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Remote Sensing Application - Regional Perspectives in Agriculture and Forestry (Hardcover, 1st ed. 2022)
Loot Price: R5,392
Discovery Miles 53 920
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Remote Sensing Application - Regional Perspectives in Agriculture and Forestry (Hardcover, 1st ed. 2022)
Series: New Frontiers in Regional Science: Asian Perspectives, 59
Expected to ship within 10 - 15 working days
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This book focuses solely on the issues of agriculture and forest
productivity analysis with advanced modeling approaches to bring
solutions to food-insecure regions of South and Southeast Asia.
Advanced modeling tools and their use in regional planning provide
an outstanding opportunity to contribute toward food production and
environments. In this book, leading-edge research methodologies
related to remote sensing and geospatial variability of soil,
water, and regional agricultural production indicators and their
applications are introduced together-a unique feature of the book
is the domain of regional policy perspectives and allied fields. In
regional policy planning, agriculture and forestry have a key role
in food security and environmental conservation that depends on the
geo-spatial variability of these factors. Over the years, nature
and climate have determined the variability of soil type, soil
quality, geographical deviation for habitat, water quality, water
sources, urban influences, population growth, carbon stock levels,
and water resources with rain-fed or irrigated land use practices.
In addition, human nutritional values and dietary habits have
brought cultural adaptation of either mono- or multi-cropping
patterns in the region. 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.
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