Quality evaluation of land reclamation in mining area based on remote sensing

Several deficiencies exist in the present evaluation of land reclamation quality in mining areas.These include the absence of an established set of evaluation index systems and standard acceptance criteria,as well as the use of traditional sam-pling techniques,which are costly and in eficiency,and time-consuming.Compared with the traditional sampling survey methods,remote sensing has the advantages of a wide detection range,diverse information collection,multiple data-acquiring strategies,high speed,and short cycle.In this study,we used the Xinzhuang coal mining field in Yongcheng,Henan Province as an example to extract information and invert surface parameters using remote sensing techniques,based on national and local reclamation regulations and standards.Subsequently,using remote sensing,we constructed an index system for evaluating land reclamation quality in three aspects:reclaiming project quality,soil quality,and ecological benefits.Through the grading standards of evaluation indicators and quantitative remote sensing models,we determined the extracted information on the area of indicators,roads,ditches,soil moisture,organic matter,and ecological benefits after reclamation.Based on this,we established a quality evaluation model for mining land reclamation using an improved index and method.The evaluation units were divided,and the weight of the evaluation index was determined using the analytic hierarchy process and data envelopment analysis(AHP-DEA)method.The land reclamation quality in the study area was comprehensively evaluated,field accuracy was verified,and the results were analyzed.The results show that,except for the removal of roads,houses,and fishponds in the study area,all 13 evaluation units achieved a score of 60 points or higher.The quality of reclamation met the standards,and the evaluation results were consistent with the conclusions of the field investigation and project acceptance report,demonstrating the reliability and feasibility of the method developed in this study.The research results will provide technical support for the scientific evaluation of land reclamation quality.

SOME KEY ISSUES ON THE APPLICATION OF SATELLITE REMOTE SENSING TO MINING AREAS

In order to apply Satellite Remote Sensing (RS) to mining areas, some key issues should be solved. Based on an introduction to relative studying background, related key issues are proposed and analyzed oriented to the development of RS information science and demands of mining areas. Band selection and combination optimization of Landsat TM is discussed firstly, and it proved that the combination of Band 3, Band 4 and Band 5 has the largest information amount in all three-band combination schemes by both N-dimensional entropy method and Genetic Algorithm (GA). After that the filtering of Radarsat image is discussed. Different filtering methods are experimented and compared, and adaptive methods are more efficient than others. Finally the classification of satellite RS image is studied, and some new methods including classification by improved BPNN(Back Propagation Neural Network) and classification based on GIS and knowledge are proposed.

Optimization of desert lake information extraction from remote sensing images using cellular automata

Desert lakes are important wetland resources in the blown-sand area of western China and play a significant role in maintain-ing the regional ecological environment.However,large-scale coal mining in recent years has considerably impacted the deposition condition of several lakes.Rapid and accurate extraction of lake information based on satellite images is crucial for developing protective measures against desertification.However,the spatial resolution of these images often leads to mixed pixels near water boundaries,affecting extraction precision.Traditional pixel unmixing methods mainly obtain water coverage information in a mixed pixel,making it difficult to accurately describe the spatial distribution.In this paper,the cellular automata(CA)model was adopted in order to realize lake information extraction at a sub-pixel level.A mining area in Shenmu City,Shaanxi Province,China is selected as the research region,using the image of Sentinel-2 as the data source and the high spatial resolution UAV image as the reference.First,water coverage of mixed pixels in the Sentinel-2 image was calculated with the dimidiate pixel model and the fully constrained least squares(FCLS)method.Second,the mixed pixels were subdivided to form the cellular space at a sub-pixel level and the transition rules are constructed based on the water coverage information and spatial correlation.Lastly,the process was implemented using Python and IDL,with the ArcGIS and ENVI software being used for validation.The experiments show that the CA model can improve the sub-pixel positioning accuracy for lake bodies in mixed pixel image and improve classification accuracy.The FCLS-CA model has a higher accuracy and is able to identify most water bodies in the study area,and is therefore suitable for desert lake monitor-ing in mining areas.

