黑龙江省耕地系统安全预警及其驱动因素空间分异

为精准把握黑龙江省耕地系统安全预警及其驱动力,该文采用探索性空间数据分析、最小二乘法和地理加权回归,探究并明确县域尺度下黑龙江省耕地系统安全预警及其驱动因素空间分异特征。结果表明:1)黑龙江省各县域(市辖区)耕地系统整体安全警情较高。72个被评定县域中,有69个处于预警状态,处于轻警、中警和重警状态的分别有28、32和9个;2)耕地系统安全预警空间差异性和集聚性特征明显。西部警情最高,东部次之,南北轴带地区最低。正、负相关类型在空间分布上呈现鲜明的对比特征,正相关类型(高-高型和低-低型)县域以"组团"形式凸显,聚集性较强,负相关类型(高-低型和低-高型)县域较少且零星分散;3)地形及气候等自然因素和投入产出、水土资源配置等社会经济因素对研究区耕地系统安全预警有着显著的影响,且回归系数空间差异明显。研究可为合理制定缓解区域耕地系统安全警情方案提供科学依据。

Spatial heterogeneity of early-warning status of cultivated land system security and its driving factors in Heilongjiang Province

Abstract: The study on spatial heterogeneity of the early-warning status of cultivated land system security (CLSS) and its driving factors on county level have an important role in accurately identifying the early-warning status of the CLSS and scientifically formulating the protection plan of cultivated land system. The purpose of this study was to explore and identify spatial heterogeneity characteristics of the early-warning status of the CLSS and its driving factors in Heilongjiang Province located in main grain producing area. In the paper, Exploratory spatial data analysis (ESDA) was used to identify spatial heterogeneity characteristics of the early-warning status in 72 counties of Heilongjiang province, and Ordinary least squares (OLS) and Geographical weighted regression (GWR) method were employed for exploring driving factors of the spatial heterogeneity of the early-warning status. The results indicated that 1) The early-warning levels of the CLSS in all counties were assessed in 72 counties, and the results showed the early-warning level in general was relatively high. Of the 72 counties, 69 counties were in the early-warning status, and 28, 32 and 10 counties were low-level warning, medium-level warning and severe-level warning, respectively. 2) The spatial distribution of the early-warning status was studied through ESDA and ArcGIS spatial analysis, and the results showed obviously heterogeneity and agglomeration characteristics of the early-warning status in the study area. The severe-level warning areas were mainly found in the southeast and west of the region, while the secure and relatively secure areas are mainly concentrated in the north-south axis belt areas. The counties with positive spatial autocorrelation (High-High type and Low-Low type) emerged by "clusters" and had a powerful agglomeration. The counties with negative spatial autocorrelation (High- Low type and Low-High type) were few and showed sporadic distribution. 3) 12 variables were preliminary selected for exploring the driving factors of the early-warning status by OLS model, and 10 variables, including 3 natural-ecological factors (slope, elevation and annual average temperature) and 7 socioeconomic factors (per capita GDP, urbanization level, investment in agricultural fixed assets, total power of agricultural machinery, water-soil coordination degree, agricultural wastes index, and road network density) were determined for further analysis by the GWR model. The regression results by GWR model and ArcGIS spatial analysis showed the natural-ecological factors, including terrain and climate, and socioeconomic factors including the land input-output, land-water resources allocation, had a significant effect on the early-warning status of the CLSS. Moreover, the regression coefficients of these driving factors showed the significant spatial heterogeneity characteristics. In conclusion, the results of studying on spatial heterogeneity of the early-warning status of the CLSS provide a reference for accurately identifying the spatial distribution of the early-warning status in Heilongjiang Province. Compared with OLS model, the GWR model visualize the non-stationary characteristics of driving factors of the spatial heterogeneity of the early-warning status of the CLSS, which can be more intuitive and in-depth to explore the spatial difference of the effect of each driving factor to the early-warning status, and provide a scientific basis for formulating appropriate measures in order to mitigate against future threats of the cultivated land system.