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RESEARCHREVIEW
Terrestrial ecosystems from space: areview of earth observation products formacroecology applicationsgeb_712 1..21
Marion Pfeifer1*, Mathias Disney2†, Tristan Quaife3† and Rob Marchant1
1York Institute for Tropical Ecosystem
Dynamics, Environment Department,
University of York, Heslington, York
YO10 5DD, UK, 2Department of Geography,
University College London, London
WC1E 6BT, UK, 3School of Geography,
University of Exeter, Cornwall Campus,
Penryn, Cornwall TR10 9EZ, UK
ABSTRACT
Aim Earth observation (EO) products are a valuable alternative to spectral veg-etation indices. We discuss the availability of EO products for analysing patterns inmacroecology, particularly related to vegetation, on a range of spatial and temporalscales.
Location Global.
Methods We discuss four groups of EO products: land cover/cover change, veg-etation structure and ecosystem productivity, fire detection, and digital elevationmodels. We address important practical issues arising from their use, such asassumptions underlying product generation, product accuracy and product trans-ferability between spatial scales. We investigate the potential of EO products foranalysing terrestrial ecosystems.
Results Land cover, productivity and fire products are generated from long-termdata using standardized algorithms to improve reliability in detecting change ofland surfaces. Their global coverage renders them useful for macroecology. Theirspatial resolution (e.g. GLOBCOVER vegetation, 300 m; MODIS vegetation andfire, � 500 m; ASTER digital elevation, 30 m) can be a limiting factor. Canopystructure and productivity products are based on physical approaches and thus areindependent of biome-specific calibrations. Active fire locations are provided innear-real time, while burnt area products show actual area burnt by fire. EOproducts can be assimilated into ecosystem models, and their validation informa-tion can be employed to calculate uncertainties during subsequent modelling.
Main conclusions Owing to their global coverage and long-term continuity, EOend products can significantly advance the field of macroecology. EO productsallow analyses of spatial biodiversity, seasonal dynamics of biomass and produc-tivity, and consequences of disturbances on regional to global scales. Remainingdrawbacks include inter-operability between products from different sensors andaccuracy issues due to differences between assumptions and models underlying thegeneration of different EO products. Our review explains the nature of EO productsand how they relate to particular ecological variables across scales to encouragetheir wider use in ecological applications.
KeywordsBiogeography, earth observation, fire patterns, fire rhythms, global patterns,LAI, land cover, productivity, remote sensing.
*Correspondence: Marion Pfeifer, York Institutefor Tropical Ecosystem Dynamics, EnvironmentDepartment, University of York, Heslington,York YO10 5DD, UK.E-mail: [email protected],[email protected]†NERC National Centre for Earth Observation(NCEO).
INTRODUCTION
The dynamics and structure of ecological systems are highly
complex. Understanding statistical patterns in the relationships
between organisms and their environment that are consistent
across large spatial and temporal scales is the central focus of
macroecology (Maurer, 2000) and of particular importance for
assessing the ecological effects of rapidly changing environments.
Land-cover change may override the importance of climate
for species distributions (Sala et al., 2000; IPCC, 2007) and
Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2011) ••, ••–••
© 2011 Blackwell Publishing Ltd DOI: 10.1111/j.1466-8238.2011.00712.xhttp://wileyonlinelibrary.com/journal/geb 1
ultimately lead to changes in spatial patterns of biomass and
ecosystem productivity. Habitat fragmentation reduces habitat
quantity and connectedness affecting species–area relationships
and species extinction dynamics (Fischer & Lindenmayer, 2007;
Fisher et al., 2010). Changes in vegetation structure may alter
heat and energy fluxes at the terrestrial surface, affecting
regional climates (Feddema et al., 2005), offsetting the impacts
of climate change (Pielke et al., 2002) or altering surface condi-
tions leading to new states of climate and ecosystems (Moor-
croft, 2003). Land use is able to control fire activity in many
areas impacting on global biogeochemical cycles and vegetation
pattern formation (Bond et al., 2005; Bowman et al., 2009).
Changes in vegetation structure may in turn alter the dynamics
of underlying drivers of these changes. However, the exact
nature of vegetation–climate feedbacks and the spatial scales on
which they act remain poorly understood (Webb et al., 2006).
Terrain topography contributes to these ecological and biogeo-
physical processes, affecting land use and the dynamics of
species movements, floods and drainage.
Assessing general patterns in macroecology and how they are
affected by human and environmental forcing (Fisher et al.,
2010) requires a substantial number of data. Field assessments
of ecological and surface properties, however, are inconsistent
over large spatial scales and patchy over temporal scales.
Bottom-down estimates of land surface properties derived using
earth observation (EO) across regional and global scales provide
data on large spatial scales (DeFries & Townshend, 1999; Kerr &
Ostrovsky, 2003; Gillespie et al., 2008). They cover areas difficult
to access due to remoteness or political instability and avoid
errors introduced by subjective sampling design and judgment
sampling (Yoccoz et al., 2001).
Since the review of DeFries & Townshend (1999) on EO
applications in detection of land surface change, a range of new
satellite sensors have been launched with improved spectral,
spatial and (crucially) temporal sampling. Studies using either
raw reflectance data or derived spectral vegetation indices (VIs)
to assess ecological traits have increasingly been employed
(Turner et al., 2003; Pettorelli et al., 2005, 2011). The use of VIs
(indicating vegetation ‘greenness’ as compound effects of veg-
etation composition, structure and function) has limitations
due to technical caveats, and EO products provide a valuable
alternative or supplement to VI. However, an ISI literature
search on topics such as ‘land cover change’, ‘ecosystem response
and global change’ or ‘ecosystem productivity’ in combination
with EO terms shows that VIs are often preferred over EO prod-
ucts (see Table S1 in Supporting Information), although studies
using fire products have increased in the last years (Chuvieco
et al., 2008; Krawchuk et al., 2009; Archibald et al., 2010).
Reasons for bias in EO information use may be found in lack of
user confidence regarding the spatial accuracy of EO products,
preference for alternative data sources (e.g. VI for productivity
studies; Sjöström et al., 2011), but also a low awareness of
product availability.
In this paper, we discuss EO products developed during the
past decade and their potential to address key topics in macro-
ecology. We assess four groups of EO end-user products; land
cover and land-cover change products, products related to veg-
etation biogeophysical structure and productivity, active fire and
burnt area products, and digital elevation models (DEMs).
These products represent the global earth’s surface as single year
or multi-year coverage, some at near-real time, at spatial reso-
lutions suitable for analysing patterns in ecology at regional to
global scales. We describe the generation of products either from
measurements by single remote sensors or multiple sensors,
discuss their accuracy and briefly address issues of transferabil-
ity between spatial scales. We hope that by increasing the under-
standing for assumptions and algorithms used for EO product
generation, we can stimulate their wider use in ecological and
vegetation sciences facilitating effective and accountable exploi-
tation of EO-derived information.
EARTH OBSERVATION FROM SPACE
Depending on the type of sensor used (see Table S2), EO
imagery is available at a wide range of scales: from high/fine
(< 10 m) to medium/moderate (10–250 m) and low/coarse
(250 m–5 km) resolutions, where resolution refers to the pixel
size in linear dimensions (Warner et al., 2009). Spatial resolution
is traded off against temporal resolution, with high spatial reso-
lution typically meaning low temporal resolution. Compared to
early products derived from Advanced Very High Resolution
Radiometer (AVHRR) data, enhanced spectral resolution and
spectral coverage of MODIS (Moderate Resolution Imaging
Spectroradiometer), VEGETATION and MERIS (MEdium-
spectral Resolution Imaging Spectrometer) have significantly
improved EO capabilities for ecosystem research (Carrão et al.,
2008). (Pseudo)-multi-angle observations by POLDER (Polar-
ization and Directionality of the Earth’s Reflectances instru-
ment) and MISR (Multi-Angle Imaging Spectro-Radiometer)
allow detailed information on atmosphere (aerosol behaviour,
cloud height), canopy structure and surface roughness
(Widlowski et al., 2004).
Work on operational products has focused on coarse-
resolution global products, which offer a range of advantages
over raw reflectance data or VIs available at higher spatial reso-
lution (Table 1). Off-the-shelf products using TM/ETM+ (The-
matic Mapper and Enhanced Thematic Mapper Plus) are almost
non-existent (partly because of the small or inconsistent
coverage) and mature products are not yet available from
fine-resolution sensors (e.g. IKONOS, KOrea Multi-Purpose
SATellite KOMPSAT) despite them providing mapping accura-
cies comparable to ground-truth vegetation mapping (Mehner
et al., 2004).
Over the past decade, much effort has been directed towards
providing EO data in inter-operable formats (e.g. HDF,
GeoTIFF) as well as in developing tools for data handling. Com-
mercial software applications now routinely provide capabilities
for reading different data formats, e.g. Matlab (MathWorks),
Mathematica (Wolfram Research), and IDL (ITT VIS). This is in
addition to commercial software tools developed specifically for
processing remotely sensed data such as ENVI (ITT VIS) and
Imagine (Leica Geosystems). Freely available software tools and
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd2
libraries are available either for converting between formats and
map projections or for more fundamental analyses, e.g. the
MODIS reprojection tool (Land Processes Distributed Active
Archive Center), the geographical information system GRASS,
the Freeware Multispectral Image Data Analysis System (Multi-
Spec), and application programming interfaces for program-
ming languages such as C/C++, Java and Python. The Oak Ridge
National Laboratory has developed a web interface that allows
the user to access MODIS data products in two different geo-
graphical projections.
