Egyptian Journal of Remote Sensing and Space Sciences最新文献

{"title":"A novel approach for optimizing regional geoid modeling over rugged terrains based on global geopotential models and artificial intelligence algorithms","authors":"Mohamed A. Elshewy ,&nbsp;Phung Trung Thanh ,&nbsp;Amr M. Elsheshtawy ,&nbsp;Mervat Refaat ,&nbsp;Mohamed Freeshah","doi":"10.1016/j.ejrs.2024.09.002","DOIUrl":"10.1016/j.ejrs.2024.09.002","url":null,"abstract":"<div><p>Accurate geoid modeling is significant in geodetic, geological, and environmental sciences. Owing to challenges in establishing reference stations, particularly in rugged terrains, such as in Northern Vietnam, leveraging global geopotential models (GGMs) is imperative. Herein, we proposed a superior method that integrates GGMs with advanced artificial intelligence (AI) algorithms to enhance the accuracy and spatial resolution of regional geoid models. A total of six contemporary GGMs (XGM2019e_2159, SGG-UGM-2, SGG-UGM-1, GECO, EIGEN-6C4, and EGM2008) were systematically evaluated to identify the optimal GGM that represents the Earth’s gravitational field in Northern Vietnam. Subsequently, sophisticated AI algorithms, including tree-based ensembles, support vector machines, Gaussian linear regression, regression trees, and linear regression models, were implemented. These AI algorithms were trained on the integrated global navigation satellite system (GNSS) leveling data and corresponding height anomalies to capture complex relationships in the geopotential field. Among the six investigated GGMs, XGM2019e_2159 shows optimal performance for Northern Vietnam, displaying a standard deviation of ±0.17 m. Rigorous assessment results from cross-validation and validation against independent datasets demonstrate satisfactory accuracy across all considered models. However, the Gaussian process regression model with an exponential kernel exhibits marginal superiority, boasting a standard deviation of approximately 0.07 m. This model is therefore chosen for the construction of the geoid model by integrating ground data with optimal GGMs, which shows superior performance, particularly in challenging topographic and geophysical conditions, thereby contributing to a marked improvement in the realized spatial resolution.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 656-668"},"PeriodicalIF":3.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232400070X/pdfft?md5=7f817a76ce47d89819547060d7ad1b59&pid=1-s2.0-S111098232400070X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mangrove species detection using YOLOv5 with RGB imagery from consumer unmanned aerial vehicles (UAVs)","authors":"Han Shen Lim ,&nbsp;Yunli Lee ,&nbsp;Mei-Hua Lin ,&nbsp;Wai Chong Chia","doi":"10.1016/j.ejrs.2024.08.005","DOIUrl":"10.1016/j.ejrs.2024.08.005","url":null,"abstract":"<div><p>Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV’s default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant <em>Rhizophora</em> mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 645-655"},"PeriodicalIF":3.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000681/pdfft?md5=94fa1361b59743d6b4ddf4f9129511c3&pid=1-s2.0-S1110982324000681-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142089025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review on payloads of unmanned aerial vehicle","authors":"Siva Sivamani Ganesh Kumar,&nbsp;Abhishek Gudipalli","doi":"10.1016/j.ejrs.2024.08.001","DOIUrl":"10.1016/j.ejrs.2024.08.001","url":null,"abstract":"<div><p>The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments’ ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 637-644"},"PeriodicalIF":3.7,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000607/pdfft?md5=535e8983fc41ceab4d0d477d48bbdb22&pid=1-s2.0-S1110982324000607-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How can aerial imagery and vegetation indices algorithms monitor the geotagged crop?","authors":"Vikneswaran Jeya Kumaran ,&nbsp;Nur Adibah Mohidem ,&nbsp;Nik Norasma Che’Ya ,&nbsp;Wan Fazilah Fazlil Ilahi ,&nbsp;Jasmin Arif Shah ,&nbsp;Zulhilmy Sahwee ,&nbsp;Norhakim Yusof ,&nbsp;Mohammad Husni Omar","doi":"10.1016/j.ejrs.2024.08.003","DOIUrl":"10.1016/j.ejrs.2024.08.003","url":null,"abstract":"<div><p>There is very little to no literature on the use of geotagging to monitor crops from aerial photos, even though many technologies have been created to do so. Current crop monitoring methods, relying on field data and lab analysis, are inefficient due to high labor, time, and potential harm, limiting their broad use. With the use of vegetation indices (VI) and geotagging, this paper highlights the benefits of crop-specific monitoring with unmanned aerial vehicles (UAV). This study systematically searched the original articles published from the 1st of January 2016 to the 7th of October 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “aerial imagery” AND “vegetation index” OR “vegetation indices“ AND “crop”. Out of the papers identified, 28 eligible studies did meet our inclusion criteria and were evaluated. This review thoroughly discusses the advantages of aerial imagery, vegetation indices, and geotagging tools in the context of crop monitoring. It was found that geotagged crop monitoring using UAV empowers farmers with data-driven insights using vegetation indices, enabling them to make informed decisions before acting, transforming agriculture towards a digital future. This study offers valuable insights for researchers and industry players, helping them identify effective and context-specific crop monitoring strategies for diverse plantations, crops, and budgets. Moreover, by utilizing the advanced computational capabilities of artificial intelligence (AI), we can analyze a wide range of vegetation indices to gain a comprehensive understanding of crop health and conduct accurate predictions.