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.

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.

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.

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.

Application of remote-sensing-image fusion to the monitoring of mining induced subsidence

We discuss remote-sensing-image fusion based on a multi-band wavelet and RGB feature fusion method. The fused data can be used to monitor the dynamic evolution of mining induced subsidence. High resolution panchromatic image data and multi-spectral image data were first decomposed with a multi-ary wavelet method. Then the high frequency components of the high resolution image were fused with the features from the R, G, B bands of the multi-spectral image to form a new high frequency component. Then the newly formed high frequency component and the low frequency component were inversely transformed using a multi-ary wavelet method. Finally, color images were formed from the newly formed R, G, B bands. In our experiment we used images with a resolution of 10 m (SPOT), and TM30 images, of the Huainan mining area. These images were fused with a trinary wavelet method. In addition, we used four indexes—entropy, average gradient, wavelet energy and spectral distortion—to assess the new method. The result indicates that this new method can improve the clarity and resolution of the images and also preserves the information from the original images. Using the fused images for monitoring mining induced subsidence achieves a good effect.

Monitoring Land Use Dynamics in Chanthaburi Province of Thailand Using Digital Remotely Sensed Images

A comprehensive method of image classification was developed for monitoring land use dynamics in Chanthaburi Province of Tailand. RS (Remote Sensing), GIS (Geographical Information System), GPS (Global Positioning System) and ancillary data were combined by the method which adopts the main idea of classifying images by steps from decision tree method and the hybridized supervised and unsupervised classification. An integration of automatic image interpretation, ancillary materials and expert knowledge was realized. Two subscenes of Landsat 5 Thematic Mapper (TM) images of bands 3, 4 and 5 obtained on December 15, 1992, and January 17, 1999, were used for image processing and spatial data analysis in the study. The overall accuracy of the results of classification reached 90%, which was verified by field check.Results showed that shrimp farm land, urban and traffic land, barren land, bush and agricultural developing area increased in area, mangrove, paddy field, swamp and marsh land, orchard and plantation, and tropical grass land decreased, and the forest land kept almost stable. Ecological analysis on the land use changes showed that more attentions should be paid on the effect of land development on ecological environment in the future land planning and management.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

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.

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.

Study on the amplitude inversion of internal waves at Wenchang area of the South China Sea

The field experiment is conducted from April 16, 2005 to July 20, 2005 at Wenchang area east of Hainan Island （19~35＇N, l12~E） of China. Internal wave packets are observed frequently with thermistor chains during the experiment. Meanwhile, internal waves are also detected from a synthetic aperture radar （SAR） image on June 19, 2005 and several other moderate-resolution imaging spectroradiometer （MODIS） images near a mooring position. The distance between the positive and negative peaks induced by the internal wave can be obtained from satellite images. Combined with remote sensing images and in situ data, a new method to inverse the amplitude of the internal wave is proposed based on a corrected nonlinear Schr6dinger （NLS） equation. Two relationships are given between the peak-to-peak distance and the characteristic wavelength of the internal wave for different nonlinear and dispersion coefficients. Based on the satellite images, the amplitude inversion of the internal waves are carried out with the NLS equation as well as the KdV equation. The calculated amplitudes of the NLS equation are close to the observation amplitude which promise the NLS equation a reliable method.

Application Advantages of Staring Area Imaging Technology(SAIT) for Microsatellites

The Staring Area Imaging Technology(SAIT) satellite continuously "images" the target over a certain time range, and can realize continuous imaging and multi-angle imaging of the area of interest. It has the characteristics of flexible imaging parameter setting and fast image preprocessing speed, enabling dynamic target detection and tracking, super-resolution, surface 3 D model construction, night-time imaging and many other application tasks. Based on the technical characteristics of the SAIT satellite, this paper analyzes the challenges in satellite development and data processing, focuses on the quasi-realtime application of SAIT satellite data, and looks at the development trend of the SAIT satellite.

Comparing Pan-Sharpening Algorithms to Access an Agriculture Area: A Mississippi Case Study