Land cover/land cover change products
Land-cover products (Table 2) are generated on the principle
that different land-cover types within a remotely sensed image
will tend to exhibit different spectral reflectance behaviour
(Townshend, 1994), and can be separated using classification
techniques (e.g. maximum likelihood analysis, decision-tree
classifier, neural networks). Post-classification techniques based
on multi-temporal image comparison are used for detection of
land-cover change, while fuzzy sets and semantic analyses are
used to handle pixel classification vagueness and sub-pixel het-
erogeneity (Ahlqvist, 2008).
The accuracy of satellite-derived land-cover information is
primarily limited by the sensor’s ability to separate the required
land-cover classes in the spectral data. High spectral resolution
can improve separation capabilities for vegetation types (Carrão
et al., 2008), but only if the additional spectral resolution permits
the extraction of uncorrelated additional information on vegeta-
tion properties. Also, subsequent classifications may lead to over-
fitting, requiring specific analyses such as feature extraction
algorithms to define precise classes (Landgrebe, 2005).
Table 1 Vegetation indices (VIs) versus global earth observation (EO) products for assessing ecological traits. Typical VI examples are thenormalized difference vegetation index (NDVI) and the soil-adjusted enhanced vegetation index (EVI). Major global EO products are listedin Tables 2–5.
Raw reflectances (RR) or spectral vegetation indices (VI) Validated EO end products
Processing requirements Expert knowledge on pre-processing images required when
using SPOT or Landsat TM data at high spatial
resolution
Direct implementation into spatial analyses software
Trade-off: spatial versus
temporal resolution
High spatial resolution � 10 m (SPOT) and � 30 m
(Landsat) but long re-visit times for specific study
region
Coarse spatial resolution (topography � 30 m; vegetation
cover � 300 m; fire � 500 m; productivity � 1 km) but
high temporal resolution (vegetation cover � annual;
fire � hourly/daily; productivity � 8-daily
Spatial extent High computational effort to cover large areas; links to
ecological traits region- or biome specific;
inter-comparisons limited because of processing effects;
data gaps; nonlinear VI-GPP/NPP/LAI relationships in
densely vegetated areas and deserts
Generated on global scales using standardized algorithms;
physically-based algorithms for generation of LAI/NPP/
GPP products
Change detection Complicated by view and sun angle impacts on RR; VI
products (NDVI, EVI: 250 m spatial resolution) easy to
use but require accounting for cloud and aerosol
contamination
Limited by spatial resolution (e.g. disadvantage in highly
heterogeneous landscapes); accurate assessments of
relative temporal changes in pixel values
Assimilation into models Limited by lack of applicability over large spatial scales;
uncertainty information easily missed
Validated products containing pixel-based accuracy
information for uncertainty assessment in subsequent
ecological modelling
GPP, gross primary production; NPP, net primary production; LAI, leaf area index.
Table 2 Main land-cover products derived from remote sensing data. BIOME (not validated), GLOBCOVER and GLC-2000 are singlemaps produced at a certain time in the past, while MCD12Q1 is a multi-annual product, which can be used for change detection.
Product Sensor Satellite SR/SC TR Format PR
GLC 20001 VGT SPOT 1 km/global + regional Annual, 2000 BIL Lat/long, WGS 84
GLOBCOVER 2005 and
20092
MERIS ENVISAT 300 m, global + regional Annual, 2004–06 and 2009 Geo-TIFF Lat/Long, WGS 84
MCD12Q1 v53 MODIS Aqua, Terra 500 m, global Annual, 2001–07 HDF-EOS Sinusoidal, EA
EA, equal area; PR, projection; SC, spatial coverage; SR, spatial resolution; TR, temporal resolution; VGT, VEGETATION.Web pages may become outdated: 1http://bioval.jrc.ec.europa.eu/products/glc2000/glc2000.php; 2http://ionia1.esrin.esa.int/; 3https://wist.echo.nasa.gov~wist/api/imswelcome/ and http://mrtweb.cr.usgs.gov/
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 3
Recent land-cover products are generated from long-term EO
data overcoming earlier problems of inconsistency between
land-cover datasets in terms of sensors and classification
(Herold et al., 2008). Some products are available as areal pro-
portions of each land-cover type per pixel, e.g. the MODIS
vegetation continuous fields product (Hansen et al., 2003), thus
picturing heterogeneous vegetation more realistically and
improving the detection of changes in complex landscapes.
However, generally, data are disseminated as discrete classes
assigned to each pixel. This can be problematic in heterogeneous
landscape mosaics affecting class accuracy and contributing to
spatial disagreement between land-cover datasets (Fig. 1)
(McCallum et al., 2006; Fritz & See, 2008). The spatial resolution
of global land-cover products may be a limiting factor for some
analyses, yet their temporal coverage enables timely estimates of
land cover and cover change (Hansen et al., 2008).
Single-year products
Mainly aimed at ecosystem assessments, the Global Land Cover
map 2000 (GLC2000, 27 classes, Bartholomé & Belward, 2005)
employs the United Nations Environment Programme (UNEP)-
Food and Agriculture Organization (FAO) land-cover classifica-
tion system. Total accuracy of GLC2000 was 68.8% assessed
using Landsat and 1265 samples distributed throughout Asia,
Africa and Europe (Mayaux et al., 2006). Accuracy varied
between regions and was considerably lower for heterogeneous
landscapes compared with homogeneous sites.
Multiple-year products
Like GLC2000, GLOBCOVER (22 classes in the global product;
up to 51 classes in regional maps) is compatible with the UNEP-
FAO land-cover classification and available in regionally opti-
mized or globally harmonized versions (Bicheron et al., 2008).
Overall accuracy is estimated as 73.1% in the 2005 product
based on 3167 globally distributed sampling points and 67.5%
(weighted by class area) in the 2009 product using 2190 points.
The MODIS MCD12Q1 product, a package of five land-cover
classifications, is derived through supervised decision-tree clas-
sifications (Cohen et al., 2006). The International Geosphere
Biosphere Programme (IGBP) scheme (Type 1: 17 classes) and
University of Maryland (UMD) scheme (Type 2: 14 classes) exist
to provide consistency with earlier land-cover maps (Hansen &
Reed, 2000). Global accuracy of the IGBP scheme (1370 training
sites) is 75%, varying between 70% and 85% for continental
regions, while user accuracy is low for savanna and deciduous
forests (< 45%) (Hodgens, 2002). Consistent processing and
updating means that changes in land-cover classes should be
accurate even if absolute land-cover classes are not. The leaf area
index (LAI)/fraction of absorbed photosynthetically active
radiation (fAPAR) scheme (Type 3: 11 classes; Myneni et al.,
1997) is an aggregation of land classes into structurally similar
biomes, used for the generation of LAI and fAPAR (400–
700 nm) maps. The Biome-BGC scheme (Type 4: 9 classes) is
designed for use with the Biome-BGC model to generate the
MODIS net primary productivity (NPP) product (Running
et al., 2004) and for providing vegetation feedback with climate
models.
Vegetation biogeophysical structure and productivity
Vegetation structure (height, LAI, leaf area density, and leaf
inclination distribution) is a key parameter when assessing eco-
system productivity and carbon fluxes (Garrigues et al., 2008)
(Table 3). LAI, in ecology broadly defined as the total canopy
area per unit ground area (m2 m-2) (Asner et al., 2003) ranges
from 0 (bare ground) to over 10 (dense forest). LAI determines
the interception of solar energy (and thus partly the fAPAR) for
photosynthesis (Sellers, 1997). To consider radiation intercep-
tion, LAI in remote sensing is usually defined as one-sided leaf
area per unit ground area (0.5 ¥ total) (Chen & Black, 1992).
Gross primary production (GPP; in units of g m-2 day-1) can be
estimated from fAPAR, which is inferred from satellite data,
based on the Monteith equation for production efficiency
(Kumar & Monteith, 1981):
GPP fAPAR PAR= × ×ε . (1)
Photosynthetically active radiation (PAR) data tend to be
acquired from re-analysis of climate observations. The efficiency
term e (in units of mass of carbon per unit of irradiance) is
defined as maximum light-use efficiency of the vegetation
limited by environmental factors, such as temperature and water
availability (Zhao & Running, 2010). The term is multiplicative
(von Liebig, 1843), however, and likely to be biased when it is
cold and dry at the same time. Weakness in the estimation of the
efficiency term seems to be the primary source of errors in the
MODIS GPP product (Sims et al., 2006). NPP represents carbon
available to plants for allocation to biomass after accounting for
autotrophic respiration Ra (g m-2 day-1), which is estimated by a
process model describing above- and below-ground carbon
allocation (Running et al., 2000), or simply by assuming Ra as
constant fraction of GPP (Veroustraete et al., 2002):
NPP GPP a= − R . (2)
Vegetation structure - spectral VIs
Vegetation traits are often estimated using spectral VIs sensitive
to the contrast between red and near-infrared reflectances of
vegetation, such as the normalized difference vegetation index
(NDVI; Pettorelli et al., 2005). For example, ECOCLIMAP LAI
estimates one LAI value for each homogeneous ecosystem and
two for mixed ecosystems from NDVI data (Masson et al., 2003;
Champeaux et al., 2005) using global (IGBP, UMD) and
regional land-cover maps. In situ measurements accounting for
vegetation clumping at plant and canopy scales assign LAI
ranges for each surface class. Comparisons with reference maps
show that ECOCLIMAP overestimates LAI, probably due to
algorithm uncertainties, poor description of surface spatial
variability and underperformance of the NDVI time series
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd4
GLC 2000
Fill value
Closed evergreen lowland forest
Degraded evergreen lowland forest
Submontane forest (900-1500 m)
Montane forest (> 1500 m)
Swamp forest
Mangroves
Mosaic forest - cropland
Mosaic forest - savanna
Closed deciduous forest
Deciduous woodland
Deciduous shrubland with sparse trees
Open deciduous shrubland
Closed grassland
Open grassland with sparse shrub
Open grassland
Sparse grassland
Swamp bushland and grassland
Cropland (> 50%)
Cropland with open woody vegetation
Irrigated cropland
Tree crops
Sandy desert and dunes
Stony desert
Bare rock
Salt hardpans
Water bodies
Cities
0 105kilometers
20 30 40
Figure 1 Land cover in north Tanzania (Mount Kilimanjaro is in the upper left corner) represented using three global land-coverproducts. Spatial resolution increases from 1000 m (GLC2000; 27 cover classes for Africa) to 500 m (MCD12Q1 type1 in 2006; 17International Geosphere Biosphere cover classes) and 300 m (GLOBCOVER; 22 classes including water bodies, snow/ice and bare areas).Note the differences in forest areas (purple) and woody vegetation (green). Significant discrepancies between products regarding certainland-cover types (McCallum et al., 2006; Fritz & See, 2008) require the user to make use of more than one dataset to assess impacts on theiranalyses.
