remote sensing (source)
Paz-Kagan, T., The Remote Sensing Laboratory, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Israel
Zaady, E., Department of Natural Resources, Agricultural Research Organization, Gilat Research Center, 85280, Israel
Salbach, C., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Schmidt, A., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Lausch, A., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Zacharias, S., Department for Monitoring and Exploration Technologies, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Notesco, G., Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Ben-Dor, E., Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Karnieli, A., The Remote Sensing Laboratory, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Israel
Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale. © 2015 by the authors.
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הספר "אוצר וולקני"
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תנאי שימוש
Mapping the spectral soil quality index (SSQI) using airborne imaging spectroscopy
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Paz-Kagan, T., The Remote Sensing Laboratory, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Israel
Zaady, E., Department of Natural Resources, Agricultural Research Organization, Gilat Research Center, 85280, Israel
Salbach, C., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Schmidt, A., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Lausch, A., Department of Computational Landscape Ecology, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Zacharias, S., Department for Monitoring and Exploration Technologies, UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany
Notesco, G., Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Ben-Dor, E., Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Karnieli, A., The Remote Sensing Laboratory, Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus, Israel
Mapping the spectral soil quality index (SSQI) using airborne imaging spectroscopy
Soil quality (SQ) assessment has numerous applications for managing sustainable soil function. Airborne imaging spectroscopy (IS) is an advanced tool for studying natural and artificial materials, in general, and soil properties, in particular. The primary goal of this research was to prove and demonstrate the ability of IS to evaluate soil properties and quality across anthropogenically induced land-use changes. This aim was fulfilled by developing and implementing a spectral soil quality index (SSQI) using IS obtained by a laboratory and field spectrometer (point scale) as well as by airborne hyperspectral imaging (local scale), in two experimental sites located in Israel and Germany. In this regard, 13 soil physical, biological, and chemical properties and their derived soil quality index (SQI) were measured. Several mathematical/statistical procedures, consisting of a series of operations, including a principal component analysis (PCA), a partial least squares-regression (PLS-R), and a partial least squares-discriminate analysis (PLS-DA), were used. Correlations between the laboratory spectral values and the calculated SQI coefficient of determination (R2) and ratio of performance to deviation (RPD) were R2 = 0.84; RPD = 2.43 and R2 = 0.78; RPD = 2.10 in the Israeli and the German study sites, respectively. The PLS-DA model that was used to develop the SSQI showed high classification accuracy in both sites (from laboratory, field, and imaging spectroscopy). The correlations between the SSQI and the SQI were R2 = 0.71 and R2 = 0.7, in the Israeli and the German study sites, respectively. It is concluded that soil quality can be effectively monitored using the spectral-spatial information provided by the IS technology. IS-based classification of soils can provide the basis for a spatially explicit and quantitative approach for monitoring SQ and function at a local scale. © 2015 by the authors.
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