Advances in Environmental Remote Sensing

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advanced remote sensing techniques for research
Aerosol Optical Depth
Aerosol Optical Thickness
Airborne Lidar
area
atmospheric correction algorithms
Biophysical Environment
Canopy Water Content
Category=KNA
Category=PD
Data Sets
digital change detection
ecosystem spatial analysis
Ecosystem structure
Environmental remote sensing
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Forest Change Analysis
Global Croplands
hyperspectral image correction
imaging
Impervious Surface
Impervious Surface Image
index
Land Cover Change Product
Landsat Imagery for Vegetation Studies
Landsat TM Imagery
LCLU Change
LCLU Type
leaf
LiDAR Data
Lidar Remote Sensing
Live Fuel Moisture
LSWI
moderate
MODIS EVI
Non-algal Particles
object
Object Based Image Analysis
Red NIR
reecting
Remotely Sensed Data
resolution
Satellite-Based Modeling
Spectral Mixture Analysis
Spectral Reectance
spectroradiometer
Surface Reectance
topographic correction methods
UHI
Urban areas
urban environmental monitoring
USGS National Land Cover Database
vegetation
Vegetation Index

Product details

  • ISBN 9781138072916
  • Weight: 1130g
  • Dimensions: 178 x 254mm
  • Publication Date: 29 Mar 2017
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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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 analyst’s 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 Earth’s 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.