To encompass all these above mentioned factors and classify regional variability for policy planning, satellite remote sensing and geographical information systems have the immense potential to increase agricultural and forest productivity to ensure the resilience of its sustainability. Therefore, the 13 chapters presented in this book introduce modeling techniques using the signatures of vegetation and water indices, land use and land change dynamics, climatic, and socioeconomic criteria through spatial, temporal, and statistical analysis. As well, remote sensing and in-depth GIS analysis are integrated with machine and deep learning algorithms to address natural uncertainties such as flash floods, droughts, and cyclones in agricultural production management.
Tofael Ahamed is an associate professor from University of Tsukuba, Japan and recognized as one of the best faculty members of the university. His research focuses on smart application of remote sensing and AI-IoT technology in agriculture. Dr. Ahamed leads research supported by the Japanese Society for Promotion Science, University of Tsukuba and international companies. Most of his research articles are published in Transactions of ASABE, Biosystems Engineering, Computer and Electronics in Agriculture, Sensors, Remote Sensing and Asia-Pacific Journal of Regional Science. He is also the author of several textbooks: Bioproduction Engineering for Precision Agronomics, Sustainability and Data to Knowledges for Agricultural Research Methodologies.