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

Scholand, Dominik ; Schmalz, Britta (2022):
Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring. (Publisher's Version)
In: Land, 10 (11), MDPI, e-ISSN 2073-445X,
DOI: 10.26083/tuprints-00021199,
[Article]

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Item Type: Article
Origin: Secondary publication via sponsored Golden Open Access
Status: Publisher's Version
Title: Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring
Language: English
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.

Journal or Publication Title: Land
Volume of the journal: 10
Issue Number: 11
Publisher: MDPI
Collation: 34 Seiten
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
600 Technik, Medizin, angewandte Wissenschaften > 630 Landwirtschaft, Veterinärmedizin
Divisions: 13 Department of Civil and Environmental Engineering Sciences > Institute of Hydraulic and Water Resources Engineering > Engineering Hydrology and Water Management
Date Deposited: 02 May 2022 11:05
Last Modified: 02 May 2022 11:05
DOI: 10.26083/tuprints-00021199
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-211994
Additional Information:

Keywords: soil erosion; USLE; P-factor; contouring; remote sensing; open data; image analysis; line segment detection

URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21199
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