Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring
The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/10/11/1279 |
| _version_ | 1797509650336186368 |
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| author | Dominik Scholand Britta Schmalz |
| author_facet | Dominik Scholand Britta Schmalz |
| author_sort | Dominik Scholand |
| collection | DOAJ |
| description | The P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD. |
| first_indexed | 2024-03-10T05:20:46Z |
| format | Article |
| id | doaj.art-7af6625caa9c4cbfad49f8727b20f3a1 |
| institution | Directory Open Access Journal |
| issn | 2073-445X |
| language | English |
| last_indexed | 2024-03-10T05:20:46Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj.art-7af6625caa9c4cbfad49f8727b20f3a12023-11-23T00:02:54ZengMDPI AGLand2073-445X2021-11-011011127910.3390/land10111279Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure ContouringDominik Scholand0Britta Schmalz1Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, 64287 Darmstadt, GermanyChair of Engineering Hydrology and Water Management, Technical University of Darmstadt, 64287 Darmstadt, GermanyThe P-factor for support practice of the Universal Soil Loss Equation (USLE) accounts for soil conservation measures and leads to a significant reduction in the modelled soil loss. However, in the practical application, the P-factor is the most neglected factor overall due to high effort for determining or lack of input data. This study provides a new method for automatic derivation of the main cultivation direction from seed rows and tramlines on agricultural land parcels using the Fast Line Detector (FLD) of the Open Computer Vision (OpenCV) package and open remote sensing data from Google Earth™. Comparison of the cultivation direction with the mean aspect for each land parcel allows the determination of a site-specific P-factor for the soil conservation measure contouring. After calibration of the FLD parameters, the success rate in a first application in the low mountain range Fischbach catchment, Germany, was 77.7% for 278 agricultural land parcels. The main reasons for unsuccessful detection were problems with headland detection, existing soil erosion, and widely varying albedo within the plots as well as individual outliers. The use of a corrected mask and enhanced parameterization offers promising improvements for a higher success rate of the FLD.https://www.mdpi.com/2073-445X/10/11/1279soil erosionUSLEP-factorcontouringremote sensingopen data |
| spellingShingle | Dominik Scholand Britta Schmalz Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring Land soil erosion USLE P-factor contouring remote sensing open data |
| title | Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring |
| title_full | Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring |
| title_fullStr | Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring |
| title_full_unstemmed | Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring |
| title_short | Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring |
| title_sort | deriving the main cultivation direction from open remote sensing data to determine the support practice measure contouring |
| topic | soil erosion USLE P-factor contouring remote sensing open data |
| url | https://www.mdpi.com/2073-445X/10/11/1279 |
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