SDSU Website USGS Website GIScCE Home

Biome Data
and Results


Boreal Forest


Temperate Forest


Dry Tropical and Subtropical Forest and Woodland


Humid Tropical Forest


Global Data


Quantifying rates of forest cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals.

Sponsored by the NASA Land-Cover and Land-Use Change Program, this project extends previous NASA-sponsored research on global forest cover dynamics (Hansen and DeFries 2004) and land cover change estimation (Loveland et al. 2002; Stehman et al. 2003) to establish a robust, operational forest monitoring and assessment system that:

  1. Quantifies rates and describes patterns of global forest cover change by biome.
  2. Identifies and quantifies sources of uncertainty of forest cover change maps and areal estimates of change.
  3. Enables repeated estimations over time that are directly comparable in monitoring forest cover trends.
  4. Generates data on forest change that are suitable for assessing impacts on ecosystem services.
Our strategy combines the strengths of global forest change mapping to produce a spatially explicit depiction of change at moderate resolution and statistical sampling to provide precise areal estimates of change in forest cover based on more accurate, higher resolution data. In addition, our monitoring strategy generates the data necessary for a statistically rigorous validation of the global forest change maps, thus successfully integrating accuracy assessment within a forest monitoring framework (Stehman et al. 2000). The research consists of four primary tasks:
  1. Use existing 2000 to 2005 MODIS percent tree cover and forest change probability maps to stratify major forest biomes into regions of high, medium and low change. The method will be extended to AVHRR 1990s data as well.

  2. Implement a remote-sensing based, probability sampling framework that combines the biome-scale forest cover and change maps with high resolution forest characterizations derived from Landsat imagery to:
    • estimate biome change and the uncertainty of each biome estimate,
    • determine the overall and individual biome accuracy of the global MODIS tree cover change maps and
    • for the MODIS layer, apply regression estimators derived using the Landsat analyses to create biome-scale forest area change products.

  3. Evaluate the strengths, weaknesses, and biases of the remote sensing inputs, and how these vary geographically for accurately characterizing forest change, including the ability to quantify deforestation versus afforestation, and natural versus anthropogenic change.
The project is conducted by Geographic Information Science Center of Excellence of the South Dakota State University.

Provided global forect cover and change data are available for use for valid scientific, conservation, and educational purposes as long as proper citations are used. We ask that you credit the Global Forest Monitoring data as follows:

Hansen M., Stehman S., Potapov P. (2010) Quantification of global gross forest cover loss.
Proceedings of the National Academy of Sciences of the U.S.A.

For further information, please contact:

Dr. Matthew Hansen
Geographic Information Science Center of Excellence - SDSU
Phone: (605) 688-6848