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Registration of Interferometric DEM by Deep Artificial Neural Networks Using GPS Control Points’ Coordinates as Network Target 以 GPS 控制点坐标为网络目标,利用深度人工神经网络对干涉测量 DEM 进行注册
IF 1.9 Pub Date : 2024-05-06 DOI: 10.26833/ijeg.1467293
A. Serwa, Abdul Baser Qasimi, Vahid Isazade
The Shuttle Radar Topography Mission (SRTM) satellite’s digital elevation model (DEM) is an important tool for studying topographic features on a medium-spacing scale. Data were collected and processed using the satellite’s orbital and navigation parameters with selected global GPS stations for verification. Distortion may be expressed by surveying measurements, such as position, distance, area, and shape. This study focuses on this distortion and proposes a new registration method to reduce its effect. Because of generality, the purpose shapes were excluded from this study. The proposed registration method depends on precise GPS control points that act as the ground truth for describing the considered surveying measurements. The processing was carried out using deep artificial neural networks (DANN) to produce a new registered DEM. A comparison was made between the original DEM and the new one, focusing on the selected surveying measurements. Another comparison was made between the GPS coordinates and SRTM polynomials to determine the potential of the proposed system. Some statistical investigations were applied to determine the level of significance of the distortion in each surveying measurement. The study shows that the distortion is highly significant; therefore, the proposed registration method is recommended to fix the distortion.
航天飞机雷达地形任务(SRTM)卫星的数字高程模型(DEM)是研究中间隔尺度地形特征的重要工具。利用卫星的轨道和导航参数收集和处理数据,并选定全球定位系统站进行验证。畸变可通过位置、距离、面积和形状等测量数据表现出来。本研究重点关注这种失真,并提出一种新的登记方法来减少其影响。出于一般性考虑,本研究不包括目的形状。所提出的配准方法依赖于精确的 GPS 控制点,这些控制点是描述所考虑的测量结果的地面实况。使用深度人工神经网络(DANN)进行处理,生成新的注册 DEM。对原始 DEM 和新 DEM 进行了比较,重点是所选的测量值。另外还对 GPS 坐标和 SRTM 多项式进行了比较,以确定拟议系统的潜力。通过一些统计调查,确定了各测量值失真程度的显著性。研究结果表明,失真非常明显;因此,建议采用拟议的登记方法来修复失真。
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引用次数: 0
Comparative Analysis of Non-Invasive Measurement Methods for Optimizing Architectural Documentation 优化建筑文件的非侵入式测量方法比较分析
IF 1.9 Pub Date : 2024-02-25 DOI: 10.26833/ijeg.1424881
Serhan Tuncer, U. Avdan
Architectural documentation not only plays a critical role in the conservation of historical structures, but also enables their detailed comprehension of the structure. This study aims to assess the most effective methods for drawing and modeling architectural structures and present their advantages and disadvantages. Measurements play a significant role in this context, and today's technology offers the potential to accelerate this process and enhance accuracy. However, the application of these technologies can impose additional burdens such as elevated expenses, the requisite for specialized personnel, and the management of substantial data volumes. Therefore, determining the appropriate measurement method in line with the quality of architectural documentation is essential. For this study, the Mosque of Kurşunlu Complex in Eskişehir was selected for its historical and topographical attributes which enabled all methods to be examined. The data produced via terrestrial laser scanning, aerial photogrammetry and terrestrial photogrammetry methods were examined in terms of the production of drawings and models for different analysis methods such as structure, daylight and building acoustics, as well as survey drawings required for the architectural documentation processes of the building. The study concluded that no single method could produce holistic data on its own, and the best results for comprehensive documentation were achieved by integrating terrestrial laser scanning and aerial photogrammetry. Furthermore, for products that do not require comprehensive data, photogrammetric methods were more efficient.
