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- W2364751469 abstract "Based on Hyperion data,NDVI and partial least squares regression(PLS) are used to estimate the vegetation cover in desertified region.The predictive accuracy of NDVI and PLS regression models are then determined and compared using calibration(n=46) and test(n=10) data.The results show that both NDVI and PLS regression models can estimate vegetation cover effectively.Compared with broadband NDVI(RMSEP=10.5618) and standard hyperspectral NDVI involving bands at 803.3 nm and 671.02 nm(RMSEP=8.3863),the NDVI computed from bands at 823.65 nm and 701.55 nm has a lower standard error of prediction(RMSEP=6.5189).PLS regression models based on original,derivative and continuum-removed spectra,which utilize all bands of Hyperion data,produce lower prediction errors(RMSEP is between 4.4998~5.1449) than NDVIs,which justly use two bands of Hyperion data.The lowest prediction error(RMSEP=4.4998,23% of mean) is obtained by PLS regression analysis involving original reflectance." @default.
- W2364751469 created "2016-06-24" @default.
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- W2364751469 date "2011-01-01" @default.
- W2364751469 modified "2023-09-23" @default.
- W2364751469 title "Estimation of Vegetation Cover in Desertified Regions from Hyperion Imageries Using NDVI and Partial Least Squares Regression" @default.
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