Mapping soil degradation using remote sensing data and ancillary data: South-East Moravia, Czech Republic
| Autor: | Kateřina Zelenková, Tereza Zádorová, Daniel Žížala, Robert Minařík, Anna Juřicová |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Czech
Atmospheric Science 010504 meteorology & atmospheric sciences unsupervised classification 0211 other engineering and technologies 02 engineering and technology 01 natural sciences Chernozem remote sensing lcsh:Oceanography Soil retrogression and degradation South east lcsh:GC1-1581 Computers in Earth Sciences 021101 geological & geomatics engineering 0105 earth and related environmental sciences General Environmental Science Hydrology Applied Mathematics lcsh:QE1-996.5 Orthophoto orthoimage language.human_language lcsh:Geology Ancillary data Remote sensing (archaeology) Soil erosion language Erosion Environmental science Sentinel-2 |
| Zdroj: | European Journal of Remote Sensing, Vol 52, Iss 0, Pp 108-122 (2019) |
| ISSN: | 2279-7254 |
| DOI: | 10.1080/22797254.2018.1482524 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|