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ARS Home » Southeast Area » Booneville, Arkansas » Dale Bumpers Small Farms Research Center » Research » Publications at this Location » Publication #401959

Research Project: Sustainable Small Farm and Organic Grass and Forage Production Systems for Livestock and Agroforestry

Location: Dale Bumpers Small Farms Research Center

Title: Identification and delineation of broad-base agricultural terraces in flat landscapes in Northeastern Oklahoma, USA

item Winzeler, Hans - Edwin
item Owens, Phillip
item Kharel, Tulsi
item Ashworth, Amanda
item Libohova, Zamir

Submitted to: Land
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/15/2023
Publication Date: 2/16/2023
Citation: Winzeler, H.E., Owens, P.R., Kharel, T.P., Ashworth, A.J., Libohova, Z. 2023. Identification and delineation of broad-base agricultural terraces in flat landscapes in Northeastern Oklahoma, USA. Land. 12(2):486.

Interpretive Summary: Broad base agricultural terraces are important features in flat landscapes of Oklahoma, as they influence water movement over and through soils, yield, and management strategies. In some cases they are difficult to delineate because of cropping patterns that may cover them and because they are only subtly variable from surrounding landscape features. We developed a computer-vision approach to delineate them automatically from digital elevation models. This technique can be broadly applied throughout northeastern Oklahoma to delineate terraces so their effect on soil and crop variability can be studied.

Technical Abstract: Broad base agricultural terraces can be difficult to delineate in flat landscapes, particularly when covered by crops, due to subtle changes in elevation over relatively wide distances. In northeastern Oklahoma, these terraces are usually less than half a meter high and 15 to 20 m wide. A technique for identifying and classifying terraces using computer vision applied to terrain derivatives cal-culated from digital elevation models of five sites is reported herein. We tested 38 terrain deriv-ative rasters representing 19 terrain derivatives, calculated using elevation models after two Gaussian smoothing strategies to provide some degree of generalization and removal of excess noise. The best subsets achieved 98% classification accuracy (kappa 0.96) and consisted of terrain derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies oc-curred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas with changes in elevation similar to elevation changes that characterize terraces. Further study will elucidate relationships between terrace “borrow” and “deposition” areas in the terrace areas and their relationships to yield and salinity issues. This work seeks to automate terrace identification for digital soil mapping on terraced fields for improved delivery of soil information for resource conservation and land use.