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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.
In: Land, 2022, 10 (11)
doi: 10.26083/tuprints-00021199
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Deriving the Main Cultivation Direction from Open Remote Sensing Data to Determine the Support Practice Measure Contouring
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Land
Volume of the journal: 10
Issue Number: 11
Collation: 34 Seiten
DOI: 10.26083/tuprints-00021199
Corresponding Links:
Origin: Secondary publication via sponsored Golden Open Access
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.

Status: Publisher's Version
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

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 630 Agriculture, veterinary medicine
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: 23 Aug 2022 07:34
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21199
PPN: 494149167
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