Evaluating MODIS data for mapping wildlife habitat distributionPublished in Remote Sensing of the

Environment in May 2008

Conservation issuesHabitat fragmentation -The division of large habitat patches into many small patches -Increases isolation of animals within habitats -Decreases amount of interior habitat, increases edge habitat Edge Effect -Edges create microclimates which are only suitable for

certain types of wildlife. Many wildlife species are extremely vulnerable in these habitats.

Home Range -Many animals have a large home range within which they

are constantly traveling to find the resources they need to survive. As habitats become increasingly fragmented, animals are unable to perform their livelihoods within these ranges.

Development and Fragmentation

Choosing which areas are most important for conservation requires extensive knowledge about the spatial distribution of the specific habitat type.

Remote Sensing can be particularly useful for this if areas are difficult to get to.

Giant Panda habitat in China is a prime example of this.

Habitat studies in the past have used data from specific sensors

LANDSAT TM multispectral scanner

Due to availability and relatively high spatial resolution (30 M/pixel)

This study compares two models using data from different satellite sensor systems to map the spatial distribution of Giant Panda habitat

First ModelCombines forest cover • derived from a digital land cover classification of Landsat

TM imagery acquired in June, 2001

o With data acquired on elevation and slope patterns

• derived from a digital elevation model obtained from topographic maps

Second Model

Based on the Ecological Niche Factor Analysis (ENFA)

Using MODIS to get time series composites of WDRVI (Wide Dynamic Range Vegetation Index) images

Time series with one week intervals in 2001

Problem with LANDSAT dataThe application of supervised and

unsupervised classification techniques to this data have not been able to detect the spectral signature of the bamboo understory layer.

Low temporal resolution fails to represent seasonal nature of the habitats

MODIS DataHas been ignored in the past due to low

spatial resolutionIs experimented with here in order to explore

the benefits provided by high temporal resolution in relation to habitat mapping

Application of MODIS data 8-day surface reflectance data were used to

calculate a new vegetation indexWDRVI- Wide dynamic range vegetation indexWDRVI=[(α+1)NDVI+(α−1)]/[(α−1)NDVI+

(α+1)]Non-linearly related to NDVI

ResultsOn average, the broadleaf deciduous pixels

with understory bamboo have 15.8% higher WDRVI values than those without understory bamboo

These areas are also significantly different at three time periods

Therefore, despite the poor spatial resolution of MODIS data, seasonal change in vegetation gives applicable info for distinguishing between suitable and non-suitable Giant Panda habitat.

Source:Vina, Bearer, H. Zhang and Z. Ouyang.

Evaluating MODIS data for mapping wildlife distribution. Remote Sensing of Environment; 112, 2008.