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: mp643@york.ac.uk,marion.pfeifer@googlemail.com†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-

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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

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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.

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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