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 5
MCD12Q1 - 2006
Water
Evergreen needleleaf forest
Evergreen broadleaf forest
Deciduous needleleaf forest
Deciduous broadleaf forest
Mixed forest
Closed shrubland
Open shrubland
Woody savanna
Savanna
Grassland
Permanent wetland
Cropland
Urban and built up
Cropland - Natural vegetation
Snow and ice
Barren and sparse
Fill value
0 10 20 30 405Kilometers
Figure 1 Continued.
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd6
GLOBCOVER 2004 - 2006
Irrigated cropland
Rainfed cropland
Cropland - Natural vegetation
Mosaic Vegetation - Crop
Broadleaf evergreen / semi-deciduous forest
Closed broadleaf deciduous forest
Open broadleaf deciduous forest
Closed needleleaf evergreen forest
Open needleleaf evergreen / deciduous forest
Mixed forest
Forest - Shrub - Grassland
Grassland - Forest - Shrub
Closed to open shrub
Closed to open grassland
Sparse
Broadleaf evergreen forest regularely flooded
Closed broadleaf forest flooded
Closed to open vegetation regularely flooded
Artifical areas
Bare areas
Water bodies
Snow and ice
No data
0 10 20 30 405Kilometers
Figure 1 Continued.
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 7
(Garrigues et al., 2008). ECOCLIMAP-2, developed as a more
precise parameter database, uses GLC2000 and VEGETATION
NDVI profiles to define homogeneous ecosystems and to
account for inter-annual variability of LAI (Champeaux et al.,
2005).
Generally, Glenn et al. (2008) argue against the use of VIs as
surrogates for canopy architecture features. VIs rely on biome-
specific calibration (Chen et al., 2002), depend on view and sun
angles, and are sensitive to background brightness, snow and
atmospheric contamination (Nagai et al., 2010).Variations inVIs
can be functions of complex,compensating biophysical processes
(Brando et al., 2010). Their sensitivity to changes in canopy
structure reduces asymptotically with increasing LAI, saturating
at values of 4–5. Recent developments such as the generation of
enhanced vegetation indices (e.g. EVI and EVI2; Huete et al.,
2002), angular model fitting (requiring multiple observations)
and minimizing variations in view and sun angle when compar-
ing across dates aim to overcome some of these limitations.
Vegetation traits - radiative transfer models
Biogeophysical traits can be estimated from the inversion of
physically based radiative transfer models (RTMs), using itera-
tive optimization approaches (Liang, 2004), artificial neural net-
works (Fang et al., 2003) or look-up-tables (Deng et al., 2006),
comparing observed and modelled reflectance for a suite of
canopy structure and environmental traits. Radiative transfer
models can make various assumptions regarding vegetation
structure and radiometric properties (Garrigues et al., 2008). In
the simplest case, the canopy is specified as a one-dimensional
(1D) homogeneous layer with light attenuated through absorp-
tion (see Fig. S1). Three-dimensional (3D) canopy models
account for more ‘realistic’ canopies, where leaves are preferen-
tially orientated, vegetation is clumped across a range of scales
(Fig. 2) and light is attenuated through absorption as well as
single and multiple scattering (Myneni et al., 1997). Model
assumptions are important when comparing EO-derived LAI
with field measurements or when integrating them with ecosys-
tem models. Model-data fusion may require the development of
transfer algorithms to avoid bias in estimates of water and gas
exchanges as well as radiation interception (Pinty et al., 2006).
MODIS LAI (range 0–10) and fAPAR (range 0–1) products
(MCD15A2) are derived via look-up table inversion of the 3D
RTM (Knyazikhin et al., 1998). MODIS biome information is
used for clumping and woody element corrections to compute
true LAI from effective LAI, which assumes randomly located
foliage elements within the canopy (Yang et al., 2006). Fixed
empirical relationships between LAI and NDVI are triggered
should the main algorithm fail, e.g. due to lack of observations.
An artificial neural network inverts the 1D RTM of Kuusk
(1995) to estimate POLDER LAI (range 0–6; Lacaze, 2006)
accounting for single and multiple scattering in the canopy, leaf
optical properties and soil spectral properties. SEVIRI LAI
(range 0–7) is derived assuming the vegetation canopy to be a
horizontally homogeneous layer, while SEVIRI fAPAR is esti-
mated from the inversion of the 1D turbid medium SAIL RTMTab
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M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd8
(Verhoef, 1984). GLOBCARBON effective LAI (range 0–10) is
generated via look-up table inversion of a physically based
model with multiple scattering (Deng et al., 2006). The iteration
method starts from a precursor effective LAI, which is derived
via land-cover-dependent LAI-VI relationships (Plummer
et al., 2006). Similar to SEVIRI LAI, the clumping index of Chen
et al. (2005) is implemented based on GLC2000 to compute true
LAI. GLOBCARBON fAPAR is calculated as difference of the
top-of-canopy PAR (amount of incoming photosynthetically
active solar radiation; derived from RED surface reflectance)
absorbance minus the PAR absorbance of soil (derived from a
look-up table based on soil maps). CYCLOPES maps of LAI
(range 0–6) and fAPAR have been generated using a neural
network to invert the SAIL RTM corrected for 3D structure
(Kuusk, 1995). The CYCLOPES algorithm accounts for clump-
ing at the landscape level (Baret et al., 2007) and can be applied
to any surface type in contrast to biome-tuned algorithms for
MODIS or GLOBCARBON.
Comparisons of POLDER LAI with MODIS LAI show good
agreement and similar trends over croplands and boreal forests
(albeit at different magnitudes), but POLDER LAI shows unre-
alistic seasonal cycles over tropical forests (Lacaze, 2006). Mean
product errors of SEVIRI products vary with geographical
region, being low for northern Africa and western Europe (< 0.1
for fAPAR, < 0.6 for LAI) but very high for regions with large
zenith view angles (e.g. northern Europe and South America),
high snow cover or persistent cloud cover (Land SAF, 2008).
Weiss et al. (2007) found good agreement between CYCLOPES
and MODIS LAI regarding seasonality and phasing (Fig. 3),
although LAI magnitude differed significantly between both
products. Overall, product accuracy varies with vegetation class
(GLOBCARBON LAI especially underestimates LAI over grass-
land and cropland), and mixed vegetation sites are generally not
well described by global LAI products (Garrigues et al., 2008).
Carbon flux products: NPP and GPP
For the MODIS GPP/NPP product (MOD17), efficiency values
are assigned a theoretical maximum as a function of land cover
(thus accuracy depends on the MODIS land-cover product)
(Zhao et al., 2006). Two MODIS NPP products are derived from
the GPP estimate; a short-time-scale NPP that only accounts for
maintenance respiration from leaves and roots, and an annual
integrated NPP that also accounts for respiration from woody
material and plant growth. The GEOSUCCESS NPP product is
modelled by the C-Fix model (equation 1; Veroustraete et al.,
2002) computing productivity as a function of temperature and
CO2 fertilization and using fAPAR estimates from NDVI.
MODIS validation shows that global annual estimates of GPP
and NPP are within 10.4% and 9% of averaged published results
(MODIS Land Team, status 2008, collection 4). The accuracy of
GPP and NPP products depends largely on the algorithm’s
ability to describe the light-use efficiency of a given plant species
and on the accuracy of the driving climatological data (Zhao
et al., 2006). Garbulsky et al. (2008) showed how using specific
photochemical reflectance index–radiation use efficiency rela-
tionships could significantly improve the accuracy of MODIS
GPP estimates. Comparisons of VI and MODIS GPP with tower
flux estimates of productivity suggest that EVI may be as good as
or better than MODIS GPP to predict vegetation productivity in
North America (Sims et al., 2006) or Africa (Sjöström et al.,
2011). Yet, relationships were highly stand-specific and relation-
ship accuracy varied among sites depending on the deciduous-
ness of species. While MODIS products have a built in
Figure 2 Spatial aggregation across scales shown for vegetation canopies from a highly simplified horizontally homogeneous case (top left,one-dimensional approximation; top right, same as top left but leaf area arranged in two different densities) through to canopies with bothhorizontal and vertical clumping (bottom left, clumping at the overstorey and understorey level; bottom right, three-dimensional model ofa savanna canopy). The same total amount of leaf material may be present in each case, but the arrangement of the material determines theradiometric response independently of the biochemical scattering properties of the leaves and soil (Widlowski et al., 2004; Pinty et al.,2006). All images ©RAMI Initiative (http://rami-benchmark.jrc.ec.europa.eu/HTML/Home.php) except lower right (M. Disney).
