Pub Date : 2025-06-01Epub Date: 2025-05-09DOI: 10.1016/j.ejrs.2025.05.002
Ahmed E. Riyad , Medhat Mokhtar , Mohamed A. Belal , Mahmoud Mohamed Bahloul
As global communication demand rises, Low Earth Orbit (LEO) satellite systems offer high-speed data transmission and extensive coverage options but face routing challenges due to dynamic topologies. This paper introduces a Q-Learning-based routing approach that converts dynamic networks into virtually static topologies at different snapshot intervals. Simulation results on a 66-satellite Starlink constellation demonstrate that Q-Learning outperforms Dijkstra’s algorithm, achieving faster convergence and reduced latency. These findings highlight the potential for Q-Learning in enhancing efficient, cost-effective satellite communications.
{"title":"A q-learning approach for enhanced routing in dynamic LEO satellite networks","authors":"Ahmed E. Riyad , Medhat Mokhtar , Mohamed A. Belal , Mahmoud Mohamed Bahloul","doi":"10.1016/j.ejrs.2025.05.002","DOIUrl":"10.1016/j.ejrs.2025.05.002","url":null,"abstract":"<div><div>As global communication demand rises, Low Earth Orbit (LEO) satellite systems offer high-speed data transmission and extensive coverage options but face routing challenges due to dynamic topologies. This paper introduces a Q-Learning-based routing approach that converts dynamic networks into virtually static topologies at different snapshot intervals. Simulation results on a 66-satellite Starlink constellation demonstrate that Q-Learning outperforms Dijkstra’s algorithm, achieving faster convergence and reduced latency. These findings highlight the potential for Q-Learning in enhancing efficient, cost-effective satellite communications.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 272-279"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-26DOI: 10.1016/j.ejrs.2025.04.004
Abdelrahman Ali Wahba , Ibrahim Fouad Ahmed , Mohamed Amin Abdelfatah , Ashraf Mohammed Ahmed Sahrawi , Gamal Saber El-Fiky
Digital photogrammetry primarily aims to extract three-dimensional coordinates (X, Y, Z or E, N, H) of feature points, which is crucial for mapping applications. The Aerial Triangulation (AT) process for aerial images must be adjusted with high precision to achieve accurate measurements. Enhancing the accuracy of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors significantly improves the AT process. Additionally, Airborne Light Detection and Ranging (LiDAR) data can produce a high-resolution Digital Elevation Model (DEM), which aids in initializing the aerial triangulation process. Modern services, such as Real-Time eXtended (RTX), are also used for GNSS/IMU corrections, further refining their accuracy.
The novelty of the current research is based on an end-to-end procedure for enhancing AT accuracy, especially in variable terrain height regions, using a hybrid airborne system. The scope is to use GNSS/IMU data coupled with a DEM from airborne LiDAR to initialize the AT process. The study cases were based in Maghagha City, Minia Governorate, Egypt, where a flight mission was carried out in 2017 using the Trimble AX60 system. This system integrates a photogrammetric camera and laser scanner with GNSS/IMU sensors. The aerial triangulation of the images was processed using MATCH-AT software. The accuracy of the results was evaluated using checkpoints. The findings indicate that AT using GNSS/IMU corrected data yields the best accuracy in AT, particularly in the Z direction, with an accuracy enhancement in check points residuals, compared with AT without using GNSS/IMU. Consequently, the final Root Mean Square (RMS) improved from 0.25 m to 0.17 m in E, from 0.2 m to 0.17 m in N, and from 3 m to 0.5 m in H. That demonstrates the significant benefit of incorporating GNSS/IMU data in improving the precision of three-dimensional spatial measurements. In addition, the DEM initialization improved the RMS slightly, also, the matching between aerial images during the triangulation process gets better values along the iteration time.