Extracting mining subsidence land from remote sensing images based on domain knowledge

Extracting mining subsidence land from RS images is one of important research contents for environment monitoring in mining area. The accuracy of traditional extracting models based on spectral features is low. In order to extract subsidence land from RS images with high accuracy, some domain knowledge should be imported and new models should be proposed. This paper, in terms of the disadvantage of traditional extracting models, imports domain knowledge from practice and experience, converts semantic knowledge into digital information, and proposes a new model for the specific task. By selecting Luan mining area as study area, this new model is tested based on GIS and related knowledge. The result shows that the proposed method is more pre- cise than traditional methods and can satisfy the demands of land subsidence monitoring in mining area.

The Massive Expansion and Spatial Transformation of Potentially Contaminated Land Across China in 1990–2020 Observed from Remote Sensing and Big-data

Identifying and monitoring the spatiotemporal patterns of potentially contaminated land(PCL) in China is a key concern of ecological governance. However, the dynamics of PCL’s expansion remain unclear nationwide. Integrating high-resolution remote sensing images, a land-use/cover change database, crawler data from websites, and other multisource data, we produced a new dataset of China’s PCL in 1990, 2000, 2010, and 2020 using data fusion technology. Then we analyzed the spatiotemporal patterns of China’s PCL from 1990 to 2020. Our study shows that the acquired vector dataset of China’s PCL is of high quality and reliability, with an overall accuracy of 93.21%. The area of China’s PCL has kept growing for the past 30 years, and the growth rate was especially rapid during2000–2010, 2.32 and 6.13 times as rapid as that during 1990–2000 and 2010–2020, respectively. PCL has also been trending toward higher aggregation over markedly enlarged areas and has transferred progressively from north and southeast of China to northwest and southwest of China and Qinghai-Tibet Plateau. The patterns of China’s PCL have been driven by the joint factors of policies, mineral resources, economy, and others, among which policies and the economy have contributed more prominently to the long-term transition.Our study promotes the access to high-quality spatial data of PCL to facilitate environmental governance of mine wastes, pollution and land management.

结合SBAS-InSAR与光学遥感的矿区地表塌陷监测

针对矿区地表塌陷过程中难以获取全面的形变信息问题,以赵固二矿采煤塌陷区为研究区,首先,基于多源多时相光学遥感影像采用综合归一化差异水体指数提取开采塌陷区积水的空间展布情况;然后,利用SBAS-InSAR技术处理63景2017年3月19日—2021年4月27日Sentinel-1A影像,获取研究区地表形变信息。结果表明:2010—2019年年均塌陷积水面积新增幅度基本平稳,2019—2021年年均塌陷积水面积新增幅度减小;研究区最大形变速率为-60.42 mm/a,形变速率小于-15 mm/a区域均有房屋墙体损坏和地裂缝现象。该方法有效地提取了开采塌陷区较长时期内地表形变信息和空间展布情况,为矿区地表塌陷监测提供新的思路,形成了一套有效的矿区地表塌陷监测工作方法和流程,达到了遥感技术在矿区地表塌陷宏观、快速、准确监测的目的,为矿区塌陷修复和生态环境治理提供基础数据支撑。

Rapideye Images in the Study of Contaminated Areas： The Case of Niccioleta （Tuscansy-ltaly）

Mining activity in Italy has been one of the main productive activities for millennia, particularly in the Tuscany region which has a great mining tradition, unfortunately characterized in the past by a management little interest to environmental problems. The area under study is the disused mine Niccioleta, in Val d＇Aspra, located about 6 km NE of Massa Marittima in the province of Grosseto. The area is characterized by the presence of four major landfills, in which prevail quantitatively fine-grained materials resulting from the treatment by flotation of pyrite. The study of satellite images offers a new approach to the study of environmental problems. The results obtained from the RapidEye images showed the presence of pyrite and chalcopyrite followed from arsenopyrite, as confirmed by the analysis of diffractometer of the samples and by bibliographic data. RapidEye images lend themselves very to be used to monitor areas of disused mining deposits of ores with primary mineralization predominantly sulphides and subject to oxidized characterized by processes of oxidation/dissolution of pyrite sulphide most common and abundant. In fact, the results of this study have highlighted the potential of remote sensing applied to the study of mining areas, noting the possible benefits, both time and cost, which could be obtained by using these techniques.