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 9
0 10 20 30 405Kilometers CYCLOPES LAI (0 - 6)
Figure 3 Maps of leaf area index (LAI) (each reclassified into 12 classes for display: increasing LAI with increasingly darker blue tones)derived from remote sensing imagery for north Tanzania in early January 2006 (Mount Kilimanjaro is in the upper left corner). Spatialresolution of the three different earth observation (EO) products is 1000 m (CYCLOPES, GLOBCARBON and MODIS). Areas with no LAIvalues (most likely because cloud cover did not allow for radiance measurements) are shown in white.
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd10
0 10 20 30 405Kilometers GLOBCARBON LAI (0 - 10)
Figure 3 Continued.
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 11
0 10 20 30 405Kilometers MODIS LAI (0 - 10)
Figure 3 Continued.
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd12
dependency on water, GEOSUCCESS NPP is not limited by
water, leading to large differences between products in areas
where soil water is limiting (Coops et al., 2009). Quaife et al.
(2008) demonstrated how different land-cover products could
produce significantly different productivity estimates. Also, the
crude assumptions underlying the production efficiency models
may not be applicable to short time-scales (Maselli et al., 2006).
FIRE PRODUCTS
Fire-related EO products include spatio-temporal maps of
active fires and burnt area/burn scar maps (Table 4; see Fig. S2),
as well as estimates of fire severity and combustion. The actual
area burnt per fire count can vary greatly (Cardoso et al., 2005),
and thus the derived amount of combusted biomass. Therefore,
burnt area maps are more relevant for modelling impacts of fire
on carbon fluxes and vegetation cover dynamics. Recently, focus
has shifted from annual to multi-annual datasets enabling
assessments of inter-relations between fire and vegetation.
Burnt area maps
The GLOBSCAR map of burnt area (Simon et al., 2004) was
derived using two global burn scar detection algorithms (K1 and
E1), applied for every burnable pixel in the IGBP vegetation map
(Kempeneers et al., 2002). The product allows seasonal and
regional tracking of fire events and their impacts (Plummer
et al., 2006). Problems at the global scale include over-detection
by the K1 algorithm, severe local under-detection by the E1
algorithm, mis-registration problems and interpolation errors
in the processing chain (Kempeneers et al., 2002). The Global
Burned Area-2000 (GBA-2000) product uses regional change-
detection algorithms adapted to land-cover types (UMD, TREES
tropical forest map; Achard et al., 1998) and climatic conditions.
GBA-2000 presented a major improvement over GLOBSCAR,
which used global algorithms, and over methods based on active
fire detection, where a single algorithm only is used (Grégoire
et al., 2003).
A multi-temporal multi-threshold burnt area algorithm was
applied to generate the Global Burned Surface dataset (GBS
82–99; Carmona-Moreno et al., 2005). Technical limitations
[e.g. increasing orbital drift with satellite lifetime, no bidirec-
tional reflectance distribution function (BRDF) effects
accounted for] make the GBS dataset unsuitable for quantitative
estimations (see Fig. S3). The dataset tends to underestimate fire
area, particularly at high latitudes (Carmona-Moreno et al.,
2005). However, the lack of systematic trend in the sensitivity of
the algorithm to calibration errors as well as the good agreement
concerning fire occurrences justifies its employment to detect
intra- and inter-annual trends in regional fire and to initiate or
validate output from dynamic vegetation models and trace gas
emission models. GLOBCARBON fire products have been gen-
erated for assimilation into carbon models. They are monthly
difference products of burnt area detected within the last month
with confidence � 80% (Plummer et al., 2006) using two global
algorithms (modified from GLOBSCAR) and two regional
GBA-2000 algorithms; fire location information derived from
active fire databases is added (Roy & Boschetti, 2009). In the
L3JRC product (Tansey et al., 2008), identification of burnt
pixels is based on a globally refined version of the boreal Eurasia
GBA2000 algorithm. L3JRC reports the amount of vegetation
burnt using GLC2000 land cover assuming that a surface cannot
be burnt more than once in the same fire season (which runs
from 1 April to 31 March). The algorithm performs well for a
range of vegetation types, while significantly underestimating
burnt areas for low vegetation cover (Tansey et al., 2008; see also
Giglio et al., 2010).
The MODIS burnt-area product MCD45A1 (Roy et al., 2005)
predicts the reflectance on a subsequent day taking into account
changing view and the effects of sun angle (variations in the
observed BRDF). A statistical measure is used to determine
whether the difference between the observed reflectance and the
model-predicted reflectance in the near- and mid-infrared
bands indicates a significant change of surface properties (Roy
et al., 2005). The Global Fire Emissions Database (GFED3.1)
provides a hybrid monthly burnt area dataset intended for use
within large-scale atmospheric and biogeochemical models (van
der Werf et al., 2006). Compiled from multiple sensors but pri-
marily based on MODIS surface reflectance data (using a differ-
ent algorithm from MCD45), it is the most comprehensive
dataset to date available for assessing inter-annual variability
and long-term trends in global burnt area and associated fire
emissions over the past 13 years (Giglio et al., 2010).
Product uncertainties can result in significant discrepancies
of burnt area estimates for some regions (Chang & Song, 2009;
see Fig. S1). MCD45A1 appears to perform best in comparison
with other fire products, partly because of the higher spatial
resolution of MODIS sensors (Roy & Boschetti, 2009). Roy &
Boschetti (2009) suggest that current global burnt area products
may still not meet the accuracy needs of local users because of
high probabilities of errors of commission and omission. Burnt
areas that are small and/or spatially fragmented relative to the
satellite observations may not be detected reliably (Laris, 2005),
which is especially relevant in cultivated and managed areas. Fire
maps contain errors because angle effects can cause a single fire
event to be detected in more than one satellite pixel and fires
may be hidden beneath clouds and canopies (discussed in
Cardoso et al., 2005). Yet, on scales relevant for macroecology
and biogeography, e.g. for testing effects of disturbance fre-
quency on ecosystem distribution and productivity, the accu-
racy of geolocation (MODIS, 50 m at nadir; VEGETATION,
300 m; Roy & Boschetti, 2009), spatial extent, time and duration
is likely to be sufficient.
Active fire data
The strong emission of mid-infrared radiation from fires
(around 4 mm) is used to derive active fire products (Giglio
et al., 2003) including MODIS thermal anomalies fire products
(MOD14A, MYD14A) (Giglio et al., 2009) and MODIS climate
modelling grids (monthly MOD14CMH, MYD14CMH, 8-day
MOD14C8H, MYD14C8H). Climate modelling grids are
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 13
Tab
le4
Act
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fire
and
burn
tar
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M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd14
gridded summaries of fire pixel information intended for use in
regional and global dynamic vegetation models. The NASA
funded Fire Information for Resource Management System
(FIRMS) and the Web Fire Mapper (http://firefly.geog.
umd.edu/firemap/) provide access to a rolling 2-month archive
of daily global MODIS hotspot/fire locations in near-real time as
well as temporal aggregates. Care should be taken interpreting
active fire locations, though, particularly at coarse spatial scales.
Fires may not be detected but information regarding missing
data or cloud obscuration may not be provided (Giglio, 2007).
Recent work has looked at how to combine estimates of active
fire products and burnt area, recognizing that active fire esti-
mates tend to underestimate the frequency and distribution of
smaller, short-lived fires which may flare up and burn out before
they are detected. Burnt area products, however, may pick up fire
disturbances, which tend to persist for some time after the fire.
DIGITAL ELEVATION MODEL (DEM)PRODUCTS
We briefly mention DEMs (Table 5) because of the importance of
topography in many earth system processes and ecological appli-
cations, especially for predictive species distribution models
(Platts et al., 2008). The Shuttle Radar Topographic Mission
(SRTM) provides elevation data from raw radar echoes collected
between 60° N and 54° S in 2000 (USGS Earth Resources Obser-
vation and Science Center). These data were filtered to produce
the 90-m (horizontal) resolution NASA product SRTM90, rep-
resenting 80% of the earth’s surface with a vertical accuracy of at
least 16 m at 90% confidence level (Rodriguez et al., 2006).
SRTM90 was further processed providing final seamless datasets
for user-defined areas (CGIAR-CSI SRTM). While terrain slope
and aspect affect the accuracy of CGIAR-CSI SRTM data, errors
are significant only on terrain with slope values exceeding 10°
(Gorokhovich & Voustianiouk, 2006). General topographic gra-
dients are captured, although some microvalley and ridges are
not,which will affect microecological studies in regions with high
topographic complexity (Jarvis et al., 2004). The ASTER global
DEM (GDEM) product is produced fully automated without
ground-control points using ephemeris and altitude data derived
from positional measurements of the TERRA platform instead,
reaching vertical accuracies of < 25 m in many cases (ASTER
Global DEM Validation Team, 2009).Accuracy problems for high
mountain conditions restrict the application for location-
specific change detection. Also, the ASTER GDEM contains
residual anomalies and artefacts that degrade its overall accuracy;
elevation of many inland lakes is inaccurate and the existence of
most water bodies is not indicated. However, the product pro-
vides topographic information on a global scale making it useful
for biogeographical studies. The ASTER GDEM has a greater
latitudinal extent (83° N to 83° S) than SRTM products aiding
research at high geographical latitudes. A multi-temporal global
land surface altimetry product (GLA14) has been generated from
the Geoscience Laser Altimeter System instrument on the Ice,
Cloud and Land Elevation Satellite (ICESat) designed to measure
changes in ice sheet elevation (Schutz et al., 2005). The geoloca-
tion accuracy of GLA14 is below 1 m and can be a valuable
supplement to other DEMs.
POTENTIAL FOR MACROECOLOGICALAPPLICATIONS
Global environmental changes, caused or accelerated by human
activities, can have severe biotic consequences, including ecosys-
tem degradation, species range shifts and changes in vegetation
phenology and productivity (Kerr et al., 2007). Satellite reflec-
tance analysis, in particular, has emerged as a major tool for
analysing such changes and underlying drivers (see Table 1).