数字摄影测量的主要目的是提取特征点的三维坐标（X、Y、Z 或 E、N、H），这对测绘应用至关重要。航空影像的空中三角测量（AT）过程必须进行高精度调整，以实现精确测量。提高全球导航卫星系统（GNSS）和惯性测量单元（IMU）传感器的精度可显著改善空中三角测量过程。此外，机载光探测和测距（LiDAR）数据可生成高分辨率的数字高程模型（DEM），有助于初始化空中三角测量过程。实时扩展（RTX）等现代服务也可用于 GNSS/IMU 校正，从而进一步提高其精确度。研究范围是使用全球导航卫星系统/IMU 数据以及机载激光雷达的 DEM 来初始化自动识别过程。研究案例基于埃及米尼亚省的马加加市，2017 年在该市使用 Trimble AX60 系统执行了一次飞行任务。该系统集成了摄影测量相机、激光扫描仪和全球导航卫星系统/IMU 传感器。图像的空中三角测量使用 MATCH-AT 软件进行处理。使用检查点对结果的准确性进行了评估。研究结果表明，与不使用全球导航卫星系统/国际海事组织的自动测试相比，使用全球导航卫星系统/国际海事组织校正数据的自动测试精度最高，特别是在 Z 方向，检查点残差的精度也有所提高。因此，最终的均方根（RMS）在 E 方向从 0.25 米提高到 0.17 米，在 N 方向从 0.2 米提高到 0.17 米，在 H 方向从 3 米提高到 0.5 米。此外，DEM 初始化略微提高了有效值，而且在三角测量过程中，航空图像之间的匹配值也随着迭代时间的延长而提高。
{"title":"Integrating GNSS/IMU and DEM data for precise aerial triangulation: Insights from airborne hybrid systems in upper Egypt","authors":"Abdelrahman Ali Wahba , Ibrahim Fouad Ahmed , Mohamed Amin Abdelfatah , Ashraf Mohammed Ahmed Sahrawi , Gamal Saber El-Fiky","doi":"10.1016/j.ejrs.2025.04.004","DOIUrl":"10.1016/j.ejrs.2025.04.004","url":null,"abstract":"<div><div>Digital photogrammetry primarily aims to extract three-dimensional coordinates (X, Y, Z or E, N, H) of feature points, which is crucial for mapping applications. The Aerial Triangulation (AT) process for aerial images must be adjusted with high precision to achieve accurate measurements. Enhancing the accuracy of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) sensors significantly improves the AT process. Additionally, Airborne Light Detection and Ranging (LiDAR) data can produce a high-resolution Digital Elevation Model (DEM), which aids in initializing the aerial triangulation process. Modern services, such as Real-Time eXtended (RTX), are also used for GNSS/IMU corrections, further refining their accuracy.</div><div>The novelty of the current research is based on an end-to-end procedure for enhancing AT accuracy, especially in variable terrain height regions, using a hybrid airborne system. The scope is to use GNSS/IMU data coupled with a DEM from airborne LiDAR to initialize the AT process. The study cases were based in Maghagha City, Minia Governorate, Egypt, where a flight mission was carried out in 2017 using the Trimble AX60 system. This system integrates a photogrammetric camera and laser scanner with GNSS/IMU sensors. The aerial triangulation of the images was processed using MATCH-AT software. The accuracy of the results was evaluated using checkpoints. The findings indicate that AT using GNSS/IMU corrected data yields the best accuracy in AT, particularly in the Z direction, with an accuracy enhancement in check points residuals, compared with AT without using GNSS/IMU. Consequently, the final Root Mean Square (RMS) improved from 0.25 m to 0.17 m in E, from 0.2 m to 0.17 m in N, and from 3 m to 0.5 m in H. That demonstrates the significant benefit of incorporating GNSS/IMU data in improving the precision of three-dimensional spatial measurements. In addition, the DEM initialization improved the RMS slightly, also, the matching between aerial images during the triangulation process gets better values along the iteration time.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 240-251"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flooding remains one of the most severe natural hazards in Pakistan, consistently leading to substantial losses in lives, livelihoods, and infrastructure. The country has experienced recurring flood events, with their frequency and intensity increasingly influenced by shifting climate patterns and irregular rainfall. The phenomena got worse over time and in 2022 all provinces of the country were severely impacted. The damage and impact of a flood may be detected, determined, and estimated with the use of remote sensing and available open geographic information system data. This study presents a scalable, efficient flood mapping framework that leverages freely available multi-source satellite data and open geospatial datasets to assess flood impact with high spatial detail. Multisource satellite imagery was utilized to detect inundation extents. Pre-processing of the remote sensing data was conducted using Google Earth Engine, and spatial integration of data layers for flood mapping was performed in ArcGIS. The results demonstrate that the 2022 Pakistan flood was the worst environmental disaster in history. The flood submerged a total area of nearly 25,000 km in the Sindh province, destroying 14,558 villages and leaving behind a trail of devastation. The methodology enables rapid, repeatable, and cost-effective flood damage assessment and is transferable to other regions. By combining cloud-based processing with open data, this framework supports timely decision-making for disaster response, prevention, and policy planning.