高光谱遥感技术在矿山地质环境调查中的应用

矿山地质环境受矿产资源开采活动的影响而产生剧烈变化,直接或间接影响了生态环境的可持续发展。近年来,高光谱遥感技术因其具备高光谱分辨率、空间信息与光谱信息合一的优势,解决了传统遥感技术仅能识别大致地物的问题,被广泛应用于矿山地质环境研究与监测。在梳理相关文献的基础上,介绍了高光谱遥感技术原理及发展现状,归纳了高光谱遥感技术在矿区植被、矿区水体、矿区土壤和矿区大气等方面的应用与监测;对矿区高光谱发展前景进行展望,认为未来随着遥感技术的发展对矿山地质环境的监测将逐渐有序化、长期化。

伊阿铁路霍拉山越岭段工程地质遥感解译与分析

伊阿铁路霍拉山越岭段地形切割强烈、地质环境复杂,部分区域为无人区,道路不便,现场勘察工作难度极大。本文利用多源遥感数据,通过对越岭段区域的断裂构造、不良地质、煤炭采空区等开展全面的解译提取分析,准确获取各类地质要素发育的位置、规模、展布形态等关键信息。结合重点区现场勘察,提出线路需尽量大角度垂直穿越区内NWW—近EW向断裂延伸区,合理避让连续危岩落石区、大型滑坡体,绕避或采用隧道工程通过风蚀垅岗、风蚀残丘地貌区,远离煤炭采空区以及由其引起的塌陷区等重要参考意见和建议。研究成果可有力支撑现场勘察以及线路方案的比选确定工作,充分发挥遥感技术独特的“天眼”优势,具有重要的理论以及实际意义。

重金属污染水体背景下的底质反射率光谱特征及其对离水反射率贡献影响分析

水体重金属遥感反演是水环境遥感领域中的难题,目前仍有相当多的基础性问题有待解决,而浅水区底质对离水反射率贡献规律是将来遥感反演模型精度其中一个重要影响因素,尤其在重金属污染这种特殊的背景下,揭示其贡献规律对提升水体重金属遥感模型精度有重要作用,测量结果对研究重金属尾矿底泥的反射率光谱特征以及区分常见水底底质有参考意义。首先利用光谱仪测量获得广东大宝山尾矿底质反射率,发现其在波长755、 1 280、 1 620和2 200 nm存在反射峰,有明显光谱特征,并与河床常见的粗沙、淤泥和石头三类底质反射率进行了对比分析,结果表明:一方面,粗沙和淤泥反射率呈一缓慢上升的曲线,与存在多个特征反射峰的矿区底泥有明显区别;另一方面,石头反射率则在波长范围550~650 nm出现一个宽而扁平的反射峰后随即在波长675 nm处出现波谷然后增大至波长750 nm后趋于平缓,其特征波长与矿区底泥均不一样。上述特征波长可作为重金属污染背景下底泥光谱的重要区分波段。测量水深为1 cm、 10 cm和深水区的离水反射率,结合水质遥感模型计算出1 cm水深情况下底质反射光、水体散射光,并将其对离水反射率贡献进行讨论。离水反射率测量结果表明,底质对浅水区影响极大,随着水深变浅,离水反射率总体不断被抬升。而水底反射光和水体散射光对离水反射率贡献规律则以波长515 nm为分界线,向短波方向以水体散射光贡献为主,向长波方向则以水底反射光为主,贡献量由底质反射能力和水体散射能力共同决定。对考虑了底质反射光后的遥感模型精度进行评价,模型计算得到波长范围350~950 nm内的离水反射率与野外测量值对比结果,二者具有显著的线性相关(R^(2)=0.964 2),相对误差在波长范围560~830 nm低于10%,甚者可低于5%,模型总体模拟精度较好,远比在不考虑底质影响时精度高,模型满足将来水中重金属遥感反演要求。研究结果对今后水体重金属遥感反演时处理底质影响提供了重要的参考数据和理论依据,有助推动该领域进一步发展。

