.291072
VImin 2.66E-15 x x x 3.22E-10 x x x 0.72534
VIrange 0.153378 0.027057 x 0.001459 x
DOYmax 0.856782 0.000803 x x 0.219958
DOYmin 0.375651 0.014866 x 0.294704
DOYrange 0.89082 0.000173 x x 0.165531
Smax 1.9E-11 x x 5.55E-12 x x 0.153712
VIS 0 x x 0.161314 0.823192
DOYS 6.36E-06 x x 0.138118 0.255492
I 0.053143 x 0.419893 0.655855
T1 3 3 5 4 1 6 0 0 1
SA2
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.448417 0.045543 0.892609
VImin 2.19E-07 x x x 3.78E-08 x x x 0.826993
VIrange 0.011018 x 0.188989 0.101129
DOYmax 1.37E-05 x x 0.000809 x x 0.001902 x x
DOYmin 0.032472 x 0.063107 0.218603
DOYrange 0.047064 0.043619 x 0.058202 x
Smax 0.211058 6.18E-05 x x 0.11204
VIS 0.000178 x x 0.039804 x 0.667244
DOYS 0 x x x 1.23E-05 x x 2.2E-09 x x x
I 0.322853 0.081668 0.322811
T1 5 4 3 6 3 2 1 2 3
96
Appendix F. MODIS versus SPOT 97
Table F.2: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for shrub based on SPOT-imagery. Signifcant differences are
indicated with ’x’. In this case, no NB data were provided for neither of both SA.
SA1
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.551946 2.63E-05 x 0.164804
VImin 0.318467 2.62E-05 x 0.642753
VIrange 0.692929 0.877441 5.33E-06 x
DOYmax 0.297132 0.22263 0.497399
DOYmin 0.174757 0.190244 0.010305 x
DOYrange 0.118026 0.948443 0.00728 x
Smax 4.25E-05 x 0.241464 2.71E-07 x
VIS 7.31E-10 x 0.015263 x 0.002228 x
DOYS 2.25E-06 x 0.106967 0.688187
I 0.102835 0.019631 x 0.600939
T1 0 3 0 0 4 0 0 5 0
SA2
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.162345 0.020371 x 0.29654
VImin 0.90899 4.72E-07 x 0.025192 x
VIrange 0.291486 3.65E-12 x 1.3E-11 x
DOYmax 0.172198 0.015444 x 0.009717 x
DOYmin 0.325035 0.001583 x 0.268525
DOYrange 0.938194 0.00506 x 0.007223 x
Smax 0.003687 x 6.6E-08 x 0.068359
VIS 0.537235 4.83E-07 x 0.217103
DOYS 0.56876 0.004694 x 0.014805 x
I 0.537123 0.003325 x 0.86837
T1 0 1 0 0 10 0 0 5 0
Appendix F. MODIS versus SPOT 98
Table F.3: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for tussock grassland based on SPOT-imagery. Signifcant differences
are indicated with ’x’.
SA1
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 5.89E-07 x x 0 x x 5.64E-14 x x x
VImin 0 x x x 0 x x x 0 x x x
VIrange 0.040523 x 9.34E-10 x x 0.293128
DOYmax 2.3E-06 x x 0.360307 0.002853 x x
DOYmin 6.6E-05 x x 0.00462 x x 0.000189 x x
DOYrange 0.000781 x 0.000486 x 0.311432
Smax 5.33E-08 x x 5.52E-06 x x 0.015891 x
VIS 1.11E-16 x x x 7.59E-09 x x 7.55E-14 x x x
DOYS 1.65E-12 x x 2.41E-11 x x x 0.000751 x
I 2.7E-07 x x x 0 x x x 7.4E-06 x x
T1 8 9 4 8 6 6 8 5 4
SA2
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.077591 0.447473 0.559709
VImin 0.058188 2.22E-07 x x 4.19E-06 x x
VIrange 0.18577 0.000471 x x 0.068916
DOYmax 0.372906 3.59E-07 x x 9.63E-05 x x
DOYmin 0.455787 0.377713 0.052616
DOYrange 0.060485 0.000145 x x 0.037633
Smax 0.109742 0.275447 0.108109
VIS 0.028528 x 0.912914 0.733374
DOYS 0.078848 3E-09 x x 2.8E-07 x x
I 0.27503 0.006741 x x 0.638878
T1 0 1 0 6 6 0 3 3 0
Appendix F. MODIS versus SPOT 99
Table F.4: The statistical output of the pairwise comparisons between the temporal trajectory met-
rics of B, UB and NB for hummock grassland based on SPOT-imagery. Signifcant differ-
ences are indicated with ’x’. In this case, no UB data were provided for SA3.