{"title":"Flood mapping and impact analysis by fusion of remote sensing and open geospatial data: Sindh case study","authors":"Munazza Usmani , Hafiz Muhammad Tayyab Bhatti , Riccardo Nanni , Francesca Bovolo , Maurizio Napolitano","doi":"10.1016/j.ejrs.2025.05.001","DOIUrl":"10.1016/j.ejrs.2025.05.001","url":null,"abstract":"<div><div>Flooding remains one of the most severe natural hazards in Pakistan, consistently leading to substantial losses in lives, livelihoods, and infrastructure. The country has experienced recurring flood events, with their frequency and intensity increasingly influenced by shifting climate patterns and irregular rainfall. The phenomena got worse over time and in 2022 all provinces of the country were severely impacted. The damage and impact of a flood may be detected, determined, and estimated with the use of remote sensing and available open geographic information system data. This study presents a scalable, efficient flood mapping framework that leverages freely available multi-source satellite data and open geospatial datasets to assess flood impact with high spatial detail. Multisource satellite imagery was utilized to detect inundation extents. Pre-processing of the remote sensing data was conducted using Google Earth Engine, and spatial integration of data layers for flood mapping was performed in ArcGIS. The results demonstrate that the 2022 Pakistan flood was the worst environmental disaster in history. The flood submerged a total area of nearly 25,000 km<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> in the Sindh province, destroying 14,558 villages and leaving behind a trail of devastation. The methodology enables rapid, repeatable, and cost-effective flood damage assessment and is transferable to other regions. By combining cloud-based processing with open data, this framework supports timely decision-making for disaster response, prevention, and policy planning.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 357-369"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-25DOI: 10.1016/j.ejrs.2025.04.003
Osman Abdelghany , Abdel-Rahman Fowler , Karim Abdelmalik , Abdelaziz Al Azzani , Mahmoud Abu Saima
SENTINEL-2 remote sensing data for Jabal Hafit mountain, south of Al Ain, UAE, were obtained for the purpose of mapping the stratigraphic units in this monotonous carbonate-dominant Lower Eocene to Oligocene sequence. The data was processed using spectral reflectance curves collected from representative rock samples. After resampling of measured spectral curves of studied samples, guided by an algorithm to find the sensitive bands, a Principal Component-based false-colour image was obtained and then improved by Decorrelation Stretch (DS). The resulting image was interpreted in a small study area in Oman where the geology was uninterrupted by human activities. Correlation of colour bands in the study area with known stratigraphic units for the region was applied to the DS image for the entire Jabal Hafit mountain area. The results show excellent discrimination of the formations and members of the Hafit Paleogene succession. Other features revealed include the extent and lateral facies changes shown by these units.
