Subpixel forest cover in central Africa from multisensor, multitemporal data

Subpixel forest cover in central Africa from multisensor, multitemporal data Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Remote Sensing of Environment Elsevier

Subpixel forest cover in central Africa from multisensor, multitemporal data

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Publisher
Elsevier
Copyright
Copyright © 1997 Elsevier Ltd
ISSN
0034-4257
D.O.I.
10.1016/S0034-4257(96)00119-8
Publisher site
See Article on Publisher Site

Abstract

Seven Landsat Multispectral Scanner (MSS) scenes in central Africa were coregistered with 8 km resolution data from the 1987 AVHRR Pathfinder Land data set. Percent forest cover in each 8 km grid cell was derived from the classified MSS scenes. Linear relationships between percent forest cover and 30 multitemporal metrics derived from all AVHRR optical and thermal channels were determined. Correlations were strongest for the mean annual normalized difference vegetation index (NDVI) and mean annual brightness temperature (AVHRR Channel 3) and weakest for those metrics, besides NDVI, based on near-infrared reflectances (AVHRR Channel 2). The relationships were used to estimate percent forest cover in various locations in the study area using multiple linear regression and regression trees. Overall, the multiple linear regression provided more accurate results. Predicted percent forest cover estimates were within 20% of the “actual” percent forest cover (derived from the MSS data) for approximately 90% of the grid cells. The RMS error for the prediction was 12% forest cover. RMS errors above 18% forest cover were obtained when using AVHRR data from a single month to derive predictive relationships. The results demonstrate that multitemporal data reflecting vegetation phenology can be used to estimate subpixel forest cover at coarse spatial resolutions.

Journal

Remote Sensing of EnvironmentElsevier

Published: Jun 1, 1997

References

  • Global discrimination of land cover types from metrics derived from AVHRR Pathfinder data
    DeFries, R.; Hansen, M.; Townshend, J.
  • Applied Regression Analysis
    Draper, N.R.; Smith, H.
  • Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys
    Michaelson, J.; Schimel, D.S.; Friedl, M.A.; Davis, F.W.; Dubayah, R.O.
  • The Vegetation of Africa
    White, F.

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