基于街区小气候环境冬夏协同优化的城市绿地生态网络精准修复

越来越多城市绿地生态网络建设进入存量更新和结构性优化并存发展的阶段,生态安全优先基础上如何进一步提升城市绿地生态网络的多种生态系统服务功能是当前风景园林实践研究的重要议题。应对全球气候变化、极端天气多发导致的人居环境宜居性下降问题,目前基于气候适应的城市绿地生态网络规划关注促进自然通风以缓解夏季热岛效应,尚忽视了其冬季可能带来的负效应。从气候调节视角切入,探讨城市绿地生态网络在协同冬夏季小气候环境中的低效空间识别与精准修复技术方法。以江苏省昆山市密集城区的189个街区单元为研究对象,通过遥感反演的方法对其冬、夏季小气候环境进行分析,聚类获得7类小气候特征街区。之后,叠加现状绿地生态网络与计算流体力学模拟风环境结果,提取现状适于通风的生态廊道,从宽度、方向、类型等方面分析其潜在通风效能,耦合街区冬夏小气候环境特征,识别研判现状绿地生态网络在冬夏温湿环境调节上存在的低效问题,并提出冬夏协同的精准修复策略,为优化人居环境宜居性、提升城市绿地生态网络生态系统服务综合效能提供技术参考。

基于Landsat遥感影像的平庄西露天矿植被恢复效果与归因分析

监测与评估生态脆弱区煤矿排土场植被恢复效果,厘清植被变化的驱动因子,能为排土场植被恢复的可持续发展提供技术和数据支撑。现有的利用长时序遥感数据进行矿区植被变化及其驱动机制的相关研究,缺少对矿区尺度下排土场全生命周期植被恢复的精细刻画,缺乏专题指标以定量描述排土场植被恢复力。针对上述问题,以平庄西露天矿的3个外排土场为研究对象,基于Landsat遥感影像生成了研究区逐年的最大INDV数据,采用时序趋势分析法对排土场植被进行全周期精细时空监测,并利用植被恢复力评价区域尺度植被恢复效果。最后,使用多元回归残差法定量分析气候变化和人类活动两类主要驱动因子对排土场植被变化的影响。实验结果表明:①2008—2023年间,3个排土场的植被指数都呈上升趋势,不同植被覆盖等级的面积比例逐步上升,绝大部分区域已经恢复至露天开采前的水平;②排土场植被恢复力呈现“先增后降再趋于稳定”的特点,山后排土场恢复力最强,其次为三家排土场,太平地排土场恢复力相对较弱;③在植被改善区,主要呈现人类活动主导影响、人类活动和气候变化共同影响2类驱动模式;在植被退化区,还存在气候变化主导影响模式,尤其在三家排土场,人类活动和气候变化共同作用影响较大。

矿山生态环境定量遥感监测与智能分析系统设计与实现

矿山生态环境监测与治理是国家生态文明建设和“双碳”目标下的重中之重,其信息化、智能化建设在新一代信息技术革命的助推下成为数字中国建设的重要一环,也是当下时代发展的必然趋势。然而,现有矿山生态环境监测系统仍然停留在单一专题、要素不全、基础量测、本地管理的初级阶段,无法满足现实环境中对矿山生态环境多要素、长时序、高频次监测与分析的需求。基于此,提出一种B/S架构下的矿山生态环境定量遥感监测与智能分析系统——矿山生态天眼,并详细介绍了其研发需求、技术架构、关键技术及核心功能。系统依托卫星遥感技术及其他监测手段,获取并聚合不同来源、信息丰富的矿山生态大数据,形成矿山分布一张图和数据资源服务;进而基于定量遥感反演矿山生态环境各生态参数,形成一套长时序、多要素的矿山生态监测产品,涵盖人类活动、自然地理条件和“植−土−水−气”各生态要素;在此基础上,系统提供GIS时空分析、统计分析及综合定量评价等工具集,分别实现对矿区土地利用、归一化植被指数(NDVI)等参数伴随采矿活动在空间上的变化监测,对土壤含水量、水体悬浮物浓度等生态要素历史统计值在不同时空位置和区域下的查询与可视化,对顾及多项生态因子的矿山生态环境质量综合定量评价,并最终形成矿山生态扰动与治理监测报告。矿山生态天眼的应用将服务于矿山生态环境全过程变化监测、数据管理、智能分析和决策应用,有望提高矿山生态环境监测与治理的效率和智能化水平,为推动生态文明信息化建设提供参考方案。