建筑文献不仅在保护历史建筑方面起着至关重要的作用,而且还能使人们详细了解建筑结构。本研究旨在评估建筑结构绘图和建模的最有效方法,并介绍其优缺点。测量在这方面发挥着重要作用,而当今的技术为加快这一过程和提高准确性提供了可能。然而,这些技术的应用也会带来额外的负担,如费用的增加、对专业人员的要求以及大量数据的管理。因此,根据建筑文献的质量确定适当的测量方法至关重要。在这项研究中,埃斯基谢希尔的库尔顺鲁清真寺建筑群因其历史和地形属性而被选中,以便对所有方法进行研究。通过地面激光扫描、航空摄影测量和地面摄影测量方法生成的数据,在绘制结构、日光和建筑声学等不同分析方法所需的图纸和模型,以及建筑文件编制过程所需的测量图纸方面进行了研究。研究得出的结论是,没有任何一种方法可以单独生成全面的数据,而综合利用地面激光扫描和航空摄影测量则可以获得最佳的全面记录效果。此外,对于不需要全面数据的产品,摄影测量方法更为有效。
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引用次数: 0
A benchmark dataset for deep learning-based airplane detection: HRPlanes 基于深度学习的飞机检测基准数据集:HRPlanes
Pub Date : 2023-10-15 DOI: 10.26833/ijeg.1107890
Tolga BAKIRMAN, Elif SERTEL
Airplane detection from satellite imagery is a challenging task due to the complex backgrounds in the images and differences in data acquisition conditions caused by the sensor geometry and atmospheric effects. Deep learning methods provide reliable and accurate solutions for automatic detection of airplanes; however, huge amount of training data is required to obtain promising results. In this study, we create a novel airplane detection dataset called High Resolution Planes (HRPlanes) by using images from Google Earth (GE) and labeling the bounding box of each plane on the images. HRPlanes include GE images of several different airports across the world to represent a variety of landscape, seasonal and satellite geometry conditions obtained from different satellites. We evaluated our dataset with two widely used object detection methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the proposed dataset can be a valuable data source and benchmark data set for future applications. Moreover, proposed architectures and results of this study could be used for transfer learning of different datasets and models for airplane detection.
由于卫星图像背景复杂,加之传感器几何形状和大气效应导致的数据采集条件差异,从卫星图像中探测飞机是一项具有挑战性的任务。深度学习方法为飞机自动检测提供可靠、准确的解决方案;然而,要获得令人满意的结果,需要大量的训练数据。在本研究中,我们通过使用来自Google Earth (GE)的图像并在图像上标记每个飞机的边界框,创建了一个名为High Resolution Planes (HRPlanes)的新型飞机检测数据集。HRPlanes包括全球几个不同机场的GE图像,以表示从不同卫星获得的各种景观,季节和卫星几何条件。我们使用两种广泛使用的目标检测方法(即YOLOv4和Faster R-CNN)来评估我们的数据集。我们的初步结果表明,所提出的数据集可以成为未来应用的有价值的数据源和基准数据集。此外,本研究提出的架构和结果可用于飞机检测的不同数据集和模型的迁移学习。
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引用次数: 1
Methodology of real-time 3D point cloud mapping with UAV lidar 无人机激光雷达实时三维点云测绘方法
Pub Date : 2023-10-15 DOI: 10.26833/ijeg.1178260
Levent CANDAN, Elif KAÇAR
Accurate and timely availability of LiDAR data is vital in some cases. To facilitate monitoring of any environmental changes, LiDAR systems can be designed, and carried by UAV platforms that can take off without major preparation. In this study, the methodology of the real-time LiDAR mapping system was developed in the laboratory. The designed system shortens the target-based flight planning and post-flight data processing. In this system, the data is taken instantly and thus the change in the mapping area can be detected quickly. The simulation system, produce 3D point cloud, and data was stored in a database for later analysis. The 3D visualization of the data obtained from our developed UAV-LiDAR system was carried out with a platform-independent interface designed as web-based. The X3D file format used in the study to produce 3D point data provide an infrastructure for AI and ML-based systems in identifying urban objects in systems containing big data such as LiDAR.