Numerous satellites collect imagery of the Earth’s surface daily, providing information to the public and private sectors. The fusion (pan-sharpening) of high-resolution panchromatic satellite imagery with lower-resolution multispectral satellite imagery has shown promise for monitoring natural resources and farming areas. It results in new imagery with more detail than the original multispectral or panchromatic images. In agricultural areas in Mississippi, landscapes can range from complex mixtures of vegetation and built-up areas to dense vegetative regions. More information is needed on pan-sharpened imagery for assessing landscapes in rural areas of Mississippi. WorldView 3 satellite imagery consisting of landscapes commonly found in rural areas of Mississippi was subjected to 17 pan-sharpening algorithms. The pan-sharpened images were compared qualitatively and quantitatively with three quality indices: 1) Erreur Relative Globale Addimensionelle de Synthese;2) Universal Image Quality Index;3) Bias. à trous wavelet transform with the injection model 3 and hyperspherical color spaced fusion methods were ranked among the best for maintaining image integrity for qualitative and quantitative analyses. The optimized high-pass filter method was often ranked last by the quality indices. The smoothing filter-based intensity modulation algorithm and the gaussian modulation transfer function match filtered with high-pass modulation injection model added artifacts to the images. Pan-sharpened satellite imagery has great potential to enhance the survey of Mississippi’s agricultural areas. The key to success is selecting an image fusion process that increases spatial content while not compromising the image integrity.

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。

不同影像处理方法在大坝施工范围识别中的应用分析

【目的】施工范围是大坝建设过程中施工精细化管理的基础内容,传统的施工范围识别方法主要是人工解译,效率低、成本高、主观性强。将影像智能解译的新方法应用于大坝施工范围识别成为一项有挑战性的技术问题。【方法】以我国贵州省凤山水库为研究区,通过对大坝施工范围特点与现有常用的遥感影像解译方法的分析,针对大坝施工范围具有的区域面积大且连续、地物复杂、人工扰动性程度高等特征,选用高分二号和哨兵二号遥感影像,研究不同的遥感影像解译方法在大坝施工范围自动识别的应用效果。【结果】文中用到的三种方法均取得较好的结果,且分别具有不同的优势。【结论】结果表明,基于影像分割的施工范围识别方法、基于变化检测的施工范围识别方法和基于影像智能分类的施工范围识别方法均能取得较好的识别结果,其中第二种方法最优,且无需人工干预;第三种方法次之,但是方法二和方法三对样本的数量和质量依赖性较高,且存在离散图斑错分的现象;第一种方法容易受阈值的选择和叠加分析的影响,识别精度低于后两种,但是处理流程简单、容易实现。

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。

基于轻量化YOLOX-S与多阈值分割的矿山遥感图像去噪算法

矿山遥感图像普遍存在大量的噪点,给后续图像分析和处理带来了很大困难。提出了一种基于轻量化目标检测模型YOLOX-S和多阈值分割的矿山遥感图像去噪算法。首先使用YOLOX-S模型对矿山遥感图像进行目标检测,得到矿山目标的位置信息。然后针对矿山目标的特点,设计了一种多阈值分割方法消除图像中的噪声点。通过将图像分为若干个子区域,并对每个子区域采用不同的阈值进行二值化处理,最终将各子区域的二值化结果合并得到去噪后的图像。试验结果表明:该算法能够有效地去除矿山遥感图像中的噪声点,并且在保留目标特征的同时,大幅提升了图像质量。此外,由于采用了轻量化模型和多阈值分割算法,使得该算法具有较快的处理速度和较低的计算成本,适用于大规模图像数据的处理任务。

基于高分辨率遥感影像的居民地分类方法研究

随着我国城镇化进程的不断深入,大量乡村居民地已经转化为城镇居民地,为了度量二者实时的转化情况,该文提出一种基于监督分类机制的居民地分类方法,该方法首先采用边缘特征及高斯函数量化影像上的局部特征,然后构建5种城镇及乡村居民地分类规则,其次创建训练样本对各类规则进行学习,最后通过城镇及乡村测试样本验证该文方法的精度。实验表明,该文方法可以对高分辨率遥感影像城镇及乡村居民地进行初级分类,为“城镇化”进程提供一个新的衡量指标。

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。

基于并联卷积神经网络的无人机遥感影像建筑区域测量

无人机遥感影像覆盖范围广,难以区分建筑区域与背景区域,导致无人机遥感影像建筑区域测量结果可靠性下降;以解决这一问题作为研究目标,提出了一种基于并联卷积神经网络的无人机遥感影像建筑区域测量方法;获取无人机遥感影像,通过静态输出、图像融合、去雾等环节完成遥感影像预处理;构建并联卷积神经网络,通过网络训练传播提取预处理后无人机遥感影像建筑区域边缘特征,经过特征匹配实现无人机遥感影像中建筑区域识别,结合面积计算结果得到建筑区域的测量结果;经过精度性能测试实验得出结论,在有雾和无雾环境下所提方法与传统区域测量方法相比的建筑区域测量误差分别降低了0.505 km^(2)和0.305 km^(2),说明该方法的测量结果可靠性更高,可以广泛应用在无人机遥感影像建筑区域测量领域。