Application of Remote Sensing and GIS in Mineral Alteration Mapping and Lineament Extraction Case of Oudiane Elkharoub (Requibat Shield, Northern of Mauritania)

The integration of remotely sensed data allowed the successful characterization of the mineral alteration zones of the Oudiane Elkharoub area in the Northeastern part of Reguibat Shield using image transformation techniques. As both chemical and geochemical analyses showed significant Au, Ag, Cu, Pb, Mn, Cr, Ni, Th and Y anomalies, it’s very interesting to apply the remote sensing and GIS in mineral resources mapping. The remote sensing is a direct adjunct to the field, lithologic and structural mapping, and more recently, GIS has played an important role in the study of mineralization areas. The integration of several evidential maps highlighted the plausible areas with high concentrations of chlorite, epidote, kaolinite, calcite, alunite, hematite, illite and sulfur among other key mineral alterations that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The methodological approach integrates geological information acquired from Aster and Landsat 8 OLI/TIRS (Operational Land Imager/Thermal InfraRed Sensor) images and a multi-criteria GIS analysis. The superimposition of various lineament and hydrothermal alteration maps and the consideration of precious and base metal indicators allowed the zoning of sites likely to contain mineral concentrations. Remote sensing becomes an important tool for locating mineral deposits in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithological mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. Ground truthing and laboratory studies including XRD analysis were utilized to verify the results.

Dynamic monitoring of surface water in the Tibetan Plateau from 1980s to 2019 based on satellite remote sensing images

The Tibetan Plateau is known as the“Asian water tower”,and changes in its surface water distribution are important indicators of global climate change and the regional response to these changes.Dynamic monitoring of the surface water on the Tibetan Plateau is an important part of the research on the functions of the“third pole”of the earth and the Asian water tower.With the support of the Google Earth Engine cloud platform,this study used a spectral index-based fast extraction method to obtain surface water data from multi-temporal Landsat(Landsat 4,5,and 8)satellite remote sensing images.Based on the extracted surface water data,we analyzed the spatiotemporal variations in the surface water of the Tibetan Plateau from 1980s to 2019.In this study,surface water area refers to the maximum coverage area of the surface water extracted from remote sensing images for one year,hereafter referred to as the surface water area.The results show that since 1980s,the overall surface water area of the Tibetan Plateau has increased,but not in a linear fashion.After a slight decrease from 1980s to 1995,the surface water area of the Tibetan Plateau increased steadily,except for a slight decrease in 2015,which may have been caused by the El Niño phenomenon.In terms of spatiotemporal distribution,different patterns exist in the various ecological regions of the Tibetan Plateau.The Inner ecological region had the greatest changes of surface water area among the ten ecological regions,accounting for 71.0%of the total surface water area increase from 1980s to 2019.The surface water bodies in the cold desert and the dry-winter subtropical climatic regions underwent the most changes,with their coefficients of variation being more than 20%.This study can provide data support for dynamic monitoring of surface water in the Tibetan Plateau.

Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

Wheat species play important role in the price of products and wheat production estimation.There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable.The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions.With the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle.In this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)data.Two thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat classification.In addition,deep neural networks are also developed.Experimental results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%accuracy.Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

Geological Context Mapping of Batouri Gold District (East Cameroon) from Remote Sensing Imagering, GIS Processing and Field Works

The Batouri area is located in the Adamawa-Yade domain in East Cameroon region, and has a high geological potential as a host for gold deposits. It is covered by thick forest where outcrops are sometime scarce. The purpose of this study is to generate and combine different geological information which makes up the specificity of the Batouri gold District, in order to contribute to the better knowledge of its geological setting. From satellite imageries, GIS tools and field data;lithological units, lineament and density maps have been dressed at regional-scale of 1/400.000. The mapping has enabled the discovery of spatial and topologic relationships between shear zones, lineaments, gold occurrences and often mineralized granitic intrusions. According to the field data, lithological and lineament maps, the lithology of the Batouri gold District is characterized by alkali granitoids (tonalite, granodiorite, syenomonzo-granite, alkaline granite) hosted by orthogneisses and migmatites as gold mineralization hosts;while the lineaments show a major shear zones trending NE-SW defined by presence of mylonites. The shear zones crosscut all lithologies, mostly granodiorite where majority of gold occurrences is observed;locally, nearest these shear zones, rocks are transformed to the mylonites and gold is concentrated along. From density map, it is shown that the high gold mineralization zone corresponds to highest lineaments density. All those data suggest that gold mineralization in the Batouri district is controlled by tectonic and lithology. It is conclusive that Batouri gold deposit is epigenetic gold set emplaced in orogenic setting, during the post-collisional stage of the Central African Fold Belt (CAFB) and the Congo Craton (CC).

