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This article was downloaded by: [Acadia University]On: 11 May 2013, At: 08:18Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK
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Ecoregion classification in theOkavango Delta, Botswana frommultitemporal remote sensingJ. McCarthy a , T. Gumbricht b & T. S. McCarthy ba Department of Land and Water Resources Engineering, RoyalInstitute of Technology (KTH), S‐100 44 Stockholm, Swedenb Department of Geology, University of the Witwatersrand, PrivateBag 3, 2050 Wits, South AfricaPublished online: 22 Feb 2007.
To cite this article: J. McCarthy , T. Gumbricht & T. S. McCarthy (2005): Ecoregion classification inthe Okavango Delta, Botswana from multitemporal remote sensing, International Journal of RemoteSensing, 26:19, 4339-4357
To link to this article: http://dx.doi.org/10.1080/01431160500113583
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Ecoregion classification in the Okavango Delta, Botswana frommultitemporal remote sensing
J. MCCARTHY{, T. GUMBRICHT{ and T. S. MCCARTHY*{{Department of Land and Water Resources Engineering, Royal Institute of Technology
(KTH), S-100 44 Stockholm, Sweden
{Department of Geology, University of the Witwatersrand, Private Bag 3, 2050 Wits,
South Africa
The Okavango inland Delta in Botswana is characterized by a high spatial and
temporal variation in vegetation patches and flooding. Predicting the effects of
escalating development projects in this pristine wildlife area is hampered by a
lack of accurate maps. Efforts using traditional statistical methods have been
futile. The processes forming this highly dynamic environment, however, give rise
to a well-documented consistency in the land cover pattern at scales ranging from
single island architecture to an overall gradient in wetland, flood plain and island
occurrence. We conducted a classification in a two-step process starting with
statistical methods, and then refining using indices and flooding data. The indices
and flooding data were created and selected to make possible the inferring of
knowledge about the patterns at different scales through declarative IF … THEN
… statements. The initial statistical classification achieved a best result of 46%
accuracy for 10 classes, whereas the rule-based classification achieved an
accuracy of 63%. Application of the derived classification for mapping islands
and topography shows a surprisingly high accuracy.
1. Introduction
The Okavango inland ‘Delta’ in Northern Botswana is one of Africa’s largest, most
pristine wetlands and a refuge for wildlife (McCarthy and Ellery 1998). It is,
however, increasingly under threat from development projects. A land cover map is
needed for portraying this system as such, and for use as a framework dataset for
modelling the hydrological, sedimentological and ecological consequences of
various development activities. This study was carried out in order to derive the
first vegetation or ecoregion (henceforth ecoregion) map of the Okavango. The map
presented herein has hitherto been used in several studies aiming at understanding
the relation between spatial patterns and temporal processes in the Okavango
(Bauer et al. 2002, 2003, Gumbricht et al. 2004a, b, Gumbricht and McCarthy 2003,
Gyllenhammar and Gumbricht 2005).
The Okavango Delta is annually flooded by water from the Angolan highlands
(10 km3 per annum) augmented by direct local rainfall (5 km3 per annum). The area
of inundation varies between 4000 and 13 000 km2, depending on the seasonal and
annual variation in incoming water flow and local precipitation (McCarthy et al.
2003). The Delta is a mosaic of channels and islands embedded in a matrix of
*Corresponding author. Email: [email protected]
International Journal of Remote Sensing
Vol. 26, No. 19, 10 October 2005, 4339–4357
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2005 Taylor & Francis
http://www.tandf.co.uk/journalsDOI: 10.1080/01431160500113583
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wetlands and flood plains. The channels vary in size from trails created by
hippopotamuses to the 100-m-wide Okavango River; islands vary in size from small
ant hills (m2) to forest-covered islands of several hundred square kilometres. The
general spatial architecture of the internal wetland environment is a channel
surrounded by wetlands, followed by flood plains that merge into island-studded
grasslands, with a tree fringe marking the shore of an island (figure 1).
The Okavango system starts with an entry corridor, the ‘Panhandle’, a tectonic
graben that is 100 km long and 10 km wide (figure 2). The Okavango River is well
defined in the Panhandle, but bifurcates (divides) into smaller channels at the apex
of the Delta proper. The Delta forms an almost perfect cone on a regional scale
(Gumbricht et al. 2001), 150 km long and with a total area of around 40 000 km2
(figure 2). The channels on the Delta surface migrate as a result of combined tectonic
and sedimentological processes; major channel positions change over timescales of
centuries (McCarthy and Ellery 1998, Gumbricht et al. 2001). The islands are born
as old channels, and associated flood plains are abandoned (Gumbricht et al. 2004).
