Delineation of groundwater potential zones using remote sensing and Geographic Information Systems(GIS)in Kadaladi region,Southern India

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram,Tamil Nadu,India,using a combination of remote sensing and Geographic Information Systems(GIS)with the Analytical Hierarchical Process(AHP).Various factors such as geology,geomorphology,soil,drainage,density,lineament density,slope,rainfall were analyzed at a specific scale.Thematic layers were evaluated for quality and relevance using Saaty's scale,and then inte-grated using the weighted linear combination technique.The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique,resulting in the final groundwater potential zone map.The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale.Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters.The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers.Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images.2 Results indicate that approximately 21.8 km of the total area exhibits high potential for groundwater recharge.Groundwater recharge is viable in areas with moderate slopes,particularly in the central and southeastern regions.

Quantifying River Bank Erosion and Accretion Patterns along the Gorai River in Kushtia, Bangladesh: A Geospatial Analysis Utilizing GIS and Remote Sensing Techniques

River bank erosion is a natural process that occurs when the water flow of a river exceeds the bank’s ability to withstand it. It is a common phenomenon that causes extensive land damage, displacement of people, loss of crops, and infrastructure damage. The Gorai River, situated on the right bank of the Ganges, is a significant branch of the river that flows into the Bay of Bengal via the Mathumati and Baleswar rivers. The erosion of the banks of the Gorai River in Kushtia district is not a recent occurrence. Local residents have been dealing with this issue for the past hundred years, and according to the elderly members of the community, the erosion has become more severe activities. Therefore, the main objective of this research is to quantify river bank erosion and accretion and bankline shifting from 2003 to 2022 using multi-temporal Landsat images data with GIS and remote sensing technique. Bank-line migration occurs as a result of the interplay and interconnectedness of various factors such as the degree of river-related processes such as erosion, transportation, and deposition, the amount of water in the river during the high season, the geological and soil makeup, and human intervention in the river. The results show that the highest eroded area was 4.6 square kilometers during the period of 2016 to 2019, while the highest accreted area was 7.12 square kilometers during the period of 2013 to 2016. However, the erosion and accretion values fluctuated from year to year.

Fusing Satellite Images Using ABC Optimizing Algorithm

Fusing satellite(remote sensing)images is an interesting topic in processing satellite images.The result image is achieved through fusing information from spectral and panchromatic images for sharpening.In this paper,a new algorithm based on based the Artificial bee colony(ABC)algorithm with peak signalto-noise ratio(PSNR)index optimization is proposed to fusing remote sensing images in this paper.Firstly,Wavelet transform is used to split the input images into components over the high and low frequency domains.Then,two fusing rules are used for obtaining the fused images.The first rule is“the high frequency components are fused by using the average values”.The second rule is“the low frequency components are fused by using the combining rule with parameter”.The parameter for fusing the low frequency components is defined by using ABC algorithm,an algorithm based on PSNR index optimization.The experimental results on different input images show that the proposed algorithm is better than some recent methods.

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.

MAPPING OF OPHIOLITES IN THE INDUS-SUTURE ZONE OF NORTHWESTERN HIMALAYA, USING SATELLITE REMOTE SENSING DATA

Ophiolites, which have been tectonically emplaced along continental margins and island arcs, are significant to the understanding of mountain belt evolution. In the Himalayas, the ophiolitic suite of rocks occur along the Indussuture zone from Hanle in the southeast to Dras\|Kargil sector in the northwest and it represents the remnant of the compressed uplifted wedge of the oceanic crust between the two colliding continental masses, the Indian and the Asian plates.. These ophiolites are temporally and spatially correlated with the culminating phase of the Himalayan orogeny. The Indus River flows to its north separating the ophiolite from the Trans Himalayan litho\|units. Geological mapping in the hostile and inaccessible mountainous terrains of the Himalaya has always posed a great challenge to geologists. Nevertheless, a number of geologists have undertaken such arduous mapping expeditions in the past and prepared fairly good geological maps of these terrains .However there always existed disputes on the accuracy of lithological boundaries and structural details in these maps because many of these boundaries and structural features were completed through extrapolations and/or interpolations as the ruggedness and inaccessibility of a large part of the terrain forbid physical examination of every outcrop. It is in this context the potential of remote sensing, especially of satellite images, is to be appreciated.