{"title":"Role of sentinel-2 remotely sensed data in assisting stratigraphic subdivision of a Paleogene carbonate sequence, Jabal Hafit, UAE-Oman","authors":"Osman Abdelghany , Abdel-Rahman Fowler , Karim Abdelmalik , Abdelaziz Al Azzani , Mahmoud Abu Saima","doi":"10.1016/j.ejrs.2025.04.003","DOIUrl":"10.1016/j.ejrs.2025.04.003","url":null,"abstract":"<div><div>SENTINEL-2 remote sensing data for Jabal Hafit mountain, south of Al Ain, UAE, were obtained for the purpose of mapping the stratigraphic units in this monotonous carbonate-dominant Lower Eocene to Oligocene sequence. The data was processed using spectral reflectance curves collected from representative rock samples. After resampling of measured spectral curves of studied samples, guided by an algorithm to find the sensitive bands, a Principal Component-based false-colour image was obtained and then improved by Decorrelation Stretch (DS). The resulting image was interpreted in a small study area in Oman where the geology was uninterrupted by human activities. Correlation of colour bands in the study area with known stratigraphic units for the region was applied to the DS image for the entire Jabal Hafit mountain area. The results show excellent discrimination of the formations and members of the Hafit Paleogene succession. Other features revealed include the extent and lateral facies changes shown by these units.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 228-239"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-14DOI: 10.1016/j.ejrs.2025.05.006
Juthi Rani Mitra , Md Sariful Islam
The urban heat island (UHI) effect is a major concern in large cities, particularly as many cities have experienced extended heatwaves in recent years. This study focuses on disparities in urban heat exposure within the city of New Orleans. Using multitemporal Landsat imagery, this research developed an Urban Heat Risk Index (UHRI) at the block group level. In addition to satellite imagery, this study incorporated socioeconomic and demographic data from the American Community Survey (ACS). To examine the relationship between UHRI and explanatory variables, a spatial lag model was applied with the maximum likelihood (ML) estimation method. The analysis revealed a positive and significant association between UHRI and population density. In contrast, the median household income, the percentage of the population aged five and under, the percentage of owner-occupied homes, and the percentage receiving cash public assistance or food stamps all exhibited a negative and significant relationship with UHRI. This study highlights significant disparities in heat exposure among different socioeconomic groups, with important implications for urban planning and public health. By identifying neighborhoods at higher risk for extreme heat, the findings can inform strategies to reduce vulnerability to heat stress, promote equitable access to green spaces, and guide policies for environmental justice. These insights can support city planners, policymakers, and community leaders in developing interventions that prioritize the needs of vulnerable populations, fostering a more resilient and just urban environment.
{"title":"Examining social disparities in urban heat exposure in New Orleans, US","authors":"Juthi Rani Mitra , Md Sariful Islam","doi":"10.1016/j.ejrs.2025.05.006","DOIUrl":"10.1016/j.ejrs.2025.05.006","url":null,"abstract":"<div><div>The urban heat island (UHI) effect is a major concern in large cities, particularly as many cities have experienced extended heatwaves in recent years. This study focuses on disparities in urban heat exposure within the city of New Orleans. Using multitemporal Landsat imagery, this research developed an Urban Heat Risk Index (UHRI) at the block group level. In addition to satellite imagery, this study incorporated socioeconomic and demographic data from the American Community Survey (ACS). To examine the relationship between UHRI and explanatory variables, a spatial lag model was applied with the maximum likelihood (ML) estimation method. The analysis revealed a positive and significant association between UHRI and population density. In contrast, the median household income, the percentage of the population aged five and under, the percentage of owner-occupied homes, and the percentage receiving cash public assistance or food stamps all exhibited a negative and significant relationship with UHRI. This study highlights significant disparities in heat exposure among different socioeconomic groups, with important implications for urban planning and public health. By identifying neighborhoods at higher risk for extreme heat, the findings can inform strategies to reduce vulnerability to heat stress, promote equitable access to green spaces, and guide policies for environmental justice. These insights can support city planners, policymakers, and community leaders in developing interventions that prioritize the needs of vulnerable populations, fostering a more resilient and just urban environment.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 295-302"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-16DOI: 10.1016/j.ejrs.2025.04.002