Island nuclei are composed of clastic sediments (i.e. sand) but, once formed, grow
through the accumulation of chemical sediments (precipitated salts). Islands grow
around their edges as a riparian tree fringe causes high transpiration and subsurface
salt accumulation. As islands grow, the tree fringe migrates with the island
expansion, and the island interior becomes saline. Mature islands are characterized
by a typical succession of species from the freshwater supplied fringes towards the
saline centres (Ellery et al. 1993). The island genesis and growth processes give rise
to typical spatial island architectures. The birth and growth processes also mean that
most islands are elongated, and parallel to the slope of the Delta surface (Gumbricht
and McCarthy 2003).
Land cover classification of wetlands from remote sensing data is hampered by
several factors, including high costs, difficult ground accessibility, steep ecological
gradients and problems of extrapolating small-scale features over large areas
Figure 1. Photograph of the Okavango Delta, Botswana, showing a channel, its adjacentwetlands and flood plain, surrounding grasslands, and islands and their typical architecture.Note the salt patches in the islands in the upper-left part of the image.
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(Harvey and Hill 2001). The similarity between different vegetation in broadband
multispectral data (cf. Smith and Fuller 2001), and the spatial complexity of the
Okavango, calls for the adoption of very-high-resolution images (Ringrose et al.
2003), hyperspectral imagery (cf. Schmidt and Skidmore 2001) or contextual
classification methods (cf. Lunetta et al. 2003). Ideally, a high-resolution
hyperspectral image could have been used in our study, but as the objective was
to create a consistent land cover map over the entire Okavango area, and, due to
financial restrictions, we had to rely on broadband multispectral Landsat Thematic
Mapper (TM) data, and the use of contextual post-classification.
In general, contextual classifiers rely on a similarity between a pixel and its
neighbours, i.e. that ground cover occurs on scales larger than a single pixel. The
simplest contextual classifier is a moving window with a 363 pixel kernel (see
Stuckens et al. 2000). More advanced methods include Markov random field
modelling (Zhou and Robson 2001) and image segmentation (Groom et al. 1996,
Lunetta et al. 2003). Lunetta et al. (2003). Both of these show convincingly that
contextual classifiers can improve both visual appearance and quantitative accuracy
Figure 2. Study area and Landsat TM 5 scenes used for the classification of ecoregions ofthe Okavango delta, Botswana. The maps are projected to Universal Transverse Mercator,South Zone 34, Cape datum (central meridian: 21; scale factor 0.9996; false easting: 500 000;false northing: 10 000 000). The coordinates are given in kilometres in the main map.
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of complex regions, including wetlands. The high spatial and temporal variability of
the Okavango system, however, violates the assumption of class resemblance
between neighbouring pixels. To overcome this problem, our classification instead
relied on initial supervised maximum likelihood and unsupervised clustering, post-
classification update based on multitemporal low-resolution images of flooding, and
contextual information extracted from different sources including the original
Landsat TM scenes (cf. King 2002).
2. Data
Four radiometric (level 1b) Landsat 5 TM scenes were mosaicked for full coverage
of the Okavango Delta (174/073 and 174/074 from 1 August 1994, 175/073 from 7
July 1994, and 175/074 from 7 July 1989; see figure 2). As vegetation patches in the
Okavango are stable over decades, the use of 5- to 10-year-old imagery as such will
not have any noticeable effects. No atmospheric correction was performed. For
geocorrection (bilinear nearest-neighbour resampling), Botswana topographic maps
printed on orthophotos at 1 : 50 000 scale were used (projected to Universal
Transverse Mercator, South Zone 34, Cape datum). The theoretical precision for
each of the four scenes was within two pixels. Positional accuracy varies, however,
between 0 and 200 m (up to six or seven pixels) when directly overlain on the
photobased maps. The same map series, together with aerial photographs at the
same scale, were also used for identifying exact locations of visited field sites and for
deriving training and evaluation data (see below, figure 3). The main river channel
system was digitized from the Landsat TM scenes, supplemented with scanned maps
at 1 : 250 000 to 1 : 350 000 scales. A series of Local Area Coverage (LAC) NOAA-
AVHRR (Advanced Very High Resolution Radiometer), ERS-ATSR (Along Track
Scanning Radiometer) and lower-spatial-resolution Landsat Multi-Spectral Scanner
(MSS) and Thematic Mapper (TM) images were used to determine the flooding
pattern between 1972 and 2000 (see Gumbricht et al. 2000 and McCarthy et al. 2003,
for details). All data were resampled to SUTM 34, Cape datum (see figure 2).
