煤炭开采导致地表沉陷积水，是东部高潜水位平原煤矿区的主要特征，对矿区开采中沉陷水体变化的长时序检测，有助于定量评估煤炭开采对土地、生态与社会的综合影响效应。在没有地下采矿信息为先导的情况下，如何识别与区分自然水体、人类地面活动导致的挖掘水体，以及沉陷水体，同时量化开采沉陷影响的边界与程度是目前单纯采用遥感手段进行监测的难题。以山东兖州煤田为例，利用Google Earth Engine（GEE）云计算平台，基于1986年以来可以获取的所有Landsat时序影像数据，结合CCDC（Continue Change Detection and Classification）算法开发了基于轨迹数据检测变化的沉陷积水年份和复垦年份时空动态制图的方法，在此基础上，以提取的沉陷水体图斑为依据，利用Sentinel-2数据分别反演了积水缓冲区反应土壤含水量的VSDI（Visible and Shortwave Infrared Drought Index）、 LSWI（Land Surface Water Index）、SMMI (Soil moisture monitoring index)三个指数，根据土壤水分空间变化规律，通过测度离沉陷积水区不同距离土壤水分空间分布特征，进一步对开采扰动的影响范围与程度进行定量分析。结果显示，（1）基于CCDC算法识别1986—2017年地下煤炭开采引起的地表沉陷积水与复垦年度时空数据，精度分别为85%、77%。（2）研究区自1990年出现沉陷水体，至2017年累计沉陷积水面积3021.08公顷，其中75.80%的沉陷积水发生在2001—2011年；沉陷积水复垦从1993年开始出现，累计面积888.37公顷，占沉陷积水总面积的29.41%，主要集中于2007年之后。（3）沉陷水体的影响主要集中在沉陷水体外围120米范围内，该区域出现剧烈的土壤水分变化；120-300米内存在扰动但是影响强度轻微；300米之外几乎无影响。本文通过分析矿区长时序的沉陷积水变化过程，并基于高潜水位矿区土壤水分的遥感反演识别开采扰动的影响范围与程度，为类似矿区开采沉陷水体的监测识别与影响评估提供了新的思路，并可推广应用至其他类似矿区。

Coal mining leads to surface subsidence and waterlogging,which is the main feature of coal mining areas in eastern plains with high underground water levels. Long term detection of changes in the subsidence water body is helpful to quantitatively evaluate the comprehensive impact of coal mining on land,ecology,and community. In the absence of underground mining knowledge as a guide,how to identify and distinguish the mining subsidence water body from the natural water body and the excavated water body caused by other human activities,and quantify the boundary and extent of the mining subsidence effects are difficult to be monitored by remote sensing only. This study takes the Yanzhou coalfield in Shandong Province as the study area. Based on all time series Landsat images  available since 1986,the authors have developed a spatiotemporal dynamic mapping method to detect the changes of subsidence water and land reclamation based on trajectory data under Google Earth Engine (GEE) platform using the CCDC (Continue Change Detection and Classification) algorithm. Three indices of VSDI (Visible and Shortwave Infrared Drought Index),LSWI (Land Surface Water Index),and SMMI (Soil moisture monitoring index) that reflect the soil moisture was inverted in the buffer zone of the subsidence water body using Sentinel 2 data respectively. According to the spatial variation law of soil moisture content,the authors measured the spatial distribution characteristics of soil moisture at different distances from the subsidence water area,further quantitatively analyzed the influence range and degree of mining disturbance. The results show that ① The annual spatial and temporal data of surface subsidence water and reclamation caused by underground coal mining from 1986 to 2017 were identified based on the CCDC algorithm with an accuracy of 85% and 77%,respectively. ② From 1990 to 2017,a waterlogged area of 3 021.08 hectares was accumulated in the study area,about 75.80% of which occurred from 2001 to 2011. Subsidence water reclamation occurred after 1993,with a cumulative area of 888.37 hectares,accounting for 29.41% of the total subsidence water area,mainly concentrated after 2007. ③ The impact of subsidence water is mainly concentrated within 120 m from the periphery of the subsided water body,where drastic soil moisture changes occur in this area. There is a disturbance within 120-300 m,but the influence intensity is slight. Almost there is no effect beyond 300 m. In this paper,the authors analyzed the long time series of subsidence water change in the mining area and identified the influence range and degree of mining disturbance based on the remote sensing inversion of soil moisture in the high phreatic level mining area. It provides a new method for monitoring,identification and impact assessment on mining subsidence water in the similar mining area.

1 研究区概况

2 数据与方法

   2.1 研究所采用的数据及预处理

   2.2 研究方法

3 研究结果

   3.1 结果精度验证

   3.2 沉陷积水与水体恢复的时序特征

   3.3 开采沉陷造成的影响范围确定

4 结论与展望