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 628-636"},"PeriodicalIF":3.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000590/pdfft?md5=edfc22e2e686d15dd63f69ec1f676497&pid=1-s2.0-S1110982324000590-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unraveling land use land cover change, their driving factors, and implication on carbon storage through an integrated modelling approach","authors":"Ogi Setiawan ,&nbsp;Anita Apriliani Dwi Rahayu ,&nbsp;Gipi Samawandana ,&nbsp;Hesti Lestari Tata ,&nbsp;I Wayan Susi Dharmawan ,&nbsp;Henti Hendalastuti Rachmat ,&nbsp;Sri Suharti ,&nbsp;Ayun Windyoningrum ,&nbsp;Husnul Khotimah","doi":"10.1016/j.ejrs.2024.08.002","DOIUrl":"10.1016/j.ejrs.2024.08.002","url":null,"abstract":"<div><p>Land Use Land Cover (LULC) change is a complex phenomenon driven by various natural and anthropogenic factors, significantly impacting carbon storage potential. By applying integrated models of ANN-CA Markov, GeoDetector, and InVEST model, this study aimed to analyze LULC change, their driving factors, and implications on carbon storage in the Forest Management Unit (FMU) of Ampang Plampang in West Nusa Tenggara, Indonesia. Several data sources were utilized in the modelling approach, including DEM (Digital Elevation Model), topographical map, Landsat imageries (2011, 2016, 2021), measured carbon density (above ground, below ground, soil, dead organic), and socio-economic data (number of populations, farmer, and agricultural land). The dryland forest in the study area constitutes the most extensive LULC that has experienced significant declines due to deforestation, predominantly transforming into agricultural land, and these are predicted to continue until 2031 with different magnitudes. The significant driving factors of LULC change were elevation, population pressure on land, and distance from settlement. The LULC change also greatly influenced the decline of carbon storage historically (2011–2016) and in projected LULC (2026–2031). The conversion of forested areas to non-forest LULCs has released carbon emissions of about 1.89 Mt CO₂-eq. The study findings implied that the integration of ANN-CA Markov, GeoDetector, and InVEST models has been helpful for comprehending complicated interactions among LULC change, driving factors, and carbon dynamics. The results also contribute to the scientific knowledge base for land management decision-making and policy formulation. Effective management of LULC changes through low carbon development is suggested to mitigate the loss of carbon storage capacities, foster sustainable development goals (SDGs), support Nationally Determined Contribution (NDC), and improve ecosystem resilience.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 4","pages":"Pages 615-627"},"PeriodicalIF":3.7,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000589/pdfft?md5=91e79cf7eb28ecfe35eb57ead4bc240f&pid=1-s2.0-S1110982324000589-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141979582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting air quality using random forest: A case study in Amman-Zarqa","authors":"Farah Alzu’bi ,&nbsp;Abdulla Al-Rawabdeh ,&nbsp;Ali Almagbile","doi":"10.1016/j.ejrs.2024.07.004","DOIUrl":"10.1016/j.ejrs.2024.07.004","url":null,"abstract":"<div><p>The Spatiotemporal variability of air quality is influenced by various factors over time. The objectives of this research are to create prediction models for Carbon monoxide (<em>CO</em>) and Nitrogen dioxide (<em>NO₂</em>) and determine the factors which that most impact <em>CO</em> and <em>NO₂</em> monthly using Random Forest Prediction. The methodology relies on Random Forest Prediction to predict air quality monthly in 2021, incorporating eight variables land surface temperature (<em>LST</em>), normalized<!--> <!-->difference<!--> <!-->built-up<!--> <!-->index (<em>NDBI</em>), built-up index (<em>BU</em> index), normalized difference<!--> <!-->vegetation index (<em>NDVI</em>), digital elevation model (<em>DEM</em>), relative humidity (<em>RH</em>), wind speed (<em>WS</em>), and wind direction (<em>WD</em>). The results indicate that <em>RH</em>, elevation, <em>WD</em>, and <em>LST</em> are the most significant factors influencing <em>CO</em> concentrations, representing 33%, 24%, 12%, and 10% respectively at annual level in 2021. Similarly, <em>WD, WS, RH</em>, elevation and <em>LST</em> are the most importance factors impacting <em>NO₂</em> concentrations, representing 24%, 21%, 18%, 12%, and 10% respectively at an annual level in 2021. Furthermore, <em>NDBI</em> and <em>BU</em> index had the lowest impact in on both <em>CO</em> and <em>NO₂</em>, with <em>BU</em> index showing a slightly higher percentage in <em>NO₂</em> models compared to <em>CO</em> models. Regarding cross-validation, the <em>MAE</em> values in <em>CO</em> models range from 0.11 to 0.18, and the <em>RMSE</em> values range from 0.14 to 0.23. Additionally, the <em>MAE</em> values in <em>NO₂</em> models ranges from 3.78 to 7.30, and <em>RMSE</em> values range from 4.93 to 9.23.