International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"Field-scale irrigated winter wheat mapping using a novel cross-region slope length index in 3D canopy hydrothermal and spectral feature space","authors":"Youming Zhang ,&nbsp;Guijun Yang ,&nbsp;Prasad S. Thenkabail ,&nbsp;Zhenhong Li ,&nbsp;Wenbin Wu ,&nbsp;Xiaodong Yang ,&nbsp;Xiaoyu Song ,&nbsp;Huiling Long ,&nbsp;Miao Liu ,&nbsp;Jing Zhang ,&nbsp;Lijun Zuo ,&nbsp;Yang Meng ,&nbsp;Meiling Gao ,&nbsp;Wu Zhu","doi":"10.1016/j.jag.2025.104628","DOIUrl":"10.1016/j.jag.2025.104628","url":null,"abstract":"<div><div>Understanding the spatial and temporal distribution of irrigated cropland at the field scale is essential for managing irrigation water use and addressing the water-food nexus. While global and regional irrigation products exist, they often classify irrigated crops based on machine learning principles, where irrigated crops outperform rainfed ones. However, these methods typically lack mechanistic representation and are rarely applicable at the field scale over long time series. Additionally, identifying irrigated cropland in dual-season systems poses challenges due to temporal heterogeneity, leading to potential misclassification. To address these issues, we constructed a 3D canopy feature space including hydrothermal characteristics (1-precipitation/P, 2-actual evapotranspiration/AET) and spectral characteristic (3-NDVI). This approach is based on two mechanisms: the impact of irrigation on water vapor cycling and its role in promoting crop growth. We introduced a novel cross-region Slope Length Index (SLI) to map irrigated and rainfed crops at the field scale. Our method involved downscaling NDVI and AET using spectral fusion techniques (STF) on Google Earth Engine (GEE), followed by fitting a robust rainfed line (AET = −125.41 + 0.84 × P, R² = 0.70) at the provincial scale, and calculating the SLI. Then A case of irrigation map (Irri_HNP) was generated by a threshold for crop water supply and demand, achieving ≥ 38 % accuracy improvement on overall accuracy (OA = 0.973) compared to existing products. The SLI method also exhibited strong stability when generalized to the national scope (AET = −74.41 + 0.82 × P, R² = 0.73), maintaining robustness in both drought and humid years (AET = −177.08 + 0.82 × P, R² = 0.69). The method’s scalability and transferability have been rigorously validated across diverse regions and environments, spanning from provincial to national scales. This validation achieved an OA of 0.922, demonstrating robust performance under heterogeneous conditions. Furthermore, the framework provides actionable insights for field-scale crop management and agricultural water governance.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104628"},"PeriodicalIF":7.6,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MCR-PFNet: A novel InSAR interferometric phase filtering method for complex noise and large gradient deformations","authors":"Shuai Wang ,&nbsp;Yu Chen ,&nbsp;Kaiwen Ding ,&nbsp;Yandong Gao ,&nbsp;Kun Tan ,&nbsp;Peijun Du","doi":"10.1016/j.jag.2025.104621","DOIUrl":"10.1016/j.jag.2025.104621","url":null,"abstract":"<div><div>Phase filtering is crucial for ensuring accurate phase unwrapping in the processing of Interferometric Synthetic Aperture Radar (InSAR) data. Traditional filtering methods often struggle to effectively suppress noise and accurately preserve phase edge information when processing InSAR interferometric phases with complex noise and large gradient deformations. In response to this challenge, we have proposed the MCR-PFNet model based on residual attention convolution for InSAR phase filtering. MCR-PFNet integrates residual blocks, the convolutional block attention module (CBAM), and a multi-head self-attention module, enabling the simultaneous extraction of both local and global phase features, while filtering out noise and preserving phase details. To further enhance the generalization capability of MCR-PFNet, additive Gaussian noise and local phase jumps were introduced into the training dataset, and the MCR-PFNet model was trained with a custom-designed periodic phase loss function. The filtering performance was evaluated on simulated and real datasets. The results demonstrate that MCR-PFNet excels in scenarios with complex noise and large gradient deformations. In the simulated wrapped phase experiments, MCR-PFNet outperformed other methods in three metrics: PSNR (55.820–64.017 dB), SSIM (0.965–0.990), and MSE (0.026–0.170 rad²). In two sets of real data experiments, MCR-PFNet significantly improved the quality of the interferograms. In the real InSAR data of coal mining subsidence, the filtered NOR decreased to 517, PRR reached 86.987 %, and Metric Q reached 95.278 %. In the real InSAR data of earthquakes, NOR was reduced by 4.016 %–79.997 % compared to other methods, while PRR and Metric Q increased by 0.116 %–25.899 % and 0.111 %–13.803 %, respectively.