SA2
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 3.22E-15 x x 0.019536 x 0.251905
VImin 1.51E-08 x x 1.38E-06 x x 0.078559
VIrange 0 x x x 0.099796 1.11E-16 x x x
DOYmax 5.12E-06 x x 8.07E-10 x x 0.000873 x
DOYmin 0.006206 x x 5.55E-16 x x 0.226478
DOYrange 1.81E-05 x 0 x x x 0.019717 x
Smax 4.54E-14 x x x 8.9E-13 x x 5.87E-06 x x
VIS 8.88E-16 x x 1.48E-06 x x 0.016204 x
DOYS 0 x x 6.35E-06 x x 0 x x
I 3.22E-15 x x x 2.16E-07 x x 0.235427
T1 8 5 9 6 5 7 3 3 4
SA3
NDVI NDWI mSAVI2
metric p B-NB B-UB NB-UB p B-NB B-UB NB-UB p B-NB B-UB NB-UB
VImax 0.040797 x 0.185212 0.00529 x
VImin 0.033996 x 1.37E-06 x 0.031717 x
VIrange 0.000383 x 4.91E-06 x 0.280226
DOYmax 5.53E-13 x 0.233497 0.018138 x
DOYmin 5.44E-08 x 0.001039 x 3.24E-07 x
DOYrange 4.14E-06 x 0.038159 x 0.267421
Smax 3.06E-09 x 4.46E-09 x 0.001098 x
VIS 0.865632 0.143027 0.013684 x
DOYS 2.26E-05 x 0.441895 0.983178
I 0.000136 x 0.054471 0.010162 x
T1 9 0 0 5 0 0 7 0 0
F.2 Results of the classification
Appendix F. MODIS versus SPOT 100
Table F.5: The results of the classification test based on prediction intervals calculated from VImax
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 48 48 0% 50 50 0%
UB 48 48 0% 50 50 0%
SA2 B 46 1 90% 50 49 2%
UB 45 0 90% 50 50 0%
Sh SA1 B 47 4 86% 50 50 0%
UB 48 2 92% 50 50 0%
SA2 B 48 46 4% 50 50 0%
UB 42 35 14% 50 50 0%
Tu SA1 B 47 0 94% 0 50 -100%
UB 45 0 90% 50 0 100%
SA2 B 49 8 82% 50 50 0%
UB 43 14 58% 50 50 0%
Hu SA2 B 48 0 96% 50 8 84%
UB 47 0 94% 50 31 38%
SA3 B 46 16 60% 50 50 0%
UB 47 29 36% 50 50 0%
Average Performance 62% 8%
Table F.6: The results of the classification test based on prediction intervals calculated from Smax
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 50 50 0% 50 44 12%
UB 50 50 0% 48 40 16%
SA2 B 49 49 0% 50 50 0%
UB 47 4 86% 50 49 2%
Sh SA1 B 48 50 -4% 50 50 0%
UB 43 32 22% 50 50 0%
SA2 B 47 1 92% 44 50 -12%
UB 47 3 88% 50 43 14%
Tu SA1 B 50 49 2% 10 49 -78%
UB 48 3 90% 49 0 98%
SA2 B 46 43 6% 50 50 0%
UB 45 45 0% 50 50 0%
Hu SA2 B 49 10 78% 50 25 50%
UB 46 30 32% 49 18 62%
SA3 B 46 0 92% 50 2 96%
UB 50 31 38% 0 0 0%
Average Performance 39% 16%
Appendix F. MODIS versus SPOT 101
Table F.7: The results of the classification test based on prediction intervals calculated from VImax-
Smax
