9783844310993 - Methods and Approaches of irrigated area mapping using Remote Sensing: Methods and Approaches of irrigated area mapping at various spatial resolutions using AVHRR, ... (9 resultados)

Condición: New. pp. 156.

Taschenbuch. Condición: Neu. Methods and Approaches of irrigated area mapping using Remote Sensing | Methods and Approaches of irrigated area mapping at various spatial resolutions using AVHRR, MODIS, and LANDSAT ETM+ data | Murali Krishna Gumma (u. a.) | Taschenbuch | 156 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844310993 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Methods and approaches of mapping irrigated areas at different resolutions using remote sensing data. Irrigated areas were mapped using: (a) AVHRR pathfinder 10-km data, (b) MODIS 500-m (c) MODIS 250-m and (d) Landsat 30-m data. The study was conducted in the Krishna River, India using satellite sensor data for nominal year 2000. The methods analyzing multiple sensors data, time-series and consisted of image segmentation approaches using SRTM data, creation of mega-file data cube, spectral matching techniques, ideal spectra data bank creation, class spectral generation, 2 dimensional brightness-greenness wetness (BGW) plot, space time-spiral curves, comprehensive protocols for class identification and labeling, resolving the mixed classes, calculation of sub-pixel areas (SPAs), and fuzzy classification accuracy assessments. The class identification protocols involved matching class spectra with ideal spectra to group classes, use of extensive ground truth data, innovative use of very high resolution Google Earth Data, and secondary data. The results clearly showed that finer the resolution greater was the irrigated areas. 156 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Gumma Murali KrishnaMurali has 12 yrs experience on internationally and author of 32 scientific papers and book chapters. His current research interests include Global rice mapping global land remote sensing Identify the best sites.

Condición: New. Print on Demand pp. 156 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 156.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Methods and approaches of mapping irrigated areas at different resolutions using remote sensing data. Irrigated areas were mapped using: (a) AVHRR pathfinder 10-km data, (b) MODIS 500-m (c) MODIS 250-m and (d) Landsat 30-m data. The study was conducted in the Krishna River, India using satellite sensor data for nominal year 2000. The methods analyzing multiple sensors data, time-series and consisted of image segmentation approaches using SRTM data, creation of mega-file data cube, spectral matching techniques, ideal spectra data bank creation, class spectral generation, 2 dimensional brightness-greenness wetness (BGW) plot, space time-spiral curves, comprehensive protocols for class identification and labeling, resolving the mixed classes, calculation of sub-pixel areas (SPAs), and fuzzy classification accuracy assessments. The class identification protocols involved matching class spectra with ideal spectra to group classes, use of extensive ground truth data, innovative use of very high resolution Google Earth Data, and secondary data. The results clearly showed that finer the resolution greater was the irrigated areas.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Methods and approaches of mapping irrigated areas at different resolutions using remote sensing data. Irrigated areas were mapped using: (a) AVHRR pathfinder 10-km data, (b) MODIS 500-m (c) MODIS 250-m and (d) Landsat 30-m data. The study was conducted in the Krishna River, India using satellite sensor data for nominal year 2000. The methods analyzing multiple sensors data, time-series and consisted of image segmentation approaches using SRTM data, creation of mega-file data cube, spectral matching techniques, ideal spectra data bank creation, class spectral generation, 2 dimensional brightness-greenness wetness (BGW) plot, space time-spiral curves, comprehensive protocols for class identification and labeling, resolving the mixed classes, calculation of sub-pixel areas (SPAs), and fuzzy classification accuracy assessments. The class identification protocols involved matching class spectra with ideal spectra to group classes, use of extensive ground truth data, innovative use of very high resolution Google Earth Data, and secondary data. The results clearly showed that finer the resolution greater was the irrigated areas.