在某些情况下,准确及时的激光雷达数据至关重要。为了便于监测任何环境变化,可以设计激光雷达系统,并由无人机平台携带,无需进行重大准备即可起飞。在本研究中,实时激光雷达测绘系统的方法是在实验室开发的。所设计的系统缩短了基于目标的飞行规划和飞行后数据处理。在该系统中,数据是即时采集的,因此可以快速检测到映射区域的变化。通过仿真系统,生成三维点云,并将数据存储在数据库中供以后分析。从我们开发的无人机-激光雷达系统中获得的数据的三维可视化是通过基于web设计的独立于平台的界面进行的。研究中用于生成3D点数据的X3D文件格式为人工智能和基于ml的系统提供了基础设施,用于识别包含激光雷达等大数据的系统中的城市物体。
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引用次数: 4
Proliferation of Dichotomy Urban Settlements Plight: Land Governance And Sustainable Built Environment In Tanzania 二元法城市住区困境的扩散:坦桑尼亚土地治理与可持续建筑环境
IF 1.9 Pub Date : 2023-04-28 DOI: 10.26833/ijeg.1149133
Amanı Uisso, Sibel CANAZ SEVGEN, Prof.dr. Harun Tanrivermi̇ş
The observation of the juxtaposition of formal and informal urban settlements in the commonwealth and sub-Saharan developing countries is been trending as a common mundane phenomenon in emerging and fast-growing cities. In Dar es Salaam for example, one of the largest, high-density, and populous businesses cities in Tanzania; dichotomy of informal and formal land rights is ubiquitous in peri-urban areas and its urban vicinities where land evolves from village to urban. The dichotomy of urban settlements occurs when the public authorities do not satisfactorily provide public services which are customarily attributed to poor governance and policies formulated to guide market forces, urban management, and growth. Different strategies and approaches have been applied by the government for at least providing proper infrastructure; however, most of the approaches are not well successful and deliver the expected results following high cost of implementation. To understand the spatial dynamics of urban typology, population density and land cover maps of Dar es Salaam were used to comprehend the developmental characteristics of Dar es Salaam urban land transformation and change detection of built-up area. According to the analysis of the maps, rapid urbanization and dramatical growth in built-up area especially between 1990-2000 years were easily observed.
在英联邦和撒哈拉以南发展中国家,正式和非正式城市住区并存的现象在新兴和快速发展的城市中一直是一种常见的现象。以达累斯萨拉姆为例,它是坦桑尼亚最大的、人口密集的商业城市之一;非正式和正式土地权利的二分法在城市周边地区及其城市附近普遍存在,土地从乡村演变为城市。当公共当局不能令人满意地提供公共服务时,就会出现城市住区的二分法,这通常归因于为指导市场力量、城市管理和增长而制定的治理不善和政策。政府采用了不同的策略和方法,至少提供适当的基础设施;然而,大多数方法都不是很成功,并且在实现成本高的情况下交付了预期的结果。为了了解城市类型的空间动态,利用达累斯萨拉姆的人口密度和土地覆被图,了解达累斯萨拉姆城市土地转型的发展特征和建成区的变化检测。通过对地图的分析,可以很容易地观察到城市化的快速发展和建成区面积的急剧增长,特别是在1990-2000年间。
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引用次数: 0
Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and Optical images on Google Earth Engine 基于谷歌地球引擎SAR和光学图像分类的Qeshm红树林时空监测
IF 1.9 Pub Date : 2023-04-19 DOI: 10.26833/ijeg.1118542
Mostafa Mahdavi̇fard, Sara KAVİANİ AHANGAR, B. Feizizadeh, Khalil Valizadeh Kamran, S. Karimzadeh
Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.