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 604-613"},"PeriodicalIF":3.7,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000565/pdfft?md5=b33e6f7b591e73da5d0849d9d150ff47&pid=1-s2.0-S1110982324000565-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141931620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing models to detect maize diseases using spectral vegetation indices derived from spectral signatures","authors":"Basani Lammy Nkuna ,&nbsp;Johannes George Chirima ,&nbsp;Solomon W. Newete ,&nbsp;Adolph Nyamugama ,&nbsp;Adriaan Johannes van der Walt","doi":"10.1016/j.ejrs.2024.07.005","DOIUrl":"10.1016/j.ejrs.2024.07.005","url":null,"abstract":"<div><p>Maize, a vital global crop, faces numerous challenges, including outbreaks. This study explores the use of spectral vegetation indices for the early detection of maize diseases in individual leaves based on crop phenology at the vegetative, tasselling, and maturity stages. The research was conducted in rural areas of Giyani in the Limpopo province, South Africa, where smallholder farmers heavily rely on maize production for sustenance. Fungal and viral diseases pose significant threats to maize crops, necessitating precise and timely disease detection methods. Hyperspectral remote sensing, with its ability to capture detailed spectral information, offers a promising solution. The study analysed spectral reflectance data collected from healthy and diseased maize leaves. Various vegetation indices derived from spectral signatures, including the Normalized difference vegetation index (NDVI), Anthocyanin Reflectance Index (ARI), photochemical Reflectance Index (PRI), and Carotenoid Reflectance Index (CRI) were investigated for their ability to show disease-related spectral variations. The results indicated that during the tasselling stage, the spectral differences had minimum absorption in the blue region. However, a distinct shift in spectral reflectance was observed during the vegetative stage with 70 % increase in reflectance. First derivative reflectance analysis revealed peaks at approximately 715 nm and 722 nm, which were useful in the discrimination of the different growth stages. Generalized Linear Models (GLM) with binomial link functions and Akaike Information Criterion (AIC) showed that individual vegetation indices performed equally well. NDVI (P&lt;0.001) and CRI (P&lt;0.000) showed the lowest AIC values across all growth stages, suggesting their potential as effective disease indicators. These findings underscores the significance of employing remote sensing technology and spectral analysis as essential tools in the endeavours to tackle the difficulties encountered by maize growers, especially those operating small-scale farms, and to advance sustainable farming practices and ensure food security.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 597-603"},"PeriodicalIF":3.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000577/pdfft?md5=4be1ca5c0f48641305e8a13b7486c590&pid=1-s2.0-S1110982324000577-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141951088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PRISMA vs. Landsat 9 in lithological mapping − a K-fold Cross-Validation implementation with Random Forest","authors":"Ali Shebl ,&nbsp;Dávid Abriha ,&nbsp;Maher Dawoud ,&nbsp;Mosaad Ali Hussein Ali ,&nbsp;Árpád Csámer","doi":"10.1016/j.ejrs.2024.07.003","DOIUrl":"10.1016/j.ejrs.2024.07.003","url":null,"abstract":"<div><p>The selection of an optimal dataset is crucial for successful remote sensing analysis. The PRISMA hyperspectral sensor (with 240 spectral bands) and Landsat OLI-2 (boasting high dynamic resolution) offer robust data for various remote sensing applications, anticipating their increased demand in the coming years. However, despite their potential, we have not identified a rigorous evaluation of both datasets in geological applications utilizing Machine Learning Algorithms. Consequently, we conduct a comprehensive analysis using Random Forest, a widely-recommended machine learning algorithm, and employ K-fold cross-validation (with <em>K</em> = 2, 5, 10) with grid-search hyperparameter tuning for enhanced performance. Toward this aim, diverse image-processing approaches, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were applied to enhance feature selection and extraction. Subsequently, to ensure better performance of the RF algorithm, this study utilized well-distributed points instead of polygons to represent each target, thereby mitigating the effects of spatial autocorrelation. Our results reveal dataset-hyperparameter dependencies, with PRISMA mainly influenced by <em>max_depth</em> and Landsat 9 by <em>max_features</em>. Employing grid-search optimally balances dataset characteristics and data splitting (folds), generating accurate lithological maps across all K values. Notably, a significant hyperparameter shift at <em>K</em> = 10 produces the best lithological maps. Fieldwork and petrographic investigations validate the lithological maps, indicating PRISMA’s slight superiority over Landsat OLI-2. Despite this, given the dataset nature and band count difference, we still advocate Landsat 9 as a potent multispectral input for future applications due to its superior radiometric resolution.