Generating a satisfactory classification image from remote sensing data is not a straightforward task. Many factors contribute to this difficulty including the characteristics of a study area availability of suitable remote sensing data ancillary and ground reference data proper use of variables and classification algorithms and the analystâs experience. An authoritative text Advances in Environmental Remote Sensing: Sensors Algorithms and Applications compiles comprehensive review articles to examine the developments in concepts methods techniques and applications as well as focused articles and case studies on the latest on a particular topic. Divided into four sections the first deals with various sensors systems or sensing operations using different regions of wavelengths. Drawing on the data and lessons learned from the U. S. Landsat remote sensing programs it reviews key concepts methods and practical uses of particular sensors/sensing systems. Section II presents new developments in algorithms and techniques specifically in image preprocessing thematic information extraction and digital change detection. It gives correction algorithms for hyperspectral thermal and multispectral sensors discusses the combined method for performing topographic and atmospheric corrections and provides examples of correcting non-standard atmospheric conditions including haze cirrus and cloud shadow. Section III focuses on remote sensing of vegetation and related features of the Earthâs surface. It reviews advancements in the remote sensing of ecosystem structure process and function and notes important trade-offs and compromises in characterizing ecosystems from space related to spatial spectral and temporal resolutions of the imaging sensors. It discusses the mismatch between leaf-level and species-level ecological variables and satellite spatial resolutions and the resulting difficulties in validating satellite-derived products. Finally Section IV examines developments in the remote sensing of air water and other terrestrial features reviews MODIS algorithms for aerosol retrieval at both global and local scales and demonstrates the retrieval of aerosol optical thickness (AOT). This section rounds out coverage with a look at remote sensing approaches to measure the urban environment and examines the most important concepts and recent research. . Language: English