A spectral index for highlighting forest cover from remotely sensed imagery

Forest cover maps are essential for current researches of biomass estimation and global change, but traditional methods to derive forest maps are complex. These methods usually need training samples or other ancillary data as input, and are time- and labor- consuming for large scale applications. To make the process of forest cover mapping simple and rapid, in this paper, a simple spectral index called forest index (FI) was proposed to highlight forest land cover in Landsat scenes. The FI is derived from three bands, green, red and near-infrared (NIR) bands and an FI image can be classified into forest/non-forest map with a threshold. The overall accuracies of classification maps in the two study areas were 97.8% and 96.2%, respectively, which indicates that the FI is efficient at highlighting forest cover.

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