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 577-596"},"PeriodicalIF":3.7,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000553/pdfft?md5=cd78548dacf563f3d654cb587e5c2940&pid=1-s2.0-S1110982324000553-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141623153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-automated mangrove mapping at National-Scale using Sentinel-2, Sentinel-1, and SRTM data with Google Earth Engine: A case study in Thailand","authors":"Surachet Pinkeaw ,&nbsp;Pawita Boonrat ,&nbsp;Werapong Koedsin ,&nbsp;Alfredo Huete","doi":"10.1016/j.ejrs.2024.07.001","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.07.001","url":null,"abstract":"<div><p>Mangroves are a crucial part of the coastal ecosystem; thus, precise and up-to-date monitoring is essential to guide regional policies and inform conservation strategies. This study investigates the capabilities of semi-automated remote sensing approaches within a Google Earth Engine framework for national-scale mangrove mapping in Thailand. Remote sensing data from 2018—10,000 data points acquired from Sentinel-1, Sentinel-2, and the Shuttle Radar Topography Mission (SRTM)—was used to train several machine learning models. The Gradient Tree Boost (GTB) proved to be the most reliable, with the least variation in validity (the lowest IQR) and the highest average Overall Accuracy of 96.75 ± 0.63 % compared to the others—96.64 ± 0.72 % for Random Forest (RF); 96.12 ± 0.80 %for Classification and Regression Trees (CART); and 95.43 ± 0.74 % for Support Vector Machines (SVM). Thus, the GTB was instrumental in mapping mangrove distribution with 10-m spatial resolution across Thailand from 2016 to 2022, the period in which the mangrove areas increased by 11 %, reflecting successful conservation efforts over the past decade. The developed framework establishes the foundation for semi-automated mangrove mapping that can be developed for other geographical contexts.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 555-564"},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S111098232400053X/pdfft?md5=bdd650004ec791bfac1bc83b674714e2&pid=1-s2.0-S111098232400053X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the usability of Göktürk-2 data and UAV data for pond construction project","authors":"Huseyin Karatas ,&nbsp;Aydan Yaman","doi":"10.1016/j.ejrs.2024.07.002","DOIUrl":"https://doi.org/10.1016/j.ejrs.2024.07.002","url":null,"abstract":"<div><p>Today, many professions need maps that can be produced quickly, precisely, and in detail, as well as the data from these maps. Land data is very important, especially in mapping engineering, both in the public and private sectors. Providing these data and maps is seen as an important expense for individuals or institutions in terms of time, cost and labor force. This study aims to investigate the usability of the data obtained by satellite images and Unmanned Aerial Vehicles (UAV), which can be easily obtained for the design of the pond/dam body within the scope of the pond construction project for irrigation purposes. Within the scope of the study, the data obtained by adding digital terrain models to Göktürk-2 satellite images were compared with the data obtained from the flight study conducted with the UAV; two separate ponds were designed using the created orthophoto and elevation data. As a result, benefit/cost ratios were calculated. The benefit/cost ratio calculated from remote sensing satellite data was 1.32, while the benefit/cost ratio calculated according to the project created with the UAV was 1.48, and the difference between the two data was calculated as 10.73%. According to this result, it was concluded that satellite images could be used in works such as ponds, closed system irrigation works, and land slope analysis, especially in preliminary project design studies. In contrast, data produced by UAV photogrammetry should be used in processes requiring higher precision. With this study, it is aimed that 25 households in the study area will benefit from the irrigation system. Furthermore, the findings of this study will enable institutions to select and utilise data that is appropriate to the purpose of the study and the desired accuracy, taking into account the benefit/cost ratios, without the need for prior fieldwork. By selecting and using the most economical data in accordance with the purpose of the work in engineering projects, optimum benefit will be obtained by saving time and labor.</p></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"27 3","pages":"Pages 565-576"},"PeriodicalIF":3.7,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1110982324000541/pdfft?md5=39a637b96b918f5094f5b44edc69ba0d&pid=1-s2.0-S1110982324000541-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141596107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}