王海潇,王勇辉.基于地形校正后Landsat 8的土壤重金属反演研究[J].干旱地区农业研究,2019,37(1):11~17
基于地形校正后Landsat 8的土壤重金属反演研究
Inversion of Landsat 8 for soil heavy metals after terrain correction
  
DOI:10.7606/j.issn.1000-7601.2019.01.02
中文关键词:  土壤重金属  Landsat 8 OLI  地形校正  预警评估
英文关键词:soil heavy metals  Landsat 8 OLI  terrain correction  warning assessment
基金项目:新疆维吾尔自治区科技计划项目:玛纳斯湖退化湿地生态恢复研究(201533109);自治区重点实验室开放课题:艾比湖湿地有机碳库研究(2016D03007);新疆师范大学博士启动基金:博斯腾湖北岸土壤地磁特性与重金属含量关系研究(xjnubs1523)
作者单位
王海潇 College of Geographical Sciences and Tourism, Xinjiang Normal University, Urumqi, Xinjiang 830054, China 
王勇辉 College of Geographical Sciences and Tourism, Xinjiang Normal University, Urumqi, Xinjiang 830054, China
2. Xinjiang Laboratory of Lake Environment and Resources in Arid Zone, Urumqi, Xinjiang 830054, China 
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中文摘要:
      为快速地了解玛纳斯流域土壤中重金属的污染状况和潜在生态风险,在Landsat 8 OLI的基础上引入DEM数据进行地形校正,同时对地形校正后的反射率进行倒数、导数和对数等数学变换,从每种变换中筛选出与土壤各重金属相关性最高的波段构建土壤各重金属PLSR预测模型,并对研究区土壤重金属分布情况进行探索,并利用生态风险评价方法对研究区进行预警。结果表明:在Landsat 8的基础上,引入DEM数据对反射率进行地形校正,以B1波段反射率和重金属Cu为例,经过地形校正后的反射率值与实测土壤表层Cu含量的R2从0.46提高至0.52,表明地形校正后的表观反射率能够更好地反映土壤重金属状况;利用土壤各重金属的最佳预测模型分别反演相应的土壤重金属含量,并引入土壤重金属生态风险指数用于评价研究区的土壤重金属生态风险,研究表明土壤重金属风险等级总体上呈现从西南方向至东北方向逐渐减弱的趋势,其生态风险排序为恢复区(C区)>退化区(B区)>湖泊入湖口(A区);为了验证基于遥感的土壤重金属生态风险预警的预测精度,将研究区土壤重金属含量实测数据也通过重金属生态风险指数进行计算,两者结果较为一致,表明可以用遥感的手段来反演该研究区的重金属分布情况,同时研究区土壤重金属污染总体上处于轻警以上级别,生态服务功能已开始退化,应该加强对该地区的重金属污染进行治理。
英文摘要:
      In order to quickly understand the pollution status and potential ecological risk of heavy metals in the Manas watershed, DEM data was introduced into Landsat 8 OLI for terrain correction, and mathematical transformations such as reciprocal, derivative, and logarithm were carried out. The PLSR prediction model of soil heavy metals was established by selecting the highest correlation between band reflectivity and concentration of heavy metals from each transformation. Additionally, the distribution of heavy metals in the study area was analyzed and using the ecological risk assessment method provided early warning for the study area. The results showed that: based on Landsat 8 OLI, the DEM data was introduced to correct the reflectivity. Taking the B1 band reflectivity and heavy metal Cu as an example, the coefficient of determination, R2, between the topographically corrected reflectance values and the measured soil Cu content increased from 0.46 to 0.52, which indicated that the apparent reflectance after topographic correction reflected the soil heavy metal condition better. The best prediction model for soil heavy metals was used to invert the corresponding soil heavy metal content, and the soil heavy metal ecological risk index was introduced to evaluate the soil heavy metal ecological risk in the study area. The risk level of heavy metals in soils generally showed a tendency to weaken gradually from southwestern region to northeastern area. The ecological risk of the order: recovery area (C area) > degraded area (B area) > lake into the lake (A area). In order to verify the prediction accuracy of soil heavy metal ecological risk warning based on remote sensing, the measured data of soil heavy metal content in the study area was also calculated by the heavy metal ecological risk index that showed consistent results. As a result, the distribution of heavy metals in the study area could be estimated by means of remote sensing. The results also indicated that the heavy metal pollution in the study area was above the alarm level, the ecological function had begun to degrade and the treatment of heavy metal pollution in the area should be strengthened.
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