| 土壤制图中多目标属性的环境因子及其尺度选择——以黑龙江鹤山农场为例 |
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| 引用本文： | 史静静,杨琳,曾灿英,朱阿兴,秦承志,梁朋.土壤制图中多目标属性的环境因子及其尺度选择——以黑龙江鹤山农场为例[J].地理研究,2018,37(3):635-646. |
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| 作者姓名： | 史静静 杨琳 曾灿英 朱阿兴 秦承志 梁朋 |
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| 作者单位： | 1. 中国科学院地理科学与资源研究所,资源与环境信息系统国家重点实验室,北京 1001012. 中国科学院大学,北京 1000493. 南京大学地理与海洋科学学院,南京 2100234. 南京师范大学地理科学学院,南京 2100235. 南京师范大学虚拟地理环境教育部重点实验室,江苏省地理环境演化国家重点实验室培育建设点,江苏省地理信息资源开发与利用协同创新中心,南京 2100236. Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA |
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| 基金项目： | 国家自然科学基金项目（41471178,41530749,41431177）;中国科学院特色研究所培育建设服务项目（TSYJS03） |
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| 摘 要： | 研究影响不同土壤属性空间分布的协同环境因子及其作用尺度,对于理解不同土壤属性的成土发展、土壤推测制图及针对多种土壤属性的空间采样设计具有重要意义。针对多种土壤属性,探索不同土壤属性的重要相关环境因子及其作用尺度,并就不同环境因子及其尺度的不同对土壤属性推测制图的影响展开研究。以黑龙江省鹤山农场为研究区,以表层砂粒、粉粒、黏粒、有机质含量和土壤厚度5种土壤属性为研究对象,根据计算邻域窗口大小的不同,生成173个不同尺度的地形因子,对单尺度地形因子和多尺度地形因子进行重要性排序,并根据重要性排序构建单尺度环境因子集1和多尺度环境因子集2,和基于专家知识选出的基准环境因子集3进行制图精度的对比。结果表明：当单尺度地形因子进行重要性排序选择时,所选出的5种土壤属性的重要相关环境因子与基准环境因子集3明显不同。当多尺度环境因子参与时,尽管对各土壤属性的作用尺度不同,各土壤属性排名靠前的因子绝大多数是基准环境因子。砂粒和粉粒的重要相关因子及作用尺度相当,但与黏粒的重要相关因子和作用尺度差别很大,有机质和土壤厚度的重要相关因子十分相似。环境因子集2较基准环境因子集3的制图精度显著提高,RMSE均值提高百分比为7.8%~21.3%,较环境因子集1的制图RMSE均值提高百分比为8.7%~16.5%。因此,针对不同的土壤属性进行制图或采样设计时,需充分考虑其环境因子和作用尺度的不同,针对基准环境因子选择适宜的尺度较选择不同的相关环境因子更重要。
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| 关 键 词： | 土壤属性制图 环境因子 随机森林 多尺度 |
| 收稿时间： | 2017-09-26 |
| 修稿时间： | 2017-12-19 |
Selection of environmental variables and their scales in multiple soil properties mapping: A case study in Heilongjiang Heshan Farm |
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| Jingjing SHI,Lin YANG,Canying ZENG,Axing ZHU,Chengzhi QIN,Peng LIANG.Selection of environmental variables and their scales in multiple soil properties mapping: A case study in Heilongjiang Heshan Farm[J].Geographical Research,2018,37(3):635-646. |
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| Authors: | Jingjing SHI Lin YANG Canying ZENG Axing ZHU Chengzhi QIN Peng LIANG |
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| Abstract: | Studying the relevant environmental variables with consideration of scales for different soil properties is meaningful to understand the generation and development of soil properties, and also necessary in multiple soil properties mapping and sampling. This study explored multiple soil properties' relevant environmental variables and their scales, and examined the impact of different environmental variables and their scales on the prediction of different soil properties. Our study area is Heshan Farm, and the target soil properties are topsoil clay content, sand content, silt content, topsoil organic matter content (SOM), and soil depth. One hundred and seventy-three multi-scale terrain variables were generated by changing neighborhood size for calculation. The single scale and multi-scale variables were ranked according to their variable importance calculated by Random Forest. Subsets 1 and 2 were selected from single scale and multi-scale variables respectively based on their variable importance with elimination of multi-collinearity. Subset 3 was taken as a reference subset and selected based on the expert knowledge. The selected subset 1 had little common with subset 3. This indicates that the environmental variables selected based on expert knowledge may be not the most important variables for the soil properties. Subset 2 had a high overlap with subset 3 though the scales were different for different environmental variables and soil properties. For the case of soil sand and silt, their relevant variables and scales were similar but quite different from soil clay's, and the SOM and soil depth had similar relevant variables. The mapping results based on the three subsets showed that using environmental variables in subset 1 was more accurate than using environmental variables in subset 3 for all soil properties except for sand content, the improvements of mean RMSEs were 1.8%~13.1%. Using environmental variables in subset 2 was more accurate than using environmental variables in subsets 1 and 3 for all the five soil properties, the improvements of mean RMSEs were 8.7%~16.5% and 7.8%~21.3%. It was shown that using reference variables with proper scales is more important than using top-ranked single scale variables for mapping. |
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| Keywords: | soil property mapping environmental variables random forest multi-scale |
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