资源科学 ›› 2017, Vol. 39 ›› Issue (10): 1975-1988.doi: 10.18402/resci.2017.10.16

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基于地理加权回归的石漠化影响因子分布研究

许尔琪()   

  1. 中国科学院地理科学与资源研究所陆地表层格局与模拟重点实验室, 北京 100101
  • 收稿日期:2017-08-23 修回日期:2017-09-14 出版日期:2017-10-20 发布日期:2017-10-20
  • 作者简介:

    作者简介:许尔琪,男,广东汕头人,博士,主要从事土地利用及空间格局、生态环境效应研究。E-mail:xueq@igsnrr.ac.cn

  • 基金资助:
    国家自然科学基金项目(41601095);国家重点基础研究发展计划项目(973 计划)(2015CB452702)

Spatial variation in drivers of karst rocky desertification based on geographically weighted regression model

Erqi XU()   

  1. Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China
  • Received:2017-08-23 Revised:2017-09-14 Online:2017-10-20 Published:2017-10-20

摘要:

分析和识别石漠化的关键影响因子,有助于有效治理和恢复石漠化。以往研究对影响因子的空间局部差异关注较少,本文以黔桂喀斯特山地为研究区,选取自然及社会经济等12个影响因子,利用地理加权回归(GWR)模型,在普通线性回归的基础上嵌入空间因素,分析石漠化影响因子的空间分异。结果表明:①黔桂喀斯特山地的石漠化Moran’s I大于正态函数在99%显著水平,存在明显空间聚集现象;②GWR模型R2(0.508)明显高于传统统计模型的R2(0.156),回归模型拟合效果显著提高;③12个影响因子与石漠化关系呈现不同数值大小、正负效应和线性组合关系的空间分布差异;④人类活动叠加在喀斯特特殊的岩性、土壤和植被组合上,显著影响石漠化分布;同时,局部区域高强度人类活动导致石漠化的急剧变化。GWR模型可揭示石漠化影响因子的空间分异规律和局部的关键影响因子,刻画多因子组合作用对石漠化的影响,有助于差别化的小流域石漠化治理。

关键词: 石漠化, 影响因子, 地理加权回归模型, 空间分异, 黔桂喀斯特山地

Abstract:

Analysis and identification of key drivers of karst rocky desertification (KRD)can contribute to effective management and restoration. Ignoring heterogeneity leads to statistical bias and influences the specificity of desertification control planning. Taking Guizhou and Guangxi Karst Mountainous Regions as the study area,this paper chose twelve factors,including socioeconomic,spatial distance,topography,climate,soil,lithology and land use,as drivers of KRD. Geographically Weighted Regression modeling (GWR)and embedding spatial factors to the traditional ordinary linear regression (OLS)model were used to analyze the spatial distribution of influence on KRD. An obvious spatial agglomeration of KRD in the study area according to Moran’s I at the significant level of 99% was found. Coefficients of determination (R2)of GWR were much higher for OLS (0.508 vs. 0.156),indicating a much better fit for the GWR model. Coefficients of GWR between twelve drivers and an obvious spatial distribution of value magnitudes,negative or positive effects and combined types. The specifics of the karst background create a fragile and vulnerable environment that is susceptible to human activities. Meanwhile,intense human activities lead to a sharp change in KRD status,which included the predatory sabotage for casing the severe KRD and the KRD restoration projects for reversing KRD to no KRD. The regression coefficients of the twelve drivers and their linear combined characteristics showed different spatial distributions based on GWR modeling. Using the GWR model revealed the spatial discrimination of the effects of driving forces on KRD and identified key drivers of KRD at the local area,revealing the spatial distribution of the joint effect of different driving forces helping provide a scientific reference to differential KRD control at small watershed scales.

Key words: Karst rocky desertification, driving force, geographically weighted regression model, spatial distribution, Guizhou and Guangxi Karst Mountainous Region