Predictive modeling of bedrock outcrops and associated shallow soil in upland glaciated landscapes
Predictive modeling of bedrock outcrops and associated shallow soil in upland glaciated landscapes
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DOI：
10.1016/j.geoderma.2020.114495
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发表时间：
2020-10
期刊：
影响因子：
6.1
通讯作者：
Olivia L. Fraser;S. Bailey;M. Ducey;K. McGuire
中科院分区：
文献类型：
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作者：
Olivia L. Fraser;S. Bailey;M. Ducey;K. McGuire
确定基岩露头和浅层土壤的面积范围有重要的意义，了解空间格局的植被组成和生产力，流化学梯度，水文和土壤性质的景观。人工方法划定基岩露头和相关的浅土壤仍然普遍采用，但他们是昂贵的，广泛的地区实施，往往受到多边形单位的代表性。很少有研究自动描绘基岩露头。这些讨论侧重于在山坡迅速侵蚀和植被稀疏的地貌中的划界方法。本研究的目的是评估视觉解释高分辨率地形图的准确性，以定位基岩露头和相关的浅土壤（BOSS）<50厘米深，在茂密的森林景观，使用视觉解释点的位置来训练预测模型，并比较预测与人工划定的多边形在高地冰川景观。在Hubbard Brook实验森林（HBEF），美国激光雷达衍生的1米阴影地形图的视觉解释导致79%的解释深层土壤位置的准确性和84%的准确性区分BOSS。我们探讨了四个概率分类BOSS使用多个激光雷达衍生的地形指标作为预测变量。所有四种方法确定了类似的预测BOSS，包括坡度和地形位置指数与15，100和200米的圆形分析窗口，分别。虽然所有分类器都产生了类似的结果，在解释上几乎没有差异，但广义加法模型预测BOSS存在的准确性略高，使用主要研究区域的独立验证数据产生85%的总体准确性，在第二个验证区域的总体准确性为86%。
Identifying the areal extent of bedrock outcrops and shallow soils has important implications for understanding spatial patterns in vegetation composition and productivity, stream chemistry gradients, and hydrologic and soil properties of landscapes. Manual methods of delineating bedrock outcrops and associated shallow soils are still commonly employed, but they are expensive to implement over broad areas and often limited by representation of polygon units. Few studies have automated the delineation of bedrock outcrops. These focused on delineation approaches in landscapes with rapidly eroding hillslopes and sparse vegetation. The objectives of this study were to assess the accuracy of visually interpreting high-resolution relief maps for locating bedrock outcrops and associated shallow soil (BOSS) <50 cm deep in a heavily forested landscape, to use visually interpreted point locations to train predictive models, and to compare predictions with manually delineated polygons in upland glaciated landscapes. Visual interpretation of Lidar-derived 1 m shaded relief maps at Hubbard Brook Experimental Forest (HBEF), USA resulted in a 79% accuracy of interpreting deep soil locations and 84% accuracy in distinguishing BOSS. We explored four probabilistic classifications of BOSS using multiple Lidar-derived topographic metrics as predictive variables. All four methods identified similar predictors for BOSS, including slope and topographic position indices with a 15, 100 and 200 m circular analysis window, respectively. Although all classifiers yielded similar results with little difference in interpretation, a generalized additive model had slightly higher accuracy predicting BOSS presence, yielding 85% overall accuracy using independent validation data across the primary study area, and 86% overall accuracy in a second validation area.