红树林被认为是最复杂和动态的生态系统之一,面临着人为干扰和气候变化带来的各种挑战。红树林生态系统覆盖地区的过度采伐和土地利用变化对这些森林构成严重威胁。因此,对这些森林进行持续和定期的监测至关重要。幸运的是,遥感数据使定期和频繁监测这类森林成为可能。这项研究有两个目标。首先,将Landsat- 8和Sentinel-2卫星的光学数据与Sentinel-1雷达数据相结合,提高地表覆盖制图精度;其次,评估谷歌Earth Engine系统中SVM机器学习算法和随机森林对森林覆盖与其他土地类型的检测和区分。结果表明,支持向量机算法在S2 + S1数据集上的kappa系数为0.94,明显优于在L8 + S1组合数据集上的kappa系数为0.88。另一方面,随机森林算法在S2 + S1和L8 + S1数据集上的kappa系数分别为0.89和0.85。这再次表明了Sentinel-2和Sentinel-1数据集优于Landsat- 8和Sentinel-1数据集。总的来说,支持向量机(SVM)算法在光学和雷达数据集上的效果优于射频随机森林算法。结果表明,谷歌地球引擎系统和机器学习算法的使用加速了红树林制图甚至变化检测的过程。
{"title":"Spatio-Temporal monitoring of Qeshm mangrove forests through machine learning classification of SAR and Optical images on Google Earth Engine","authors":"Mostafa Mahdavi̇fard, Sara KAVİANİ AHANGAR, B. Feizizadeh, Khalil Valizadeh Kamran, S. Karimzadeh","doi":"10.26833/ijeg.1118542","DOIUrl":"https://doi.org/10.26833/ijeg.1118542","url":null,"abstract":"Mangrove forests are considered one of the most complex and dynamic ecosystems facing various challenges due to anthropogenic disturbance and climate change. The excessive harvesting and land-use change in areas covered by mangrove ecosystems are critical threats for these forests. Therefore, the continuous and regular monitoring of these forests is essential. Fortunately, remote sensing data has made it possible to regularly and frequently monitor this type of forest. This study has two goals. Firstly, it combines optical data of Landsat- 8 and Sentinel-2 with Sentinel-1 radar data to improve land cover mapping accuracy. Secondly, it aims to evaluate the SVM machine learning algorithms and random forest to detection and differentiate forest cover from other land types in the Google Earth Engine system. The results show that the support vector machine (SVM) algorithm in the S2 + S1 dataset with a kappa coefficient of 0.94 performs significantly better than when used in the L8 + S1 combination dataset with a kappa coefficient of 0.88. On the other hand, the kappa coefficients of 0.89 and 0.85 were estimated for the random forest algorithm in S2 + S1 and L8 + S1 datasets. This again indicates the superiority of Sentinel-2 and Sentinel-1 datasets over Landsat- 8 and Sentinel-1 datasets. In general, the support vector machine (SVM) algorithm yielded better results than the RF random forest algorithm in optical and radar datasets. The results showed that the use of the Google Earth engine system and machine learning algorithms accelerates the process of mapping mangrove forests and even change detection.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44575572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Transformation from e-Government to e-land administration in Turkey: A SWOT-based assessment analysis 土耳其从电子政府向电子土地管理的转变:基于SWOT的评估分析
IF 1.9 Pub Date : 2023-03-27 DOI: 10.26833/ijeg.1152715
Z. Polat
Communication between citizen and government has gained a new dimension with the developing information communication Technologies (ICT). The advantage of this technology in terms of both time and cost in service delivery has accelerated the use of these technologies in public administration. With this change, the concept of e-government took its place in the literature and started to be used in many countries. In particular, many public institutions both in the world and Turkey since the 2000s, began to provide many services in different areas such as health, safety, tax, education through e-government. Land management is one of these areas. The Land Registry and Cadastre Directorate, which operates under the Ministry of Environment and Urbanization, and many other public institutions have started to provide many services through e-government within the scope of land management. Thus, the foundations of the transition to e-land administration began to be laid. In this study, it is aimed to determine the necessary strategies for transition to e-land administration considering the role of e-government platform in the provision of public services relating to land management in Turkey. Therefore, the current e-service structure related to land management has been analyzed with the SWOT technique. Thus, the strengths and weaknesses of the existing structure were revealed, and the opportunities and threats faced by this structure were identified. As a result of the analysis, various suggestions were made for the institutional policies that should be implemented in transition to e-land administration.
