Abstract
Land Cover Classification Accuracy Assessment Using Full-Waveform LiDAR Data
Highlights
Land cover is the physical material on the Earth’s surface, including grass, asphalt, trees, bare ground, and water
Orthorectified aerial images in the NIR band and FW LiDAR data were employed in this study to extract spectral features [i.e., the normalized differential vegetation index (NDVI)] and greenness index), geomorphometric features [i.e., the normalized digital surface model], slope, and aspect), waveform features from the LiDAR data [i.e., amplitude and pulse width (Lin and Mills 2010)], and their corresponding textural features
Because the reflected waveforms are affected by the type of material and properties of detected land objects, the waveform features can be applied to analyze specific characteristics associated with land-cover classification (LCC)
Summary
Land cover is the physical material on the Earth’s surface, including grass, asphalt, trees, bare ground, and water. These terms are frequently used interchangeably, land cover is distinct from land use, which refers to how people use the land for socioeconomic activities (Comber et al 2005). Traditional methods for land-cover mapping include photogrammetry and remote sensing. The types of sensors used for land-cover mapping are passive (e.g., optical satellite or digital camera), active (e.g., radar or LiDAR), or a combination of both. The advantages of employing remote sensing technology to examine LULC include extensive range, immediacy and periodicity of the obtainable information. Remote sensing technology can be applied to monitor current changes in LULC (Chang et al 2012b)
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