基于Landsat8卫星OLI的水体信息提取研究

选取Landsat8卫星OLI的武汉地区部分遥感影像,采用基于阈值的单波段法、谱间关系法、基于阈值的指数法提取水体影像,结果表明:单波段阈值法的水体提取可以达到89.44%;谱间关系法提取出的水体含有较多的建筑物信息,错分像元较多;基于阈值的指数法中MNDWI精度最高,可作为Landsat8卫星提取水体的重要方法。

基于Radarsat2与Landsat8协同反演植被覆盖地表土壤水分的一种新方法

主动微波遥感与被动光学遥感在反演地表土壤水分方面分别具有各自的优缺点,为了将这两者的优势结合弥补缺点,提出了一种基于Radarsat 2与Landsat 8数据协同反演植被覆盖地表土壤水分的半经验耦合模型.该模型基于水云模型,将光学遥感反演得到的植被冠层含水量作为水云模型的关键输入参数,并同时考虑植被冠层与土壤以及其之间的部分对雷达后向散射系数的影响,以此来去除雷达回波中的植被部分.最后选用内蒙古呼伦贝尔市额尔古纳市大兴安岭西侧研究区的Radarsat 2与Landsat 8遥感数据,利用新的耦合模型反演得到植被覆盖区土壤水分含量,并利用地面测量数据对模型进行验证.结果表明:利用Landsat 8数据反演植被含水量算法精度较高(R2=0.89),论文提出的耦合模型反演植被覆盖地表土壤水分精度比之前算法也有了较大的提高,其中HH极化效果最好,R2由0.27提高至0.65.这表明该耦合模型具有较好的反演精度,可以应用于植被覆盖区土壤水分含量的反演.

新型Landsat8卫星影像的反射率和地表温度反演

Landsat 8卫星自2013年2月发射以来,其影像的定标参数经过了不断调整和完善,针对Landsat 8开发的各种算法也相继问世.本文采用最新的参数、算法和引入COST算法建立的大气校正模型,对Landsat 8多光谱和热红外波段进行了处理,反演出它们的反射率和地表温度,并与同日的Landsat 7数据和实测地表温度数据进行了对比.结果表明,现有Landsat 8多光谱数据的定标参数和大气顶部反射率反演算法已有很高的精度,本文引入COST算法建立的Landsat 8大气校正模型也与Landsat 7的COST模型所获得的结果几乎相同,相关系数可高达0.99.但是现有针对Landsat 8提出的地表温度反演算法仍不理想,已提出的劈窗算法误差都较大.鉴于TIRS 11热红外波段的定标参数仍不理想,因此在现阶段建议采用单通道算法单独反演TIRS 10波段来求算地表温度,但要注意根据大气水汽含量的情况选用正确的大气参数计算公式.

基于ASTER与Landsat8的遥感蚀变信息提取——以新疆西昆仑塔什库尔干地区老并铁矿区为例

应用ASTER与Landsat 8数据,选用主成分分析法提取新疆西昆仑塔什库尔干地区老并铁矿区的遥感蚀变异常信息,将两者提取的综合结果进行比较分析后,与研究区内已知磁铁矿体信息叠加,结果表明两者提取的蚀变异常信息的准确性与可靠性均较好,且ASTER能辅助Landsat 8细化并确定铁染蚀变异常信息,缩小找矿靶区。

Landsat8卫星OLI遥感影像在生态环境监测中的应用研究

利用美国2013年2月发射的Landsat 8卫星上运营性陆地成像仪OLI获取的太湖流域遥感影像数据,开展了其在大气能见度、蓝藻水华及建设用地等生态环境监测领域中的应用研究。研究表明,Landsat 8卫星向未来延续了Landsat系列的长时期对地观测能力,且观测性能有所优化,适合对区域能见度、湖泊蓝藻水华、生态系统格局等生态环境问题进行反演和分析,是生态环境监测的重要遥感信息源。

基于Landsat8和DEM的老龙口水库库容遥感分析方法研究

本研究采用多期Landsat8 OLI影像数据结合数字高程模型(DEM)的方法对吉林省珲春市老龙口水库2015～2017年的库容变化进行定量分析,并利用已知的库容量对研究结果进行校验。