Coverage of global spatial and continuous temporal scales of
EO-derived land cover and GPP/NPP products permits investi-
gation of trends in distribution and productivity of ecosystems
over time and space, at intra-annual time-scales. Detection of
temporal change of vegetation cover, structure (LAI, fAPAR)
and NPP/GPP allows one to analyse seasonal patterns of vegeta-
tion traits and biological responses to climate and environmen-
tal change, such as leaf flushing and abscission in response to dry
Table 5 Digital elevation modelproducts. Product Sensor Satellite SR Format PR
SRTM1 SIR-C, X-SAR SSE 30 m (US territory),
90 m (near global)
BIL Lat/long, WGS 84
ASTGTM2 ASTER-VNIR Terra 30 m GeoTIFF Lat/long, WGS 84
GLA142, 3 GLAS IceSat < 100 m BIN GLAS eEllipsoid3
SRTM products are available in three spatial resolutions: SRTM1 (1 arcsec ~ 30 m at the equator),SRTM3 (3 arcsec ~ 90 m at the equator) and SRTM30 (30 arcsec ~ 1 km at the equator). The ASTERglobal digital elevation model covers land surfaces between 83° N and 83° S. The GLAS ICESat landaltimetry product covers the globe between 86° N and 86° S. Geolocations are attributed to the centresof the 60-m footprints, which are separated by 172 m in the along-track direction.PR, projection; SR, spatial resolution.Web pages may become outdated: 1http://dds.cr.usgs.gov/srtm/ or http://edcsns17.cr.usgs.gov/EarthExplorer/; 2https://wist.echo.nasa.gov/~wist/api/imswelcome/; 3NCIDS GLAS altimetry eleva-tion extractor tool (NGAT) and IDL ellipsoid conversion tool are available at http://nsidc.org/data/icesat/tools.html.
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 15
and wet seasons to enhance photosynthetic gain (Myneni et al.,
2007), dynamics of carbon capture across regions and biomes
(Garbulsky et al., 2010) and drought-inductions in global eco-
system productivity (Zhao & Running, 2010).
Information on changes in landscape connectivity, area and
energy allows analyses of environmental mechanisms underlying
regional diversity (Turner, 2004; Honkanen et al., 2010) and
invasibility of communities (Richardson & Pyšek, 2006). EO
products provide information on disturbances, which may mask
macroecological patterns such as species–area relationships
(Higgins, 2007) and can lead to regional climate–vegetation mis-
matches. Fire, a major disturbance agent in many parts of the
world (Bowman et al., 2009), is estimated by a range of continu-
ously generated global EO products. Information on fire distri-
bution combined with products on vegetation productivity and
rainfall is increasingly exploited to predict wildfire distribution
and changes in the susceptibility of vegetation to fire under
climate changes (Bucini & Hanan, 2007; Krawchuk et al., 2009;
Bradstock, 2010).
CHALLENGES: SCALE AND UNCERTAINTY
Spatial accuracy can be a major deterrent for ecologists attempt-
ing to use EO data. However, alternatives covering large geo-
graphical areas are rare. The current challenges faced by
ecologists include issues of: (1) how ecological system properties
are related to EO products at a specific aggregation level (sensu
scale) and (2) accounting for uncertainties associated with spe-
cific EO products (‘validation’) for successful model–data fusion
(Williams et al., 2008).
Sub-pixel surface variation is intrinsic in spatial observations
of a natural surface (Cracknell, 1998) and is best described by
‘grain’. ‘Grain’ in biodiversity modelling describes field sampling
units, in landscape ecology it defines the smallest interval in the
observation and in remote sensing it is equivalent to the spatial,
spectral and temporal resolutions of the image (Clark & Pellikka,
2007) (see Fig. S4). Up-scaling of ecological information, often
collected at scales smaller than a few metres, to the spatial grain of
satellite remote sensing can introduce scaling bias if the relation-
ship between observation and process is nonlinear (Tian et al.,
2002; Stoy et al., 2009). When land cover varies at a spatial
frequency that is finer than the image grain, aggregation effects
lead to overestimation of areas of more common land-cover
types and vice versa (Fernandes et al., 2004; Verburg et al., 2011).
Clumping of vegetation at multiple scales can introduce scaling
bias on LAI estimates when nonlinear LAI retrieval algorithms
calibrated at the patch scale were applied on moderate-resolution
scales (Tian et al., 2002). Minimizing aggregation errors to
reduce spatial mismatches between data, products and models,
e.g. by maintaining more information about the underlying fine-
scale spectral signal (Rastetter et al., 1992) or by developing
transfer functions in which relationships between LAI and spec-
tral measurements vary as a function of scale (Williams et al.,
2008), has received increasing attention (Garrigues et al., 2008).
Global validation initiatives such as VALERIE (VAlidation of
Land European Remote sensing Instruments; Morisette et al.,
2006) and BigFoot were launched to establish uncertainties in
EO products, especially of those relating to the terrestrial carbon
cycle (Cohen et al., 2006). These uncertainties may arise from
aggregation effects, random errors and systematic errors (e.g.
due to instrument capabilities and algorithms). Note that sites
used for calibrating algorithms can be biased towards certain
geographical regions and biomes (Baret et al., 2006), with a lack
of data in tropical regions. Nevertheless, uncertainty assess-
ments make the use of EO products for ecological modelling
accountable and allow the results to be generalized.
CONCLUSION AND PERSPECTIVES
Many of the practical obstacles to the routine use of EO data in
ecological applications that existed a decade ago, such as the lack
of validated time series, tools and specialist expertise, have been
overcome. Users are left with the challenge of making an
informed decision about which product to choose for their
Figure 4 Vision of an overarchingdistribution and exchange network thatintegrates existing earth observation (EO)product databases (across sensors) andcollections of ecological data underone roof to foster and improveinterdisciplinary approaches in theanalyses of earth–atmosphere processes.Information exchange via traininginitiatives, workshops, seminars andcross-disciplinary conferences will breathelife into the shared platform, and willinform future developments of productsand remote sensing missions as well asenable a more thorough understanding ofthe processes that shape terrestrial andatmospheric components of the earth.
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd16
specific question, being aware that understanding assumptions
about the structural properties of the surface underlying these
models is the key to effective exploitation of EO products (Grace
et al., 2007). The range of EO products available provides huge
opportunities for advancing our understanding of ecological
processes and patterns at scales relevant for macroecology incor-
porating temporal dynamics as requested by Fisher et al. (2010).
EO is a fast evolving field and new products are constantly
generated (e.g. Space Agency Climate Change Initiative, global
albedo products). Effort has increased to integrate information
across sensors and scales with the potential to improve future
assessments of carbon fluxes and biodiversity and to inform
environmental policy on impacts of global change (Fig. 4).
Large-footprint LIDAR information is fused with MODIS data
to generate forest height maps (Lefsky, 2010) and the P-band of
the Synthetic Aperture Radar (SAR) shows good agreement with
boreal forest biomass. These developments will hopefully allow
large-scale forest structure and biomass assessments. Extending
field campaigns and increased information exchange between
disciplines will inform future developments of products and
missions will bring both research fields one step further, envis-
aged in an overarching distribution network.
ACKNOWLEDGEMENTS
Marion Pfeifer is supported by the Marie Curie Intra-European
fellowship IEF Programme (EU FP7-People-IEF-2008 Grant
Agreement no. 234394). Thomas Gillespie and referees provided
invaluable comments.
REFERENCES
Achard, F., Eva, H., Glinni, A., Mayaux, P., Richards, T. & Stibig,
H.J. (1998) Identification of deforestation hot spot areas in the
humid tropics. European Commission Publication EUR 18079
EN. European Commission, Luxembourg.
Ahlqvist, O. (2008) Extending post-classification change detec-
tion using semantic similarity metrics to overcome class het-
erogeneity: a study of 1992 and 2001 U.S. National Land
Cover Database changes. Remote Sensing of Environment, 112,
1226–1241.
Archibald, S., Nickless, A., Govender, N., Scholes, R.J. & Lehsten,
V. (2010) Climate and the inter-annual variability of fire in
southern Africa: a meta-analysis using long-term field data
and satellite-derived burnt area data. Global Ecology and Bio-
geography, 19, 794–809.
Asner, G.P., Scurlock, J.M.O. & Hicke, J.A. (2003) Global syn-
thesis of leaf area index observations: implications for eco-
logical and remote sensing studies. Global Ecology and
Biogeography, 12, 191–205.
ASTER Global DEM Validation Team (2009) ASTER GDEM
Validation Summary Report. Available at: http://www.pdfcari.
com/ASTER-Global-DEM-Validation-Summary-Report.htm
(accessed 14 August 2011).
Baret, F., Morissette, J.T., Fernandes, R.A., Champeaux, J.L.,
Myneni, R.B., Chen, J., Plummer, S., Weiss, M., Bacour, C.,
Garrigues, S. & Nickeson, J.E. (2006) Evaluation of the repre-
sentativeness of networks of sites for the global validation and
intercomparison of land biophysical products: proposition of
the CEOS-BELMANIP. IEEE Transactions on Geoscience and
Remote Sensing, 44, 1794–1803.
Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M.,
Berthelot, B., Nino, F., Weiss, M. & Samain, O. (2007) LAI,
fAPAR and fCover CYCLOPES global products derived from
VEGETATION. Part 1: principles of the algorithm. Remote
Sensing of Environment, 110, 275–286.
Bartholomé, E. & Belward, A. (2005) GLC2000: a new approach
to global land cover mapping from Earth observation data.
International Journal of Remote Sensing, 26, 1959–1977.
Bicheron, P., Huc, M., Henry, C., Bontemps, S. & Partners, G.
(2008) GLOBCOVER Products Description Manual. ESA Glob-
Cover Project led by Medias France.
