Characterizing the spatial distribution of giant pandas in China using multitemporal MODIS data and landscape metrics

X. P. Ye*, T. J. Wang, A. K. Skidmore, A. G. Toxopeus

*Corresponding author for this work

Research output: Contribution to journalConference paper


Although forest fragmentation and degradation have been recognized as one of major threats to wild panda population, little is known about the relationship between panda distribution and forest fragmentation. This study is unique as it presents a first attempt at understanding the effects of forest fragmentation on panda spatial distribution for the entire wild panda population. Using a moving window with a radius of 3 km, landscape metrics were calculated for two classes of forest (i.e. dense forest and sparse forest) which derived from a complete year of MODIS 250 m EVI multitemporal data in 2001. Eight fragmentation metrics that had highest loadings in factor analyses were selected to quantify the spatial heterogeneity of forests. It was found that the eight selected metrics were significantly different (P < 0.05) between panda presence and absence. The relationship between panda distribution and forest heterogeneity was explored using forward stepwise logistic regression. Giant pandas appear sensitive to patch size and isolation effects associated with forest fragmentation. The R2 value (0.45) of the final regression model indicates that landscape metrics partly explain the distribution of giant pandas, though a knowledge-based control for elevation and slope improved the explanation to 74.9%. Findings of this study imply that the design of effective conservation area for wild panda must include large, contiguous and adjacent forest areas.

Original languageEnglish
Pages (from-to)1039-1046
Number of pages8
JournalThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Issue numberPart B8
Publication statusPublished - 2008
Externally publishedYes
Event21st International Congress for Photogrammetry and Remote Sensing, ISPRS 2008 - Beijing, China
Duration: 3 Jul 200811 Jul 2008


  • Multitemporal
  • Forest
  • Landscape
  • Spatial
  • Configuration
  • Modelling
  • Knowledge Base


Dive into the research topics of 'Characterizing the spatial distribution of giant pandas in China using multitemporal MODIS data and landscape metrics'. Together they form a unique fingerprint.

Cite this