Mapping soil organic matter in small low-relief catchments using fuzzy slope position information
Mapping soil organic matter in small low-relief catchments using fuzzy slope position information
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DOI：
10.1016/j.geoderma.2011.06.006
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发表时间：
2012-02
期刊：
影响因子：
6.1
通讯作者：
C. Qin;A. Zhu;A. Zhu;W. Qiu;Yanjun Lu;Yanjun Lu;Baolin Li;T. Pei
中科院分区：
文献类型：
--
作者：
C. Qin;A. Zhu;A. Zhu;W. Qiu;Yanjun Lu;Yanjun Lu;Baolin Li;T. Pei
斜坡位置（地形位置）之间的空间转换通常是渐进的。已经开发了各种方法来使用模糊斜率位置来量化过渡。然而，很少有研究将模糊坡位的定量信息用于数字土壤制图或其他与地形相关的地理建模。本文探讨了使用这些信息映射土壤有机质含量（SOM）在一个有目的的（或定向）的抽样框架预测土壤制图。首先，五个斜坡位置系统（即，在此基础上，采用基于典型坡位定位的方法，确定模糊坡位。利用基于地形属性和领域知识的规则提取典型坡位位置。其次，利用模糊坡位进行定向取样，确定每种坡位类型的典型SOM值;然后将典型土壤有机质值与模糊坡位数据相结合，利用加权平均模型--模糊坡位加权（FSPW）模型，对中国东北某低海拔小流域10- 15 cm和35- 40 cm两个土层土壤有机质的空间分布进行了预测。研究区域包括两个部分：一个约4km 2的区域用于模型开发，一个约60 km 2的区域用于模型外推和验证。评估结果表明，我们的FSPW模型产生了更好的预测SOM比多元线性回归（MLR）模型提供的。在模型开发领域的定量措施，包括相关系数，平均绝对误差，误差的均方根，表明FSPW模型的性能与5个建模点从目的抽样相比，毫不逊色于MLR结果为48建模点。在模型外推区域的102个样本点的验证集的基础上的定量评估的证据表明，FSPW模型的表现优于MLR模型，这表明，模糊的斜坡位置的信息是有用的，在该地区的数字土壤制图。
Spatial transitions between slope positions (landform positions) are often gradual. Various methods have been developed to quantify the transitions using fuzzy slope positions. However, few studies have used the quantitative information on fuzzy slope positions in digital soil mapping or other terrain-related geographic modeling. This paper examines the use of such information for mapping soil organic matter content (SOM) within a purposive (or directed) sampling framework for predictive soil mapping. First, a five slope position system (i.e., ridge, shoulder slope, back slope, foot slope, channel) was adopted and the fuzzy slope positions were derived through an approach based on typical slope position locations. The typical slope position locations were extracted using a set of rules based on terrain attributes and domain knowledge. Secondly, the fuzzy slope positions were used to direct purposive sampling, which determined the typical SOM value for each slope position type. Typical SOM values were then combined with fuzzy slope position data to map the spatial variation of SOM using a weighted-average model – the fuzzy slope position weighted (FSPW) model – to predict the spatial distribution of SOM for two soil layers at depths of 10–15cm and 35–40cm in a low-relief watershed in north-eastern China. The study area comprised two portions: an area of about 4km2used for model development, and an area of about 60km2for model extrapolation and validation. Evaluation results show that our FSPW model produces a better prediction of the SOM than that provided by a multiple linear regression (MLR) model. Quantitative measures in the model-development area, including correlation coefficient, mean absolute error, and root mean square of error, show that the performance of the FSPW model with five modeling points from purposive sampling compares favorably with MLR results for 48 modeling points. Evidence from the quantitative assessment based on a validation set of 102 sample points in the model-extrapolation area shows that the FSPW model performs better than the MLR model, which suggests that information on fuzzy slope position was useful in aiding digital soil mapping over the area.