Bond, W.J., Woodward, F.I. & Midgley, G.F. (2005) The global
distribution of ecosystems in a world without fire. New Phy-
tologist, 165, 525–537.
Bowman, D.M.J.S., Balch, J.K., Artaxo, P. et al. (2009) Fire in the
earth system. Science, 324, 481–484.
Bradstock, R.A. (2010) A biogeographic model of fire regimes in
Australia: current and future implications. Global Ecology and
Biogeography, 19, 145–158.
Brando, P., Goetz, S., Baccini, A., Nepstad, D.C., Beck, P.S.A. &
Christman, M.C. (2010) Seasonal and interannual variability
of climate and vegetation indices across the Amazon. Proceed-
ings of the National Academy of Sciences USA, 107, 14685–
14690.
Bucini, G. & Hanan, N.P. (2007) A continental-scale analysis of
tree cover in African savannas. Global Ecology and Biogeogra-
phy, 16, 593–605.
Cardoso, M.F., Hurtt, G.C., Moore, B. III, Nobre, C.A. & Bain, H.
(2005) Field work and statistical analyses for enhanced inter-
pretation of satellite fire data. Remote Sensing of Environment,
96, 212–227.
Carmona-Moreno, C., Belward, A., Malingreau, J.-P., Hartley,
A., Garcia-Alegre, M., Antonovskiy, M., Buchshtaber, V. &
Pivovarov, V. (2005) Characterizing interannual variations in
global fire calendar using data from Earth observing satellites.
Global Change Biology, 11, 1537–1555.
Carrão, H., Gonçalves, P. & Caetano, M. (2008) Contribution of
multispectral and multitemporal information from MODIS
images to land cover classification. Remote Sensing of Environ-
ment, 112, 986–997.
Champeaux, J.L., Masson, V. & Chauvin, F. (2005) ECOCLI-
MAP: a global database of land surface parameters at 1 km
resolution. Meteorological Applications, 12, 29–32.
Chang, D. & Song, Y. (2009) Comparison of L3JRC and MODIS
global burned area products from 2000 to 2007. Journal
of Geophysical Research, 114, D16106. doi:10.1029/
2008JD011361.
Chen, J.M. & Black, T.A. (1992) Defining leaf area index for
non-flat leaves. Plant, Cell and Environment, 15, 421–429.
Chen, J.M., Pavlic, G., Brown, L., Cihlar, J., Leblanc, S.G., White,
H.P., Hall, R.J., Peddle, D.R., King, D.J., Trofymow, J.A., Van der
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 17
Sanden, J.J., Pellikka, P.K.E. & Swift, D.E. (2002) Derivation
and validation of Canada-wide coarse-resolution leaf area
index maps using high-resolution satellite imagery and
ground measurements. Remote Sensing of Environment, 80,
165–184.
Chen, J.M., Menges, C.H. & Leblanc, S. (2005) Global derivation
of the vegetation clumping index from multi-angular satellite
data. Remote Sensing of Environment, 97, 447–457.
Chuvieco, E., Giglio, L. & Justice, C. (2008) Global characte-
rization of fire activity: toward defining fire regimes from
Earth observation data. Global Change Biology, 14, 1488–
1502.
Clark, B. & Pellikka, P. (2007) Mapping land cover change in the
Taita Hills, Kenya, utilizing multi-scale segmentation and
object-oriented classification of SPOT imagery. Geoscience
and Remote Sensing Symposium, 2007. IGARSS 2007. doi:
10.1109/IGARSS.2007.4423201.
Cohen, W.B., Maiersperger, T.K., Turner, D.P., Ritts, W.D., Pflug-
macher, D., Kennedy, R.E., Kirschbaum, A., Running, S.W.,
Costa, M. & Gower, S.T. (2006) MODIS land cover and LAI
collection 4 product quality across nine sites in the western
hemisphere. IEEE Transactions on Geoscience and Remote
Sensing, 44, 1843–1857.
Coops, N.C., Ferster, C.J., Waring, R.H. & Nightingale, J. (2009)
Comparison of three models for predicting gross primary
production across and within forested ecoregions in the con-
tiguous United States. Remote Sensing of Environment, 113,
680–690.
Cracknell, A.P. (1998) Synergy in remote sensing: what’s in a
pixel? International Journal of Remote Sensing, 19, 2025–2047.
DeFries, R.S. & Townshend, J.R.G. (1999) Global land cover
characterization from satellite data: from research to opera-
tional implementation? Global Ecology and Biogeography, 8,
367–379.
Deng, F., Chen, J.M., Plummer, S. & Chen, M. (2006) Algorithm
for global leaf area index retrieval using satellite imagery. IEEE
Transactions on Geoscience and Remote Sensing, 44, 2219–
2229.
Fang, H., Liang, S. & Kuusk, A. (2003) Retrieving leaf area index
using a genetic algorithm with a canopy radiative transfer
model. Remote Sensing of Environment, 85, 257–270.
Feddema, J.J., Oleson, K.W., Bonan, G.B., Mearns, L.O., Buja,
L.E., Meehl, G.A. & Washington, W.M. (2005) The impor-
tance of land-cover change in simulating future climates.
Science, 310, 1674–1678.
Fernandes, R., Fraser, R., Latifovic, R., Cihlar, J., Beaubien, J. &
Du, Y. (2004) Approaches to fractional land cover and con-
tinuous field mapping: a comparative assessment over the
BOREAS study region. Remote Sensing of Environment, 89,
234–251.
Fischer, J. & Lindenmayer, D.B. (2007) Landscape modification
and habitat fragmentation: a synthesis. Global Ecology and
Biogeography, 16, 265–280.
Fisher, J.A.D., Frank, K.T. & Leggett, W.C. (2010) Dynamic mac-
roecology on ecological time-scales. Global Ecology and Bio-
geography, 19, 1–15.
Fritz, S. & See, L.M. (2008) Identifying and quantifying uncer-
tainty and spatial disagreement in the comparison of global
land cover for different application. Global Change Biology, 14,
1057–1075.
Garbulsky, M.F., Peñuelas, J., Papale, D. & Filella, I. (2008)
Remote estimation of carbon dioxide uptake by a Mediterra-
nean forest. Global Change Biology, 14, 2860–2867.
Garbulsky, M.F., Peñuelas, J., Papale, D., Ardö, J., Goulden, M.L.,
Kiely, G., Richardson, A.D., Rotenberg, E., Veenendaal, E.M. &
Filella, I. (2010) Patterns and control of the variability of
radiation use efficiency and primary productivity across ter-
restrial ecosystems. Global Ecology and Biogeography, 19, 253–
267.
Garrigues, S., Lacaze, R., Baret, F., Morisette, J.T., Weiss, M.,
Nickeson, J.E., Fernandes, R., Plummer, S., Shabanov, N.V.,
Myneni, R.B., Knyazikhin, Y. & Yang, W. (2008) Validation
and intercomparison of global leaf area index products
derived from remote sensing data. Journal of Geophysical
Research, 113, G02028. doi:10.1029/2007JG000635.
Giglio, L. (2007) MODIS collection 4 active fire product user’s
guide. Version 2.3. Science Systems and Applications, Inc. Avail-
able at: http://modis-fire.umd.edu/Documents/MODIS_
Fire_Users_Guide_2.3.pdf (accessed 15 August 2011).
Giglio, L., Descloitres, J., Justice, C.O. & Kaufman, Y.J. (2003) An
enhanced contextual fire detection algorithm for MODIS.
Remote Sensing of Environment, 87, 273–382.
Giglio, L., Loboda, T., Roy, D.P., Quayle, B. & Justice, C.O. (2009)
An active fire-based burned area mapping algorithm for
the MODIS sensor. Remote Sensing of Environment, 113, 408–
420.
Giglio, L., Randerson, J.T., van der Werf, G.R., Kasibhatla, P.S.,
Collatz, G.J., Morton, D.C. & Defries, R.S. (2010) Assessing
variability and long-term trends in burned area by merging
multiple satellite fire products. Biogeosciences, 7, 1171–1186.
Gillespie, T.W., Foody, G.M., Rocchini, D., Giorgi, A.P. &
Saatchi, S. (2008) Measuring and modelling biodiversity from
space. Progress in Physical Geography, 32, 203–221.
Glenn, E.P., Huete, A.R., Nagler, P.L. & Nelson, S.G. (2008) Rela-
tionship between remotely-sensed vegetation indices, canopy
attributes, and plant physiological processes: what vegetation
indices can and cannot tell us about the landscape. Sensors, 8,
2136–2160.
Gorokhovich, Y. & Voustianiouk, A. (2006) Accuracy assessment
of the processed SRTM-based elevation data by CGIAR using
field data from USA and Thailand and its relation to the
terrain characteristics. Remote Sensing of Environment, 104,
409–415.
Grace, J., Nichol, C., Disney, M.I., Lewis, P., Quaife, T. & Bowyer,
P. (2007) Can we measure photosynthesis from space? Global
Change Biology, 13, 1484–1497.
Grégoire, J.-M., Tansey, K. & Silva, J.M.N. (2003) The GBA2000
initiative: developing a global burnt area database from
SPOT-VEGETATION imagery. International Journal of
Remote Sensing, 24, 1369–1376.
Hansen, M.C. & Reed, B. (2000) A comparison of the IGBP
DISCover and University of Maryland 1 km global land cover
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd18
products. International Journal of Remote Sensing, 21, 1365–
1373.
Hansen, M.C., DeFries, R.S., Townshend, J.R.G., Carroll, M.,
Dimiceli, C. & Sohlberg, R.A. (2003) Global percent tree cover
at a spatial resolution of 500 meters: first results of the MODIS
vegetation continuous fields algorithm. Earth Interactions, 7,
1–15.