基于Landsat8的绿地格局对地表热环境的影响研究

在快速城市化背景下,城市的热环境问题不断加剧。本文以2017年8月4日和2018年1月27日的厦门岛Landsat 8数据为数据源,提取绿地空间信息并进行地表温度反演。实验结果表明:夏季绿地面积与温度呈现出中等程度负相关,冬季绿地面积与温度保持弱负相关;水体面积与温度在这两个季节里都表现出较强的负相关;聚集度较好的水体在这两个季节均表现出降温作用,绿地对温度的影响不显著。

基于Landsat8-OLI的热融湖提取方法研究

介绍了热融湖和水体提取的研究背景、研究区及数据来源,依据NDWI等9种水体指数算法,结合随机森林算法,对水体特征进行提取,并以人工解译出研究区水体,结合神经网络分类作为验证样本,验证各方法的精度,探寻一种适用于热融湖提取的方法。结果发现,传统的NDWI与EWI8能较好地区分冰雪区域与水体;在建立样本数据集后,随机森林的分类总体精度达到了90%。由此可以得出,机器学习能够为遥感分类提供更高精度的方法,并为水文环境监测、地表生态监测提供有力的工具。

Geological Controls and Prospectivity Mapping for Manganese Ore Deposits Using Predictive Modeling Comparison:An Integration of Outcrop and Remote Sensing Data,Sinai Microplate,Egypt

One of the most controversial minerals in their origin and occurrence around the world is manganese deposits.The Abu Zenima area is rated one of the most economically important places where manganese ore deposits(Mn ODs)are located in the southwest Sinai microplate,Egypt.These deposits are confined with the Um Bogma Formation(UBF)and the reserves of this region are relatively small.In this study,optical and radar data are used in a new challenge as an attempt to reach the closest controls and setting of Mn ODs.Moreover,Frequency Ratio(FR)and Logistic Regression(LogR)predictive models are applied to integrate different geospatial thematic maps,to predict new potential resource zones for increasing the ranges of mining quarries.Landsat8 OLI,Sentinel-2A Multi Spectral Instrument and Radar(Sentinel-1B)data are combined for mapping Mn ODs locations and their relationship with geological structures and the surrounding rocks.Band ratio,Principal and Independent Component Analysis techniques and four classification algorithms were implemented to the optical’VNIR and SWIR bands.Unusually,the interferometric processing steps for Sentinel-1 data were made for understanding the tectonic features in the area.The FR and LogR models are validated during fieldwork with known Mn ODs locations.Results indicate that processed images are capable of differentiation of UBF which broadly distributed in the central and southern parts of the area.Mn ODs possibly formed by thermal events that attributed to paleo-volcanic events before the rift stage.The high accuracy of LogR model(0.902)suggests that high Mn ODs potential zones are identified within the intersected fault zones near granitic units.This integration is recommended for discriminating hydrothermally Mn ODs in other arid geographic regions.

Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Water quality monitoring in a slightly-polluted inland water body through remote sensing-Case study of the Guanting Reservoir in Beijing,China

This study focused on the water quality of the Guanting Reservoir,a possible auxiliary drinking water source for Beijing.Through a remote sensing(RS)approach and using Landsat 5 Thematic Mapper(TM)data,water quality retrieval models were established and analyzed for eight common water quality variables,including algae content,turbidity,and concentrations of chemical oxygen demand,total nitrogen,ammonia nitrogen,nitrate nitrogen,total phosphorus,and dissolved phosphorus.The results show that there exists a statistically significant correlation between each water quality variable and remote sensing data in a slightly-polluted inland water body with fairly weak spectral radiation.With an appropriate method of sampling pixel digital numbers and multiple regression algorithms,retrieval of the algae content,turbidity,and nitrate nitrogen concentration was achieved within 10% mean relative error,concentrations of total nitrogen and dissolved phosphorus within 20%,and concentrations of ammonia nitrogen and total phosphorus within 30%.On the other hand,no effective retrieval method for chemical oxygen demand was found.These accuracies were acceptable for the practical application of routine monitoring and early warning on water quality safety with the support of precise traditional monitoring.The results show that performing the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.

