International Journal of Applied Earth Observation and Geoinformation最新文献

{"title":"Corrigendum to “Optimization of pre-hospital emergency facility layout in Nanjing: A spatiotemporal analysis using multi-Source big data” [Int. J. of Appl. Earth Obs. Geoinf. 133 (2024) 104112]","authors":"Bing Han, Wanqi Hu, Xilu Tang, Jiemin Zheng, Mingxing Hu, Zhe Li","doi":"10.1016/j.jag.2024.104325","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104325","url":null,"abstract":"","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"30 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874793","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":"Utilization of Sentinel-2 satellite imagery for correlation analysis of shoreline variation and incident waves: Application to Wonpyeong-Chogok Beach, Korea","authors":"Euihyun Kim, Changbin Lim, Jung Lyul Lee","doi":"10.1016/j.jag.2024.104316","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104316","url":null,"abstract":"Satellite images have been adopted in recent years for identifying topographical features on the Earth’s surface. Researchers have also published reports on the use of satellite images to analyze shoreline changes or to verify shoreline change in numerical models. But reports that demonstrate the reverse process of using satellite images to estimate the incident waves to a beach are rare, particularly to a place where protective coastal structures exist. This paper describes a once-thriving coastal townsite with two fishing ports in Korea which has been transformed into a typical example that relies on protective structures with occasional artificial nourishment to maintain its shoreline stability in the past 20 years. Unlike many others, this study proposes a new methodology to estimate the deepwater wave heights based on the analysis of shoreline data extracted from satellite images over 5 years (2019–2023) for Wonpyeong-Chogok Beach, its median sediment grain sizes <mml:math altimg=\"si6.svg\"><mml:msub><mml:mi>D</mml:mi><mml:mn>50</mml:mn></mml:msub></mml:math>, and the known empirical relationship between sediment and waves. The entire shoreline of 2,860 m in length is divided into 39 transects, of which one-half of it is protected by submerged and emergent detached breakwaters, where shoreline has advanced, while the rest has eroded. From the standard deviation values ​​calculated from the extracted shoreline location data, the influence of long-term trends was excluded, and the intrinsic standard deviation is obtained by applying sediment size information, and then the incident deep-water (average annual maximum) wave height of 4.363 m was estimated. Applying this methodology to the beach area where the coastal structure was placed, the wave transmission of the coastal structure was calculated 0.91 and 0.72 for LCSs and TT-DBWs, respectively, through the reduction ratio of the standard deviation. Finally, discussions are made on how the resolution of the Sentinel-2 satellite images in affecting the standard deviation and long-term trend results in the shoreline data.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"19 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825346","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":"SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity","authors":"Yun Luo, Shiliang Su","doi":"10.1016/j.jag.2024.104315","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104315","url":null,"abstract":"A wide variety of spatially explicit modeling algorithms has recently mushroomed in geoinformation research. These algorithms establish local models with data from spatially confined subsets, thereby offering a new impetus for addressing the issue of spatiotemporal non-stationarity. However, a significant challenge persists in literature that local models are primarily predicated on linear assumptions, limiting their capacity to capture the non-linear relationships prevalent in real-world geographical phenomena. This study remedies this gap through proposing a novel approach that integrates the bagging and stacking approaches of ensemble learning into the spatially explicit modeling framework. We specifically develop the SpatioTemporal Random Forest (STRF) and SpatioTemporal Stacking Tree (STST) algorithms<ce:cross-ref ref><ce:sup loc=\"post\">1</ce:sup></ce:cross-ref><ce:footnote><ce:label>1</ce:label><ce:note-para view=\"all\">Python package link: <ce:inter-ref xlink:href=\"https://github.com/46319943/GeoRegression\" xlink:type=\"simple\">https://github.com/46319943/GeoRegression</ce:inter-ref>.</ce:note-para></ce:footnote>, which capture and interpret the non-linearity in the spatial and temporal context more effectively. Additionally, we introduce the ‘local importance score’ and ‘spatiotemporally accumulated local effects’ as novel interpretable metrics for visualizing and unraveling the dynamics of non-stationarity in spatial analyses. Simulation and real data experiments validate that the STRF and STST outperform over traditional spatially explicit modeling algorithms to a large content. This study contributes to the methodological innovation of spatially explicit modeling by bringing the nonlinearity in spatiotemporal non-stationarity to the fore.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"26 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825360","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":"Diffuse attenuation coefficient and bathymetry retrieval in shallow water environments by integrating satellite laser altimetry with optical remote sensing","authors":"Changda Liu, Huan Xie, Qi Xu, Jie Li, Yuan Sun, Min Ji, Xiaohua Tong","doi":"10.1016/j.jag.2024.104318","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104318","url":null,"abstract":"Shallow water environmental information is crucial for the study of marine ecosystems and human activities. There have been numerous satellite remote sensing studies focused on this area. However, accurate information acquisition from remote sensing data remains difficult in this region due to the complexity of the environment and the coupling between benthic reflectance and water column scattering. In this study, we developed a method to retrieve the diffuse attenuation coefficient (&lt;mml:math altimg=\"si2.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;K&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;d&lt;/mml:mi&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;), seafloor classification, and bathymetric maps by combining satellite laser altimetry and optical remote sensing imagery in shallow water areas. Firstly, the relationships between remote sensing reflectance (&lt;mml:math altimg=\"si3.