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A Geoinformatics Approach to Water ErosionEarth Observations

A Geoinformatics Approach to Water Erosion: Earth Observations [To the soil conservationalist, remote sensing methodologies provide profuse and updated information about soil propertiesSoil properties, rock exposure, vegetationVegetation attributes, and built infrastructure characteristics. This chapter provides an overview of the main characteristics of remotely sensed images, and their use in mapping spatial properties of agricultural catchmentsCatchment. It offers several spectral indicesSpectral indices for quantifying soil and vegetationVegetation properties, using visible and near-infrared data, by means of algebraic band-ratio methods. Next, it describes soft and hard classificationClassification techniques, including pixel-based and object-based classificationObject-based classification. Another useful application suggests the use of classified remotely sensed data as a tool for computing and mapping runoffRunoff coefficients, as well as channel network data for guiding drainage determination. Finally, in the past decade, the use of high-resolution dronesDrone for land useLand use/land cover classificationClassification has become common in soil erosionSoil erosionstudies. Extracting DEMsDigital Elevation Model (DEM) and topographic properties has also become an important part in soil degradationSoil degradation analyses; in particular, change detectionChange detection of soil lossSoil loss and soil depositionDeposition can be used to estimate sediment budgets. The tools reviewed in this chapter further expand the notion that remotely sensed data can be used to provide evidence of the state and dynamics of a given catchmentCatchment—thereby providing a general framework for depicting and simulating the mechanisms of water erosionErosion in agricultural catchmentsCatchment.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Geoinformatics Approach to Water ErosionEarth Observations

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Publisher
Springer International Publishing
Copyright
© Springer Nature Switzerland AG 2022
ISBN
978-3-030-91535-3
Pages
151 –204
DOI
10.1007/978-3-030-91536-0_5
Publisher site
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Abstract

[To the soil conservationalist, remote sensing methodologies provide profuse and updated information about soil propertiesSoil properties, rock exposure, vegetationVegetation attributes, and built infrastructure characteristics. This chapter provides an overview of the main characteristics of remotely sensed images, and their use in mapping spatial properties of agricultural catchmentsCatchment. It offers several spectral indicesSpectral indices for quantifying soil and vegetationVegetation properties, using visible and near-infrared data, by means of algebraic band-ratio methods. Next, it describes soft and hard classificationClassification techniques, including pixel-based and object-based classificationObject-based classification. Another useful application suggests the use of classified remotely sensed data as a tool for computing and mapping runoffRunoff coefficients, as well as channel network data for guiding drainage determination. Finally, in the past decade, the use of high-resolution dronesDrone for land useLand use/land cover classificationClassification has become common in soil erosionSoil erosionstudies. Extracting DEMsDigital Elevation Model (DEM) and topographic properties has also become an important part in soil degradationSoil degradation analyses; in particular, change detectionChange detection of soil lossSoil loss and soil depositionDeposition can be used to estimate sediment budgets. The tools reviewed in this chapter further expand the notion that remotely sensed data can be used to provide evidence of the state and dynamics of a given catchmentCatchment—thereby providing a general framework for depicting and simulating the mechanisms of water erosionErosion in agricultural catchmentsCatchment.]

Published: Feb 17, 2022

Keywords: Classification; Drainage structure; Drones; Spectral indices; Remote sensing

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