灰色评估模型定量评价于田绿洲土壤盐渍化风险

针对土壤盐渍化这一干旱区重大生态环境问题,以新疆维吾尔自治区于田绿洲为研究靶区,在野外踏探、室内试验的基础上,将土壤电导率作为评价盐渍化风险生态终点,选择地面蒸散发、地表温度、地表反照率、地面高程、地下水埋深、地下水电导率、地上生物量、叶面积指数、归一化植被指数、表层土壤pH值、表层土壤含水率、土地利用/覆被类型、人口密度和人均耕地面积共14个评价指标作为主要盐渍化风险源,通过遥感与GIS技术获取这些评价因子空间数据集,同时进行数据标准化、叠加并生成相应的栅格图层集,采用Pearson相关性分析法确定评价因子风险权重,引入灰色系统分析法构建研究区盐渍化风险灰色评价模型,构建了土壤盐渍化风险评价模型,并对研究区的盐渍化风险进行定量评价与分析。结果表明:整个研究区盐渍化风险值介于0.053～0.747之间,平均值达到0.190。总体以一般风险为主,盐渍化高度风险占23.37%,虽然分布面积不大,但对绿洲北部区域的生态环境和农业生产影响深远。研究可为干旱区绿洲的土地资源管理、农田系统的合理布局及农业可持续发展中的风险决策提供数据基础与参考依据。

Quantitative evaluation of soil salinization risk in Keriya Oasis based on grey evaluation model

Abstract: Soil salinization is a global issue of concern and the biggest global natural disaster. Salt-affected soil is also the most prominent environmental problem in arid and semi-arid regions in China. In this study, the Keriya Oasis in the arid zone of Xinjiang, Northwestern China was chosen as study area, a geodatabase was created with multiple field observations together with laboratory analyses and related datasets including attribute, vector and raster data. Topsoil electrical conductivity (TS_EC) was selected as the ecological endpoint for evaluating the salinization risk. And 14 evaluation indicators were chosen as the main sources of soil salinity risk which included ground evapotranspiration (ET), land surface temperature (LST), surface albedo (Albedo), digital elevation model (DEM), normalized difference vegetation index (NDVI), leaf area index (LAI), aboveground biomass (Biomass), groundwater depth (GWD), groundwater electrical conductivity (GW_EC), topsoil water content (SWC), topsoil pH value (pH), land use land / cover type (LULC), population density (PD) and per capita arable land (PCAL). An index system for soil salinization risk assessment was established. Through remote sensing (RS) techniques and quantitative inversion, 7 risk factors were derived such as: NDVI, LAI, Albedo, LST, ET, Biomass, DEM; the other factors were spatially interpolated, then data normalization was applied to all these datasets and overlayed GIS database of soil salinity risk factors was built. Risk weights of evaluation factors were determined and weight coefficients were calculated by adopting Pearson correlation analysis method. The theory of grey relational analysis system was introduced into soil salinization risk assessment, and risk assessment model was constructed in the study area. Then the soil salinity risk of the region was quantitatively assessed and classified, and finally soil salinity risk map was elaborated. The results showed that: the salinization risk values of the whole study area varied from 0.053 to 0.747, with a mean value of 0.190. Spatial distribution heterogeneity of different risks in the Keriya Oasis was prominent, and soil salinity risk was mainly demonstrated moderate risk. The area of risk rating 3 was the largest, and it accounted for 48.94% of total study area, soil salinity risk was moderate, belonging to potential risk area; The area of rating 4 accounted for 27.69%, and it belonged to the low risk region. Rating 2 risk region accounted for 19.35%, and soil salinity risk was relatively high. Rating 1 risk area accounted for only 4.02%, but it was characterized with very high risk soil salinity. Although the high risky area was smaller in size, it might lead a negative influence on the ecological environment and agricultural production in the northern region of the oasis. In conclusion, the quantitative assessment and mapping results of soil salinization risk in Keriya Oasis could be used to make appropriate decisions related to crop production, prevention of soil salinization, and it might offer scientific evidence and consulting for obtaining sustainable development of agriculture and eco-environment in arid and semi-arid regions.