Assessment of Soil Degradation by Erosion Based on Analysis of Soil Properties Using Aerial Hyperspectral Images and Ancillary Data, Czech Republic

Abstract

Abstract:The assessment of the soil redistribution and real long-term soil degradation due to erosion on agriculture land is still insufficient in spite of being essential for soil conservation policy. Imaging spectroscopy has been recognized as a suitable tool for soil erosion assessment in recent years. In our study, we bring an approach for assessment of soil degradation by erosion by means of determining soil erosion classes representing soils differently influenced by erosion impact. The adopted methods include extensive field sampling, laboratory analysis, predictive modelling of selected soil surface properties using aerial hyperspectral data and the digital elevation model and fuzzy classification. Different multivariate regression techniques (Partial Least Square, Support Vector Machine, Random forest and Artificial neural network) were applied in the predictive modelling of soil properties. The properties with satisfying performance (R 2 > 0.5) were used as input data in erosion classes determination by fuzzy C-means classification method. The study was performed at four study sites about 1 km 2 large representing the most extensive soil units of the agricultural land in the Czech Republic (Chernozems and Luvisols on loess and Cambisols and Stagnosols on crystalline rocks). The influence of site-specific conditions on prediction of soil properties and classification of erosion classes was assessed. The prediction accuracy (R 2 ) of the best performing models predicting the soil properties varies in range 0.8-0.91 for soil organic carbon content, 0.21-0.67 for sand content, 0.4-0.92 for silt content, 0.38-0.89 for clay content, 0.73-089 for Fe ox , 0.59-0.78 for F ed and 0.82 for CaCO 3 . The performance and suitability of different properties for erosion classes' classification are highly variable at the study sites. Soil organic carbon was the most frequently used as the erosion classes' predictor, while the textural classes showed lower applicability. The presented approach was successfully applied in Chernozem and Luvisol loess regions where the erosion classes were assessed with a good overall accuracy (82% and 67%, respectively). The model performance in two Cambisol/Stagnosol regions was rather poor (51%-52%). The results showed that the presented method can be directly and with a good performance applied in pedologically and geologically homogeneous areas. The sites with heterogeneous structure of the soil cover and parent material will require more precise local-fitted models and use of further auxiliary information such as terrain or geological data. The future application of presented approach at a regional scale promises to produce valuable data on actual soil degradation by erosion usable for soil conservation policy purposes.

Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic

“…• 50 samples from the site Sardice field survey performed in 2016 (Hrabalíková et al, 2016;Žížala et al, 2017 2016. This part of the dataset was used as a calibration set for the classification, or more precisely, as support data for supervised cluster categorization during post-processing after unsupervised classification.…”

“…The basic principle of the identification of eroded and accumulated (AC) soils is based on the assumption that the spectral reflectance of eroded or AC soils has different attributes than non-eroded (NE) "healthy" soils. These differences are caused by changes in the chemical and physical properties of the upper layer of soils due to the processes connected with soil removal, soil transportation and accumulation (Chabrillat et al, 2014;Chappell, Zobeck, & Brunner, 2005;Conforti et al, 2013;Demattê & Focht, 1999;Lin, Zhou, Wu, Zhu, & Dang, 2014;Schmid et al, 2012Schmid et al, , 2016Žížala, Zádorová, & Kapička, 2017). Soil properties affected by erosion processes that have a spectral response in soil spectra at the same time can be identified as spectral soil erosion indicators.…”

“…Soil properties affected by erosion processes that have a spectral response in soil spectra at the same time can be identified as spectral soil erosion indicators. These properties are either affected by the preferential removal and transportation of light surface particles, such as soil organic matter content and soil texture (Schmid et al, 2016), or are related to the removal of topsoil and its mixture with subsoil, such as the content of carbonates (Curzio & Magliulo, 2010b;Žížala et al, 2017), content of iron oxides (Chabrillat, 2006;Frazier & Cheng, 1989;Lin et al, 2014;Mathieu, Cervelle, Rémy, & Pouget, 2007), or content of coarse fragments (Hill, Mégier, & Mehl, 1995;Hill, Mehl, & Altherr, 1994;Hill & Schütt, 2000). The possibility and accuracy of the delineation of eroded soils using spectral images is highly dependent on the intensity of the erosion processes on the one hand, and on the corresponding changes in the spectral characteristic of disturbed soils on the other hand.…”

“…The application of the fuzzy classification (Meléndez-Pastor, Pedreño, Lucas, & Zorpas, 2017), the spectral mixture (sub-pixel) analysis (Haboudane, Bonn, Royer, Sommer, & Mehl, 2002;Rabah & Farah, 2016;Schmid et al, 2016) or the object classification ("spatio-contextual" image classification) (Mayr, Rutzinger, Bremer, & Geitner, 2016;Nobrega et al, 2006;Wang, Huang, Du, Hu, & Han, 2013) represents another solution to overcome the above mentioned difficulties related to the pixelbased methods (Li et al, 2014). Simultaneously, using higher resolution data in the spectral domain (hyperspectral data) promises an increase in the accuracy of the classification of erosion-degraded soils (Chabrillat et al, 2003(Chabrillat et al, , 2014Haubrock, Chabrillat, & Kaufmann, 2004Hill et al, 1994;Schmid et al, 2016;Žížala et al, 2017). The further development and wider application of this method can be expected with the forthcoming spaceborne hyperspectral sensors, such as German EnMAP, Italian PRISMA, Japanese HISUI, Israel-Italian SHALOM or Chinese TianGong-1 (Demattê et al, 2015).…”

Untitled

“…First-derivative (FD), second-derivative (SD), absorbance (A), continuum-removal (CR) are effective preprocessing methods that are important spectral significance in the field of spectral analysis because they can eliminate background noise to some extent (Cheng et al 2019). These methods enhance spectral absorption and reflection characteristics (Liu & Han 2017;Žížala et al 2017). Effective pretreatment helps capture subtle differences in spectral data and improves the estimation accuracy of surface parameters.…”