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Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring

Author

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  • Dominik Scholand

    (Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, 64287 Darmstadt, Germany)

  • Britta Schmalz

    (Chair of Engineering Hydrology and Water Management, Technical University of Darmstadt, 64287 Darmstadt, Germany)

Abstract

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.

Suggested Citation

  • Dominik Scholand & Britta Schmalz, 2021. "Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring," Land, MDPI, vol. 10(11), pages 1-34, November.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:11:p:1279-:d:684882
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    References listed on IDEAS

    as
    1. Krishna Bhandari & Jagannath Aryal & Rotchanatch Darnsawasdi, 2015. "A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 321-342, January.
    2. Ranghu Wang & Shuwen Zhang & Jiuchun Yang & Luoman Pu & Chaobin Yang & Lingxue Yu & Liping Chang & Kun Bu, 2016. "Integrated Use of GCM, RS, and GIS for the Assessment of Hillslope and Gully Erosion in the Mushi River Sub-Catchment, Northeast China," Sustainability, MDPI, vol. 8(4), pages 1-20, March.
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