Hansen, M.C., Stehamn, S.V., Potapov, P.V., Loveland, T.R.,
Townshend, J.R.G., DeFries, R.S., Pittman, K.W., Arunarwati,
B., Stolle, F., Steininger, M.K., Carroll, M. & DiMiceli, C.
(2008) Humid tropical forest clearing from 2000 to 2005
quantified by using multitemporal and multiresolution
remotely sensed data. Proceedings of the National Academy of
Sciences USA, 105, 9439–9444.
Herold, M., Mayaux, P., Woodcock, C.E., Baccini, A. & Schmul-
lius, C. (2008) Some challenges in global land cover mapping:
an assessment of agreement and accuracy in existing 1km
datasets. Remote Sensing of Environment, 112, 2538–2556.
Higgins, P.A.T. (2007) Biodiversity loss under existing land use
and climate change: an illustration using northern South
America. Global Ecology and Biogeography, 16, 197–204.
Hodgens, J. (2002) Validation of the consistent-year v003 MODIS
land cover product. MODIS user guide. Available at: http://
www-modis.bu.edu/landcover/userguidelc/consistent.htm
(accessed 15 August 2011).
Honkanen, M., Roberge, J.-M., Rajasärkkä, A. & Mönkkönen,
M. (2010) Disentangling the effects of area, energy and
habitat heterogeneity on boreal forest bird species richness
in protected areas. Global Ecology and Biogeography, 19,
61–71.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. & Fer-
reira, L.G. (2002) Overview of the radiometric and biophysi-
cal performance of the MODIS vegetation indices. Remote
Sensing of Environment, 83, 195–213.
IPCC (2007) Fourth assessment report (AR4). Climate change
2007: synthesis report. IPCC, Geneva.
Jarvis, A., Rubiano, J., Nelson, A., Farow, A. & Mulligan, M.
(2004) Practical use of SRTM data in the tropics – comparisons
with digital elevation models generated from cartographic data.
Working document 198. CIAT, Cali, Colombia.
Kempeneers, P., Swinnen, E. & Fierens, F. (2002) GLOBSCAR
final report. Tap/n7904/ff/fr-001 Version 1.2. ESA, Ispra.
Kerr, J.T. & Ostrovsky, M. (2003) From space to species: ecologi-
cal applications for remote sensing. Trends in Ecology and
Evolution, 18, 299–305.
Kerr, J.T., Kharouba, H.M. & Currie, D.J. (2007) The macroeco-
logical contribution to global change solutions. Science, 316,
1581–1584.
Knyazikhin, Y., Martonchik, J.V., Myneni, R.B., Diner, D.J. &
Running, S.W. (1998) Synergistic algorithm for estimating
vegetation canopy leaf area index and fraction of absorbed
photosynthetically active radiation from MODIS and MISR
data. Journal of Geophysical Research, 103, 32257–32274.
Krawchuk, M.A., Moritz, M.A., Parisien, M.-A., van Dorn, J. &
Hayhoe, K. (2009) Global pyrogeography: the current and
future distribution of wildfire. PLoS ONE, 4, e5102.
Kumar, M. & Monteith, J.L. (1981) Remote sensing of crop
growth. Plants and the daylight spectrum (ed. by H. Smith), pp.
133–144. Academic Press, London.
Kuusk, A. (1995) A fast invertible canopy reflectance model.
Remote Sensing of Environment, 51, 342–350.
Lacaze, R. (2006) POLDER-2. Land surface level-3 products. User
manual. Algorithm description and product validation. Issue
1.41. Available at: http://postel.mediasfrance.org/IMG/pdf/
P2-TE3-UserManual-I1.41.pdf (accessed 15 August 2011).
Land SAF (2008) Product user manual – vegetation parameters
(FVC, LAI, FAPAR). Ref: SAF/LAND/UV/PUM_VEGA/2.1.
Issue 2.1. Available at: http://landsaf.meteo.pt/GetDocument.
do?id=300 (accessed 15 August 2011).
Landgrebe, D. (2005) Multispectral land sensing: where from,
where to? IEEE Transactions on Geoscience and Remote
Sensing, 43, 414–421.
Laris, P. (2005) Spatiotemporal problems with detecting and
mapping mosaic fire regimes with coarse-resolution satellite
data in savanna environments. Remote Sensing of Environ-
ment, 99, 412–424.
Lefsky, M. (2010) A global forest canopy height map from the
Moderate Resolution Imaging Spectroradiometer and the
Geoscience Laser Altimeter System. Geophysical Research
Letters, 37, L15401. doi:10.1029/2010GL043622.
Liang, S. (2004) Quantitative remote sensing of land surfaces.
Wiley, New York.
McCallum, I., Obersteiner, M., Nilsson, S. & Shvidenko,
A. (2006) A spatial comparison of four satellite derived 1
km global land cover datasets. International Journal of
Applied Earth Observation and Geoinformation, 8, 246–
255.
Maselli, F., Barbati, A., Chiesi, M., Chirici, G. & Corona, P.
(2006) Use of remotely sensed and ancillary data for estimat-
ing forest gross primary productivity in Italy. Remote Sensing
of Environment, 100, 563–575.
Masson, V., Champeaux, L., Chauvin, F., Meriguer, C. & Lacaze,
R. (2003) A global database of land surface parameters at 1 km
resolution in meteorological and climate models. Journal of
Climate, 16, 1261–1282.
Maurer, B.A. (2000) Macroecology and consilience. Global
Ecology and Biogeography, 9, 275–280.
Mayaux, P., Strahler, A., Eva, H., Herold, M., Agrawal, S.,
Naumov, S., De Miranda, E.E., Di Bella, C.M., Ordoyne, C.,
Kopin, Y. & Roy, P.S. (2006) Validation of the Global Land
Cover 2000 map. IEEE Transactions on Geoscience and Remote
Sensing, 44, 1728–1739.
Mehner, H., Cutler, M., Fairbairn, D. & Thompson, G. (2004)
Remote sensing of upland vegetation: the potential of high
spatial resolution satellite sensors. Global Ecology and Bioge-
ography, 13, 359–369.
Moorcroft, P.R. (2003) Recent advances in ecosystem–
atmosphere interactions: an ecological perspective. Proceed-
ings of the Royal Society B: Biological Sciences, 270, 1215–
1227.
Morisette, J.T., Baret, F., Jeffrey, L.P. et al. (2006) Validation of
global moderate-resolution LAI products: a framework
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 19
proposed within the CEOS Land Product Validation Sub-
group. IEEE Transactions on Geoscience and Remote Sensing,
44, 1804–1817.
Myneni, R.B., Nemani, R.R. & Running, S.W. (1997) Estimation
of global leaf area index and absorbed PAR using radiative
transfer models. IEEE Transactions on Geoscience and Remote
Sensing, 35, 1380–1393.
Myneni, R.B., Yang, W., Nemani, R.R. et al. (2007) Large sea-
sonal swings in leaf area of Amazon rainforests. Proceedings
of the National Academy of Sciences USA, 104, 4820–
4823.
Nagai, S., Saigusa, N., Muraoka, H. & Nasahara, K.N. (2010)
What makes the satellite-based EVI–GPP relationship unclear
in a deciduous broad-leaved forest? Ecological Research, 25,
359–365.
Pettorelli, N., Vik, J.O., Mysterud, A., Gailllard, J.-M., Tucker, C.J.
& Stenseth, N.C. (2005) Using the satellite-derived NDVI to
assess ecological responses to environmental change. Trends in
Ecology and Evolution, 20, 503–510.
Pettorelli, N., Ryan, S., Mueller, T., Bunnefeld, N., Jedrzejewska,
B., Lima, M. & Kausrud, K. (2011) The normalized difference
vegetation index (NDVI): unforeseen successes in animal
ecology. Climate Research, 46, 15–27.
Pielke, R.A. Sr, Marland, G., Betts, R.A., Chase, T.N., Eastman,
J.L., Niles, J.O., Niyogi, D.D.S. & Running, S.W. (2002) The
influence of land-use change and landscape dynamics on the
climate system: relevance to climate change policy beyond the
radiative effect of greenhouse gases. Philosophical Transactions
of the Royal Society A: Mathematical, Physical and Engineering
Sciences, 360, 1705–1719.
Pinty, B., Lavergne, T., Dickinson, R.E., Widlowski, J.-L.,
Gobron, N. & Verstraete, M.M. (2006) Simplifying the inter-
action of land surfaces with radiation for relating remote
sensing products to climate models. Journal of Geophysical
Research, 111, D02116. doi:10.1029/2005JD005952.
Platts, P., McClean, C.J., Lovett, J.C. & Marchant, R. (2008) Pre-
dicting tree distributions in an East African biodiversity
hotspot: model selection, data bias and envelope uncertainty.
Ecological Modelling, 218, 121–134.
Plummer, S., Arino, O., Simon, M. & Steffen, W. (2006) Estab-
lishing an earth observation product service for the terres-
trial carbon community: the GLOBCARBON initiative.
Mitigation and Adaptation Strategies for Global Change, 11,
97–111.
Quaife, T., Quegan, S., Disney, M., Lewis, P., Lomas, M. & Wood-
ward, F.I. (2008) Impact of land cover uncertainties on esti-
mates of biospheric carbon fluxes. Global Biogeochemical
Cycles, 22, GB4016. doi:10.1029/2007GB003097.
Rastetter, E.B., King, A.W., Cosby, B.J., Hornberger, G.M.,
O’Neill, R.V. & Hobbie, J.E. (1992) Aggregating fine-scale eco-
logical knowledge to model coarser-scale attributes of ecosys-
tems. Ecological Applications, 2, 55–70.