A Review of Satellite Remote Sensing Monitoring Methods for Sea Surface Oil Spill

Using satellite remote sensing to monitor oil spill on the sea is an advanced means of oil spill monitoring, and it has the characteristics of wide coverage, speediness and real time, synchronization, continuity, and low cost. Hence, accelerating the research on this technology and establishing a satellite remote sensing monitoring mechanism suitable for oil spill emergency situations is of great significance to improve China's oil spill monitoring capability and prevent or reduce the pollution damage caused by oil spill in the marine environment.This paper analyzes and studies the current situation using satellite remote sensing to monitor oil spills at home and abroad. Based on the basic principle of satellite remote sensing, this paper systematically studies the satellite remote sensing monitoring oil spill principles, satellite data processing methods and oil spill information identification, and summarizes an oil spill identification system that can realize oil spill information reproduction. This system provides an important means of support for the handling of oil spill accidents.

Study of the propagation direction of the internal waves in the South China Sea using satellite images

Internal wave propagation carries considerable vertical shear which can lead to turbulence and mixing. Based on the analysis of more than 2 500 synthetic aperture radar （SAR） and optical satellite images, the in- ternal wave propagation in the whole South China Sea was investigated systematically. The results show that （1） in the northeastern South China Sea, most internal waves propagate westward from the Luzon Strait and are diffracted by coral reefs near the Dongsha Islands. Some impinge onto the shelf and a few are reflected; （2） in the northwestern South China Sea, most internal waves are generated at the shelf and propagate northwestward or westward to the coast; （3） in the western South China Sea, most internal waves propagate westward to the Vietnamese coast, except a few propagate southward to the deep sea; and （4） in the southern South China Sea, most internal waves propagate southwestward to the coast. Some prop- agate southeastward to the coast of Kalimantan Island, and a few propagate southeastward because of the influence of the Mekon~ River.

Estimating Rice Yield by HJ-1A Satellite Images

As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.

Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing

Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders.

An evaluation of the role played by remote sensing technology following the World Trade Center attack

Remote sensing technology has been widely recognized for contributing to emergency response efforts after the World Trade Center attack on September 11th, 2001. The need to coordinate activities in the midst of a dense, yet relatively small area, made the combination of imagery and mapped data strategically useful. This paper reviews the role played by aerial photography, satellite imagery, and LIDAR data at Ground Zero. It examines how emergency managers utilized these datasets, and identifies significant problems that were encountered. It goes on to explore additional ways in which imagery could have been used, while presenting recommendations for more effective use in future disasters and Homeland Security applications. To plan adequately for future events, it was important to capture knowledge from individuals who responded to the World Trade Center attack. In recognition, interviews with key emergency management and geographic information system (GIS) personnel provide the basis of this paper. Successful techniques should not be forgotten, or serious problems dismissed. Although widely used after September 11th, it is important to recognize that with better planning, remote sensing and GIS could have played an even greater role. Together with a data acquisition timeline, an expanded discussion of these issues is available in the MCEER/NSF report “Emergency Response in the Wake of the World Trade Center Attack; The Remote Sensing Perspective” (Huyck and Adams, 2002) Keywords World Trade Center (WTC) - terrorism - emergency response - emergency management - ground zero - remote sensing - emergency operations - disasters - geographic information systems (GIS) - satellite imagery - synthetic aperture radar (SAR) - light detection and ranging imagery (LIDAR)

无人机可见光遥感影像地物目标提取技术研究

无人机可见光遥感影像中地物目标边界清晰度较低,容易导致地物目标与背景之间的区分度降低,进而难以提取地物目标;为此,提出无人机可见光遥感影像地物目标提取方法;从光谱特征、纹理特征和边缘特征3个方面分析无人机可见光遥感影像特征;结合3种影像特征对无人机可见光遥感影像数据集实行增广处理;对完成增广后的数据集定义影像编码标签,以此确定地物目标增强权重,通过参量化处理地物目标光谱特征,计算光谱吸收指数,获取地物目标提取表达式,从而实现无人机可见光遥感影像地物目标提取;实验结果表明,所提方法能够保证地物目标边界的清晰度,具有较强的地物目标提取能力。