3. Classification scheme
The land cover classification scheme in this study was determined based on earlier
vegetation surveys (e.g. SMEC 1989, Ellery and Ellery 1997), and on the intended
use of the map in hydrological, sedimentological and ecological studies, for example.
The field surveys were done using species and species community-level data, and
translated to ecoregion classes as a step in the rule-based classification. Based on the
earlier surveys, we intended to identify 12 ecoregions (table 1). Our botanical
knowledge was not sufficient to unambiguously distinguish dry grasslands with and
without occasional flooding, nor salt crusts (or salt pans) with and without
occasional flooding during a single field visit. With additional information derived
from flooding frequency and island/channel juxtaposition, we could however
distinguish these classes in the classification. As these classes have a significant
meaning for deriving other information from the ecoregion map (see below), we
decided to include them despite the ambiguity. Aggregated classifications in 10 and
6 classes were also done, and used for accuracy evaluation. The 10-class version
(column C2 in table 1) represents an unambiguous classification. Distinguishing
between different dry woodland classes (dominated by Acacia, Mopane and
Combretum, respectively), albeit of ecological importance, turned out to be difficult
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as the broadband spectral properties are similar and no supplementary data exists
that could be utilized. In a further simplified version of the ecoregion map, the
woodland and forest classes were joined, as were the two flood-plain classes (column
C3 in table 1).
4. Training and evaluation data
Training and evaluation data for the classification were collected on four different
occasions, in June and September 2000, and January and May 2001. Site data were
collected on foot, from boat, from motor vehicles and from a helicopter survey. Thefield survey was done using handheld GPS with about 10 m accuracy, and sought to
identify areas with homogenous vegetation cover of at least 10 pixels (about 1 ha).
Figure 3. Sub-regions used in the ecoregion classification, and major sites used for collectingtraining and evaluation field data indicated by rectangles. Black rectangles indicate areasscanned from aerial photographs, and grey rectangles indicate areas scanned fromorthophoto maps. Both these datasets were used for assisting in capturing and georeferencingthe surveyed field data (see text). The maximum extent of flooding is indicated.
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Inaccessibility and extremely dangerous conditions (i.e. wildlife) confined field-data
collection to along channels, around camps and along airfields. As penetrating a
back-swamp area or a single island can take a full day, field data had to be sampled
on the basis of accessibility.
A total of 170 sample sites were surveyed and manually delimited to an exact
position in the Landsat TM mosaic. Dominant species were recorded, and transects
of hemispherical photographs taken for the estimation of vegetation cover (Sohlman
2001). The total area of training and evaluation data amounted to about 6.5 km2, of
which one-quarter (1.6 km2) were used for training, and three-quarters were used in
the evaluation. All primary training and evaluation data used were inspected in the
field. Several of the key sites visited were also scanned from orthophoto maps at
1 : 50 000 scale and/or as aerial photographs (figure 3). These maps/aerial photo-
graphs are especially helpful for delimiting the depth of riparian fringe areas (and
other vegetation patches), which is difficult to achieve in the field. Additional
Table 1. Ecoregion classification scheme for the Okavango Delta, Botswana and aggregatedclassifications used in the accuracy evaluation (C1512 classes, C2510 classes, C356 classes),
ecoregion classes, and key species belonging to each class.
C1 C2 C3 Ecoregions/land cover classesExample of key
species/vegetation types
1 1 1 River/madiba/backswamp lakes Nymphaea spp.2 2 2 Permanent swamp communities Cyperus papyrus, Vossia
cuspidata, Phragmites communisL., Typha capensis
3 3 3 Primary flood plain Miscanthus junceus, Phragmitescommunis L., Cyperusarticulatus, Schoenoplectuscorymbosus
4 4 3 Secondary flood plain Panicum repens, Sorgastrumfriesii, Imperata cylindrica
5 5 4 Grassland occasionally flooded Different grasses, sparsethickets, Pechuel-loeschealeubnitziae
6 5 4 Dry grassland/savannah thickets Different grasses, sparsethickets, Pechuel-loeschealeubnitziae
7 6 5 Sparse dry grassland/salt crust Sporobolus spicatus,Pechuel-loeschea leubnitziae,Cynodon dactylon
8 6 5 Sparse dry grassland/salt crustoccasionally flooded
Sporobolus spicatus,Pechuel-loeschea leubnitziae,Cynodon dactylon
9 7 6 Riparian forest Ficus natalensis, F. sycomorus,F. verrucolosa, Diospyrosmespiliformis, Phoenix reclinata,Syzygium cordatum, Garcinialivingstonei
10 8 6 Dry woodland (dominated byAcacia spp.)