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;R&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi mathvariant=\"normal\"&gt;r&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;s&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt;), water depth, and &lt;mml:math altimg=\"si2.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;K&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;d&lt;/mml:mi&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt; were established based on radiative transfer theory. This method allows for the retrieval of &lt;mml:math altimg=\"si2.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;K&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;d&lt;/mml:mi&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt; in shallow water regions, overcoming the limitations present in previous studies. Secondly, we eliminated the water column attenuation and obtained the bottom reflectance index (BRI). The BRI allowed us to determine the bottom reflectance and classify the seafloor using the Gaussian mixture model clustering method. This approach can effectively reduce the error in bathymetric inversion caused by variations in bottom reflectance. Finally, we developed a neural network model for bathymetric inversion. The model inputs consist of &lt;mml:math altimg=\"si3.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;R&lt;/mml:mi&gt;&lt;mml:mrow&gt;&lt;mml:mi mathvariant=\"normal\"&gt;r&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;s&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt; data and spectral shape data containing physical constraint information, aiming to achieve a robust estimation performance. We conducted the study in two experimental areas (the Bimini Islands and the Yongle Atoll) and compared the results with validation data to evaluate the algorithm performance. The results indicated an agreement between the estimated &lt;mml:math altimg=\"si2.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mi mathvariant=\"normal\"&gt;K&lt;/mml:mi&gt;&lt;mml:mi mathvariant=\"normal\"&gt;d&lt;/mml:mi&gt;&lt;/mml:msub&gt;&lt;/mml:mrow&gt;&lt;/mml:math&gt; and the validation data (inferred &lt;mml:math altimg=\"si2.svg\"&gt;&lt;mml:mrow&gt;&lt;mml:msub&gt;&lt;mml:mrow&gt;&lt;mml:mi mathvariant=\"normal\"&gt;K&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;mml:mrow&gt;&lt;mml:mi mathvariant=\"normal\"&gt;d&lt;/mml:mi&gt;&lt;/mml:mrow&gt;&lt;/mml:msub&gt;&lt;mml:mn&gt;490&lt;/m","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"16 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825347","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":"Local uncertainty maps for land-use/land-cover classification without remote sensing and modeling work using a class-conditional conformal approach","authors":"Denis Valle, Rodrigo Leite, Rafael Izbicki, Carlos Silva, Leo Haneda","doi":"10.1016/j.jag.2024.104288","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104288","url":null,"abstract":"Land use/land cover (LULC) is one of the most impactful global change phenomenon. As a result, considerable effort has been devoted to creating large-scale LULC products from remote sensing data, enabling the scientific community to use these products for a wide range of downstream applications. Unfortunately, uncertainty associated with these products is seldom quantified because most approaches are too computationally intensive. Furthermore, uncertainty maps developed for large regions might fail to perform adequately at the spatial scale in which they will be used and might need to be customized to suit the specific applications of end-users.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"41 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825367","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":"Spatial-Topological-Semantic alignment for cross domain scene classification of remote sensing images with few source labels","authors":"Binquan Li, Lishuang Gong, Qiao Wang, Xin Guo, Zhiqiang Li","doi":"10.1016/j.jag.2024.104313","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104313","url":null,"abstract":"Domain adaptation is crucial for information integration of remote sensing systems, such as satellite constellations and space stations, to intelligently achieving full domain awareness. The conventional methods focus on aligning spatial features without fully considering the topological structure and semantic information in the scene, resulting in loss of useful information and suboptimal classification results. This situation becomes more severe and further complicated to deal with under the condition of few labels available in the source domain. To address the above problems, a spatial-topological-semantic alignment method called STSA is proposed to implement unsupervised domain adaptation (UDA) with few source labels, fully exploring multiple types of information and their complementarity in remote sensing images (RSIs). The proposed method is applied to complete the classification task on a multi-modal cross-domain datasets with synthetic aperture radar (SAR), thermal infrared (TI), near infrared (NI), and short wavelength infrared (SW) images derived from Chinese Tiangong-2 manned spacecraft, as well as a Single modal cross-domain datasets with optical images. Compared with the state of the art UDA methods, even with only one labeled RSI in the source domain, the proposed methods still perform better and achieve satisfying accuracy. It properly explores valuable knowledge from unlabeled RSIs and improves the robustness and flexibility of the model, which is more suitable for UDA with few source labels in RSIs scene classification.