Hyper-scale digital soil mapping and soil formation analysis
文献类型：SCI/SSCI论文
| 作者 | Behrens T. ; Schmidt K. ; Ramirez-Lopez L. ; Gallant J. ; Zhu A. X. ; Scholten T. |
| 发表日期 | 2014 |
| 关键词 | Hyper-scale analysis Digital soil mapping Soil formation Digital terrain analysis Pedology Geomorphic signature Data mining ConStat random forests classification prediction selection stocks |
| 英文摘要 | Landscape characteristics show local, regional and supra-regional components. As a result pedogenesis and the spatial distribution of soil properties are both influenced by features emerging at multiple scales. To account for this effect in a predictive model, descriptors of the geomorphic signature are required at multiple scales. In this study, we present a new hyper-scale terrain analysis approach, referred to as Contextual Statistical Mapping (ConStat), which is based on statistical neighborhood measures derived for growing sparse circular neighborhoods. The statistical measures tested comprise basic descriptors such as the minimum, maximum, mean, standard deviation, and skewness, as well as statistical terrain attributes and directional components. We propose a data mining framework to determine the relevant statistical measures at the relevant scales to analyze and interpret the influence of these statistical measures and to map the geomorphic structures influencing soil formation and the regions where a statistical measure shows influence. We introduce ConStat on two landscape-scale DSM examples with different soil genesis regimes where the ConStat terrain features serve as proxies for multi-scale variations of climate and parent material conditions. The results show that ConStat provides high predictive power. The cross-validated R-2 values range from 0.63 for predicting topsoil clay content in the Piracicaba area (Brazil) to 0.68 for topsoil silt content in the Rhine-Hesse area (Germany). The results obtained from data mining analysis allow for interpretations beyond conventional concepts and approaches to explain soil formation. As such it overcomes the trade-off between accuracy and interpretability of soil property predictions. (C) 2013 Elsevier B.V. All rights reserved. |
| 出处 | Geoderma |
| 卷 | 213 |
| 页 | 578-588 |
| 收录类别 | SCI |
| 语种 | 英语 |
| ISSN号 | 0016-7061 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/29971]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Behrens T.,Schmidt K.,Ramirez-Lopez L.,et al. Hyper-scale digital soil mapping and soil formation analysis. 2014. |
入库方式： OAI收割
来源：地理科学与资源研究所
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