Estimation Of Annual Average Soil Loss: An Application Of Remote Sensing And Gis. Cet article n’est pas disponible.
Langue : anglais
Edité par Lap Lambert Academic Publishing, 2013
- Livre broché
- Neuf
Vendeur : Revaluation Books, Exeter, Royaume-UniRevaluation Books
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A propos de cet article
68 pages. 8.66x5.91x0.16 inches. In Stock.
N° de réf. du vendeur 3659466204
- Titre
- Estimation Of Annual Average Soil Loss: An Application Of Remote Sensing And Gis
- Auteur
- Mandal, Sakti; Mandal, Sakti
- Éditeur
- Lap Lambert Academic Publishing
- Année de publication
- 2013
- État de l'article
- Brand New
- Reliure
- Paperback
- Langue
- anglais
- ISBN à 10 chiffres
- 3659466204
- ISBN à 13 chiffres
- 9783659466205
- Poids de l'article
- 0,15 kilogramme
Remote Sensing (RS) and Geographic Information Systems (GIS) are useful tools in hydrological analysis and natural resource management. The application of RS and GIS techniques leads to estimate soil loss based on different parameters. RUSLE (Revised Universal Soil Loss Equation) model is used for soil loss estimation. Different parameters, namely the rainfall and runoff factor (R), soil erodibility factor (K), slope length and steepness factor (LS), crop management factor (C) and conservation practice factor (P), that are the mandatory inputs to RUSLE, have been either derived from remote sensing data or through conventional data collection systems.
« Synopsis » peut appartenir à une autre édition de cet ouvrage.
Présentation de l'éditeur
Remote Sensing (RS) and Geographic Information Systems (GIS) are useful tools in hydrological analysis and natural resource management. The application of RS and GIS techniques leads to estimate soil loss based on different parameters. RUSLE (Revised Universal Soil Loss Equation) model is used for soil loss estimation. Different parameters, namely the rainfall and runoff factor (R), soil erodibility factor (K), slope length and steepness factor (LS), crop management factor (C) and conservation practice factor (P), that are the mandatory inputs to RUSLE, have been either derived from remote sensing data or through conventional data collection systems.
« A propos de ce titre » peut appartenir à une autre édition de cet ouvrage.