随着信息通信技术的不断发展,公民与政府之间的沟通有了新的层面。这项技术在提供服务方面的时间和成本优势加速了这些技术在公共行政中的使用。随着这一变化,电子政务的概念在文献中占有一席之地,并开始在许多国家使用。特别是,自2000年代以来,世界和土耳其的许多公共机构开始通过电子政务在健康、安全、税务、教育等不同领域提供许多服务。土地管理就是其中之一。隶属于环境和城市化部的土地登记和地籍局以及许多其他公共机构已开始在土地管理范围内通过电子政务提供许多服务。因此,向电子土地管理过渡的基础开始奠定。在这项研究中,考虑到电子政务平台在土耳其提供与土地管理有关的公共服务方面的作用,旨在确定向电子土地管理过渡的必要战略。因此,本文运用SWOT分析方法,对当前与土地管理相关的电子服务结构进行了分析。因此,揭示了现有结构的长处和短处,并确定了该结构面临的机遇和威胁。分析的结果是,对向电子土地管理过渡期间应执行的体制政策提出了各种建议。
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引用次数: 0
Monitoring and Classification of Karst Rocky Desertification with Landsat 8 OLI Images Using Spectral Indices, Multi-Endmember Spectral Mixture Analysis and Support Vector Machine Landsat 8 OLI遥感影像对喀斯特石漠化的监测与分类
IF 1.9 Pub Date : 2023-03-16 DOI: 10.26833/ijeg.1149738
Çağan Alevkayali, Onur Yayla, Yıldırım Atayeter
Karst Rocky Desertification (KRD) is the reduction of vegetative productivity of this land with the release of bedrock as a result of the full or partial transportation of the fertile soil through natural processes and human activities in karst landscapes. The purpose of this study is to reveal the effectiveness of Remote Sensing methods in monitoring, mapping, and evaluating KRD. Landsat 8 OLI images were used to carry out these procedures. In monitoring this process, Karst Bare Rock Index (KBRI), Normalized Difference Rock Index (NDRI), Carbonate Rock Index 2 (CRI2), Normalized Difference Build-Up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Dimidiate Pixel Model (DPM), Multi Endmember Spectral Mixture Analysis (MESMA) and Support Vector Machine (SVM) were used from the spectral indices. In order to evaluate the results obtained, KRD was divided into 4 basic classes such as none, mild, moderate, and severe. According to these classification levels, it was determined that SVM method had the highest accuracy. For this reason, it was concluded that the SVM method can be used effectively in determining KRD. In the study, it was concluded that the KRD strengthens as one goes from south to north and from west to east in the research area. This study points out KRD is one of the effective land problems in the Mediterranean region, Turkey.
喀斯特石漠化(KRD)是指由于喀斯特景观中的自然过程和人类活动使肥沃的土壤全部或部分迁移,导致该土地的植被生产力下降,基岩释放。本研究的目的是揭示遥感方法在监测、绘制和评估KRD方面的有效性。Landsat 8 OLI图像用于执行这些程序。在监测这一过程中,从光谱指数中使用了岩溶裸露岩石指数(KBRI)、归一化差异岩石指数(NDRI)、碳酸盐岩指数2(CRI2)、归一化差分累积指数(NDBI)、归一化差值植被指数(NDVI)、二分像素模型(DPM)、多端元光谱混合分析(MESMA)和支持向量机(SVM)。为了评估所获得的结果,KRD被分为4个基本类别,如无、轻度、中度和重度。根据这些分类水平,确定SVM方法具有最高的准确度。因此,可以得出结论,SVM方法可以有效地用于确定KRD。研究表明,研究区KRD呈自南向北、自西向东的增强趋势。该研究指出,KRD是土耳其地中海地区的一个有效的土地问题。
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引用次数: 2
Identification of Groundwater Potential for Urban Development Using Geospatial Techniques and Analytical Hierarchy Process 基于地理空间技术和层次分析法的城市发展地下水潜力识别
IF 1.9 Pub Date : 2023-02-18 DOI: 10.26833/ijeg.1190998
Rajaveni SUNDARA PANDİAN, S. U, P. K, L. R
Detailed knowledge regarding the availability of potential groundwater sources is a prerequisite for the sustainable development of cities and towns in a planned manner. The present study is carried out to identify the potential groundwater sources for the growth of towns and cities around Virudhunagar district, India by integrated geospatial techniques and analytical hierarchy method. The groundwater potential zones are divided into four groups: low, medium, high, and very high. It is observed that 18.41% and 34.1% fall under the low and medium zones, respectively. The high and very high groundwater potential zone encompasses an area of 11.95% and 35.23% of the total area respectively. Finally, the identified groundwater potential zones are validated with reported potential yield data of various wells which shows a good correlation. The findings of this study will assist urban planners and decision-makers in better planning and development of future cities and towns.