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"21 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825361","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":"Using lightweight method to detect landslide from satellite imagery","authors":"Jinchi Dai, Xiaoai Dai, Renyuan Zhang, JiaXin Ma, Wenyu Li, Heng Lu, Weile Li, Shuneng Liang, Tangrui Dai, Yunfeng Shan, Donghui Zhang, Lei Zhao","doi":"10.1016/j.jag.2024.104303","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104303","url":null,"abstract":"Accurate, rapid, and automated landslide detection is crucial for early warning, emergency management, and landslide mechanism analysis. Increasingly general-purpose detection models are being deployed for these complex and dynamic tasks involving features that are difficult to characterize. However, these models are computationally expensive and memory-hungry, while the accuracy and detection efficiency remain wanting. To address the above problems, this paper proposes an end-to-end model with high-precision and lightweight design for integrated landslide detection and segmentation. Here, we customized the backbone utilizing the advanced Efficient MOdel (EMO), and further used the linear cheap operation from GhostNet to reduce computational complexity. As a result, the total parameters of our models were reduced by up to 48.13%, compared to the baseline. Building on this, we employed a dynamic detection head with multiple attention mechanisms, and proposed a lightweight attention enhancement module for strengthened multi-scale feature extraction and fusion. The results demonstrate that our model outperforms the baseline on all metrics, achieving an outstanding F1 score of 96.75%.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"63 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825362","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":"An approach for predicting landslide susceptibility and evaluating predisposing factors","authors":"Wanxin Guo, Jian Ye, Chengbing Liu, Yijie Lv, Qiuyu Zeng, Xin Huang","doi":"10.1016/j.jag.2024.104217","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104217","url":null,"abstract":"Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information among samples leads to reduced prediction accuracy and challenges in quantifying landslide risk covariates. This study proposes a landslide susceptibility assessment method that integrates ensemble learning with geographically weighted concepts. Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. Additionally, we constructed a deep learning geographically weighted regression (GW-DNN) model based on the deep learning principles and geographically weighted regression to quantify the impacts of landslide-predisposing factors.The experimental results show that the CRNN-LSTM model achieved AUC values of 0.977 and 0.961 on the training and validation sets, significantly outperforming the individual classifiers (AUC of 0.944 and 0.940 for the 1D-CNN model, 0.950 and 0.948 for the RNN model, and 0.956 and 0.952 for the LSTM model). Additionally, the GW-DNN model achieved R<ce:sup loc=\"post\">2</ce:sup> coefficients of 0.876 and 0.860 during the training and validation phases. These findings indicate that our proposed method not only highly accurately predicts landslide susceptibility but also provides a precise quantitative assessment of the impact of landslide-predisposing factors at specific spatial points (landslide units) in high-risk areas. These findings offer valuable technical support for landslide disaster prevention and mitigation.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"120 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825364","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":"SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps","authors":"Zhiyong Zhou, Cheng Fu, Robert Weibel","doi":"10.1016/j.jag.2024.104236","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104236","url":null,"abstract":"Building generalization is an essential task in generating multi-scale topographic maps. The progress of deep learning offers a new paradigm to overcome the coordination challenges faced by conventional building generalization algorithms. Some studies have confirmed the feasibility of several original semantic segmentation networks, such as U-Net and its variants and the conditional generative adversarial network (cGAN), for building generalization in image maps. However, they suffer from critical deformation effects, especially for large and geometrically complex buildings. Since learning building generalization essentially means modeling the subtle transformation of building footprints across scales, we argue that the spatial awareness of a neural network, for instance, regarding building size and shape, is crucial to effective learning. Thus, we propose a spatially-aware generative adversarial network, SpaGAN. It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. The results demonstrate that SpaGAN outperforms existing baseline models (U-Net, ResU-Net, pix2pix) for building generalization, particularly in the real-world dataset. The new model can achieve more reasonable aggregation, simplification, and squaring generalization operators.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"11 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825363","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":"Reconstruction of Petermann glacier velocity time series using multi-source remote sensing images","authors":"Zongze Li, Jinsong Chong, Yawei Zhao, Lijie Diao","doi":"10.1016/j.jag.2024.104307","DOIUrl":"https://doi.org/10.1016/j.jag.2024.104307","url":null,"abstract":"Glacier velocity is one of the crucial parameters in the research of glacier dynamics. Synthetic aperture radar (SAR), as an active microwave sensor, represents a common method to monitor glacier velocity. However, the changes of glacier surface could cause the data missing of glacier velocity due to incoherence. To meet the demand for glacier velocity monitoring, this paper employs the SAR images of Sentinel-1 in long time series and optical images of Sentinel-2 to investigate the velocity of Petermann glacier in 2021. Firstly, the time series of glacier velocity in the whole year of 2021 is obtained by using SAR images. The glacier velocity extracted from the optical image pairs is used as the initial value of the large missing part of the glacier velocity field. Then the spatiotemporal glacier velocity matrix is constructed and empirical orthogonal function (EOF) analysis is carried out. Among them, the glacier velocity is reconstructed by the glacier velocity estimation method based on confidence, and the complete glacier velocity time series is obtained by iterating to minimize the error of the reconstructed glacier velocity. Finally, the obtained time series of Petermann Glacier velocity in 2021 were statistically analyzed. The statistical results quantified the seasonal differences of Petermann Glacier. In addition, the analysis results show that the temporal and spatial variations of Petermann Glacier velocity are affected by topography and temperature.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"20 1","pages":""},"PeriodicalIF":7.5,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142793511","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}