我国尾矿库数量众多, 分布广泛, 在低含水量条件下, 风力作用引起的尾砂扬尘会对周边环境造成污染。 而尾矿库表面积大, 含水量变化快, 传统的含水量监测方法效率低、 安全性差、 成本高, 难以实现尾矿库含水量的大面积、 实时、 快速的监测。 目前, 基于光谱特征的遥感模型虽可以较为准确地预测土壤含水量, 但矿区尾砂与常规土壤在成分上存在差异性, 使得土壤含水量的光谱预测遥感模型可能无法适用于尾矿库含水量的预测。 为此, 选择辽宁省风水沟尾矿库作为研究区, 采集尾砂配置成不同含水量的样品, 测试其可见光-近红外光谱, 分析不同含水量样品的光谱特征以及含水量与光谱特征之间的关系, 建立针对尾砂的含水量遥感预测模型, 并应用于辽宁省风水沟尾矿库表面含水量的预测。 结果表明: (1)含水量对尾砂的光谱特征有显著影响, 二者存在高度的相关性, 光谱反射率随含水量增加而下降, 且波长越长, 含水量对光谱的影响越显著; (2)构建了基于尾砂光谱特征的含水量遥感预测模型, 选择Landsat8-OLI传感器的B6和B7波段, 定义了比值指数(RTI)、 归一化差异指数(NDTI)和差值指数(DTI)3种尾砂光谱指数, 并将这3种指数作为输入自变量, 使用随机森林方法进行训练以及含水量的建模预测, 并与B7波段建立的对数反射率预测模型进行比较。 结果表明, 光谱指数+随机森林的预测模型效果优于基于B7波段建立的对数反射率模型。 (3)使用光谱指数+随机森林的预测模型, 通过Landsat8-OLI数据对实地尾矿库提取了含水量的空间分布图, 结果表明模型预测的含水量与实测结果之间的决定系数R2达0.798, 均方根误差RMSE为0.077, 相对分析误差RPD为1.970, 平均相对精度ARE为20.1%, 在现有技术条件下, 达到了较好的预测效果。 该研究为变质型铁矿尾矿库含水量的预测提供一种大面积、 实时、 快速的实用方法。

The tailing ponds are widely distributed in China. Once the surface tailings are under low moisture content, the tailing dust would cause severely environmental pollution driven by the wind action. Because of the large area and rapidly variation of moisture content of tailing ponds, the traditional method with the limitation on low efficiency, safety and high cost cannot meet the requirement of quick and dynamic moisture content monitoring. Currently, although the remote sensing technology based on spectral model can provide accurate prediction of soil moisture content, this model is not fit to tailing moisture content prediction because of the different characteristics and components between soil and tails. Therefore, the Fengshuigou tailing pond in Liaoning Province was selected as the study area. First, the samples of tailings at different moisture content were collected and configured. Then, the visible-near infrared spectra of the samples were measured and analyzed. Furthermore, the relationship between moisture contents and spectral characteristics was established. Finally, the remote sensing inversion model for moisture contents prediction was built and applied for mapping the moisture content in this study area. This study yielded the following results: (1) The moisture content has a significant effect on the spectral characteristics of tailings, and the reflectance decreased obviously as the moisture content increased. The longer the wavelength is, the more significant the effect of water content on the spectrum is. (2) The remote sensing model based on the spectral characteristic for tailings moisture content prediction was established. In terms of the band 6 and band 7 from Landsat8-OLI imagery, the ratio index (RTI), normalized difference index (NDTI) and difference index (DTI) of tailings were proposed and selected as the input data for the random forest model. By comparing the random forest model and Log reflectance model, the random forest model can generate more accurate predicting results. (3) The tailings moisture content map was generated by applying the random forest predicting model with spectral index based on the Landsat-OLI imagery. From the field verification, the coefficie of determination (R2), RMSE, RPD, and ARE is 0.798, 0.077, 1.970 and 20.1% respectively between the predicted and field measured moisture content. The results could provide an effective and real-time method for large scale moisture content predicting of the tailing ponds from the metamorphic iron ore area.