Soil Organic Carbon Mapping Using Hyperspectral Remote Sensing and ANN
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Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Sparbuchladen, [3602074].
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. -, 27.06.2017, Taschenbuch, Neuware, 220x150x4 mm, 106g, 60, Internationaler Versand, offene Rechnung (Vorkasse vorbehalten), Selbstabholung und Barzahlung, PayPal, Banküberweisung.

Lieferung aus: Deutschland, Versandkosten nach: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Syndikat Buchdienst, [4235284].
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. 2017, Taschenbuch / Paperback, Neuware, H: 220mm, B: 150mm, 60, Internationaler Versand, Selbstabholung und Barzahlung, PayPal, offene Rechnung, Banküberweisung.

Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
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.

Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, European-Media-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: LAP Lambert Academic Publishing | 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. | Format: Paperback | Language/Sprache: english | 60 pp.

Lieferung aus: Deutschland, Versandfertig in 1 - 2 Werktagen, Versandkostenfrei.
Von Händler/Antiquariat, dodax-shop-eu.
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