International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"BSG-WSL: BackScatter-guided weakly supervised learning for water mapping in SAR images","authors":"Kai Wang, Zhongle Ren, Biao Hou, Weibin Li, Licheng Jiao","doi":"10.1016/j.jag.2025.104385","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104385","url":null,"abstract":"Extracting and analyzing water resources in Synthetic Aperture Radar (SAR) images is crucial for flood management and environmental resource planning due to the ability to monitor ground all-weather and all-time. However, extracting water entirely from high-resolution SAR images in diverse scenarios is challenging due to variable water shapes, many low-intensity land covers similar to water, and scarce labels. In this article, a BackScatter-Guided Weakly Supervised Learning (BSG-WSL) framework based on image-level labels is proposed for water extraction with the requirement of high generalization and low labeling. In BSG-WSL, a BackScatter-Guided Network (BSGNet) is proposed, where the backscatter information of water is used to guide the feature extraction process, yielding precise Class Attention Maps (CAMs) of water. Then, a morphological pseudo-label optimization algorithm is designed to employ CAMs to generate high-quality pseudo-labels. Finally, a confidence cross-entropy loss is introduced to utilize pseudo-labels to train the extraction model and achieve precise water extraction in different scenarios. Experiments on three datasets of SAR images from the GF-3 and Sentinel-1B satellites verify that the proposed method achieves state-of-the-art performance compared to other weakly supervised methods based on image-level annotations.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"84 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the impact of pandemic on the urban thermal environment over megacities in China: Spatiotemporal analysis from the perspective of heat anomaly variations","authors":"Jianfeng Gao, Qingyan Meng, Linlin Zhang, Xinli Hu, Die Hu, Jiangkang Qian","doi":"10.1016/j.jag.2025.104396","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104396","url":null,"abstract":"Influenced by lockdown policies and anomalies in human activities, emergencies such as pandemic significantly altered the urban thermal environment. However, the spatiotemporal heat anomaly changes across and within cities during emergencies and their drivers have not been fully investigated. This study quantified the changes in the urban thermal environment in China before and during the COVID-19 pandemic. Based on z-scores and multiscale geographically weighted regression models, heat anomaly changes and transfer patterns of different land uses in cities with varying degrees of pandemic impact and drivers were estimated. During the entire year, we found that although the pandemic significantly reduced surface urban heat island intensity during 5 % to 35 % of days, it did not change significantly throughout 2020. During the first-level public health emergency response, the land surface temperatures of residential and commercial lands notably affected by the pandemic decreased by −0.195°C and −0.371°C, and the shifting of strong heat anomaly zones in industrial lands increased heat anomaly and no heat anomaly zones by 6.1 % and 1.4 %, respectively. Furthermore, thermal anomalies were highly correlated with changes in biophysical parameters during the pandemic. These findings provide insights and mitigation strategies for the fluctuations in the urban thermal environment caused by emergencies.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"207 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models","authors":"Li Zhang, Xiaodong Gao, Shuyi Zhou, Zhibo Zhang, Tianjie Zhao, Yaohui Cai, Xining Zhao","doi":"10.1016/j.jag.2025.104388","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104388","url":null,"abstract":"Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However, accurate identification of standing dead trees (SDTs) within plantations using remote sensing techniques remains underexplored, and the spatial distribution patterns of SDTs across the CLP are poorly understood. Therefore, this study leveraged unmanned aerial vehicle (UAV) remote sensing to capture high-resolution RGB images of <ce:italic>Robinia pseudoacacia</ce:italic> plantations. These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. Particularly, the YOLOv9-ECA was developed by incorporating the ECA module into key network layers to enhance channel dependency modeling and improve feature representation for SDTs detection. Its merit lies in adaptively reweighting feature channels, enabling efficient detection in resource-constrained environments. As expected, the YOLOv9-ECA model demonstrated significant advancements, achieving a detection speed of 123.5f/s, a mAP of 97.8%, and an F<ce:inf loc=\"post\">1</ce:inf> score of 0.97, outperforming other models in both detection efficiency and accuracy. Subsequently, the model was employed to quantify the spatial distribution of SDTs across the CLP by estimating the number of dead trees per unit area. Results revealed an increasing trend in the number of dead trees per unit along decreasing precipitation gradients, emphasizing the vulnerability of <ce:italic>Robinia pseudoacacia</ce:italic> plantations in drier regions. Additionally, the number of dead trees per unit varied with slope aspect, with sunny slopes exhibiting the highest values and shady slopes the lowest. This study highlights the potential of YOLOv9-ECA as a powerful tool for the efficient detection of SDTs, offering insights for the sustainable management of <ce:italic>Robinia pseudoacacia</ce:italic> plantations on the CLP and holding potential applicability to similar environments globally.