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• W2342397389 abstract "Timely and accurate information about land cover is an important and extensively used application of remote sensingdata. After successful launch of Landsat 8 is providing a new data source for monitoring land cover, which has thepotential to improve the earth surface features characterization. Mapping of Leaf area Index (LAI) in larger area may beimpossible when we rely on field measurements. Remote sensing data have been continuing efforts to develop differentmethods to estimate LAI. In this present study, an attempt has been made to discriminate various land cover features andempirical equation is used for retrieve biophysical parameter (LAI) for satellite NDVI data. Support vector machineclassification was performed for Muzaffarnagar district using LANDSAT 8 operational land imager data to separate outmajor land cover classes (water, fallow, built up, sugarcane, orchard, dense vegetation and other crops). Ground truthdata was collected using JUNO GPS which was used in developing the spectral signatures for each classes. The LAI-NDVIexisting empirical equation is used to prepare LAI map. It is found that the LAI values in village foloda regionmaximum LAI pixels in the range 3.10 and above and minimum in the range 1.0 to 1.20. It is also concluded that theLAI values between 1.70 and 3.10 is having most of the sugarcane crop pixels at maximum vegetative growth stage. Itshows that the sugarcane crop condition in the study area was very good." @default.
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• W2342397389 date "2016-04-30" @default.
• W2342397389 modified "2023-09-25" @default.
• W2342397389 title "Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data" @default.
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• W2342397389 doi "https://doi.org/10.1117/12.2224430" @default.
• W2342397389 hasPublicationYear "2016" @default.
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