Durmuş Ali Çelik , Arif Oguz Altunel
Alterations in land-cover significantly influence global climate fluctuations. To utilize land resources rationally and sustainably, it is essential to identify the open-source remote sensing capabilities, the resulting products, and assess their geographical accuracies. This study conceptualized over Kastamonu province in northwestern Türkiye, focused on comparing three of the high-resolution (10 m) land-cover products; Environmental Systems Research Institute (ESRI) 2022, European Space Agency (ESA) World-Cover 2021 and Google-The World Resources Institute, Dynamic Word (DW) 2022, and 2022 Google Earth imagery were utilized for spatial comparisons. The overall accuracy (OA) and Kappa coefficient were computed, along with additional accuracy assessment metrics. OAs of land-cover maps (local accuracy), from highest to lowest, were ESRI2022; 76 %, ESA2021; 75.8 % and DW2022; 73.4 %. The Kappa coefficients for the three land-cover maps were calculated as 0.703 (very good) for ESA2021 and 0.69 and 0.68 (very good) for ESRI2022 and DW2022, respectively. The maximum user accuracy value was recorded at 92.23 % for the crops and 92.21 % for the built area classes in ESA2021. A comparison was also conducted among the corresponding class definitions. The most exemplary portrayal was observed in the categories of water, trees, and crops. Consequently, ESRI, ESA, and DW datasets were found to be fairly comparable to one another and can serve as auxiliary data in research pertaining to water, forestry and cultivated land resources.
土地覆盖的变化显著影响全球气候波动。为实现土地资源的合理和可持续利用，必须对开源遥感能力、成果进行识别，并对其地理精度进行评估。本研究以土耳其西北部的Kastamonu省为例，重点比较了三种高分辨率（10米）土地覆盖产品；利用环境系统研究所（ESRI） 2022、欧洲空间局（ESA） World- cover 2021和谷歌-世界资源研究所、Dynamic Word (DW) 2022和2022谷歌地球图像进行空间比较。计算总体精度（OA）和Kappa系数，以及附加的精度评估指标。土地覆盖图的OAs（局部精度）从高到低依次为ESRI2022；76%，至2021年；75.8%和DW2022；73.4%。ESA2021的Kappa系数为0.703（非常好），ESRI2022和DW2022的Kappa系数分别为0.69和0.68（非常好）。在ESA2021中，农作物的最大用户精度值为92.23%，建成区类别的最高用户精度值为92.21%。并对相应的类定义进行了比较。在水、树木和农作物类别中观察到最典型的写照。因此，ESRI、ESA和DW数据集相互之间具有相当的可比性，可以作为水、林业和耕地资源研究的辅助数据。
{"title":"Is dynamic world a contender in global land-cover making race? A swift field assessment from Kastamonu, Türkiye","authors":"Durmuş Ali Çelik , Arif Oguz Altunel","doi":"10.1016/j.ejrs.2025.04.002","DOIUrl":"10.1016/j.ejrs.2025.04.002","url":null,"abstract":"<div><div>Alterations in land-cover significantly influence global climate fluctuations. To utilize land resources rationally and sustainably, it is essential to identify the open-source remote sensing capabilities, the resulting products, and assess their geographical accuracies. This study conceptualized over Kastamonu province in northwestern Türkiye, focused on comparing three of the high-resolution (10 m) land-cover products; Environmental Systems Research Institute (ESRI) 2022, European Space Agency (ESA) World-Cover 2021 and Google-The World Resources Institute, Dynamic Word (DW) 2022, and 2022 Google Earth imagery were utilized for spatial comparisons. The overall accuracy (OA) and Kappa coefficient were computed, along with additional accuracy assessment metrics. OAs of land-cover maps (local accuracy), from highest to lowest, were ESRI2022; 76 %, ESA2021; 75.8 % and DW2022; 73.4 %. The Kappa coefficients for the three land-cover maps were calculated as 0.703 (very good) for ESA2021 and 0.69 and 0.68 (very good) for ESRI2022 and DW2022, respectively. The maximum user accuracy value was recorded at 92.23 % for the crops and 92.21 % for the built area classes in ESA2021. A comparison was also conducted among the corresponding class definitions. The most exemplary portrayal was observed in the categories of water, trees, and crops. Consequently, ESRI, ESA, and DW datasets were found to be fairly comparable to one another and can serve as auxiliary data in research pertaining to water, forestry and cultivated land resources.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 205-213"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-03-26DOI: 10.1016/j.ejrs.2025.03.002
Ameereh Seyedzadeh , Mohamed Okasha , Alia Alblooshi , Wan Faris Aizat , Abdul Halim Jallad , Erwin Sulaeman
