LC-22 Abstract

Integrating Coarse and Fine Resolution Satellite Data to Monitor Land Cover Change throughout Amazônia  

Ruth DeFries, University of Maryland (US-PI)
Yosio Edemir Shimabukuro, INPE (SA-PI)

This proposal addresses a key need to accurately monitor land cover change at repeated intervals throughout Amazônia.  Spatially-explicit data on land cover dynamics in response to land use change are essential to extrapolate process-level understanding of carbon, nutrient, and trace gas fluxes over the larger region.  Currently, monitoring efforts to quantify land cover change based on analysis of Landsat data are time-consuming, laborious, and data-intensive.  Estimates from different analyses often yield conflicting results, highlighting differences in land cover definitions and methodologies applied by various researchers.

We propose to collaborate with colleagues from the Brazilian National Institute for Space Research (INPE) to develop efficient, accurate, and repeatable methods for monitoring land cover dynamics throughout the region.  The emphasis will be on integrating data from different sensors at multiple resolutions to apply methods that are as automated and practicable as possible.  Specifically, we propose to:

1) develop and test a nested approach using coarse resolution (250-500m) MODIS data to identify locations undergoing land cover change and Landsat data for more detailed characterization and analysis of those identified locations.  Very high resolution (1-4m) IKONOS data and in situ observations will be used to validate the results.  Through a nested approach, we aim to reduce the amount of high resolution data requiring time consuming analysis without sacrificing wall-to-wall coverage of the entire region.

2) examine an alternative approach to describe land cover dynamics in terms of subpixel percentage tree cover to circumvent the definitional differences in terms such as “forest”, “nonforest”, “degraded forest”, “deforestation”, and “regrowth.”  Through such an approach, we aim to improve the ability to identify subpixel changes in tree cover and provide estimates of forest area and change that are consistent in space and time.

3) Establish collaborative relationships and exchanges of methods and data between INPE, the University of Maryland, and the LBA-ECO team.

4) Provide subsets of 250m and 500m MODIS data and improved estimates of land cover dynamics throughout the region to the LBA community through the University of Maryland Global Land Cover Facility (already linked to the LBA data distribution system).