Acacia erioloba, A. nigrescens,Combretum spp., Lonchocarpusspp.
11 9 6 Dry woodland (dominated bymopane)
Colophospermum mopane
12 10 6 Dry woodland (dominated byCombretum spp.)
Combretum spp.
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training data used only for the rule structuring included sites visited by collaborative
partners with known local conditions (e.g. McCarthy et al. 1993).
5. Classification
5.1 Statistical classification
Initial categorization was done by maximum likelihood classification (MLC)
covering the whole study area with training data representing species levels (see
table 2). The MLC classification was iterated several times and evaluated against the
training areas themselves. Rather than changing the training areas, the iterated
classifications were based on incorporating a priori likeliness of occurrence for the
species classes concerned (ER Mapper 2003). The two most satisfactory MLC
classifications were evaluated against the independently evaluated field dataset and
then used as input in the subsequent rule-based classification. Unsupervised
classification for the whole area was made in ER Mapper using the ISOCLASS
algorithm (ER Mapper 2003). Supervised (MLC) and unsupervised (ISOCLASS)
classifications were also made separately for the Panhandle region and the Delta
proper (figure 3), using only training data related to the specific environment of each
region (MLC). For the MLC classifications, posterior probability and class
typicality were calculated for each training area and included as information layers
(‘bands’) in the subsequent rule structuring. Typicality of a pixel is a measure of how
confidently a pixel belongs to the class, using a function of the Mahalanobis distance
of a pixel to the class mean. The posterior probability (P) index indicates for each
pixel the relative probabilities of falling into each class, i (sum of Pi51). In the rule-
based classification, the typicality and posterior probability index were used, among
other data, to modify pixels with doubtful classification (Pedroni 2003).
Table 2. Training areas used in the classification (total area of training data51.6 km2).
Species/vegetation type Number of training areas Class relation
Water 4 WaterNymphaea spp. 1 WaterCyperus papyrus 6 Permanent swamp communitiesPhragmites communis L. 9 Permanent swamp communitiesPennisetum glaucocladum 1 Permanent swamp communitiesTypha capensis 1 Permanent swamp communitiesVossia cuspidata 1 Permanent swamp communitiesSchoenoplectus corymbosus 2 Primary flood plainCyperus articulatus 1 Primary flood plainMiscanthus junceus 2 Primary flood plainGrass seasonal flood plain(mixed species)
1 Secondary flood plain
Dry grassland (mixed species) 2 Dry grasslandSporobolus spicatus 1 Dry grasslandPechuel-loeschea leubnitziae 1 Dry grasslandSparse grassland/salt crust 1 Sparse grassland/salt crustRiparian forest (mixed species) 3 Riparian forestAcacia spp. 3 Acacia woodlandColophospermum mopane 2 Mopane woodlandCombretum spp. 1 Combretum woodland
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5.2 Textural indices
From the mosaicked Landsat TM scenes, the Normalized Difference Vegetation
Index (NDVI) was calculated as (Band4 – Band3)/(Band4 + Band3) (Spanner et al.
1990). Wetness and Brightness were derived from tasselled cap calculations (Crist
et al. 1986), and a land-water mask was created from the thresholding of tasselled
cap wetness and band ratioing between TM bands 2 and 5. Because of differences in
aquatic vegetation density over open water, the Panhandle region and the Delta
proper were done separately.
5.3 Inundation and flooding frequency
As a Landsat TM scene only represents a snapshot in time, and the inundated area
of the Delta varies over time, the ecoregion classification was supplemented with a
flooding mask in 1 km resolution derived from time-series data of NOAA-AVHRR,
ERS-ATSR and composites of Landsat MSS/TM data—see Gumbricht et al. (2000)
and McCarthy et al. (2003) for details. In total, 212 images were combined into a
map of flooding frequency in the Delta going back in time as far as 1972 (Landsat
MSS). To achieve a flooding map representative of an average annual situation,
normalization was done by forcing each month to be represented by the same
number of flooding images (by assigning a factor to maps of all months). The
flooding frequency was subdivided into five classes of inundation: (1) permanent
swamps (.80% of time), (2) seasonal swamps (30–80% of time), (3) regularly
flooded areas (10–30% of time), (4) sparsely flooded areas (5–10% of time), and
(5) areas flooded in singular events.
