College of Geomatics, Xi'an University of Science and Technology, Xi'an, 710054, People's Republic of China.
Key Laboratory of Mine Geological Hazards Mechanism and Control, Xi'an, 710054, China.
Environ Monit Assess. 2020 Mar 15;192(4):237. doi: 10.1007/s10661-020-8174-9.
A set of indicators that focus only on numerical values is constructed based on remotely sensed images to assess soil moisture conditions. The quantitative evaluation of soil moisture variation in two periods is rarely referred to in the current literature. In this study, a scaled soil moisture monitoring index (SSMMI) was established to monitor the soil moisture status during 2010-2018 in the Daliuta Coal Mining Area (DCMA), China, based on SPOT-5, SPOT-6, and Sentinel-2 images. We also employed a gradient-based structural similarity (GSSIM) algorithm to quantitatively analyze the characteristics of the spatial distribution of the soil moisture in the DCMA. The optimal scale for exploring the spatial heterogeneity of the soil moisture was determined by local variance and semivariance methods. The results showed that the soil moisture decreased at a rate of 0.0213/a from 2010 to 2018. The areas with the extremely dry and dry levels, which were mainly located in the northwest, some regions of the central area, and the southeast of the DCMA, decreased from 14.48% in 2010 to 13.66% in 2018. The proportion of the no dry level was improved by 14.62%, while the area of the extremely wet and wet levels decreased by 13.79%. The mean value of the soil moisture in the unmined area was greater than that in the DCMA, which was larger than that in the mined area. The result of the GSSIM analysis indicated that the area of dramatic change, where the soil moisture changed substantially, was chiefly distributed in the north, west, some central regions, and some parts of the south and east of the DCMA. The region where the substantial change occurred was surrounded by a moderate-change area, which was encompassed by a low-change area. The area with dramatic and moderate decreases in the soil moisture accounted for 64.52% of the region, which was greater than that with incremental soil moisture changes, which accounted for 5.85% of the region. The area also showed decreased soil moisture from 2010 to 2018. Soil moisture changes are closely related to variations in land cover. For instance, vegetative cover over an open-pit mining area can cause a dramatic reduction in soil moisture. Ninety-three meters was the optimal scale used for monitoring the soil moisture in the DCMA, which indicates that we can adopt the SPOT-5, SPOT-6, and Sentinel-2 images to evaluate the soil moisture conditions in the DCMA.
构建了一套仅基于遥感图像的指标体系，用于评估土壤水分状况。当前文献中很少提到对两个时期土壤水分变化的定量评价。本研究基于 SPOT-5、SPOT-6 和 Sentinel-2 图像，建立了规模土壤水分监测指数（SSMMI），以监测 2010-2018 年中国大柳树矿区（DCMA）的土壤水分状况。我们还采用基于梯度的结构相似性（GSSIM）算法对 DCMA 土壤水分空间分布特征进行了定量分析。通过局部方差和半方差方法确定了探测土壤水分空间异质性的最佳尺度。结果表明，2010 年至 2018 年期间，土壤水分以 0.0213/a 的速率减少。极度干旱和干旱水平的面积主要位于矿区西北部、中部一些地区和东南部，从 2010 年的 14.48%减少到 2018 年的 13.66%。无干旱水平的比例提高了 14.62%，而极湿和湿水平的面积减少了 13.79%。未开采区土壤水分的平均值大于 DCMA，而 DCMA 又大于开采区。GSSIM 分析结果表明，土壤水分发生显著变化的区域主要分布在矿区的北部、西部、中部一些地区以及南部和东部的一些地区。土壤水分发生显著变化的区域被一个中变化区域包围，中变化区域被一个低变化区域包围。土壤水分急剧和中度减少的区域占该区域的 64.52%，大于土壤水分增量变化的区域，占该区域的 5.85%。该区域从 2010 年到 2018 年也表现出土壤水分减少。土壤水分变化与土地覆盖变化密切相关。例如，露天矿区的植被覆盖会导致土壤水分急剧减少。93 米是监测 DCMA 土壤水分的最佳尺度，这表明我们可以采用 SPOT-5、SPOT-6 和 Sentinel-2 图像来评估 DCMA 的土壤水分状况。
