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Since January 2020, the COVID-19 pandemic has been escalating from North America to Asia. Various studies projected the spread of pandemic globally, using air passenger data from an infected area. But there could be various parameters that can be the basis for the forecasting of the pandemic. Current research adopts the Medical GIS approach and incorporates critical parameters from various domains to create a global alertness scale to combat the pandemic. The finding of the study ranks the countries on a 1 to 9 scale based on the spatial alertness In this context, the study focuses on the role of GIS techniques as an enabler to fight against the global pandemic and could be beneficial for the authorities to adopt timely preventive actions.

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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.