5.4 Contextual information extraction
Keeping in mind the spatial architecture of the Okavango at different scales, we
created intelligible contextual indices to use in the rule-based classification.
Proximity was adopted for calculating distances from digitized rivers, from the
land-water mask (using the land as source and calculating proximity over water, and
vice versa), and from island cores (defined as grassland or salt crust classes confined
to within the maximum area of the Delta). Cost grow functions were adopted for the
same sources using various other initial MLC classes as friction surfaces (table 3,
figure 4). The derived distance indices assisted us in distinguishing inter alia island
and channel fringe vegetation.
A second type of extended contextual (focal) information was created by
calculating the area and perimeter of each cluster of contiguous pixels from the
MLC classification and subsequently from the rule-based classes. Area and
perimeter relations of contiguous groups of pixels were used for identifying spatial
forms of the clusters. Elongated regions were assumed to indicate either a river
channel or an elongated island. The area of the contiguous groups was also used to
discard small, isolated groups of contiguous pixels, using the mode class value as
derived from neighbourhood (filtering) analyses.
5.5 Rule-based classification
The rule-based method used in this study expresses knowledge in the form of IF …
THEN … declarative statements, usually found as part of expert systems (Lein and
Lemons 1997). Such logical rules developed by domain experts have been shown to
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give improved classification results in various landscapes (e.g. Chmiel and
Gumbricht 1996). By creating a multi-band image in ER Mapper including the
datasets described above as individual layers (‘bands’), logical rules declaring
relations of flooding and spatial architecture could be combined and processed
without using a separate inference engine. Data layers with information pertaining
to local, focal and regional scales (see table 3) were combined in a forward-driven
classification that was driven by a combination of our knowledge of the spatial
architecture of the Okavango and the data created. Rules were strictly given based
on knowledge of the patterns and processes, and no random or ‘chance’ rules were
used.
Table 3. Image data, derived indices and ancillary data (information) used in the rule-basedclassification.
Data type Information content Origin
Spectral (local) Maximum-likelihood classes(species wise)
Landsat TM
Class typicality (one pertraining area, all not used)
MLH classification
Class Posterior Probability(one per training area, all notused)
MLH classification
Unsupervised classification Landsat TMNormalized differencevegetation index (NDVI)
Landsat TM
Wetness (tasselled captransformation)
Landsat TM
Brightness (tasselled captransformation)
Landsat TM
Flooding frequency NOAA AVHRR, ATSR, LandsatMSS/TM/ETM
Water/land mask Landsat TM, 1 : 350 000 papermaps
Contextual (focal) Average 363 filter ofclassifications
MLH classification/rule-basediteration
Mode filter of classifications MLH classification/rule-basediteration
High-pass filter for linearfeatures
Landsat TM
Contextual (regional) Distance from open water(including madiba)
MLH classification
Distance from island core MLH classificationDistance from rivers Digitized rivers from paper maps/
MLH classificationCost distance from island core MLH classificationCost distance from rivers Digitized rivers from paper maps/
MLH classificationArea of the land cover classes MLH classification/rule-based
iterationShape (perimeter in relationto area)
MLH classification/rule-basediteration
Digitized Rivers Landsat TM and paper mapsDelta outline (maximumextent)
NOAA AVHRR, ATSR, LandsatMSS/TM/ETM, 1 : 350 000 papermaps
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The rules only refer to a per-pixel relation, with contextual information being
available on a per-pixel basis (table 3, figure 5). The first rule statement was always a
class relation, initially from the MLC classifications, and subsequently from
classifications derived from earlier rules—when one set of logical rules was finished,
the resulting map was saved as a new data layer and used as input for the next set of
rules. In a sense, the whole rule-based classification can thus be seen as an
adjustment of the statistical classifications using physically interpretable data and
related knowledge.
The Landsat TM images used in the classification represent winter conditions
(cloud-free season) when flooding is at its maximum. Hence, the statistical
classification over-represents wet classes. This was adjusted based on the flooding
duration map. The categorization between permanent swamp communities, primary
and secondary flood plains, and dry and occasionally flooded grasslands, was
largely engendered using the flooding information, supplemented with contextual
relations (e.g. cost distance to rivers).
