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30 April 2016 Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data
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Timely and accurate information about land cover is an important and extensively used application of remote sensing data. After successful launch of Landsat 8 is providing a new data source for monitoring land cover, which has the potential to improve the earth surface features characterization. Mapping of Leaf area Index (LAI) in larger area may be impossible when we rely on field measurements. Remote sensing data have been continuing efforts to develop different methods to estimate LAI. In this present study, an attempt has been made to discriminate various land cover features and empirical equation is used for retrieve biophysical parameter (LAI) for satellite NDVI data. Support vector machine classification was performed for Muzaffarnagar district using LANDSAT 8 operational land imager data to separate out major land cover classes (water, fallow, built up, sugarcane, orchard, dense vegetation and other crops). Ground truth data was collected using JUNO GPS which was used in developing the spectral signatures for each classes. The LAI-NDVI existing empirical equation is used to prepare LAI map. It is found that the LAI values in village foloda region maximum LAI pixels in the range 3.10 and above and minimum in the range 1.0 to 1.20. It is also concluded that the LAI values between 1.70 and 3.10 is having most of the sugarcane crop pixels at maximum vegetative growth stage. It shows that the sugarcane crop condition in the study area was very good.
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Amit Kumar Verma, P. K. Garg, K. S. Hari Prasad, and V. K. Dadhwal "Study of land cover classes and retrieval of leaf area index using Landsat 8 OLI data", Proc. SPIE 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI, 988025 (30 April 2016);

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