The purpose of this book is to explore the utility of remotely sensed data acquired for land-use /land-cover (LULC) classifications. The Resourcesat-1 Data is a high resolution (i.e. 5.8-meter), multispectral (3 bands: red, green and near infrared) dataset. Resourcesat-1 imagery has been selected for this study because of its low cost and potential for small scale land use and land cover classifications similar to the success the Landsat (30 meter, multispectral: 7 band) imagery has achieved with large scale classifications ...
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The purpose of this book is to explore the utility of remotely sensed data acquired for land-use /land-cover (LULC) classifications. The Resourcesat-1 Data is a high resolution (i.e. 5.8-meter), multispectral (3 bands: red, green and near infrared) dataset. Resourcesat-1 imagery has been selected for this study because of its low cost and potential for small scale land use and land cover classifications similar to the success the Landsat (30 meter, multispectral: 7 band) imagery has achieved with large scale classifications. The study has been done using a subdivision in Jharia Coal Field region i.e. Jharia town and the surrounding (rural) property. Supervised (parametric and nonparametric) classification procedures were conducted for the Jharia town area using ERDAS Imagine 9.2. Random sample points were generated for accuracy assessment via a ground based visual assessment of each point's LULC class. By using a 6 class LULC scheme, a supervised classification of the Resourcesat-1 imagery resulted in classification accuracy of 83.07%.
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Choose your shipping method in Checkout. Costs may vary based on destination.
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