矿山开采损害InSAR/UAV融合监测关键技术及应用

煤炭开采引起的地质环境损害问题已成为关注和研究的焦点;尤其西部矿区以大规模、高强度开采主,引起的岩层及地表移动也呈现出变形速度快、损害程度深、波及范围广的特点,对其进行快速、准确、全面地监测是矿区生态环境保护、绿色矿山建设的关键。传统的地表移动观测方法和单一测量技术均难以满足需求;无人机(UAV)摄影测量与合成孔径雷达干涉(InSAR)优势互补,二者融合在地质环境损害监测方面具有优势,提出了融合UAV/InSAR监测矿山开采损害的关键理论、技术和方法。重点论述了UAV/InSAR融合新型多尺度观测站建立模式和二者协同观测方法;探讨了UAV/InSAR跨尺度异质遥感数据融合策略;提出了基于UAV/InSAR特征级融合的地表沉陷变形及关键环境因素精确提取的思路和方法;并以内蒙古王家塔煤矿为例进行了应用研究,首先采用UAV摄影测量和InSAR技术建立了工作面尺度“点-线-面”结合新模式观测站,同时获得研究区域的UAV光学影像、InSAR影像和主断面关键点水准数据;利用UAV/InSAR的特征级融合方法提取了矿区沉陷盆地,并利用UAV沉陷盆地和融合沉陷盆地分别进行求参。研究结果表明:UAV监测的最大下沉值为2487 mm,中误差为81 mm,UAV测量误差对于沉陷盆地边界影响较大,导致无法精确获取盆地边界区域;InSAR监测的最大下沉为110 mm,远小于实际,InSAR受时空失相干影响,无法准确获取大变形区域沉降;通过InSAR/UAV特征级融合,得到了整体和边界区域精度更高融合下沉盆。在求参方面,单独UAV数据求取的下沉系数与水准结果的相对误差为1.4%,主要影响角正切偏差较大,约20%,主要为UAV下沉盆地边界误差较大所致。与单独UAV求参相比,融合下沉盆地数据求出的参数更准确,主要影响角正切的相对误差仅为5%,融合数据很好解决了单一UAV反演tanβ误差偏大的问题。该工程案例表明了UAV/InSAR融合技术在西部矿区地表沉陷变形监测中具有显著的优势,可为西部煤矿开采地表损害监测提供技术支撑。

基于校正的遥感数据方法估算地下水补给量:以鄂尔多斯盆地台格庙矿区为例

地下水补给是影响区域地下水资源的关键因素,准确评价地下水净补给量对地下水资源的合理开发利用和区域水文循环过程的研究具有基础性意义。通常情况下,在站点尺度可以使用蒸渗仪等设备直接监测地下水补给,而在区域尺度获取地下水补给则难度较大,因此亟待构建一种简便量化区域尺度地下水补给量的方法。本文提出一种基于遥感数据校正来估算区域尺度多年地下水补给量的方法,并在黄河中游内蒙古鄂尔多斯盆地新街台格庙矿区进行试点应用研究。通过对多源降水数据的筛选和蒸发遥感数据的校正,评价台格庙矿区地下水多年净补给量,并基于实测数据的Hydrus-1D数值模拟验证评价方法的有效性。研究结果显示,TPDC来源的降水量数据集更适用于反映研究区降水量的空间分布;MOD16遥感数据低估了矿区实际蒸散量,实际蒸散量是MOD16数据的1.41倍;矿区多年平均净补给量变化范围在-65.70~73.93 mm之间,超过80%的区域净补给量在-30~30 mm之间。新方法估算的地下水净补给量与基于实测数据的土壤水流数值模拟结果接近,表明该方法在缺乏地表产流或地表径流观测资料翔实的地区具有较强的实际应用价值。

