9783330326033 - Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN de Tiwari, Sudheer Kumar; Saha, S. K.; Kumar, Suresh (6 resultados)

Paperback. Condición: Brand New. 60 pages. 8.66x5.91x0.14 inches. In Stock.

Taschenbuch. Condición: Neu. Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN | Sudheer Kumar Tiwari (u. a.) | Taschenbuch | 60 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9783330326033 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content. 60 pp. Englisch.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Tiwari Sudheer KumarMr. Sudheer Kumar Tiwari is working as Scientist in Andhra Pradesh Space Applications Centre (APSAC), Planning Department, Govt. of Andhra Pradesh. He has completed his M.Tech. in Remote Sensing and GIS with disti.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 60 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Soil organic carbon (SOC) is an important and reliable indicator of soil quality. In this study, soil spectra were characterized and analyzed to predict the spatial SOC content, using multivariate predictive modeling technique-artificial neural network (ANN). EO1-Hyperion (400 - 2500 nm) hyper-spectral image, field and laboratory scale data sets (350 - 2500 nm) were generated, consisting of laboratory estimated SOC content of collected soil samples (dependent variable) and their corresponding reflection data of SOC sensitive spectral bands (predictive variables). For each data set, ANN predictive models were developed and three data set (image-scale, field-scale and lab-scale) revealed significant network performances for training, testing and validation, indicating a good network generalization for SOC content. ANN based analysis showed high prediction of SOC content at image (R2 = 0.93, and RPD = 3.19), field (R2 = 0.92 and RPD = 3.17), and lab scale (R2 = 0.95 and RPD = 3.16). Validation results of ANN indicated that predictive models performed well (R2 = 0.90) with RMSE 0.070. The result showed that ANN methods have a great potential for estimating SOC content.