Sengupta somesh (3 risultati)

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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
Contatta il venditoreVenditore con 4 stelleCondizione: Usato - Come nuovo
EUR 155,54
EUR 29,96 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 1 disponibile
Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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- Print on Demand
Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 45,45
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sharma SachinSachin Sharma (Professional Environmentalist)did Environmental Science & Engineering in 2010 from ISM, Dhanbad, India. Completed Master of Science in 2007. Author of two more international books on the same study area. C. …

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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 62,49
EUR 35,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibile
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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%. …