Abstract
Southwest China is considered to be one of the three major karst regions in the world. Fractional vegetation cover (FVC) can be an important factor in delineating the various steps in the karst regional evolutionary process that is expected to results in rocky desertification. Remote sensing using unmanned aerial vehicles (UAVs) is a new method of investigating, extracting, and monitoring vegetation. In this method, error correction of the results of five other classification methods that involve imaging using visible-light bands (EXcess Green, Normalized Green-Red Difference Index, Normalized Green-Blue Difference Index, Red-Green Ratio Index and Visible-band Difference Vegetation Index)), it was found that the classification accuracy for nonvegetation areas is improved after combining the normalized difference vegetation indices of the remote sensing images.
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