Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

Sustainable management of natural resources is an urgent need, given the changing climatic conditions of Earth systems. The ability to monitor natural resources precisely and accurately is increasingly important. New and advanced remote sensing tools and techniques are continually being developed to monitor and manage natural resources in an effective way. Remote sensing technology uses electromagnetic sensors to record, measure and monitor even small variations in natural resources. The addition of new remote sensing datasets, processing techniques and software makes remote sensing an exact and cost-effective tool and technology for natural resource monitoring and management. Advances in Remote Sensing for Natural Resources Monitoring provides a detailed overview of the potential applications of advanced satellite data in natural resource monitoring. The book determines how environmental and - ecological knowledge and satellite-based information can be effectively combined to address a wide array of current natural resource management needs. Each chapter covers different aspects of remote sensing approach to monitor the natural resources effectively, to provide a platform for decision and policy. This important work: Provides comprehensive coverage of advances and applications of remote sensing in natural resources monitoring Includes new and emerging approaches for resource monitoring with case studies Covers different aspects of forest, water, soil- land resources, and agriculture Provides exemplary illustration of themes such as glaciers, surface runoff, ground water potential and soil moisture content with temporal analysis Covers blue carbon, seawater intrusion, playa wetlands, and wetland inundation with case studies Showcases disaster studies such as floods, tsunami, showing where remote sensing technologies have been used This edited book is the first volume of the book series Advances in Remote Sensing for Earth Observation.

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

Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies to provide data for research and operational applications in a wide range of disciplines related to management of forest ecosystems. This book provides a comprehensive, state-of-the-art review of the research and application of ALS in a broad range of forest-related disciplines, especially forest inventory and forest ecology. However, this book is more than just a collection of individual contributions – it consists of a well-composed blend of chapters dealing with fundamental methodological issues and contributions reviewing and illustrating the use of ALS within various domains of application. The reviews provide a comprehensive and unique overview of recent research and applications that researchers, students and practitioners in forest remote sensing and forest ecosystem assessment should consider as a useful reference text.

Methods for obtaining accurate, spatially explicit estimates of biomass density in tropical forests are required to reduce uncertainties in the global carbon cycle, and to support international climate agreements and emerging carbon markets. Three-dimensional (3-D) remote sensing techniques sensitive to the vertical structure of vegetation provide a unique opportunity for mapping and monitoring forest carbon stocks across large areas in the tropics. However, approaches to forest biomass estimation from remotely sensed structure have yet to be fully developed to deliver the required biomass accuracy. In this research, we use airborne laser scanning (ALS), space-based lidar observations (ICESat/GLAS), and detailed in situ measurements made at the Tapajós National Forest, Brazil, to advance methods of biomass estimation in tropical regions from remotely sensed structure. The overall objectives were to (1) test and refine methods for the extraction of structural information from lidar data; (2) determine the accuracy of lidar estimates of structure in relation to detailed field measurements of vertical structure; and (3) develop and validate structure-based models for optimal prediction of aboveground biomass. Because remote sensing approaches to biomass estimation begin with field estimates of biomass, field plot data collected at Tapajós were also used to gain a better understanding of the uncertainty associated with plot-level biomass estimates obtained specifically for calibration of remote sensing data. This included an evaluation of the error resulting from spatial disagreement between field and remote sensing measurements (i.e., co-location error), and the error introduced when accounting for temporal differences in data acquisition. Results show that a new approach to biomass estimation based on Fourier transforms of lidar profiles significantly improves predictions of aboveground biomass ranging from 2 to 538 Mg ha−1 in primary and secondary forests. Data from two different regions in the Amazon were used to demonstrate and test this method in a range of conditions. The improvement in biomass estimation performance was consistent across sites and the approach was integrated in a multi-stage scaling strategy to biomass estimation to produce a wall-to-wall map of biomass across a large area in the Amazon.