基于多时相RSEI的生态环境质量评价——以新民市为例

以辽宁省新民市作为研究对象,基于2014年、2017年、2020年的相近月份(5—6月)Landsat 8 OLI_TRIS数据,提取4个生态因子[绿度(NDVI)、湿度(WET)、干度(NDBSI)、热度(LST)],采用主成分分析法构建遥感生态指数(RSEI),对研究区域生态环境质量时空演变特征进行评价。结果表明,2014年、2017年、2020年新民市RSEI的均值分别为0.397、0.348、0.506,呈先降后升的趋势。2014—2020年,生态环境质量等级为差和较差的区域主要分布在西北区域,面积占比由62.5%降至33.2%;生态环境质量等级为较好和好的区域主要分布在东南区域,面积占比呈明显的先降低后升高趋势,由21.3%先下降到18.4%后上升到37.0%。4个因子中绿度和湿度对生态环境质量起到正面作用,其中湿度的正面影响较为显著;干度和热度起到负面作用,其中干度的负影响较为显著。

多源多特征集成的南水北调工程丹江库区湿地时空格局演变

丹江口水库是中国南水北调工程的关键水源区。随着城镇化发展以及大坝二次建设,库区湿地生态系统变化显著,亟需湿地生态科学监测。以丹江库区为例,依托Google Earth Engine(GEE)平台,首先采用已有土地覆盖数据集生成湿地样本集,其次整合Landsat影像、DEM等数据构建多源特征集合,并基于Jeffries-Matusita(JM)距离进行特征优选,使用随机森林(RF)算法实现了1985~2023年丹江库区湿地制图。结果表明:①本文提出的样本采集流程可有效提高样本质量,为长时序分类样本采集提供参考;②湿地分类特征优选后特征数由37个减为27个,分类总体精度变化不大,优选后的特征应用于丹江库区湿地分类的平均总体精度(OA)以及平均数量和分配分歧指数(QADI)分别为89.53%和0.0802,说明特征优选有效减少信息冗余,提高影像分类效率;③1985~2023年,丹江库区湿地面积呈波动增加趋势,从1985年的17839.85 ha扩大到2023年的28872.48 ha,面积增长38.12%。总体来说,丹江库区湿地生态系统呈现出逐步恢复和优化的良好态势。

遥感技术新进展赋能水利行业应用

随着遥感技术的不断发展和水利信息化进程的加快,水利遥感有了飞速的发展,与水利现代化发展同步。目前,遥感技术在水利行业的应用范围非常广,在为水利高质量发展提供有力支撑和强力驱动的数字孪生水利建设中发挥了重要的作用。近十多年来,快速发展的遥感技术为提升水利遥感应用能力提供了强有力的支撑,具体表现在数据源的丰富化和多源化、数据获取的速度和效率显著提升、定量估算与反演精度大大提高、专题信息可获得性不断增加、数据处理和信息提取的智能化水平取得长足进步。介绍了遥感技术发展赋能水利遥感应用领域的现状和前景展望,为遥感技术赋能水利新质生产力提供参考。

母亲河复苏行动河湖遥感监测分析

遥感监测技术弥补了传统监测手段的不足,为母亲河复苏行动实施与监管提供了强有力的技术支撑。纳入水利部《母亲河复苏行动河湖名单(2022—2025年)》的有79条河流、9个湖泊,共88条(个)河湖。对比分析母亲河复苏行动实施前(2021年4月)和母亲河复苏行动实施后(2024年4月)的遥感监测影像,结果表明:全线有水河流从27条增至48条;存在断流的河流从52条、断流河长1339 km减少至31条、断流河长582 km;9个湖泊水面面积从390 km^(2)增加至417 km^(2)。总体分析,88条(个)河湖复苏行动后总有水河长9379 km、水面面积1348 km^(2),较复苏行动前同期分别增加756 km、211 km^(2)。在分析遥感监测数据基础上,提出以遥感监测推动母亲河复苏行动的建议。

1986—2020年山东省地表水时空变化特征分析

针对地表水提取过程中部分细小河流和半干涸河道提取较困难的问题,以1986—2020年的山东省内陆地表水为研究对象,基于改进的归一化水体指数(MNDWI)模型,引入建筑物指数模型和植被指数模型,提出了一种新型水体指数(GMNDWI),较明显地提高了地表水体的提取精度。在此基础上,运用空间网格化处理与分区统计、动态度等方法分析了山东省内陆地表水的时空变化特征,并通过相关性分析对选取的影响因子进行了讨论。结果表明:研究区内地表水时空分布不均衡,旱雨季水面积之比稳定在3∶5,均呈现先增加后减少的变化趋势;降雨量和耕地面积与地表水面积的相关系数明显高于其他因素,表明二者的共同作用是导致研究区地表水面积变化的关键因素。