Please use this identifier to cite or link to this item:
|Title:||Tropical Forest Area Measurement from Global Land-Cover Classifications: Inverse Calibration Models Based on Spatial Textures|
|Authors:||MAYAUX Philippe; LAMBIN Eric f.|
|Citation:||Remote Sensing of Environment vol. 59 p. 29-43|
|Type:||Articles in Journals|
|Abstract:||Retrieving area estimates from broad scale land-cover maps is unaccurate dude to the spatial aggregation. In a previous study, we tested a method to calibrate area estimates of tropical forest cover by inverting a model of the influence of the spatial fragmentation on the spatial aggregation bias. In this study, improvements of this previous model are sought by better accounting for the spatial variability using texture measures and by integrating spatial information in the mixed pixel estimator. The integration of spatial information into a correction model to retrieve fine resolution proportiones from coarse resolution data improves the reliability of the estimates by up to 35%.|
|JRC Institute:||Joint Research Centre Historical Collection|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.