基于不同数据源的数字孪生小流域底板模型精度检验

为解决小流域综合治理规划设计中基础地形数据获取困难等问题,基于合成孔径雷达卫星遥感影像、倾斜摄影影像、光探测测距影像等3种主流遥感数据源,分别建立栖龙湾小流域数字孪生底板模型,进而提取主沟道纵断面、2块梯田样地边界、10个林地样区的郁闭度,与人工解译和实测结果对比进行精度检验,结果表明:以光探测测距影像为数据源的提取精度最高,以倾斜摄影影像为数据源的提取精度次之,以孔径雷达卫星遥感影像为数据源的提取精度最低。鉴于3种数据源小流域底板模型各有优缺点,可根据实际工作需要进行多源数据融合,以改善数字孪生技术在小流域综合治理规划设计中的应用效果。在栖龙湾小流域内选择石坎梯田地块进行合成孔径雷达卫星遥感影像与倾斜摄影影像数据融合、选择科普展览馆进行倾斜摄影影像与光探测测距影像数据融合,均取得良好的效果。

基于无人机影像的冬小麦株高提取与LAI估测模型构建

株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。

基于中低分辨率卫星影像中舰船目标识别方法

目前,中低分辨率成像侦察卫星已经成为远距离情报保障的重要手段之一。然而,中低分辨率成像侦察卫星对海上机动舰船的侦察存在目标检测、识别难等问题,大大影响了情报使用效益。针对上述问题,提出基于霍夫变换的舰船开尔文尾迹提取方法,用于匹配并识别舰船型号。通过仿真得出不同型号舰船的开尔文尾迹尖头波平均距离,可以有效的识别出真实遥感影像中舰船属性,与实际影像数据对比分析,证明上述算法具有一定可行性。因此,基于霍夫变换的舰船开尔文尾迹提取方法可以在一定条件下基本实现目标舰船型号的识别,提升中低分辨率成像侦察卫星对海上机动舰船目标的识别准确率。

用于高分辨率遥感影像地类识别的Deeplabv3+改进模型

在高分辨率遥感影像地类识别上,语义分割网络Deeplabv3+表现优异,但是所需参数非常多,训练时间久。遥感影像中的地类与普通RGB图片中的对象相比颗粒度非常大,其具有更显著的特征以及更少的类别,并不需要过深过大的网络。因此,文中提出了一种基于轻量级网络的语义分割模型(Thin-Deeplabv3+),对Deeplabv3+的编码器进行了改进,利用轻量级膨胀网络(Light and Dilated Network,LDNet)提取输入遥感影像的特征,然后利用膨胀系数分别为2、12、24和36的空洞空间金字塔池化(Atrous Spatial Pyramid Pooling,ASPP)模块加强特征提取。在高分遥感影像数据集(Gaofen Image Dataset,GID)以及DeepGlobe土地覆盖分类挑战数据集(DeepGlobe Land Cover Classification Challenge,DLCCC)上的实验结果表明,Thin-Deeplabv3+的识别精度高于Deeplabv3+,并且所需参数仅约为Deeplabv3+的1/10。

遥感卫星在侧视和偏航导引模式下太阳帆板光照角变化分析

遥感卫星通常工作在太阳同步轨道,姿态控制较为复杂。卫星可采用姿态侧视来扩大成像载荷探测范围,并通过姿态偏航导引来消除或补偿成像畸变。上面两种模式的姿态机动对太阳帆板光照角产生了较大影响,使得卫星一年当中不同时段太阳电池阵光照角差异很大,这对卫星电源系统设计有很大影响。文中通过分解卫星姿态和帆板安装位置影响因素,分析二者的影响,并利用坐标矩阵转换将光线矢量和帆板法线映射到同一坐标系下进行解算,从而得到光照角关系式。经过仿真验证该计算方法对光照角分析的有效性。

