TY - JOUR
T1 - Airborne imaging spectroscopy for assessing land-use effect on soil quality in drylands
AU - Levi, Nathan
AU - Karnieli, Arnon
AU - Paz-Kagan, Tarin
N1 - Funding Information:
This project has received funding from the European Union's Horizon 2020 research and innovation program “European Long-Term Ecosystem, Critical Zone, and Socio-Ecological systems Research Infrastructure PLUS” (eLTER PLUS) under grant agreement no. 871128. The authors wish to thank Mr. Alexander Goldberg for soil collection, analysis, and logistical support in the field and laboratory work, Dr. Natalia Panov and Dr. Jisung Chang for hyperspectral images preprocessing, and Mr. Vladislav Dubinin for statistical and methodological advice.
Publisher Copyright:
© 2022
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Global population growth has resulted in land-use (LU) changes in many natural ecosystems, causing deterioration in the environmental conditions that affect soil quality. The effect of LU on soil quality is acute in water-limited systems that are characterized by insufficient availability of soil organic resources. Thus, the main objective of this study was to assess the effects of human activities (i.e., land-uses as grazing, modern agriculture, and runoff harvesting systems) on soil quality using imaging spectroscopy (IS) in the arid regions of Israel. For this, 12 physical, biological, and chemical soil properties were selected and further integrated into the soil quality index (SQI) as a method to assess the significant effects of LU changes in an arid area in southern Israel. A flight campaign of the AisaFENIX hyperspectral airborne sensor was used to develop an IS prediction model for the SQI on a regional scale. The spectral signatures, extracted from the hyperspectral image itself, were well separable among the four LUs using the partial least squares-discriminant analysis (PLS-DA) classification method (OA = 95.31%, Kc = 0.90). The correlation was performed using multivariate support vector machine-regression (SVM-R) models between the spectral data and the measured soil indicators and the overall SQI. The SVM-R models were significantly correlated for several soil properties, including the overall SQI (R2adjVal = 0.87), with the successful prediction of the regional SQI mapping (R2adjPred = 0.78). Seven individual soil properties, including fractional sand and clay, SOM, pH, EC, SAR, and P, were successfully used for developing prediction maps. Applying IS, and statistically integrative methods for comprehensive soil quality assessments enhances the prediction accuracy for monitoring soil health and evaluating degradation processes in arid environments. This study establishes a precise tool for sustainable and efficient land management and could be an example for future potential IS earth-observing space missions for soil quality assessment studies and applications.
AB - Global population growth has resulted in land-use (LU) changes in many natural ecosystems, causing deterioration in the environmental conditions that affect soil quality. The effect of LU on soil quality is acute in water-limited systems that are characterized by insufficient availability of soil organic resources. Thus, the main objective of this study was to assess the effects of human activities (i.e., land-uses as grazing, modern agriculture, and runoff harvesting systems) on soil quality using imaging spectroscopy (IS) in the arid regions of Israel. For this, 12 physical, biological, and chemical soil properties were selected and further integrated into the soil quality index (SQI) as a method to assess the significant effects of LU changes in an arid area in southern Israel. A flight campaign of the AisaFENIX hyperspectral airborne sensor was used to develop an IS prediction model for the SQI on a regional scale. The spectral signatures, extracted from the hyperspectral image itself, were well separable among the four LUs using the partial least squares-discriminant analysis (PLS-DA) classification method (OA = 95.31%, Kc = 0.90). The correlation was performed using multivariate support vector machine-regression (SVM-R) models between the spectral data and the measured soil indicators and the overall SQI. The SVM-R models were significantly correlated for several soil properties, including the overall SQI (R2adjVal = 0.87), with the successful prediction of the regional SQI mapping (R2adjPred = 0.78). Seven individual soil properties, including fractional sand and clay, SOM, pH, EC, SAR, and P, were successfully used for developing prediction maps. Applying IS, and statistically integrative methods for comprehensive soil quality assessments enhances the prediction accuracy for monitoring soil health and evaluating degradation processes in arid environments. This study establishes a precise tool for sustainable and efficient land management and could be an example for future potential IS earth-observing space missions for soil quality assessment studies and applications.
KW - Agriculture
KW - Arid environment
KW - Grazing
KW - Runoff harvesting system
KW - Soil quality index
KW - Support vector machine-regression
UR - http://www.scopus.com/inward/record.url?scp=85124606219&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2022.01.018
DO - 10.1016/j.isprsjprs.2022.01.018
M3 - Article
AN - SCOPUS:85124606219
VL - 186
SP - 34
EP - 54
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -
