Egyptian Journal of Remote Sensing and Space Sciences最新文献_第6页

Examining human activities in response to land surface temperature in Sekota watershed, northern Ethiopia 研究人类活动对埃塞俄比亚北部塞科塔流域地表温度的响应

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-08 DOI: 10.1016/j.ejrs.2025.05.004

Mulat Amare Tshayu , Teshome Betru Tadesse , Kindu Setalem Meshesha , Mohammed Habib Afkea , Mohammed Motuma Assen

The alteration of land use/land cover change (LULCC) is an environmental issue that impacts affects ecosystems by increasing the land surface temperature (LST). This study aimed to investigate the influence of human activities on LST in the Sekota watershed northern Ethiopia. This study used Landsat images and a supervised support vector machine (SVM) classification algorithm to map LU/LC and estimate LST. The findings revealed that farmland exhibited the most substantial expansion, with a net gain of 16,970.84 ha, while shrubland experienced the most significant decline, with a net loss of 20,768.57 ha. Moreover, forest cover by 329.73 ha, bare land by 2048.97 ha, and settlements by 131.07 ha increased from 2000 to 2022. The mean LST increased from 32.31 °C in 2000 to 36.01 °C in 2014, followed by a gradual decrease to 34.18 °C in 2022. The overall accuracy and kappa coefficients of the LULC maps were 87.6 % (0.8421), 91.5 % (0.8901), and 92 % (0.8973) in 2000, 2014, and 2022, respectively. This study also investigated the correlation between the normalized difference vegetation index (NDVI) and LST. The results demonstrated a negative relationship, with correlation coefficient R² values of 0.70, 0.65, and 0.75 for 2000, 2014, and 2022, respectively. This indicates that non-vegetated e areas had higher LST levels than forested areas. As a result, it is recommended that government agencies and local communities focus on preserving vegetation cover and adopting practices such as planting perennial fruit crops and implementing agroforestry systems in the study area.

土地利用/土地覆盖变化（LULCC）是一个通过增加地表温度对生态系统产生影响的环境问题。本研究旨在探讨人类活动对埃塞俄比亚北部Sekota流域地表温度的影响。本研究使用Landsat图像和监督支持向量机（SVM）分类算法来绘制LU/LC和估计LST。结果表明，耕地面积扩大幅度最大，净增加16970.84 ha，而灌木林地面积减少幅度最大，净减少20768.57 ha。2000 - 2022年，森林面积增加329.73 ha，裸地面积增加2048.97 ha，居民点面积增加131.07 ha。平均地表温度从2000年的32.31°C上升到2014年的36.01°C，随后逐渐下降到2022年的34.18°C。2000年、2014年和2022年的总体精度和kappa系数分别为87.6%（0.8421）、91.5%（0.8901）和92%（0.8973）。本文还研究了归一化植被指数（NDVI）与地表温度的相关性。2000年、2014年和2022年的相关系数R2分别为0.70、0.65和0.75，呈负相关。这表明非植被地区的地表温度水平高于森林地区。因此，建议政府机构和当地社区将重点放在保护植被上，并采取诸如种植多年生水果作物和实施农林复合系统等做法。

{"title":"Examining human activities in response to land surface temperature in Sekota watershed, northern Ethiopia","authors":"Mulat Amare Tshayu , Teshome Betru Tadesse , Kindu Setalem Meshesha , Mohammed Habib Afkea , Mohammed Motuma Assen","doi":"10.1016/j.ejrs.2025.05.004","DOIUrl":"10.1016/j.ejrs.2025.05.004","url":null,"abstract":"<div><div>The alteration of land use/land cover change (LULCC) is an environmental issue that impacts affects ecosystems by increasing the land surface temperature (LST). This study aimed to investigate the influence of human activities on LST in the Sekota watershed northern Ethiopia. This study used Landsat images and a supervised support vector machine (SVM) classification algorithm to map LU/LC and estimate LST. The findings revealed that farmland exhibited the most substantial expansion, with a net gain of 16,970.84 ha, while shrubland experienced the most significant decline, with a net loss of 20,768.57 ha. Moreover, forest cover by 329.73 ha, bare land by 2048.97 ha, and settlements by 131.07 ha increased from 2000 to 2022. The mean LST increased from 32.31 °C in 2000 to 36.01 °C in 2014, followed by a gradual decrease to 34.18 °C in 2022. The overall accuracy and kappa coefficients of the LULC maps were 87.6 % (0.8421), 91.5 % (0.8901), and 92 % (0.8973) in 2000, 2014, and 2022, respectively. This study also investigated the correlation between the normalized difference vegetation index (NDVI) and LST. The results demonstrated a negative relationship, with correlation coefficient R<sup>2</sup> values of 0.70, 0.65, and 0.75 for 2000, 2014, and 2022, respectively. This indicates that non-vegetated e areas had higher LST levels than forested areas. As a result, it is recommended that government agencies and local communities focus on preserving vegetation cover and adopting practices such as planting perennial fruit crops and implementing agroforestry systems in the study area.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 261-271"},"PeriodicalIF":3.7,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921672","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}

引用次数: 0

Integrating PRISMA hyperspectral data with Sentinel-1, Sentinel-2 and Landsat data for mapping crop types and land cover in northeast Thailand 将PRISMA高光谱数据与Sentinel-1、Sentinel-2和Landsat数据整合，用于泰国东北部作物类型和土地覆盖制图

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-05-03 DOI: 10.1016/j.ejrs.2025.04.005

Savittri Ratanopad Suwanlee , Zahid Naeem Qaisrani , Jaturong Som-ard , Surasak Keawsomsee , Kemin Kasa , Narissara Nuthammachot , Siwa Kaewplang , Sarawut Ninsawat , Enrico Borgogno Mondino , Samuele De Petris , Filippo Sarvia

Accurate crop types and land cover maps are pivotal for effective land management and agricultural policy, particularly in regions with complex agricultural landscapes and small field sizes. Northeast Thailand, a significant agricultural hub, faces challenges in crop classification due to its diverse crop patterns, cloud cover, and smallholder plots. This study integrates satellite data from PRISMA, Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8/9 (L8/9) imagery to address these challenges. A total of 1305 reference point were randomly collected between November and December 2022 to train and validate the proposed crop classification. Specifically, 15 different combinations using a random forest (RF) classifier were tested. The combination of all datasets achieved the highest overall accuracy (OA) of 91.5 %, followed by S1 + S2 + L8/9 (89.8 %), while PRISMA alone yielded a lower accuracy (63.8 %). The study identified nine dominant land cover classes, with cassava, rice, and sugarcane as primary crops. A strong correlation (r = 0.91) with the official Land Development Department (LDD) statistics demonstrates the robustness of the method. This research highlights the technical advantage of multi-sensor integration in overcoming the limitations of single-sensor data, providing a reliable tool for accurate crop mapping, and supporting sustainable agricultural practices in challenging environments.

准确的作物类型和土地覆盖图对于有效的土地管理和农业政策至关重要，特别是在农业景观复杂和农田面积小的地区。泰国东北部是一个重要的农业中心，由于其多样化的作物模式、云量和小农地块，在作物分类方面面临挑战。该研究整合了PRISMA、Sentinel-1 （S1）、Sentinel-2 （S2）和Landsat-8/9 （L8/9）图像的卫星数据，以解决这些挑战。在2022年11月至12月期间，随机收集1305个参考点，对提出的作物分类进行训练和验证。具体来说，使用随机森林（RF）分类器测试了15种不同的组合。所有数据集的组合获得了最高的总体精度（OA），为91.5%，其次是S1 + S2 + L8/9(89.8%)，而单独使用PRISMA的精度较低（63.8%）。该研究确定了九个主要的土地覆盖类别，其中木薯、水稻和甘蔗是主要作物。与官方土地发展部（LDD）统计数据的强相关性（r = 0.91）表明该方法的稳健性。该研究强调了多传感器集成在克服单传感器数据局限性方面的技术优势，为精确的作物制图提供了可靠的工具，并支持具有挑战性环境下的可持续农业实践。

{"title":"Integrating PRISMA hyperspectral data with Sentinel-1, Sentinel-2 and Landsat data for mapping crop types and land cover in northeast Thailand","authors":"Savittri Ratanopad Suwanlee , Zahid Naeem Qaisrani , Jaturong Som-ard , Surasak Keawsomsee , Kemin Kasa , Narissara Nuthammachot , Siwa Kaewplang , Sarawut Ninsawat , Enrico Borgogno Mondino , Samuele De Petris , Filippo Sarvia","doi":"10.1016/j.ejrs.2025.04.005","DOIUrl":"10.1016/j.ejrs.2025.04.005","url":null,"abstract":"<div><div>Accurate crop types and land cover maps are pivotal for effective land management and agricultural policy, particularly in regions with complex agricultural landscapes and small field sizes. Northeast Thailand, a significant agricultural hub, faces challenges in crop classification due to its diverse crop patterns, cloud cover, and smallholder plots. This study integrates satellite data from PRISMA, Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8/9 (L8/9) imagery to address these challenges. A total of 1305 reference point were randomly collected between November and December 2022 to train and validate the proposed crop classification. Specifically, 15 different combinations using a random forest (RF) classifier were tested. The combination of all datasets achieved the highest overall accuracy (OA) of 91.5 %, followed by S1 + S2 + L8/9 (89.8 %), while PRISMA alone yielded a lower accuracy (63.8 %). The study identified nine dominant land cover classes, with cassava, rice, and sugarcane as primary crops. A strong correlation (r = 0.91) with the official Land Development Department (LDD) statistics demonstrates the robustness of the method. This research highlights the technical advantage of multi-sensor integration in overcoming the limitations of single-sensor data, providing a reliable tool for accurate crop mapping, and supporting sustainable agricultural practices in challenging environments.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 252-260"},"PeriodicalIF":3.7,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902008","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}

引用次数: 0

Integrating GNSS/IMU and DEM data for precise aerial triangulation: Insights from airborne hybrid systems in upper Egypt 整合GNSS/IMU和DEM数据用于精确的空中三角测量：来自上埃及机载混合系统的见解

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-04-26 DOI: 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":"2025-04-26","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}

引用次数: 0

Role of sentinel-2 remotely sensed data in assisting stratigraphic subdivision of a Paleogene carbonate sequence, Jabal Hafit, UAE-Oman sentinel-2遥感数据在协助古近系碳酸盐岩层序地层细分中的作用，Jabal Hafit， uae -阿曼

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-04-25 DOI: 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.

利用SENTINEL-2遥感资料，对阿联酋Al Ain南部Jabal Hafit山进行了以碳酸盐岩为主的单调下始新统至渐新统层序地层单元制图。利用代表性岩石样品的光谱反射率曲线对数据进行处理。对研究样品的实测光谱曲线进行重采样，在寻找敏感波段算法的指导下，得到基于主成分的伪彩色图像，然后进行去相关拉伸（DS）改进。得到的图像在阿曼的一个小研究区域进行了解释，那里的地质不受人类活动的影响。将研究区彩色带与该地区已知地层单元的相关性应用于整个Jabal Hafit山区的DS图像。结果对海菲特古近系演替的组和段具有较好的识别能力。揭示的其他特征包括这些单元所显示的范围和侧向相变化。

{"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":"2025-04-25","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}

引用次数: 0

Spatiotemporal variations of aquatic vegetation in Maracaibo Lake: Remote sensing and machine learning approach with Google Earth Engine 马拉开波湖水生植被时空变化：基于谷歌Earth Engine的遥感与机器学习方法

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-04-22 DOI: 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.

水生植物入侵危及湖泊生物多样性和生态系统服务，给当地社区造成重大经济损失。因此，准确地划定开发的范围和频率是至关重要的，但传统的方法成本高，而且在偏远地区。需要具有成本效益的方法，例如卫星监测。本研究利用谷歌Earth Engine （GEE）中的随机森林分类模型和Landsat 7和8图像对淡水生态系统中水生植被的入侵进行了监测。该方法通过将Otsu-Canny Edge算法应用于植被指数的动态调整来自动选择训练样本，允许每月更新，同时最大限度地减少人为偏差。将该方法应用于委内瑞拉马拉开波湖，在2013年至2021年期间，浮动水生植被覆盖率显著增加，从2013年的≤10%增加到2021年的25.63%，特别是沿西北海岸和马拉开波湖海峡。这种增加可归因于降水模式等自然过程和人类活动增加的人为输入的结合。从混淆矩阵和跨传感器比较可以看出，该模型达到了较高的精度（>0.80）。这种方法提供了一种可以应用于其他富营养化湖泊的持续长期监测工具，提高了我们对入侵植被影响的理解，并协助资源管理者和政策制定者制定可持续管理战略。

{"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":"2025-04-22","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}

引用次数: 0

Is dynamic world a contender in global land-cover making race? A swift field assessment from Kastamonu, Türkiye 动态世界是全球土地覆盖竞赛的竞争者吗？来自<s:1>基耶州Kastamonu的快速现场评估

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-04-16 DOI: 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":"2025-04-16","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}

引用次数: 0

Thermal analysis and experimental validation of the thermal subsystem of AlAinSat-1 AlAinSat-1热分系统的热分析与实验验证

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-03-26 DOI: 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.

本研究考察了AlAinSat-1忍受极端空间条件所采用的热管理策略。它通过数值模拟对卫星的热行为进行了深入分析，并通过实验测试验证了其在太空中的功能。AlAinSat-1是一颗设计成立方体形状的纳米卫星，配备了地球观测有效载荷。热分析使用西门子NX软件进行，遵循一个结构化的过程，包括理想化、网格划分和边界条件的应用。进行了模拟，以评估立方体卫星在最坏的冷热情况下的性能，预测任务成功所需的温度范围。仿真结果证实，AlAinSat-1可以承受极端空间条件，所有组件都保持在其工作温度范围内。为了验证这些发现，使用小型热真空室（TVAC）进行了烘烤和热真空循环测试。烘烤测试在50°C下进行5小时，旨在消除CubeSat敏感组件中的挥发性污染物，降低排气风险。该测试取得了0.1%的总质量损失，表明成功。热真空循环测试包括四个循环，范围从- 20°C到+ 50°C，每个循环停留时间为1小时。这些测试确认了立方体卫星组件的工作温度范围。实验结果与模拟结果一致，表明AlAinSat-1的所有部件在其指定的温度范围内都能有效地工作。这种校准验证了热管理方法，并确保立方体卫星为空间部署做好准备。

{"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":"2025-03-26","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}

引用次数: 0

Olipier cultural site vulnerability analysis in East Belitung, Indonesia: Cultural resources vulnerability (CRV) methods 印尼东勿里洞奥利皮尔文化遗址脆弱性分析：文化资源脆弱性（CRV）方法

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-03-20 DOI: 10.1016/j.ejrs.2025.03.001

Amanda Tri Persada , Yulius , Syamsul B. Agus , Hadiwijaya L. Salim , Ira Dillenia , Taslim Arifin , Aida Heriati , Joko Prihantono , Dini Purbani , Sri Endah Purnamaningtyas , Didik Wahju Hendro Tjahjo , Muhammad Ramdhan , Siti Hajar Suryawati , Ary Wahyono , Ulung Jantama Wisha , Zulfiandi , Fery Kurniawan

The significance of this research lies in its contribution to Olipier cultural site vulnerability caused by coastal erosion and climate change impacts in East Belitung, Indonesia. Therefore, this study employs the Coastal Vulnerability Index (CVI) and Cultural Resource Vulnerability (CRV) methods to assess coastal vulnerability and site susceptibility, which integrates physical parameters, such as elevation, beach slope, geomorphology, land use, tidal range, significant wave height, shoreline change, distance from shoreline to sites, and sea-level rise. The CVI analysis results indicate that approximately 12.68 km of the observed coastline is very highly vulnerable, 8.72 km is highly vulnerable, and the remnant 10.91 km coastline is categorized as low vulnerability. On the other hand, the CRV method emphasizes specific vulnerable locations, identifying that approximately 53.34 % oil refineries are highly vulnerable zones due to their proximity to the shoreline, low elevation, and slope. This study also underscores the importance of proactive conservation measures, whereby implementing coastal protection structures, mangrove rehabilitation, and coral reef transplantation are possible. Collaboration between local and central governments is essential for effective coastal management and conservation of cultural heritage sites. Overall, this research provides valuable insights for coastal management strategies to mitigate risks and preserve cultural heritage in East Belitung Regency.

本研究的意义在于对印尼东勿里洞地区因海岸侵蚀和气候变化影响造成的奥利皮尔文化遗址脆弱性做出贡献。因此，本研究采用海岸脆弱性指数（Coastal Vulnerability Index， CVI）和文化资源脆弱性（Cultural Resource Vulnerability， CRV）方法，综合高程、滩坡、地貌、土地利用、潮差、显著浪高、岸线变化、岸线至遗址距离、海平面上升等物理参数，对海岸脆弱性和遗址易感性进行评估。CVI分析结果表明，观测岸线中约12.68 km的岸线高度脆弱，8.72 km的岸线高度脆弱，剩余的10.91 km岸线为低脆弱。另一方面，CRV方法强调了特定的脆弱区域，确定了大约53.34%的炼油厂是高度脆弱区域，因为它们靠近海岸线、低海拔和坡度。这项研究还强调了积极主动的保护措施的重要性，从而实施海岸保护结构，红树林恢复和珊瑚礁移植是可能的。地方和中央政府之间的合作对于有效的海岸管理和文化遗产保护至关重要。总体而言，本研究为东别里东摄政的海岸管理策略提供了有价值的见解，以减轻风险并保护文化遗产。

{"title":"Olipier cultural site vulnerability analysis in East Belitung, Indonesia: Cultural resources vulnerability (CRV) methods","authors":"Amanda Tri Persada , Yulius , Syamsul B. Agus , Hadiwijaya L. Salim , Ira Dillenia , Taslim Arifin , Aida Heriati , Joko Prihantono , Dini Purbani , Sri Endah Purnamaningtyas , Didik Wahju Hendro Tjahjo , Muhammad Ramdhan , Siti Hajar Suryawati , Ary Wahyono , Ulung Jantama Wisha , Zulfiandi , Fery Kurniawan","doi":"10.1016/j.ejrs.2025.03.001","DOIUrl":"10.1016/j.ejrs.2025.03.001","url":null,"abstract":"<div><div>The significance of this research lies in its contribution to Olipier cultural site vulnerability caused by coastal erosion and climate change impacts in East Belitung, Indonesia. Therefore, this study employs the Coastal Vulnerability Index (CVI) and Cultural Resource Vulnerability (CRV) methods to assess coastal vulnerability and site susceptibility, which integrates physical parameters, such as elevation, beach slope, geomorphology, land use, tidal range, significant wave height, shoreline change, distance from shoreline to sites, and sea-level rise. The CVI analysis results indicate that approximately 12.68 km of the observed coastline is very highly vulnerable, 8.72 km is highly vulnerable, and the remnant 10.91 km coastline is categorized as low vulnerability. On the other hand, the CRV method emphasizes specific vulnerable locations, identifying that approximately 53.34 % oil refineries are highly vulnerable zones due to their proximity to the shoreline, low elevation, and slope. This study also underscores the importance of proactive conservation measures, whereby implementing coastal protection structures, mangrove rehabilitation, and coral reef transplantation are possible. Collaboration between local and central governments is essential for effective coastal management and conservation of cultural heritage sites. Overall, this research provides valuable insights for coastal management strategies to mitigate risks and preserve cultural heritage in East Belitung Regency.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 167-184"},"PeriodicalIF":3.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143682320","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}

引用次数: 0

Decadal and seasonal oceanographic trends influenced by climate changes in the Gulf of Thailand 受气候变化影响的泰国湾年代际和季节海洋学趋势

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-03-12 DOI: 10.1016/j.ejrs.2025.02.003

Muhammad Zainuddin Lubis , Muhammad Ghazali , Andrean V.H. Simanjuntak , Nelly F. Riama , Gumilang R. Pasma , Asep Priatna , Husnul Kausarian , Made Wedanta Suryadarma , Sri Pujiyati , Fredrich Simanungkalit , Batara , Kutubuddin Ansari , Punyawi Jamjareegulgarn

Our study investigates the decadal and seasonal variability of sea surface height (SSH) and sea surface temperature (SST) in the Gulf of Thailand (GoT) using data from CMEMS from 1993 to 2021. We employed statistical analyses utilizing GLM and GAM to assess the variables comprehensively. The reveals a significant upward trend in SSH, increasing from ∼0.79 m in 1993–1998 to ∼0.89 m in 2017–2021, highlighting the impacts of climate change. SST analysis revealed fluctuations, with a maximum reaching ∼30.6 °C in 2019–2020, correlating with climatic events such as El Niño. Our study results at station 1 (near Bangkok) showed that the average SSH in 1998 during strong El Niño years was equal to 0.82 m, while the maximum SST was equal to 29.89 °C. Seasonal patterns indicated SSH peaks in DJF and SON at ∼0.92 m, while SST peaked in spring MAM and summer JJA at ∼30.7 °C. Volume transport analysis showed significant variability, with 0.3634 Sv (0–55 m) at longitude 99°E-107° E and latitude 6° N, indicating complex circulation patterns influenced by bathymetry and wind. Time series analysis revealed an average SSH increase of 0.0038 m/year, with a high pseudo-R-squared of 0.99. Our findings underscore the critical influence of climate variability on oceanographic conditions in the GoT, emphasizing the need for ongoing monitoring to address the implications of rising sea levels and temperature fluctuations. In conjunction with increased SSH, the rising SST heightens the risk of flooding in low-lying areas, exacerbating vulnerabilities for local populations and necessitating adaptive management strategies to mitigate these impacts.

利用1993 - 2021年CMEMS数据，研究了泰国湾（GoT）海表高度（SSH）和海表温度（SST）的年代际和季节变化。我们采用GLM和GAM进行统计分析，对变量进行综合评估。海平面高度呈显著上升趋势，从1993-1998年的~ 0.79 m增加到2017-2021年的~ 0.89 m，突出了气候变化的影响。海温分析揭示了波动，2019-2020年的最大值达到~ 30.6°C，与El Niño等气候事件相关。研究结果表明，1998年强El Niño年的平均海平面为0.82 m，最大海温为29.89°C。季节模式表明，DJF和SON的海温峰值在~ 0.92 m，而SST峰值在春季MAM和夏季JJA在~ 30.7°C。在经度99°E-107°E和纬度6°N处，体积输运分析显示出显著的变异，为0.3634 Sv (0-55 m)，表明受水深和风的影响，环流模式较为复杂。时间序列分析显示，平均海平面上升为0.0038 m/年，伪r平方高，为0.99。我们的研究结果强调了气候变率对北半球海洋学状况的重要影响，强调需要进行持续监测，以解决海平面上升和温度波动的影响。与海平面上升相结合，海温上升增加了低洼地区发生洪水的风险，加剧了当地居民的脆弱性，需要适应性管理策略来减轻这些影响。

{"title":"Decadal and seasonal oceanographic trends influenced by climate changes in the Gulf of Thailand","authors":"Muhammad Zainuddin Lubis , Muhammad Ghazali , Andrean V.H. Simanjuntak , Nelly F. Riama , Gumilang R. Pasma , Asep Priatna , Husnul Kausarian , Made Wedanta Suryadarma , Sri Pujiyati , Fredrich Simanungkalit , Batara , Kutubuddin Ansari , Punyawi Jamjareegulgarn","doi":"10.1016/j.ejrs.2025.02.003","DOIUrl":"10.1016/j.ejrs.2025.02.003","url":null,"abstract":"<div><div>Our study investigates the decadal and seasonal variability of sea surface height (SSH) and sea surface temperature (SST) in the Gulf of Thailand (GoT) using data from CMEMS from 1993 to 2021. We employed statistical analyses utilizing GLM and GAM to assess the variables comprehensively. The reveals a significant upward trend in SSH, increasing from ∼0.79 m in 1993–1998 to ∼0.89 m in 2017–2021, highlighting the impacts of climate change. SST analysis revealed fluctuations, with a maximum reaching ∼30.6 °C in 2019–2020, correlating with climatic events such as El Niño. Our study results at station 1 (near Bangkok) showed that the average SSH in 1998 during strong El Niño years was equal to 0.82 m, while the maximum SST was equal to 29.89 °C. Seasonal patterns indicated SSH peaks in DJF and SON at ∼0.92 m, while SST peaked in spring MAM and summer JJA at ∼30.7 °C. Volume transport analysis showed significant variability, with 0.3634 Sv (0–55 m) at longitude 99°E-107° E and latitude 6° N, indicating complex circulation patterns influenced by bathymetry and wind. Time series analysis revealed an average SSH increase of 0.0038 m/year, with a high pseudo-R-squared of 0.99. Our findings underscore the critical influence of climate variability on oceanographic conditions in the GoT, emphasizing the need for ongoing monitoring to address the implications of rising sea levels and temperature fluctuations. In conjunction with increased SSH, the rising SST heightens the risk of flooding in low-lying areas, exacerbating vulnerabilities for local populations and necessitating adaptive management strategies to mitigate these impacts.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 2","pages":"Pages 151-166"},"PeriodicalIF":3.7,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611517","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}

引用次数: 0

Comparison of random forest, gradient tree boosting, and classification and regression trees for mangrove cover change monitoring using Landsat imagery 随机森林、梯度树增强和分类回归树在Landsat影像红树林覆盖变化监测中的比较

IF 3.7 3区地球科学 Q2 ENVIRONMENTAL SCIENCES

Egyptian Journal of Remote Sensing and Space Sciences

Pub Date : 2025-03-01 DOI: 10.1016/j.ejrs.2025.02.002

Nirmawana Simarmata , Ketut Wikantika , Trika Agnestasia Tarigan , Muhammad Aldyansyah , Rizki Kurnia Tohir , Adam Irwansyah Fauzi , Anggita Rahma Fauzia

Ineffective land use in coastal areas negatively impacts the environment and destroys mangrove ecosystems, contributing to increasing greenhouse gas emissions and decreasing carbon sequestration. This study aimed to monitor the land use changes in mangrove areas with Landsat data using several machine learning (ML) methods. According to the random forest (RF), gradient tree boosting (GTB), and classification and regression trees algorithms (CART), the mangrove area exhibited significant fluctuations over the study period, with the largest expansion observed from 1999 to 2008 (4,240.57 ha), followed by a slight increase in 2023 (368.36 ha from 2019). Accuracy assessment revealed distinct performance levels across the models. The RF algorithm demonstrated the highest overall accuracy (OA) of 98.8 %, with kappa values ranging from 0.96 to 0.98, indicating high consistency and reliable predictions over time. The CART algorithm, while accurate, showed more variability, especially between 1991 and 1994, with an OA ranging from 85.3 % to 92.5 % and kappa values between 0.92 and 0.96. The GTB algorithm had moderate performance, with OA values between 85.6 % and 95.7 % and kappa values ranging from 0.92 to 0.96, suggesting reliable results but with some inconsistency compared to RF. The RF algorithm’s superior OA and consistency make it the most suitable long-term land cover monitoring method. Future studies can benefit from incorporating RF in assessing ecosystem changes, including carbon sequestration potential in mangrove forests.

沿海地区土地利用不当会对环境产生负面影响，破坏红树林生态系统，导致温室气体排放增加，碳固存减少。本研究旨在利用几种机器学习（ML）方法，利用Landsat数据监测红树林地区的土地利用变化。根据随机森林（RF）、梯度树增强（GTB）和分类与回归树算法（CART），红树林面积在研究期间呈现显著波动，1999 - 2008年最大扩张（4,240.57 ha），随后在2023年略有增加（较2019年增加368.36 ha）。准确性评估揭示了不同模型的不同性能水平。RF算法显示出最高的总体准确率（OA）为98.8%，kappa值在0.96 ~ 0.98之间，表明随着时间的推移，预测的一致性和可靠性很高。CART算法虽然准确，但表现出更大的变异性，特别是在1991年至1994年之间，OA在85.3%至92.5%之间，kappa值在0.92至0.96之间。GTB算法性能一般，OA值在85.6% ~ 95.7%之间，kappa值在0.92 ~ 0.96之间，结果可靠，但与RF相比存在一定的不一致性。RF算法具有较好的OA性和一致性，是最适合长期土地覆盖监测的方法。未来的研究可以受益于将RF纳入评估生态系统变化，包括红树林的碳固存潜力。

{"title":"Comparison of random forest, gradient tree boosting, and classification and regression trees for mangrove cover change monitoring using Landsat imagery","authors":"Nirmawana Simarmata , Ketut Wikantika , Trika Agnestasia Tarigan , Muhammad Aldyansyah , Rizki Kurnia Tohir , Adam Irwansyah Fauzi , Anggita Rahma Fauzia","doi":"10.1016/j.ejrs.2025.02.002","DOIUrl":"10.1016/j.ejrs.2025.02.002","url":null,"abstract":"<div><div>Ineffective land use in coastal areas negatively impacts the environment and destroys mangrove ecosystems, contributing to increasing greenhouse gas emissions and decreasing carbon sequestration. This study aimed to monitor the land use changes in mangrove areas with Landsat data using several machine learning (ML) methods. According to the random forest (RF), gradient tree boosting (GTB), and classification and regression trees algorithms (CART), the mangrove area exhibited significant fluctuations over the study period, with the largest expansion observed from 1999 to 2008 (4,240.57 ha), followed by a slight increase in 2023 (368.36 ha from 2019). Accuracy assessment revealed distinct performance levels across the models. The RF algorithm demonstrated the highest overall accuracy (OA) of 98.8 %, with kappa values ranging from 0.96 to 0.98, indicating high consistency and reliable predictions over time. The CART algorithm, while accurate, showed more variability, especially between 1991 and 1994, with an OA ranging from 85.3 % to 92.5 % and kappa values between 0.92 and 0.96. The GTB algorithm had moderate performance, with OA values between 85.6 % and 95.7 % and kappa values ranging from 0.92 to 0.96, suggesting reliable results but with some inconsistency compared to RF. The RF algorithm’s superior OA and consistency make it the most suitable long-term land cover monitoring method. Future studies can benefit from incorporating RF in assessing ecosystem changes, including carbon sequestration potential in mangrove forests.</div></div>","PeriodicalId":48539,"journal":{"name":"Egyptian Journal of Remote Sensing and Space Sciences","volume":"28 1","pages":"Pages 138-150"},"PeriodicalIF":3.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143551910","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}

引用次数: 0