| 基于MGWR的土壤pH值空间建模及其影响因素分析 |
| 摘要点击 942 全文点击 325 投稿时间：2022-12-04 修订日期：2023-02-14 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 土壤pH 多尺度地理加权回归(MGWR) 混合地理加权回归 分位数回归 数字土壤制图 |
| 英文关键词 soil pH multi-scale geographically weighted regression(MGWR) mixed geographically weighted regression quantile regression digital soil mapping |
| 作者 | 单位 | E-mail | | 赵明松 | 安徽理工大学空间信息与测绘工程学院, 淮南 232001
矿山采动灾害空天地协同监测与预警安徽省教育厅重点实验室, 淮南 232001
矿区环境与灾害协同监测煤炭行业工程研究中心, 淮南 232001 | zhaomingsonggis@163.com | | 陈宣强 | 安徽理工大学空间信息与测绘工程学院, 淮南 232001
矿山采动灾害空天地协同监测与预警安徽省教育厅重点实验室, 淮南 232001
矿区环境与灾害协同监测煤炭行业工程研究中心, 淮南 232001 | | | 徐少杰 | 安徽理工大学空间信息与测绘工程学院, 淮南 232001 | | | 邱士其 | 安徽理工大学空间信息与测绘工程学院, 淮南 232001 | | | 王世航 | 安徽理工大学空间信息与测绘工程学院, 淮南 232001
矿山采动灾害空天地协同监测与预警安徽省教育厅重点实验室, 淮南 232001
矿区环境与灾害协同监测煤炭行业工程研究中心, 淮南 232001 | |
|
| 中文摘要 |
| 以安徽、河南、江苏和山东省为研究区，利用599个土壤样点数据，从地形、气候和生物等方面选取与土壤pH相关的9个环境因子，采用多尺度地理加权回归（MGWR）、混合地理加权回归（Mixed GWR）、地理加权回归（GWR）和多元线性回归（MLR）这4种模型对研究区土壤pH空间分布进行建模，并结合MGWR与分位数回归揭示环境因子对土壤pH作用的空间差异性.结果表明：①研究区土壤pH在不同空间距离上呈不同程度的显著全局和局部空间自相关性，聚集特征明显.② 4种模型中MGWR模型最优，MGWR、Mixed GWR、GWR和MLR的建模集Radj2为0.64、0.62、0.59和0.48.MGWR的残差独立分布性最强，其空间自相关性最弱，全局Moran's I仅为0.07.③ 3种GWR预测结果显示，研究区土壤pH值空间分布总体由北至南逐渐降低，河南北部最高，安徽南部最低.④ MGWR回归结果表明年均降雨量（MAP）、多尺度谷底平坦度（MRVBF）和海拔对土壤pH的影响较强，且存在较强的空间异质性.在江苏北部和山东大部分地区，MAP对土壤pH的影响较强；在江苏北部和山东西部，MRVBF对土壤pH的正向作用较强；在江苏北部和中部，海拔对土壤pH的负向作用最强.⑤ MAP对不同分位数水平上的土壤pH均呈显著负作用，作用强度随分位数水平增加呈减弱趋势；MRVBF对低分位数水平（θ为0.1~0.4）上的土壤pH呈显著负作用，对高分位数水平（θ为0.5~0.9）的土壤pH作用不显著.研究结果可为利用MGWR开展大区域土壤属性影响因素分析及预测制图提供参考. |
| 英文摘要 |
| Anhui, Henan, Jiangsu, and Shandong provinces were selected as the study area. A total of 599 soil samples and nine environmental factors of soil pH were collected. The spatial distribution of soil pH was modeled based on multi-scale geographically weighted regression(MGWR), mixed geographically weighted regression(Mixed GWR), geographically weighted regression(GWR), and multiple linear regression(MLR) models. Then, the spatial difference in the effect of environmental factors on soil pH was revealed using MGWR and quantile regression models. The results showed that:① soil pH showed significant global and local spatial autocorrelation at different spatial distances, and the clustering characteristics were obvious. ② The MGWR model was the best among the four models, and the Radj2 of MGWR, Mixed GWR, GWR, and MLR were 0.64, 0.62, 0.59, and 0.48, respectively. The residual of MGWR had the strongest independent distribution and the weakest spatial autocorrelation with a global Moran's I of 0.07. ③ Three types of GWR predictions showed that the spatial distribution of soil pH decreased gradually from north to south in the study area, with the highest in northern Henan and the lowest in southern Anhui. ④ MGWR modeling results showed that there was strong spatial heterogeneity of mean annual precipitation(MAP), multi-resolution valley bottom flatness(MRVBF), and elevation affecting soil pH. MAP had a stronger effect on soil pH in northern Jiangsu and most parts of Shandong. The positive effect of MRVBF on soil pH was stronger in northern Jiangsu and western Shandong. The negative effect of elevation on soil pH was stronger in northern and central Jiangsu. ⑤ The quantile regression analysis showed that the mean annual precipitation had a significant negative effect on soil pH at different quantile levels of soil pH, and influence intensity decreased with the increase in pH quantile level. MRVBF had a significant negative effect on soil pH at a low quantile level(θ=0.1 to 0.4) but had no significant effect on soil pH at a high quantile level(θ=0.5 to 0.9). These results can provide an important reference for mapping soil properties and analyzing its influence factors based on the MGWR model in large regions. |
|
|
|
