In this edited volume, information on climate-induced impacts for flooding, flash floods, and drought impact on agricultural crops is provided to address possible solutions for food security in south Asia, southeast Asia, and some regions of Africa. Leading-edge research methodology is presented as it relates to remote sensing applications for regional science and allied fields. In regional policy planning, agriculture and forestry play key roles in food security along with environmental conservation and depend on geo-spatial variability. Satellite remote sensing and geographical information systems have an immense potential to encompass all these factors and to catalogue the regional variability of climate change and climate economics. In the satellite remote sensing domain, advanced modeling tools, deep learning applications, and cloud-based earth engines significantly increase the flexibility of decision making and its application for regional perspectives. The result can increase agricultural and forest productivity and ensure its resilience and sustainability.
The book's chapters introduce modeling techniques such as machine learning and fuzzy expert system using satellite remote sensing datasets based on cloud application. These methods assist regional planners to increase crop production, land use, and detection of changes in land cover in order to better understand their vulnerability to climate-related disaster. Furthermore, remote sensing and in-depth GIS analysis are integrated with machine learning to address natural uncertainties such as flash floods, droughts, and cyclones so that emergency responses for agricultural production management can be adopted more effectively.
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