Identification and Delineation of Broad-Base Agricultural Terraces in Flat Landscapes in Northeastern Oklahoma, USA

Hans Edwin Winzeler (), Phillip R. Owens, Tulsi Kharel, Amanda Ashworth and Zamir Libohova
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Hans Edwin Winzeler: USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA
Phillip R. Owens: USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA
Tulsi Kharel: USDA ARS, 141 Experiment Station Road, P.O. Box 350, Stoneville, MS 38776, USA
Amanda Ashworth: USDA ARS, Poultry Production and Product Safety Research Unit, 1260 W. Maple St., Fayetteville, AR 72701, USA
Zamir Libohova: USDA ARS, Dale Bumpers Small Farms Research Center, 6883 AR-23, Booneville, AR 72927, USA

Land, 2023, vol. 12, issue 2, 1-12

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. The objective of this research was to develop and test a technique for identifying and classifying terraces using computer vision applied to terrain derivatives calculated from digital elevation models at five sites. We tested 38 terrain-derivative grid combinations or sets that represented 19 terrain characteristics, calculated from elevation models after two Gaussian smoothing strategies to provide some degree of generalization and a removal of excess noise. The best subsets achieved a 98% classification accuracy (kappa 0.96) and consisted of derivatives representing hydrology, morphometry, and visibility categories. Inaccuracies occurred primarily at the edges of some of the study sites, where agricultural fields bordered incised drainage areas where changes in elevation were similar to those for the terraces. Further study will elucidate the 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 the improved delivery of soil information for resource conservation and land use.

Keywords: terrain derivatives; computer vision; digital elevation models; terraces; classification; topographic wetness index; Oklahoma; soils; broad-base terraces (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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