Springer Spatial Modeling in Forest Resources Management: Rural Livelihood and Sustainable Development (Environmental Science and Engineering)

This book demonstrates the measurement, monitoring, mapping, and modeling of forest resources. It explores state-of-the-art techniques based on open-source software & R statistical programming and modeling specifically, with a focus on the recent trends in data mining/machine learning techniques and robust modeling in forest resources. Discusses major topics such as forest health assessment, estimating forest biomass & carbon stock, land use forest cover (LUFC), dynamic vegetation modeling (DVM) approaches, forest-based rural livelihood, habitat suitability analysis, biodiversity and ecology, and biodiversity, the book presents novel advances and applications of RS-GIS and R in a precise and clear manner. By offering insights into various concepts and their importance for real-world applications, it equips researchers, professionals, and policy-makers with the knowledge and skills to tackle a wide range of issues related to geographic data, including those with scientific, societal, and environmental implications.   From the Back Cover This book demonstrates the measurement, monitoring, mapping, and modeling of forest resources. It explores state-of-the-art techniques based on open-source software & R statistical programming and modeling specifically, with a focus on the recent trends in data mining/machine learning techniques and robust modeling in forest resources. Discusses major topics such as forest health assessment, estimating forest biomass & carbon stock, land use forest cover (LUFC), dynamic vegetation modeling (DVM) approaches, forest-based rural livelihood, habitat suitability analysis, biodiversity and ecology, and biodiversity, the book presents novel advances and applications of RS-GIS and R in a precise and clear manner. By offering insights into various concepts and their importance for real-world applications, it equips researchers, professionals, and policy-makers with the knowledge and skills to tackle a wide range of issues related to geographic data, including those with scientific, societal, and environmental implications.   About the Author Pravat Kumar Shit is an Assistant Professor at the PG Department of Geography, Raja N. L. Khan Women’s College (Autonomous), West Bengal, India. He received his M.Sc & Ph.D. degrees in Geography from Vidyasagar University and PG Diploma in Remote Sensing & GIS from Sambalpur University. His research interests include soil erosion spatial modelling, groundwater, forest resources and natural resources mapping & modeling. He has published five books (three books in Springer) and more than 50 papers in peer-reviewed journals.  Hamid Reza Pourghasemi is an Associate Professor of Watershed Management Engineering at the College of Agriculture, Shiraz University, Iran. He holds a B.Sc, an M.Sc, and PhD in Watershed Management Engineering from the University of Gorgan, Iran, and Tarbiat Modares University, Iran, respectively. His main research interests are GIS-based spatial modeling using machine learning/data mining techniques in different fields, such as landslide, flood, gully erosion, forest fire, land subsidence, species distribution modeling, and groundwater/hydrology. He has published more than 100 peer-reviewed papers in high-quality journals. Also, he published two books with Springer. Pulakesh Das received his Ph.D. from the Indian Institution of Technology Kharagpur, India. Currently he is teaching as an Assistant Professor at the Department of Remote Sensing & GIS, Vidyasagar University, West Bengal, India. He completed his B.Sc in Physics and M.Sc in Remote Sensing & GIS at the same university. His research areas include land use and forest cover modeling, hydrological modeling, study of forest cover dynamics and climate change, and digital image processing. He has published more than 15 research articles in respected, peer-reviewed journals. Gouri Sankar Bhunia received his Ph.D. from the University of Calcutta,