Sammenlign metoder

	Universel Jordtabsligning	HEC-RAS	NDVI
Fagområde	Geofysik	Geofysik	Geofysik
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Oprindelsesår	1978	1995	1973
Ophavsperson	Waldo Wischmeier and Dwight Smith	US Army Corps of Engineers Hydrologic Engineering Center	Rouse, Haas, Schell, and Deering
Type	Empirical soil erosion prediction model	1D/2D river hydraulics and flood inundation modeling	Spectral index for vegetation assessment
Oprindelig kilde	Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses: A guide to conservation planning. USDA Agricultural Handbook 537.	Brunner, G. W. (2010). HEC-RAS river analysis system hydraulic reference manual. US Army Corps of Engineers Hydrologic Engineering Center.	Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite Symposium Proceedings, 1, 309-317.
Aliasser	USLE, Revised USLE	HEC-RAS	NDVI
Relaterede	3	3	3
Resumé	The Universal Soil Loss Equation (USLE) is an empirical model that estimates annual soil loss due to sheet and rill erosion on hillslopes caused by rainfall and runoff. Developed by Wischmeier and Smith in 1978 from decades of erosion plot experiments, USLE has become a standard tool for erosion risk assessment, conservation planning, and best management practice design. The Revised USLE (RUSLE) updated the original model with improved factor algorithms.	HEC-RAS (Hydrologic Engineering Center River Analysis System) is a hydraulic modeling software developed by the US Army Corps of Engineers that computes water surface elevation and velocity in open channels and floodplains, and depicts inundation extent and depth. Since its introduction in 1995, HEC-RAS has become the de facto standard for floodplain delineation, dam break analysis, and flood risk assessment for regulatory and engineering purposes.	The Normalized Difference Vegetation Index (NDVI) is a spectral index computed from satellite or aerial multispectral imagery that quantifies vegetation greenness and vigor. Introduced by Rouse and colleagues in 1973 using Landsat data, NDVI has become the most widely used remote sensing metric for vegetation monitoring, drought assessment, crop productivity forecasting, and land cover change detection.