Evaluation and predictability of water erosion based on spectral information analysis

Rabah, Zouhaier Ben; Farah, Imed Riadh

doi:10.1109/atsip.2016.7523157

Cited by 2 publications

(1 citation statement)

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“…Fulajtár (2001) noted that additional ancillary data is needed for the classification of erosion patterns with required accuracy, and other authors have also recommended a combination of automatic classification approach and visual interpretation (Báčová & Krása, 2016;Pilesjoe, 1992;Šarapatka & Netopil, 2010;Smetanová, 2009). 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).…”

Section: Introductionmentioning

confidence: 99%

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

Žížala

Juřicová

Zádorová

et al. 2018

European Journal of Remote Sensing

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Data on the real extent of soil that is degraded by erosion represent important information for the purposes of conservation policy. However, this type of data is rarely available for large areas. A remote-sensing-based method for identifying of eroded areas at the regional scale has been tested using a combination of time series of free access Sentinel-2 image data, airborne orthoimages and ground-truth data. The unsupervised classification ISODATA of the Sentinel-2A images has been performed. The minimum distance method has been applied for the assignment of unsupervised classes to four erosion classes using the ground-truth data. The automatic classification of eroded soils achieved an overall accuracy of 55.2% for three distinguished classes. An accumulated class has been eliminated as no unsupervised classes were assigned to this erosion class. A simplified classification of two classes (strongly eroded and other soils) reached an accuracy of 80.9%. The overall accuracy of the simplified classification increased to 86.9% after the visual refinement using orthoimages. This study shows the potential of the tested approach to produce valuable data on actual soil degradation by erosion. The limitations of the method are related to the soil cover variability, masking effect of clouds, vegetation or litter and the spectral separability of individual classes.

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