This study examines the thermal management strategies employed by AlAinSat-1 to endure extreme space conditions. It provides an in-depth analysis of the satellite’s thermal behavior through numerical simulations and validates its ability to function in space using experimental testing. AlAinSat-1 is a nanosatellite designed in the shape of a cube, equipped with an Earth observation payload. The thermal analysis was performed using Siemens NX software, following a structured process that included idealization, meshing, and the application of boundary conditions. Simulations were conducted to evaluate the CubeSat’s performance in the worst-case hot and cold scenarios, predicting the temperature range required for mission success. Simulation results confirm that AlAinSat-1 can withstand extreme space conditions, with all components remaining within their operational temperature ranges. To validate these findings, bakeout and thermal vacuum cycling tests were performed using a small Thermal Vacuum Chamber (TVAC). The bakeout test, conducted at 50 °C for five hours, aimed to eliminate volatile contaminants from the CubeSat’s sensitive components, reducing the risk of outgassing. This test achieved a 0.1 % total mass loss, indicating success. The thermal vacuum cycling test involved four cycles ranging from −20 °C to + 50 °C, with a dwell time of one hour per cycle. These tests confirmed the operational temperature range of the CubeSat’s components. The experimental results were consistent with the simulations, demonstrating that all components of AlAinSat-1 functioned effectively within their designated temperature limits. This alignment validates the thermal management approach and ensures the CubeSat’s readiness for space deployment.
{"title":"Thermal analysis and experimental validation of the thermal subsystem of AlAinSat-1","authors":"Ameereh Seyedzadeh , Mohamed Okasha , Alia Alblooshi , Wan Faris Aizat , Abdul Halim Jallad , Erwin Sulaeman","doi":"10.1016/j.ejrs.2025.03.002","DOIUrl":"10.1016/j.ejrs.2025.03.002","url":null,"abstract":"<div><div>This study examines the thermal management strategies employed by AlAinSat-1 to endure extreme space conditions. It provides an in-depth analysis of the satellite’s thermal behavior through numerical simulations and validates its ability to function in space using experimental testing. AlAinSat-1 is a nanosatellite designed in the shape of a cube, equipped with an Earth observation payload. The thermal analysis was performed using Siemens NX software, following a structured process that included idealization, meshing, and the application of boundary conditions. Simulations were conducted to evaluate the CubeSat’s performance in the worst-case hot and cold scenarios, predicting the temperature range required for mission success. Simulation results confirm that AlAinSat-1 can withstand extreme space conditions, with all components remaining within their operational temperature ranges. To validate these findings, bakeout and thermal vacuum cycling tests were performed using a small Thermal Vacuum Chamber (TVAC). The bakeout test, conducted at 50 °C for five hours, aimed to eliminate volatile contaminants from the CubeSat’s sensitive components, reducing the risk of outgassing. This test achieved a 0.1 % total mass loss, indicating success. The thermal vacuum cycling test involved four cycles ranging from −20 °C to + 50 °C, with a dwell time of one hour per cycle. These tests confirmed the operational temperature range of the CubeSat’s components. The experimental results were consistent with the simulations, demonstrating that all components of AlAinSat-1 functioned effectively within their designated temperature limits. This alignment validates the thermal management approach and ensures the CubeSat’s readiness for space deployment.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 185-204"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705505","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}
Pub Date : 2025-06-01Epub Date: 2025-05-27DOI: 10.1016/j.ejrs.2025.05.009
You Wu , Chen Wang
Unsupervised road extraction methods based on traditional point processes have long faced challenges such as bottlenecks in processing efficiency and deficiencies in topological connectivity. To improve these drawbacks, this study proposes a new integrated modeling framework. First step is to construct the integrated point-line-network model based on the tree-shaped point process, in which the relationships between point, line and network are supposed to be constrained according to topological structure features like branching, trend, connectivity of road. In second step, integrated point-line-network model is further constrained by spectral Gaussian mixture model and Kullback-Leibler divergence of road, and then extraction model is obtained. Third step is to redesign transfer kernels of Reversible Jump Markov Chain Monte Carlo (RJMCMC) for simulation and optimization of road extraction. Finally, different scales of sub-meter-level remote sensing images are tested, and the results show that efficiency of the proposed method is higher than traditional methods, and the connectivity is well maintained.