Generalization rules were applied as an interwoven iterative step where a majority
filter together. An area of contingency was used for different classes and area
thresholds. Details were kept for the hydrologically important classes, so that
majority filters did not reduce the sparsely occurring classes of riparian forest and
rivers.
Region masking was used to overcome the problems of satellite scene differences.
The Panhandle and the central delta were classified separately from the rest of the
Figure 4. Illustration of the difference between proximity analysis (Euclidean distance) andcost grow analysis. In both cases the channel was used as the source area. The cost growanalysis used the flood plain as a friction surface and the grassland as a barrier. In thesubsequent rule-based classification, small flood-plain areas with no connection to the rivercould be further analysed and the classification modified accordingly.
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Delta. The final classification was done by merging the three classification regions,
and applying rule structuring for pixels with conflicting classifications in over-
lapping areas. The resulting ecoregion map in 12 categories was finally aggregated
into maps with 10 and 6 classes before final evaluation of the result.
Image processing and rule structuring were done using ER Mapper. Raster GIS
functionality was written in Pascal, using functions from the GIS-software IDRISI
when applicable.
6. Results
Based on 130 independent ground data samples, the best MLC-derived land cover
classification had an estimated accuracy of 46% for 10 classes (table 4). With the
rule-based classifier, an estimated overall accuracy of 63% was achieved for the 10-
class land cover map (table 5) and 74% for the 6-class map (table 6, figure 6). It is
well known that fewer classes give a better accuracy, but also that the Kappa index
(taking into consideration the probability of correct classification due to chance)
improves.
The results from the classifications are illustrated in figure 7, showing a small area
with an oxbow lake as an aerial photograph, from the Landsat TM image, from the
MLC classification and the rule-based classification. On a scale of one to a few
pixels, the MLC classification was highly fragmented. Visual comparison with
scanned aerial photographs revealed that many areas of riparian forests were
identified as wetland classes, and vice versa.
Figure 5. Schematic diagram of data processing.
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Table 4. Error matrix and accuracy for the MLC classification in 10 classes (the columns represent the ground-data image and the rows the classified image).
Wat PSC P FP S FP GL SP RF Aca Mop Com Total ErrorC
Uncl 7 0 0 0 0 0 0 0 0 0 7 1.000 Uncl Unclassified pixelsWat 840 0 8 23 0 0 0 0 0 0 871 0.036 Wat WaterPSC 14 1010 523 73 7 0 419 143 8 3 2200 0.541 PSC Permanent swamp
communitiesP FP 29 134 525 185 2 11 110 76 8 0 1080 0.514 P FP Primary flood plainS FP 0 0 20 36 44 0 4 104 10 0 218 0.835 S FP Secondary flood plainGL 0 0 19 28 145 20 9 260 76 1 558 0.740 GL GrasslandSP 0 0 0 0 54 75 2 58 21 3 213 0.648 SP Sparse grassland/salt
crustRF 0 24 3 34 3 2 170 40 3 15 294 0.422 RF Riparian forestAca 0 1 146 75 25 1 71 145 29 5 498 0.709 Aca Acacia woodlandMop 0 7 102 52 3 3 29 146 79 4 425 0.814 Mop Mopane woodlandCom 0 0 56 26 12 9 12 73 1 0 189 1.000 Com Combretum woodlandTotal 890 1176 1402 532 295 121 826 1045 235 31 6553 ErrorC Error of commissionErrorO 0.056 0.141 0.626 0.932 0.509 0.380 0.794 0.861 0.664 1.000 0.538 ErrorO Error of omission
The abbreviations correspond to the 10 ecoregion classes. Overall accuracy546.2%; Kappa index of agreement50.37.
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Table 5. Error matrix and accuracy for the rule-based classification in 10 classes (the columns represent the ground-data image and the rows the classifiedimage).