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"39 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting glacial lake water quality indicators from RGB surveillance images via deep learning","authors":"Zijian Lu, Xueyan Zhu, Jinfeng Li, Mingyue Li, Jie Wang, Wenqiang Wang, Yili Zheng, Qianggong Zhang","doi":"10.1016/j.jag.2025.104392","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104392","url":null,"abstract":"Global warming has accelerated glacier retreat, subsequently leading to the formation of glacial lakes in high-altitude mountainous regions. These lakes represent emerging ecological water systems and could potentially pose significant hazards. Observations of these systems are constrained by their remote locations and the lack of cost-effective monitoring methods, resulting in limited understanding of their dynamics. In this study, we synchronized surveillance monitoring with in-situ water quality measurements at a typical high-altitude glacial lake on the Qinghai-Tibet Plateau. We aim to use images from surveillance cameras to estimate the turbidity parameter, a key indicator of changes in the water environment and the impacts of climate change on high-altitude ecosystems. We segmented RGB images and applied regression modeling with field-measured water turbidity data, and then used deep learning models to accurately estimates turbidity levels and their changes. Our study demonstrates the potential of RGB imagery and deep learning for the long-term, continuous, and high-resolution monitoring of water quality in remote glacial lakes. It presents a novel and cost-effective approach for monitoring these newly emerged and swiftly changing water systems at high altitudes, offering a significant advancement in tracking environmental changes in these critical high mountain regions.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"166 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic mapping of urban tree canopy height using ICESat-2 data and GF-2 imagery","authors":"Xiaodi Xu, Ya Zhang, Peng Fu, Chaoya Dang, Bowen Cai, Qingwei Zhuang, Zhenfeng Shao, Deren Li, Qing Ding","doi":"10.1016/j.jag.2024.104348","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104348","url":null,"abstract":"Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (<ce:italic>R</ce:italic>) = 0.53, root mean square error (<ce:italic>RMSE</ce:italic>) = 2.9 m, and mean absolute error (<ce:italic>MAE</ce:italic>) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"77 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network","authors":"Haoyi Wang, Weitao Chen, Xianju Li, Qianyong Liang, Xuwen Qin, Jun Li","doi":"10.1016/j.jag.2025.104383","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104383","url":null,"abstract":"Multibeam sounding is a high-precision remote sensing method for seabed detection. Seabed topography classification is crucial for marine science research, resource exploration and engineering. When using multibeam data for seabed topography automatic classification, the fuzzy boundaries of different topographic entities, redundancy of multimodal data, and the lack of geological knowledge guidance have led to low classification accuracy. Thus, a knowledge graph-guided vision mamba seabed topography classification network (CUG-STCN) was constructed, consisting of three modules: (1) The long sequence modeling mamba-based encoder addresses the fuzzy seabed topography boundary. It uses 2D-selective-scan to create image blocks in different scanning directions. By combining with the selective state space model to capture long-range dependencies and ensure transmission of spatial context information while maintaining linear computational complexity. (2) The cross-modal information interaction and fusion module addresses the redundancy of multimodal information. By employing a bidirectional information interaction mechanism, it captures the correlations of seabed topography between different modalities and achieving feature fusion. (3) The seabed topography knowledge graph-guided semantic perception module guides the geological knowledge. It constructs seabed topography knowledge vectors through entity query and word embedding, using the similarity between vectors to create a similarity measurement matrix. It provides geological knowledge, enhancing the modeling capability of complex seabed topography relationship. CUG-STCN achieves OA of 90.11% and mIOU of 48.50%, outperforming six mainstream networks, which at most, achieve the OA and mIOU improvements of 5.37% and 14.18%. Notably, the application of CUG-STCN in other regions demonstrates its strong generalization performance.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"530 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A tree crown edge-aware clipping algorithm for airborne LiDAR point clouds","authors":"Shangshu Cai, Yong Pang","doi":"10.1016/j.jag.2025.104381","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104381","url":null,"abstract":"Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness of tree crowns within predefined clipping boundaries (e.g., rectangles), we develop a tree crown edge-aware (E-A) point cloud clipping algorithm, named E-A algorithm. Firstly, the crown edge and distance features are enhanced and quantified using mathematical morphology and nearest neighbor pixel methods. Then, these two features are linearly weighted and integrated for cutting line detection. Finally, the optimal cutting lines are detected by exploring a set of edges with the minimum sum of integrated feature values. E-A algorithm was tested with airborne LiDAR point clouds collected from China’s Saihanba Forest Farm, comparing it against regular clipping methods. The results indicate that E-A algorithm can automatically and effectively emphasize preserving tree crown completeness within predefined clipping boundaries. It reduces crown fragmentation errors by 73.29% on average and maintains an average area difference of 6.42% compared to regular clippings. This algorithm provides a crucial tool for forest point cloud applications.