Richardson, D.M. & Pyšek, P. (2006) Plant invasions: mer-
ging the concepts of species invasiveness and community
invasibility. Progress in Physical Geography, 30, 409–
431.
Rodriguez, E., Morris, C.S. & Belz, J.E. (2006) A global assess-
ment of the SRTM performance. Photogrammetric Engineer-
ing and Remote Sensing, 72, 249–260.
Roy, D.P. & Boschetti, L. (2009) Southern Africa validation of
the MODIS, L3JRC, and GLOBCARBON burned-area prod-
ucts. IEEE Transactions on Geoscience and Remote Sensing, 47,
1032–1044.
Roy, D.P., Jin, Y., Lewis, P.E. & Justice, C.O. (2005) Prototyping a
global algorithm for systematic fire-affected area mapping
using MODIS time series data. Remote Sensing of Environ-
ment, 97, 137–162.
Running, S.W., Thornton, P., Nemani, R. & Glassy, J.M. (2000)
Global terrestrial gross and net primary productivity from the
Earth Observing System. Methods in ecosystem science (ed. by
O. Sala, R. Jackson and H. Mooney), pp. 44–57. Springer-
Verlag, New York.
Running, S.W., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves,
M. & Hashimoto, H. (2004) A continuous satellite-derived
measure of global terrestrial primary production. Bioscience,
54, 547–560.
Sala, O.E., Chapin, F.S. III, Armesto, J.J., Berlow, E., Bloomfield,
J., Dirzo, R., Huber-Sanwald, E., Huenneke, L.F., Jackson, R.,
Kinzig, A., Leemans, R., Lodge, D., Mooney, H.A., Oesterheld,
M., Poff, L., Sykes, M.T., Walker, B.H., Malker, W. & Wall, D.
(2000) Global biodiversity scenarios for the year 2100. Science,
287, 1770–1774.
Schutz, B., Zwally, H., Schuman, C., Hancock, D. & Dimarzio, J.
(2005) Overview of the ICESat mission. Geophysical Research
Letters, 32, L21S01.
Sellers, P.J. (1997) Modeling the exchanges of energy, water, and
carbon between continents and the atmosphere. Science, 275,
602–609.
Simon, M., Plummer, S., Fierens, F., Hoelzmann, J.J. & Arino, O.
(2004) Burnt area detection at global scale using ATSR-2: the
GLOBSCAR products and their qualification. Journal of Geo-
physical Research, 109, D14S02. doi:10.1029/2003JD003622.
Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldoc-
chi, D.D., Flanagan, L.B., Goldstein, A.H., Hollinger, D.Y.,
Misson, L., Monson, R.K., Oechel, W.C., Schmid, H.P., Wofsy,
S.C. & Xu, L. (2006) On the use of MODIS EVI to assess gross
primary productivity of North American ecosystems. Journal
of Geophysical Research, 111, G04015. doi:10.1029/
2006JG000162.
Sjöström, M., Ardö, J., Arneth, A., Boulain, N., Cappelaere, B.,
Eklundh, L., Grandcourt de, A., Kutsch, W.L., Merbold, L.,
Nouvellon, Y., Scholes, R.J., Schubert, P., Seaquist, J. &
Veenendaal, E.M. (2011) Exploring the potential of MODIS
EVI for modeling gross primary production across African
ecosystems. Remote Sensing of Environment, 115, 1081–1089.
Stoy, P.C., Williams, M., Disney, M., Prieto-Blanco, A., Huntley,
B., Baxter, R. & Lewis, P. (2009) Upscaling as ecological infor-
mation transfer: a simple framework with application to
Arctic ecosystem carbon exchange. Landscape Ecology, 24,
971–986.
Tansey, K., Grégoire, J.-M., Defourny, P., Leigh, R., Pekel, J.-F.,
Barros, A., Silva, J., van Bogaert, E., Bartholomé, E. & Bon-
M. Pfeifer et al.
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd20
temps, S. (2008) A new, global, multi-annual (2000–2007)
burnt area product at 1 km resolution. Geophysical Research
Letters, 35, L01401. doi:10.1029/2007GL031567.
Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V.,
Zhou, L., Zhang, Y., Buermann, W., Dong, J., Viekkanen, B.,
Hame, T., Anderson, K., Ozdogan, M., Knyazikhin, Y. &
Myneni, R.B. (2002) Multiscale analysis and validation of the
MODIS LAI product I. Uncertainty assessment. Remote
Sensing of Environment, 83, 414–430.
Townshend, J. (1994) Global data sets for land applications from
the Advanced Very High-Resolution Radiometer – an intro-
duction. International Journal of Remote Sensing, 15, 3319–
3332.
Turner, J.R.G. (2004) Explaining the global biodiversity gradi-
ent: energy, area, history and natural selection. Basic and
Applied Ecology, 5, 435–448.
Turner, W., Spector, S., Gardiner, N., Fladeland, M., Sterling, E.
& Steininger, M. (2003) Remote sensing for biodiversity
science and conservation. Trends in Ecology and Evolution, 18,
306–314.
Verburg, P.H., Neumann, K. & Nol, L. (2011) Challenges in
using land use and land cover data for global change studies.
Global Change Biology, 17, 974–989.
Verhoef, W. (1984) Light scattering by leaf layers with applica-
tion to canopy reflectance modelling: the SAIL model. Remote
Sensing of Environment, 16, 125–141.
Veroustraete, F., Sabbe, H. & Eerens, H. (2002) Estimation of
carbon mass fluxes over Europe using the C-Fix model and
Euroflux data. Remote Sensing of Environment, 83, 376–399.
von Liebig, J.F. (1843) Chemistry in its application to agriculture
and physiology. J. Owen, Cambridge, MA.
Warner, T.A., Nellis, M.D. & Foody, G.M. (eds) (2009) The
SAGE handbook of remote sensing. SAGE Publishing, London.
Webb, T.J., Gaston, K.J., Hannah, L. & Woodward, F.I. (2006)
Coincident scales of forest feedback on climate and conserva-
tion in a diversity hot spot. Proceedings of the Royal Society B:
Biological Science, 273, 757–765.
Weiss, M., Baret, F., Garrigues, S. & Lacaze, R. (2007) LAI and
fAPAR CYCLOPES global products derived from VEGETA-
TION. Part 2: validation and comparison with MODIS col-
lection 4 products. Remote Sensing of Environment, 110, 317–
331.
van der Werf, G.R., Randerson, J.T., Giglio, L., Collatz, G.J.,
Kasibhatla, P.S. & Arellano, A.F. (2006) Interannual variability
in global biomass burning emissions from 1997 to 2004.
Atmospheric Chemistry and Physics, 6, 3423–3441.
Widlowski, J.-L., Pinty, B., Gobron, N., Verstraete, M.M., Diner,
D.J. & Davis, A.B. (2004) Canopy structure parameters
derived from multi-angular remote sensing data for terrestrial
carbon studies. Climatic Change, 7, 403–415.
Williams, M., Bell, R., Spadavecchia, L., Street, L.E. & van Wijk,
M.T. (2008) Upscaling leaf area index in an arctic landscape
through multiscale observations. Global Change Biology, 14,
1517–1530.
Yang, W., Tan, B., Huang, D., Rautiainen, M., Shabanov, N.V.,
Wang, Y., Privette, J.L., Huemmrich, K.F., Fensholt, R., Sand-
holt, I., Weiss, M., Ahl, D.E., Gower, S.T., Nemani, R.R.,
Knyazikhin, Y. & Myneni, R. (2006) MODIS leaf area index
products: from validation to algorithm improvement. IEEE
Transactions on Geoscience and Remote Sensing, 44, 1885–
1898.
Yoccoz, N.G., Nichols, J.D. & Boulinier, T. (2001) Monitoring of
biological diversity in space and time. Trends in Ecology and
Evolution, 16, 446–453.
Zhao, M. & Running, S.W. (2010) Drought-induced reduction
in global terrestrial net primary production from 2000
through 2009. Science, 329, 940–943.
Zhao, M., Running, S.W. & Nemani, R.R. (2006) Sensitivity of
Moderate Resolution Imaging Spectroradiometer (MODIS)
terrestrial primary production to the accuracy of meteoro-
logical reanalyses. Journal of Geophysical Research – Biogeo-
sciences, 111, G01002.
SUPPORTING INFORMATION
Additional Supporting Information may be found in the online
version of this article:
Figure S1 Schematic representation of photon transport in veg-
etation canopies.
Figure S2 Maps of fire products overlaid on precipitation maps
derived from WorldClim database.
Figure S3 Remote sensing essentials.
Figure S4 Effects of aggregation on canopy spatial information
generated from a detailed three-dimensional model of a
savanna-type canopy.
Table S1 Results of ISI Web Of Knowledge literature search using
earth observation terms in combination with keywords charac-
terizing ecological systems.
Table S2 Spatial resolution (SR) and number of spectral bands
(SB) of passive satellites and sensors. Launched–time of satellite
launch.
As a service to our authors and readers, this journal provides
supporting information supplied by the authors. Such materials
are peer-reviewed and may be re-organized for online delivery,
but are not copy-edited or typeset. Technical support issues
arising from supporting information (other than missing files)
should be addressed to the authors.
BIOSKETCH
Marion Pfeifer is a Marie Curie IEF research fellow in
the York Institute for Tropical Ecosystem Dynamics,
UK. In her research, she focuses on the response of
plants to environmental change across spatial scales,
using demographic, genetic and modelling approaches.
She is currently studying LAI–vegetation relationships
for eastern Africa using KOMPSAT, SPOT and MERIS
data provided by the European Space Agency (Cat-1
User Agreement).
Editor: Thomas Gillespie
Satellite products for macroecology
Global Ecology and Biogeography, ••, ••–••, © 2011 Blackwell Publishing Ltd 21