L波段D-InSAR在矿区地面沉降监测中的应用

合成孔径雷达差分干涉测量(differential interferometry synthetic aperture radar,D-InSAR)是目前矿区地面沉降监测的常用技术之一,阐述与分析了基于GAMMA的双轨D-InSAR技术的数据处理流程及其关键技术,对三景L波段的ALOS PALSAR雷达影像进行处理,得到兖州矿区地面沉降图。借助Arcgis、ENVI等对研究区2007-12-02—2008-02-06、2008-02-06—2008-03-23的沉降进行详细对比分析,得到其沉降分布图、东滩矿区地面沉降图、剖面图、部分特征点的沉降情况等。研究结果表明,利用L波段双轨D-InSAR技术可以对矿区的地面沉降位置、分布、沉降量等信息进行直观、详细地对比分析和重现,为DInSAR技术监测矿区地面沉降的应用与分析提供新的思路。

2000—2020年三峡库区生态环境质量综合评估

客观综合地评估区域生态环境质量是现今全球关注和研究的热点问题。三峡库区自蓄水后,生态环境就变得比较脆弱,因此评价三峡库区生态环境质量十分必要。基于2000、2010、2020年的遥感数据,结合生态环境指数(EI)和遥感生态指数(RSEI)构建了三峡库区生态环境质量综合评价体系。结果表明,构建的生态环境质量综合评价体系能够有效实现三峡库区大尺度长时间序列的生态环境质量评价,精度可靠。2000—2010年三峡库区生态环境质量呈现下降趋势,而2010—2020年呈现上升趋势,2000—2020年整体上表现为上升趋势。空间上,三峡库区生态环境质量较好的区域主要分布在东部、北部和东南部的巫溪县、宜昌县、兴山县、石柱县和巴东市等;质量较差的区域主要分布在西南部和长江沿岸城市化进程较快的地区。

利用改进型遥感生态指数的岩溶区生态环境质量评价

本文选取绿度(NDMVI)、湿度(Wet)、石漠化(RDI)和热度(LST)4个指数,构建了改进型遥感生态指数(IRSEI),以实现县域岩溶地区的生态环境质量评价。基于2003与2023年的县域尺度遥感影像与其他地理信息数据,通过若干方面定量对比IRSEI与RSEI指数。结果表明,IRSEI包含了更丰富的山地植被与石漠化状况信息,能够更好地表征和评价岩溶地区生态环境质量,适合作为岩溶地区生态环境质量评价的定量指标。研究区在2003—2023年间,生态环境质量改善和退化情况均有发生,但生态环境质量总体下降了7.46%,生态等级由良变为中等。从空间分布来看,罗平县研究区生态质量差的区域主要分布在北部,生态环境质量优的区域主要分布在中部。

联合深度学习与面向对象分析的卫宁北山露天矿山采场信息提取

卫宁北山地区是宁夏开展矿山生态环境恢复治理工作的关键节点区域。快速准确地获取区域内采场空间信息,监测矿山生态修复工程建设进展,已成为自治区矿政管理的重要工作之一。本文以卫宁北山地区为研究区,提出了一种联合深度学习与面向对象分析的国产高分辨率遥感卫星影像露天矿山采场信息提取方法。该方法首先采用支持小样本学习的U-Net模型进行露天采场的初步识别;然后结合面向对象分析与空间分析方法实现露天采场边界的精细化提取。经验证,该方法识别露天采场空间位置的精度为0.71,平均空间范围的提取精度为0.78。在此基础上,对卫宁北山地区露天矿山在2019—2021年的恢复治理情况开展识别与分析,识别出的125处露天矿山采场中有43.2%已开展生态修复工程,含44处采坑填埋与覆土整平、6处重新开发利用及4处人工复绿。结果表明,该方法在无须进行特征工程的前提下能够较为快速地对露天采场空间信息进行精细化提取,可为宁夏矿山遥感监测提供技术参考。

基于DInSAR技术与WorldView-2影像的煤矿塌陷区信息提取研究

结合DIn SAR与World View-2光学遥感解译技术,对研究区进行多源数据处理,提取研究地区的煤矿塌陷区信息,然后对处理后的两种结果进行对比。二者既有区别,又可以互补,最终在研究区形成完整的塌陷区信息。