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Comparison of Terrain Indices and Landform Classification Procedures in Low-Relief Agricultural Fields

机译:低洼农业区地形指数和地貌分类程序的比较

摘要

Landforms control the spatial distribution of numerous factors associated with agronomy and water quality. Although curvature and slope are the fundamental surface derivatives used in landform classification procedures, methodologies for landform classifications have been performed with other terrain indices including the topographic position index (TPI) and the convergence index (CI). The objectives of this study are to compare plan curvature, the convergence index, profile curvature, and the topographic position index at various scales to determine which better identifies the spatial variability of soil phosphorus (P) within three low relief agricultural fields in central Illinois and to compare how two methods of landform classification, e.g. Pennock et al. (1987) and a modified approach to the TPI method (Weiss 2001, Jenness 2006), capture the variability of spatial soil P within an agricultural field. Soil sampling was performed on a 0.4 ha grid within three agricultural fields located near Decatur, IL and samples were analyzed for Mehlich-3 phosphorus. A 10-m DEM of the three fields was also generated from a survey performed with a real time kinematic global positioning system. The DEM was used to generate rasters of profile curvature, plan curvature, topographic position index, and convergence index in each of the three fields at scales ranging from 10 m to 150 m radii. In two of the three study sites, the TPI (r ≥ -0.42) was better correlated to soil P than profile curvature (r ≤ 0.41), while the CI (r ≥ -0.52) was better correlated to soil P than plan curvature (r ≥ -0.45) in all three sites. Although the Pennock method of landform classification failed to identify footslopes and shoulders, which are clearly part of these fields’ topographic framework, the Pennock method (R² = 0.29) and TPI method (R² = 0.30) classified landforms that captured similar amounts of soil P spatial variability in two of the three study sites. The TPI and CI should be further explored when performing terrain analysis at the agricultural field scale to create solutions for precision management objectives.
机译:地形控制着与农学和水质相关的众多因素的空间分布。尽管曲率和坡度是地形分类程序中使用的基本表面导数,但已使用其他地形指数(包括地形位置指数(TPI)和会聚指数(CI))来进行地形分类的方法。这项研究的目的是在不同尺度上比较平面曲率,会聚指数,剖面曲率和地形位置指数,以确定哪个能更好地识别伊利诺伊州中部和北部三个低起伏农业田地中土壤磷的空间变异性。比较两种地貌分类方法,例如Pennock等。 (1987年)和TPI方法的改进方法(Weiss 2001,Jenness 2006),捕获了农田内空间土壤P的变异性。在位于伊利诺伊州迪凯特附近的三个农田内的0.4公顷网格上进行了土壤采样,并对样品中的Mehlich-3磷进行了分析。通过使用实时运动学全球定位系统进行的调查,还生成了三个字段的10 m DEM。 DEM用于在半径10 m至150 m范围内的三个场中的每一个中生成轮廓曲率,平面曲率,地形位置指数和会聚指数的栅格。在三个研究地点中的两个中,TPI(r≥-0.42)与土壤P的相关性比轮廓曲率(r≤0.41)好,而CI(r≥-0.52)与土壤P的相关性比平面曲率(r≥-0.52)好。 r≥-0.45)在所有三个站点中。尽管Pennock方法的地貌分类方法无法识别出明显属于这些田地地形框架的山坡和肩膀,但Pennock方法(R²= 0.29)和TPI方法(R²= 0.30)对捕获相似土壤P的地貌进行了分类。三个研究地点中有两个的空间变异性。在农业规模上进行地形分析时,应进一步探索TPI和CI,以创建精确管理目标的解决方案。

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