{"title":"Integrated point-line-network model for road extraction based on tree-shaped point process","authors":"You Wu , Chen Wang","doi":"10.1016/j.ejrs.2025.05.009","DOIUrl":"10.1016/j.ejrs.2025.05.009","url":null,"abstract":"<div><div>Unsupervised road extraction methods based on traditional point processes have long faced challenges such as bottlenecks in processing efficiency and deficiencies in topological connectivity. To improve these drawbacks, this study proposes a new integrated modeling framework. First step is to construct the integrated point-line-network model based on the tree-shaped point process, in which the relationships between point, line and network are supposed to be constrained according to topological structure features like branching, trend, connectivity of road. In second step, integrated point-line-network model is further constrained by spectral Gaussian mixture model and Kullback-Leibler divergence of road, and then extraction model is obtained. Third step is to redesign transfer kernels of Reversible Jump Markov Chain Monte Carlo (RJMCMC) for simulation and optimization of road extraction. Finally, different scales of sub-meter-level remote sensing images are tested, and the results show that efficiency of the proposed method is higher than traditional methods, and the connectivity is well maintained.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 348-356"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-04-22DOI: 10.1016/j.ejrs.2025.04.001
Karen Escalona , Rodrigo Abarca-del-Río , María Pedreros-Guarda , Oscar Parra
Aquatic plant invasions endanger lake biodiversity and ecosystem services, resulting in significant economic losses for local communities. Therefore, it is crucial to accurately delineate the extent and frequency of development, but traditional methods are costly and in remote areas. Cost-effective methods, such as satellite monitoring are required. This study uses a Random Forest classification model in the Google Earth Engine (GEE) with Landsat 7 and 8 images to monitoring aquatic vegetation invasion in freshwater ecosystems. The methodology automates the selection of training samples through a dynamic adjustment that incorporates the Otsu-Canny Edge algorithms applied to a vegetation index, allowing for monthly updates while minimizing human bias. Applying this methodology to Lake Maracaibo, Venezuela, between 2013 and 2021, there was a significant increase in floating aquatic vegetation cover, from ≤10 % in 2013 to 25.63 % in 2021, particularly along the northwest coast and the Strait of Maracaibo. This increase could be attributed to a combination of natural processes like precipitation patterns and increased anthropogenic inputs from human activities. The model achieved high accuracy (>0.80), as evidenced by the confusion matrix and cross-sensor comparison. This approach provides a tool for continuous long-term monitoring that can be applied to other eutrophic lakes, improving our understanding of the effects of invasive vegetation, and assisting resource managers and policymakers in developing sustainable management strategies.