Wat PSC P FP S FP GL SP RF Aca Mop Com Total ErrorC
Wat 861 20 21 0 0 0 1 0 0 0 903 0.046 Wat WaterPSC 15 1000 234 21 0 5 106 1 0 0 1382 0.276 PSC Permanent swamp
communitiesP FP 9 135 980 279 27 0 160 2 0 3 1595 0.386 P FP Primary flood plainS FP 1 14 57 149 2 0 78 8 0 1 310 0.519 S FP Secondary flood plainGL 0 0 97 76 191 11 46 328 56 2 807 0.763 GL GrasslandSP 0 0 0 0 71 98 2 73 15 2 261 0.624 SP Sparse grassland/salt
crustRF 4 7 13 5 4 3 382 190 0 20 628 0.392 RF Riparian forestAca 0 0 0 2 0 4 34 362 57 3 462 0.216 Aca Acacia woodlandMop 0 0 0 0 0 0 14 79 106 0 199 0.467 Mop Mopane woodlandCom 0 0 0 0 0 0 3 2 1 0 6 1.000 Com Combretum woodlandTotal 890 1176 1402 532 295 121 826 1045 235 31 6553 ErrorC Error of commissionErrorO 0.033 0.150 0.301 0.720 0.352 0.190 0.538 0.654 0.549 1.000 0.370 ErrorO Error of omission
The abbreviations correspond to the 10 ecoregion classes. Overall accuracy563.0%; Kappa index of agreement50.57.
Table 6. Error matrix and accuracy for the rule-based method in six classes (the columns represent the ground-data image and the rows the classified image).
Wat PSC FP GL SP For Total ErrorC
Wat 861 20 21 0 0 1 903 0.046 Wat WaterPSC 15 1000 255 0 5 107 1382 0.276 PSC Permanent swamp
communitiesFP 10 149 1465 29 0 252 1935 0.231 FP Flood plainGL 0 0 173 191 11 432 807 0.763 GL GrasslandSP 0 0 0 71 98 92 261 0.624 SP Sparse grassland/salt crustFor 4 7 20 4 7 1253 1295 0.032 For ForestTotal 890 1176 1934 295 121 2137 6553 ErrorC Error of commissionErrorO 0.033 0.150 0.242 0.352 0.190 0.414 0.257 ErrorO Error of omission
The abbreviations correspond to the six aggregated ecoregion classes. Overall accuracy574.3%; Kappa index of agreement50.67.
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7. Discussion
The high spatial and temporal variability in the Okavango Delta makes ecoregion
classification from remotely sensed data a non-trivial task. The spatial variation
of channels, wetlands, flood plains and islands prompts the use of high
spatial resolution remotely sensed data, e.g. Landsat (E)TM. We feel that a
grain size of 28.5 m was relevant, and that in lower-resolution data, too much of
the spatial architecture would be lost. The high spatial variability at a scale
corresponding to the grain size of the image data makes the use of multitemporal
high-resolution images almost impossible. Simultaneously, several of the ecoregions
(vegetation communities) that compose the Okavango have very similar spectral
properties in the (broad) bands available from Landsat (E)TM. Not only do
various grasslands and flood plains resemble each other, but papyrus (Cyperus
papyrus) and reed (Phragmites communis L.) have very similar spectral properties
Figure 6. Ecoregions of the Okavango Delta, Botswana, derived from a combined statisticaland contextual rule-based post-classification.
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to various tree species. Statistical (both supervised and unsupervised) classi-
fication hence gave poor results. Other studies (e.g. Schmidt and Skidmore
2001) indicate that a high spatial hyperspectral image should have been more
favourable.
We used inundation metrics from multitemporal remote sensing to separate
classes in dry vs. wet areas at a regional (.1 km) scale. Knowledge about tolerance/
requirement for inundation periods was used to infer rules concerning the
occurrence of primary and secondary flood plains, for example. The coarser
resolution of the flooding data, however, introduces an artificial crisp boundary
between the flood-plain classes, for example.
The internal spatial architecture of the Okavango in general is a drying and
increasing salinity gradient from channel–wetland–flood plain–grassland–island rim
(riparian forest)–island centre (grassland to salt crust). The channel and the island
central salt crust are easily identified and delimited from Landsat (E)TM data.
Hence, proximity and cost grow analysis, and contextual filtering based on those
classes were used to assist in improving the classification. Islands and channels are
born and developed from unidirectional fluvial processes, and hence have typical
forms. Thus, form parameters related to the shape of contiguous clusters of pixels
were also used to modify the classification. Yet another characteristic is that the very
nutrient-poor water can only sustain dense vegetation growth adjacent to the
channels; hence, high-pass filtering could be used to assist in identifying channels of
sub-pixel width.