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"119 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Change detection of slow-moving landslide with multi-source SBAS-InSAR and Light-U2Net","authors":"Jianao Cai, Dongping Ming, Feng Liu, Xiao Ling, Ningjie Liu, Liang Zhang, Lu Xu, Yan Li, Mengyuan Zhu","doi":"10.1016/j.jag.2025.104387","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104387","url":null,"abstract":"Interferometric Synthetic Aperture Radar (InSAR) techniques are commonly used approach for identifying Slow-moving Landslide (SML). However, most SML boundary identification with deep learning are based on single-source InSAR data, which cannot fully explore the dynamic process of destabilization, and are inefficient due to high model complexity. Meanwhile, research on automatic procession with multi-source InSAR data is few. To enhance efficiency in geohazard monitoring, this paper proposed an automatic framework for Boundary-Changed Slow-moving Landslide (BCSML) detection by integrating multi-source Small Baseline Subset InSAR (SBAS-InSAR), Convolutional Neural Network (CNN), and change detection methodologies. Firstly, surface deformation was estimated using multi-source SBAS-InSAR. Then, a novel and effective Light-U<ce:sup loc=\"post\">2</ce:sup>Net was constructed with decreased complexity to identify Significant Deformation Zone (SDZ) and locate SML candidate. Finally, BCSMLs were identified using a change detection approach based on newly defined geometric measurements. Two study areas were selected to test the model’s performance: Zayu County and the Nu-Lancang River parallel flow (NLPF) area (in China). The proposed Light-U<ce:sup loc=\"post\">2</ce:sup>Net model achieves high Precision (80.1 %), Recall (80.2 %), and F1-scores (80.1 %) in Zayu County. Additionally, the model’s complexity has reduced by 42.4 % without compromising identification accuracy compared to the original model. The pre-trained model was then applied to the NLPF area, and a total of 273 BCSMLs were detected, with 176 identified as expanding and 97 as shrinking. BCSML identification accuracy can reach to 90.47 %. The results have proved that the proposed framework with the Light-U<ce:sup loc=\"post\">2</ce:sup>Net model is effective and practically potential in landslide disaster prevention.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"7 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reduced sediment load and vegetation restoration leading to clearer water color in the Yellow River: Evidence from 38 years of Landsat observations","authors":"Ke Xia, Xintao Li, Taixia Wu, Shudong Wang, Hongzhao Tang, Yingying Yang","doi":"10.1016/j.jag.2025.104369","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104369","url":null,"abstract":"The Yellow River (YR), the fifth largest river in the world, plays a crucial role in regional development, making water quality assessment essential. Remote sensing provides a rapid and convenient means of observation, but water quality inversion models are often limited by the complex optical properties of inland waters and the availability of limited in-situ samples. The Forel-Ule color index (FUI), combined with satellite data, is effective for large-scale, comprehensive water quality assessments. However, the long-term water color dynamics of YR and its response to environmental changes have not been systematically studied. This study developed an improved hue angle (α, FUI conversion parameter) inversion model using Landsat data and assessed YR’s water color dynamics from 1985 to 2023. The results showed that YR’s FUI was generally high (mean FUI: 16.84 ± 1.85), with notable seasonal variation and spatial distribution patterns significantly influenced by dam construction and environmental features. Over the past 38 years, 86.47 % of river sections exhibited a declining α trend, particularly in the Loess Plateau. Reduced sediment load and increased vegetation, driven by engineering measures and the “Grain for Green” policy, were the primary factors behind long-term water color changes. Overall, the shift of YR water color towards greener hues was a positive signal, indicating improvements in water quality and ecosystem recovery. This study is significant for a deeper understanding of YR’s water quality changes and for informing watershed ecological restoration and management strategies.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"38 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143049666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment and validation of Meteosat SEVIRI fire radiative power (FRP) retrievals over Kruger National Park","authors":"Gareth Roberts, Martin. J. Wooster, Tercia Strydom","doi":"10.1016/j.jag.2025.104375","DOIUrl":"https://doi.org/10.1016/j.jag.2025.104375","url":null,"abstract":"Satellite burned area, active fire and fire radiative power (FRP), are key to quantifying fire activity and are one of 54 essential climate variables (ECV) and it is important to validate these data to ensure their consistency. This study investigates some of the factors that influence FRP retrieval and uses Meteosat Spinning Enhanced Visible and InfraRed Imager (SEVIRI) data to do so. Analysis of the influence of a fire’s location within a SEVIRI pixel on FRP was carried out using fire simulations which indicate that FRP varies by up to 14 % at nadir for a single sensor and by up to 55 % when intercomparing simulated FRP from different SEVIRI sensors. Intercomparison between actual MET-11 and MET-08 FRP data on a per-pixel basis reveals a high degree of scatter (81.9 MW), strong correlation (R = 0.72), low bias (∼1 MW) and an average percentage difference of 15.7 %. Variability is reduced when aggregated to fire ‘clusters’ which improves the correlation (R = 0.96) and reduces the average percentage difference (4.2 %). Validation of MET-08 and MET-11 FRP retrievals using FRP from helicopter mounted longwave infrared (LWIR) and midwave infrared (MWIR) thermal cameras is carried out over five prescribed burns. The results reveal good agreement between the SEVIRI and thermal camera FRP although the SEVIRI FRP is typically overestimated compared to that from the LWIR camera. This study illustrates some of the challenges validating satellite FRP which should be accounted for when defining uncertainty thresholds for product requirements and in developing FRP validation protocols.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"6 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}