手法を比較

	Universal Soil Loss Equation	HEC-RAS	NDVI
分野	地球物理学	地球物理学	地球物理学
系統	Process / pipeline	Process / pipeline	Process / pipeline
提唱年	1978	1995	1973
提唱者	Waldo Wischmeier and Dwight Smith	US Army Corps of Engineers Hydrologic Engineering Center	Rouse, Haas, Schell, and Deering
種類	Empirical soil erosion prediction model	1D/2D river hydraulics and flood inundation modeling	Spectral index for vegetation assessment
原典	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.
別名	USLE, Revised USLE	HEC-RAS	NDVI
関連	3	3	3
概要	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.