Abstract
Sustainable management of natural forest resources is a vital requirement in the changing climatic conditions on Earth. Two advances techniques, hyperspectral remote sensing (HRS) and LIDAR (light detection and ranging) remote sensing (LRS), provide more enhanced and accurate measurements than that of conventional optical remote sensing (ORS). Hyperspectral sensor like AVIRIS, which has hundreds of narrow bands, have advantages over a broadband multispectral sensor. In addition, the fusion of HRS and LRS can play an essential role in assessing biophysical and biochemical variables of forest species. In this chapter, the authors reviewed the extant literature and tried to understand the position of HRS, LRS, and their integration with the machine and deep learning algorithms for the effective monitoring and management of natural forest resources. Further, scopes and challenges are also discussed to enhance the effectiveness of these techniques in natural forest resources management.
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