Wildland fires are becoming one of the most critical environmental factors affecting a wide range of ecosystems worldwide. In Mediterranean ecosystems (including also South-Africa, California, parts of Chile and Australia), wildland fires are recurrent phenomena every summer, following the seasonal drought. As a result of changes in traditional land use practices, and the impact of recent climate warming, fires have more negative impacts in the last years, threatening lives, socio-economic and ecological values. The book describes the ecological context of fires in the Mediterranean ecosystems, and provides methods to observe fire danger conditions and fire impacts using Earth Observation and Geographic Information System technologies.

Highlighting new technologies, Remote Sensing of Natural Resources explores advanced remote sensing systems and algorithms for image processing, enhancement, feature extraction, data fusion, image classification, image-based modeling, image-based sampling design, map accuracy assessment and quality control. It also discusses their applications for

The ability to anticipate the impacts of global environmental changes on natural resources is fundamental to designing appropriate and optimised adaptation and mitigation strategies. However, this requires the scientific community to have access to reliable, large-scale information on spatio-temporal changes in the distribution of abiotic conditions and on the distribution, structure, composition, and functioning of ecosystems. Satellite remote sensing can provide access to some of this fundamental data by offering repeatable, standardised, and verifiable information that is directly relevant to the monitoring and management of our natural capital. This book demonstrates how ecological knowledge and satellite-based information can be effectively combined to address a wide array of current natural resource management needs. By focusing on concrete applied examples in both the marine and terrestrial realms, it will help pave the way for developing enhanced levels of collaboration between the ecological and remote sensing communities, as well as shaping their future research directions. Satellite Remote Sensing and the Management of Natural Resources is primarily aimed at ecologists and remote sensing specialists, as well as policy makers and practitioners in the fields of conservation biology, biodiversity monitoring, and natural resource management.

A volume in the three-volume Remote Sensing Handbook series, Remote Sensing of Water Resources, Disasters, and Urban Studies documents the scientific and methodological advances that have taken place during the last 50 years. The other two volumes in the series are Remotely Sensed Data Characterization, Classification, and Accuracies, and Land Reso

Volume III of the Six Volume Remote Sensing Handbook, Second Edition, is focused on agriculture; food security; vegetation; phenology; rangelands; soils; and global biomass modeling, mapping, and monitoring using multi-sensor remote sensing. It discusses the application of remote sensing in agriculture systems analysis, phenology, cropland mapping and modeling, terrestrial vegetation studies, physically based models, food and water security, precision farming, crop residues, global view of rangelands, and soils. This thoroughly revised and updated volume draws on the expertise of a diverse array of leading international authorities in remote sensing and provides an essential resource for researchers at all levels interested in using remote sensing. It integrates discussions of remote sensing principles, data, methods, development, applications, and scientific and social context. FEATURES Provides the most up-to-date comprehensive coverage of remote sensing science in agriculture, vegetation, and soil studies. Discusses and analyzes data from old and new generations of satellites and sensors spread across 60 years. Provides comprehensive assessment of modeling, mapping, and monitoring agricultural crops, vegetation, and soils from wide array of sensors, methods, and techniques. Includes numerous case studies on advances and applications at local, regional, and global scales. Introduces advanced methods in remote sensing such as machine learning, cloud computing, and AI. Highlights scientific achievements over the last decade and provides guidance for future developments. This volume is an excellent resource for the entire remote sensing and GIS community. Academics, researchers, undergraduate and graduate students, as well as practitioners, decision makers, and policymakers, will benefit from the expertise of the professionals featured in this book, and their extensive knowledge of new and emerging trends.

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research. Hyperspectral Remote Sensing of Vegetation integrates this knowledge, guiding readers to harness the capabilities of the most recent advances in applying hyperspectral remote sensing technology to the study of terrestrial vegetation. Taking a practical approach to a complex subject, the book demonstrates the experience, utility, methods and models used in studying vegetation using hyperspectral data. Written by leading experts, including pioneers in the field, each chapter presents specific applications, reviews existing state-of-the-art knowledge, highlights the advances made, and provides guidance for the appropriate use of hyperspectral data in the study of vegetation as well as its numerous applications, such as crop yield modeling, crop and vegetation biophysical and biochemical property characterization, and crop moisture assessment. This comprehensive book brings together the best global expertise on hyperspectral remote sensing of agriculture, crop water use, plant species detection, vegetation classification, biophysical and biochemical modeling, crop productivity and water productivity mapping, and modeling. It provides the pertinent facts, synthesizing findings so that readers can get the correct picture on issues such as the best wavebands for their practical applications, methods of analysis using whole spectra, hyperspectral vegetation indices targeted to study specific biophysical and biochemical quantities, and methods for detecting parameters such as crop moisture variability, chlorophyll content, and stress levels. A collective "knowledge bank," it guides professionals to adopt the best practices for their own work.