The applicability of our approach in improving the vegetation classification of the
Okavango using expert rules pertaining to different spatial patterns and temporal
processes is dependent on several factors:
N the internal spatial architecture (channels–wetlands–flood plains–islands) of
the Okavango is consistent throughout the system;
N the vegetation communities are stable and have a memory length for the
flooding regime;
N flooding being variable in area but regular in time; and
Figure 7. Comparison of a detailed area (see figure 3) in the Okavango Delta, Botswana,from (a) aerial photograph, (b) Landsat TM scene, (c) initial MLC classification, and (d ) rule-based post-classification.
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N suitable remote sensors of different spatial and temporal resolution could be
applied for creating indices and metrics relating to both spatial and temporal
variations.
Without these prerequisites, our approach would not have been possible.
Great efforts were made to obtain a correct representation of riparian forests, as
they have a disproportionally large influence on hydrology, sedimentology and
ecology. As many riparian forests are confined to a width of only one pixel
(approximately 30 m), the selection of training and evaluation areas was tedious and
difficult. The final classification of a densely vegetated pixel (i.e. high NDVI) with
typicality/posterior probability representing both a tree and an aquatic grass was
determined based on both the typicality/probability and the pixel juxtaposition vis-
a-vis channels and island centres.
Finally, we must note that the class definitions are ambiguous, even if distinct
species composition is suggested by Ellery and Ellery (1997), for example. The
identification and coverage of various species in a patch in the field is difficult. Part
of the classification error stems from this. Another artificial error is introduced from
spatial generalizations at pixel levels (identified training and evaluation areas are
sometimes so thin/small, that a shift in one pixel introduces an error—the water
class is in reality 100% correct, but because of generalizations, the errors reported in
tables 5 and 6 are introduced). The use of the map created from this study has
however been successfully adopted for deriving maps of islands (Gumbricht et al.
2004) and microtopography (Gumbricht et al. 2004a) of the Okavango Delta, both
evaluated against independent datasets with a surprisingly high accuracy.
8. Conclusions
Ecoregion classification of the Okavango Delta was achieved by supplementing
snapshot high-spatial-resolution Landsat TM images with ancillary GIS data,
flooding data derived from low-resolution NOAA-AVHRR, ERS-ATSR and
Landsat MSS/TM data, and by indices derived from the original Landsat TM
scenes. Initial statistical classification could unambiguously identify a few key
classes (most notably water and salt crust), but largely failed to separate different
vegetation classes. The use of the broadband multispectral Landsat TM sensor was
insufficient for achieving a satisfactory land cover classification in this fragmented
landscape. Although fragmented and dynamic, the pristine Okavango environment
has a logical architecture with channels, wetlands, flood plains and islands being
closely associated with flooding regime and vegetation communities. The system is
well understood and has evolved into a meta-stable system where rivers and
vegetation patches have a memory length extending on decadal scales. Knowledge
about the internal spatial architecture and the flooding regime allowed us to use an
expert system approach for improving the initial statistical classification. This was
achieved by creating indices and metrics representing different features of the
Okavango system. Some misclassifications from the statistical method could be
corrected, resulting in a more accurate classification.
We conclude that for the regional scale, the use of low-resolution multitemporal
images for deriving flooding frequency could be applied as an ancillary data layer
for regional segmentation, albeit it introduced into an artificial crisp boundary
between some classes. Known relations of water needs and confinement on a species
level could be inferred to adjust the classification to more accurate outcomes.
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For the internal mosaic of the Okavango, we created indices and metrics by
combining textural and contextual information derived from the original Landsat
TM Scenes. As indices of vegetation density, brightness, wetness, proximity,
connectedness and form were derived from the same image source as the statistical
classifications, the usual problems with geopositional accuracies were avoided. The
conclusion from this part of the study is that for a landscape composed of a large
number of logical (i.e. natural) juxtapositioned patches, the adoption of knowledge-
based rules is a superior classification technique compared with statistical methods.To be feasible, the knowledge rules must be anchored in intelligible indices for
facilitating expert knowledge to be translated into declarative rules.
Acknowledgements
Data were kindly supplied by Anglo American. The post doc for T.G. was financed
by the Royal Swedish Academy of Sciences and the scholarship for J.M. by The
Swedish Foundation for International Cooperation in Research and HigherEducation (STINT). University of the Witwatersrand supported the participation
of T.M. Support was also given by the Royal Institute of Technology, through
Fredrik Bjorns fund, and from the Swedish International Development Agency
(SIDA). This study was part of the SAFARI2000 Southern African Regional
Science Initiative. The authors thank anonymous reviewers for helping to improve
the manuscript.
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