Forest Cover Estimates Research Articles

Regional forest cover estimation via remote sensing: the calibration center concept

A method for combining Landsat Thematic Mapper (TM), Advanced Very High Resolution Radiometer (AVHRR) imagery, and other biogeographic data to estimate forest cover over large regions is applied and evaluated at two locations. In this method, TM data are used to classify a small area (calibration center) into forest/nonforest; the resulting forest cover map is then used in combination with AVHRR spectral data from the same area to develop an empirical relationship between percent forest cover and AVHRR pixel spectral signature; the resultant regression relationship between AVHRR band values and percent forest cover is then used to extrapolate forest cover for several hundred kilometers beyond the original TM calibration center. In the present study, the method was tested over two large regions in the eastern United States: areas centered on Illinois and on the Smoky Mountains on the North Carolina-Tennessee border. Estimates of percent forest cover for counties, after aggregating AVHRR pixel estimates within each county, were compared with independent ground-based estimates. County estimates were aggregated to derive estimates for states and regions. For the Illinois region, the overall correlation between county cover estimates was 0.89. Even better correlations (up to r = 0.96) resulted for the counties close to one another, in the same ecoregion, or in the same major land resource region as the calibration center. For the Smokies region, the correlations were significant but lower due to large influences of pine forests (suppressed spectral reflectance) in counties outside the hardwood-dominated calibration center. The method carries potential for estimating forest cover across the globe. It has special advantages in allowing the assessment of forest cover in highly fragmented landscapes, where individual AVHRR pixels (1 km2) are forested to varying degrees.

Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution ∼1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.

Subpixel measurement of tropical forest cover using AVHRR data

Abstract Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km2. For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km2 needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.