基于无人机可见光遥感的马铃薯植株氮素累积估测与验证

为探究无人机可见光遥感技术快速、无损监测马铃薯氮素营养的可能性,于2020—2022年在贵州省威宁县开展马铃薯不同氮肥梯度大田试验,设置7个氮素水平(0、60、120、180、240、300和360 kg·hm^(-2)),利用无人机搭载可见光传感器获取不同年份马铃薯块茎形成期冠层RGB高清影像,并同步测定马铃薯地上部氮素含量、生物量和氮素累积量等氮素营养指标,以2020—2021年数据作为建模数据,以2022年数据作为验证数据,建立氮素营养指标估测方程模型并绘制实测值和预测值的1∶1线性关系图。结果表明,与其他冠层光谱参数相比,红光和蓝光比值(R/B)能更好地表征马铃薯氮素营养指标,其与地上部氮素含量、生物量、氮素累积量的相关性均达到极显著水平(P<0.01),其中二次函数模型相关性均优于其他函数模型。利用2022年相同独立试验验证该模型准确性,地上部氮素含量、生物量、氮素累积量实测值与预测值的决定系数(R2)分别为0.949、0.977、0.977,均方根误差(RMSE)和相对误差(MRE)分别为0.368、0.149、0.073和10.42%、6.05%、8.85%,表明模型预测精度较好。综上,无人机可见光遥感可用于马铃薯氮素营养的评估预测,块茎形成期最佳预测参数为R/B,二次函数模型为最佳预测模型。本研究为马铃薯氮素营养无损评估预测提供了理论与实践依据。

低成本小型化遥感光学系统设计

随着航空航天成像技术的发展,遥感成像观测系统需求不断增加,为了简化光学系统的加工装配复杂度,缩小光学系统体积,该文提出了一种低成本、小型化遥感成像光学系统设计方法。在全球面镜的基础上,配合氟化钙等低色散材料,设计了一种焦距2000 mm,适用于5.08 cm(2 inch)探测器的光学成像系统,该系统在50 lp/mm处MTF(调制传递函数)优于0.41,体积与R-C系统接近,系统的球面主、次镜避免了在加工装调时使用非球面补偿器,降低了加工装调成本。同时设计了新型百叶窗遮光罩结构,有效避免了传统外遮光罩结构形式体积过大的问题,规避角大于12°后PST(点源透射比)值均小于10^(-6),且光学体积仅为ф227×421 mm^(3),远小于传统外遮光罩形式。该系统具有低成本、结构小型化、轻量化、像质优良、杂散光抑制良好等优点,在工程应用中可以满足航空航天成像系统的使用要求。

基于多时相Sentinel-2卫星影像的冬小麦面积提取

及时准确地提取冬小麦种植信息,对开展冬小麦农情遥感监测具有重要的意义.以杭州市余杭区冬小麦越冬期(2021-12-04)、扬花期(2022-04-08)和乳熟期(2022-05-03)Sentinel-2遥感影像为数据源,分别采用最大似然法、支持向量机、归一化差值植被指数(normalized difference vegetation index,NDVI)相加和相减合成运算提取冬小麦种植面积.结合冬小麦地面调查数据与实测种植面积,对不同方法的提取结果进行精度评价.结果显示,利用越冬期影像NDVI阈值将常绿植被区(茶园、林地)掩膜处理,对非常绿植被区(建筑、水体、冬小麦)扬花期与乳熟期影像NDVI值进行和值运算,是提取余杭区冬小麦种植面积的最佳方法,面积精度为91.96%,说明基于多时相遥感影像结合植被物候特征与典型地物类型,能够实现冬小麦种植面积的高精度提取.

基于UNet-ResNet14^(*)半监督学习的无人机影像森林树种分类

无人机遥感在森林树种精细和高效分类制图中具有巨大的潜力。为了快速准确获取森林的优势树种分布信息,该研究探讨了半监督学习方法在树种分类方面的有效性。以福建省福州市、龙岩市和三明市的4个试验区为例,构建精简的ResNet18为主干的UNet树种分类模型(UNet-ResNet14^(*)),使用交叉熵和Dice系数的联合损失函数来优化模型参数,对比分析Self-training和Mean Teacher两种不同的半监督学习方法在无人机影像森林树种分类模型的泛化能力。结果表明,以ResNet14^(*)作为主干的分类模型与其他模型相比精度更高且预测速度更快,当联合损失函数权重值为0.5的情况下模型预测效果最好,总体精度达到了91.15%。经过Self-training的模型在木荷、马尾松、杉木3个样本充足的类别中精度均有所提升,总精度为91.08%,比原始模型略低,但在独立验证区的精度为88.50%,比原始模型高;Mean Teacher方法的总精度为88.56%,在独立验证区的精度为73.56%。因此,研究认为可以采用Self-trainin半监督方法结合UNet-ResNet14^(*)的方案快速得到试验区的树种组成信息。