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 48 48 0% 50 44 12%
UB 48 48 0% 48 40 16%
SA2 B 46 1 90% 50 49 2%
UB 45 0 90% 50 49 2%
Sh SA1 B 45 4 82% 50 50 0%
UB 43 1 84% 50 50 0%
SA2 B 46 1 90% 44 50 -12%
UB 39 2 74% 50 43 14%
Tu SA1 B 47 0 94% 0 49 -98%
UB 43 0 86% 49 0 98%
SA2 B 45 8 74% 50 50 0%
UB 42 14 56% 50 50 0%
Hu SA2 B 47 0 94% 50 5 90%
UB 43 0 86% 49 10 78%
SA3 B 46 0 92% 50 2 96%
UB 47 27 40% 0 0 0%
Average Performance 71% 19%
Table F.8: The results of the classification test based on prediction intervals calculated from VImax-
DOYmax
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 45 44 2% 50 50 0%
UB 45 46 -2% 50 50 0%
SA2 B 44 0 88% 50 49 2%
UB 44 0 88% 50 50 0%
Sh SA1 B 47 4 86% 50 50 0%
UB 43 1 84% 50 50 0%
SA2 B 45 33 24% 50 50 0%
UB 42 34 16% 50 50 0%
Tu SA1 B 47 0 94% 0 44 -88%
UB 44 0 88% 49 0 98%
SA2 B 41 5 72% 50 50 0%
UB 42 14 56% 50 50 0%
Hu SA2 B 46 0 92% 50 8 84%
UB 47 0 94% 50 31 38%
SA3 B 43 0 86% 50 1 98%
UB 46 0 92% 0 0 0%
Average Performance 66% 15%
Appendix F. MODIS versus SPOT 102
Table F.9: The results of the classification test based on prediction intervals calculated from VImax-
Smax-I
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 48 47 2% 50 44 12%
UB 48 48 0% 48 40 16%
SA2 B 46 1 90% 50 49 2%
UB 44 0 88% 50 49 2%
Sh SA1 B 45 4 82% 47 50 -6%
UB 40 1 78% 50 50 0%
SA2 B 45 1 88% 44 50 -12%
UB 37 0 74% 50 43 14%
Tu SA1 B 46 0 92% 0 43 -86%
UB 43 0 86% 48 0 96%
SA2 B 45 7 76% 50 50 0%
UB 41 9 64% 50 50 0%
Hu SA2 B 47 0 94% 50 5 90%
UB 43 0 86% 49 10 78%
SA3 B 43 0 86% 50 2 96%
UB 47 25 44% 0 0 0%
Average Performance 71% 19%
Table F.10: The results of the classification test based on prediction intervals calculated from VImax-
VImin-VIrange
Sensor MODIS SPOT
VT SA Basis PI Correct Incorrect Performance Correct Incorrect Performance
Fo SA1 B 47 46 2% 50 37 26%
UB 46 48 -4% 50 47 6%
SA2 B 46 1 90% 43 49 -12%
UB 37 0 74% 50 49 2%
Sh SA1 B 47 4 86% 50 50 0%
UB 44 2 84% 50 1 98%
SA2 B 46 0 92% 50 50 0%
UB 32 0 64% 31 6 50%
Tu SA1 B 45 0 90% 0 25 -50%
UB 44 0 88% 50 0 100%
SA2 B 48 8 80% 50 50 0%
UB 41 14 54% 50 49 2%
Hu SA2 B 47 0 94% 49 8 82%
UB 41 0 82% 36 0 72%
SA3 B 43 3 80% 50 50 0%
UB 43 2 82% 0 0 0%
Average Performance 71% 24%
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