{"title":"Spatiotemporal variations of aquatic vegetation in Maracaibo Lake: Remote sensing and machine learning approach with Google Earth Engine","authors":"Karen Escalona , Rodrigo Abarca-del-Río , María Pedreros-Guarda , Oscar Parra","doi":"10.1016/j.ejrs.2025.04.001","DOIUrl":"10.1016/j.ejrs.2025.04.001","url":null,"abstract":"<div><div>Aquatic plant invasions endanger lake biodiversity and ecosystem services, resulting in significant economic losses for local communities. Therefore, it is crucial to accurately delineate the extent and frequency of development, but traditional methods are costly and in remote areas. Cost-effective methods, such as satellite monitoring are required. This study uses a Random Forest classification model in the Google Earth Engine (GEE) with Landsat 7 and 8 images to monitoring aquatic vegetation invasion in freshwater ecosystems. The methodology automates the selection of training samples through a dynamic adjustment that incorporates the Otsu-Canny Edge algorithms applied to a vegetation index, allowing for monthly updates while minimizing human bias. Applying this methodology to Lake Maracaibo, Venezuela, between 2013 and 2021, there was a significant increase in floating aquatic vegetation cover, from ≤10 % in 2013 to 25.63 % in 2021, particularly along the northwest coast and the Strait of Maracaibo. This increase could be attributed to a combination of natural processes like precipitation patterns and increased anthropogenic inputs from human activities. The model achieved high accuracy (>0.80), as evidenced by the confusion matrix and cross-sensor comparison. This approach provides a tool for continuous long-term monitoring that can be applied to other eutrophic lakes, improving our understanding of the effects of invasive vegetation, and assisting resource managers and policymakers in developing sustainable management strategies.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 214-227"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01Epub Date: 2025-05-21DOI: 10.1016/j.ejrs.2025.05.008
Alshimaa Y. Abo Gharbia , Ahmed Gomaa , Mohamed Saleh , Ashraf Elkutb Mousa , Ibrahim Atiatallah Abbas , Moatamad R. Hassan
Estimating Global Navigation Satellite System (GNSS) velocities is essential for understanding crustal deformation and motion. This work employs the Random Forest (RF) and Gradient Boosting Machines (GBM), two machine learning (ML) techniques, to estimate horizontal velocities at specific locations using GNSS data. Crustal deformation data were acquired through Global Positioning System (GPS) techniques, with positions of eleven stations determined from eight GPS measurement campaigns. Eighty percent of the GNSS velocity data from stations in the Abu-Dabbab region were used for training, while twenty percent were reserved for testing the models. RF demonstrated superior performance in estimating east geodetic GPS velocities with the lowest mean absolute error (MAE), while GBM excelled in predicting north geodetic GPS velocities, also achieving the lowest MAE. The maximum differences between model-predicted and reference velocities were 0.09 mm/year for RF and 0.1 mm/year for GBM, underscoring the precision of these methods. Despite data constraints the study confirms the efficacy of ML techniques, particularly RF and GBM, in providing accurate GNSS velocity estimates.
{"title":"GNSS geodetic velocity prediction using ensemble tree models in Abu-Dabbab, Egypt","authors":"Alshimaa Y. Abo Gharbia , Ahmed Gomaa , Mohamed Saleh , Ashraf Elkutb Mousa , Ibrahim Atiatallah Abbas , Moatamad R. Hassan","doi":"10.1016/j.ejrs.2025.05.008","DOIUrl":"10.1016/j.ejrs.2025.05.008","url":null,"abstract":"<div><div>Estimating Global Navigation Satellite System (GNSS) velocities is essential for understanding crustal deformation and motion. This work employs the Random Forest (RF) and Gradient Boosting Machines (GBM), two machine learning (ML) techniques, to estimate horizontal velocities at specific locations using GNSS data. Crustal deformation data were acquired through Global Positioning System (GPS) techniques, with positions of eleven stations determined from eight GPS measurement campaigns. Eighty percent of the GNSS velocity data from stations in the Abu-Dabbab region were used for training, while twenty percent were reserved for testing the models. RF demonstrated superior performance in estimating east geodetic GPS velocities with the lowest mean absolute error (MAE), while GBM excelled in predicting north geodetic GPS velocities, also achieving the lowest MAE. The maximum differences between model-predicted and reference velocities were 0.09 mm/year for RF and 0.1 mm/year for GBM, underscoring the precision of these methods. Despite data constraints the study confirms the efficacy of ML techniques, particularly RF and GBM, in providing accurate GNSS velocity estimates.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 337-347"},"PeriodicalIF":3.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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