详细了解潜在地下水资源的可得性,是有计划地实现城镇可持续发展的先决条件。本研究采用综合地理空间技术和层次分析法,为印度Virudhunagar地区周边城镇的发展确定潜在的地下水资源。地下水潜势区分为低、中、高、极高四类。18.41%和34.1%分别属于低、中区域。地下水高潜势区和极高潜势区分别占总面积的11.95%和35.23%。最后,将识别出的地下水潜力带与各口井报告的潜在产量数据进行了验证,结果显示出较好的相关性。这项研究的结果将有助于城市规划者和决策者更好地规划和发展未来的城镇。
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引用次数: 0
Determination of the appropriate zone on dam surface for floating photovoltaic system installation using RS and GISc technologies 利用RS和GISc技术确定浮动光伏系统在坝面的合适安装区域
Pub Date : 2023-02-15 DOI: 10.26833/ijeg.1052556
Osman Salih YILMAZ, Fatih GÜLGEN, Ali Murat ATEŞ
This study aims to reveal suitable places where floating photovoltaic-solar power plants (FPV-SPPs) can be installed on the dam surface using the possibilities of remote sensing (RS) and geographical information science (GISc) technologies. Past satellite images from Landsat and Sentinel platforms allow researchers to analyse shoreline changes in the dam surface. Shoreline extraction is a crucial process for the FPV-SPP to stay afloat despite external constraints. In this study, changes in dam water levels were determined by classifying 20-year satellite images and analysing a 32-year global surface water dynamics dataset. The water surface area was calculated as 1,562.40 ha using the random forest (RF) algorithm and the normalized differences water index (NDWI) on Google Earth Engine (GEE) cloud platform. In addition, solar analysis was carried out with GISc using annual solar radiation maps shuttle radar topography mission (SRTM) data, which directly affects the energy production of FPV-SPPs. It has been calculated that the solar radiation on the water surface varies between 1,554 kWh/m2-year and 1,875 kWh/m2-year. These calculated values were divided into five different classes, and it was observed that 88.5% of the dam surface had a very high level of solar radiation compared to other areas. Higher efficiency will be obtained from the FPV-SPP to be installed in this region compared to the systems to be installed in other regions. It has been observed that the radiation values in other parts of the water surface are lower due to topographic shading. These analyses revealed energy zones with high production potential, thereby easing the decision-making process for investors planning to establish FPV-SPPs.
本研究旨在利用遥感(RS)和地理信息科学(GISc)技术的可能性,揭示可在大坝表面安装浮动光伏太阳能发电厂(FPV-SPPs)的合适地点。过去来自陆地卫星和哨兵平台的卫星图像使研究人员能够分析大坝表面的海岸线变化。岸线提取是FPV-SPP不受外部限制而保持漂浮的关键过程。在这项研究中,通过分类20年的卫星图像和分析32年的全球地表水动力学数据集,确定了大坝水位的变化。采用随机森林(RF)算法和谷歌Earth Engine (GEE)云平台上的归一化差水指数(NDWI)计算水体面积为1,562.40 ha。此外,利用航天飞机雷达地形任务(SRTM)的年度太阳辐射图数据,利用GISc进行了太阳辐射分析,这直接影响了fpv - spp的能源生产。经计算,水面太阳辐射在1554 ~ 1875 kWh/m2-年之间变化。将这些计算值分为5个不同的类别,观察到88.5%的大坝表面与其他地区相比太阳辐射水平很高。与在其他地区安装的系统相比,在该地区安装的FPV-SPP将获得更高的效率。观测到,由于地形遮阳,水面其他部分的辐射值较低。这些分析揭示了具有高生产潜力的能源区,从而简化了计划建立fpv - spp的投资者的决策过程。
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引用次数: 3
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International Journal of Engineering and Geosciences
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