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104621"},"PeriodicalIF":7.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying native grasslands and key phenological stages using time series Sentinel-2 data and deep learning models","authors":"Yihan Pu ,&nbsp;Amy Nixon ,&nbsp;Beatriz Prieto ,&nbsp;Xulin Guo","doi":"10.1016/j.jag.2025.104619","DOIUrl":"10.1016/j.jag.2025.104619","url":null,"abstract":"<div><div>Canadian prairies are<!--> <!-->among the world’s most endangered ecosystems, and the identification of native grasslands is crucial for supporting grassland management and wildlife conservation. However, the current amount, distribution, and dynamic changes of native grassland remain uncertain, partly due to the difficulty of separating native grassland with other land cover types, especially tame grassland in landcover classification products. This study aims to identify native grasslands in the Mixed Grasslands ecoregion of Saskatchewan using Sentinel-2 Normalized Difference Vegetation Index (NDVI) time series data through deep learning and multi-temporal approaches. Three classifiers, one-dimensional convolutional (Conv1D), Attention Long Short-Term Memory (At-LSTM), and Random Forest (RF), were applied to distinguish the native and tame grasslands, analyzing the key phenological stages. The results show that the Conv1D-based model outperformed the others in both the South (accuracy: 0.88; F1 score: 0.87) and North (accuracy: 0.78; F1 score: 0.77) sub-regions. In contrast, the At-LSTM and RF models performed worse, particularly in the North sub-region, with F1 scores of 0.64 and 0.68, respectively. The early July period (Day of Year 170 to 200) was critical for distinguishing native and tame grassland landcover, especially in the South sub-region. Additionally higher resolution data (5-day intervals) generally improved model accuracy compared to 10-day and 30-day intervals. Overall, the study demonstrates the effectiveness of time series NDVI data and deep learning approaches for identifying native grasslands, offering valuable information to assist in grassland ecosystem management and conservation strategies in the Canadian prairies.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104619"},"PeriodicalIF":7.6,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrieving 4D landslide displacement using Pléiades satellite stereo pairs on the La Valette landslide","authors":"Sheng Fu ,&nbsp;Steven M. de Jong ,&nbsp;Wiebe Nijland ,&nbsp;Mathieu Gravey ,&nbsp;Philip Kraaijenbrink ,&nbsp;Tjalling de Haas","doi":"10.1016/j.jag.2025.104613","DOIUrl":"10.1016/j.jag.2025.104613","url":null,"abstract":"<div><div>Slow-moving landslides pose a substantial threat to communities and infrastructure, with annual creeping distances ranging from a few mm to hundreds of meters. To protect local communities from the landslide motion, landslide displacement retrieving or monitoring is necessary. However, traditional field investigations are time- and labor-consuming, which may limit the understanding of the landslide evolution and thereby mitigation. We propose a new 4D landslide displacement framework, combining satellite-based structure-from-motion, cross-correlation feature tracking for horizontal ground-surface deformation measurements with COSI-Corr, and DEM differencing for vertical ground-surface deformation. We apply this method to very high resolution (0.5 m) optical stereo images acquired by the Pléiades satellite constellation. We use our method to monitor the annual movement of the ‘La Valette’ landslide in the French Alps, between 2012 and 2022. During this period, the landslide moved most actively during the years 2012 and 2013, with average 3D displacement rates of 1.22 and 0.89 cm / day, respectively. Furthermore, we found a decelerating trend in movement rates from 2012 to 2022, which we attribute to warmer weather, decreasing precipitation rates, drier air conditions, and the implementation of a drainage installation. Our study demonstrates the potential of very-high resolution satellite stereo imagery for near-real time accurate monitoring of 4D landslide displacement, which benefits the tracking and hazard management of slow-moving landslides.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104613"},"PeriodicalIF":7.6,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An impervious surfaces extraction method based on optical, ascending and descending SAR remote sensing imagery in high-density urban core areas","authors":"Aizhu Zhang ,&nbsp;Zheng Han ,&nbsp;Genyun Sun ,&nbsp;Xiaolin Chen ,&nbsp;Ji Cheng ,&nbsp;Honghsheng Zhang","doi":"10.1016/j.jag.2025.104595","DOIUrl":"10.1016/j.jag.2025.104595","url":null,"abstract":"<div><div>Accurately monitoring impervious surface (IS) changes is vital for assessing the effects of urbanization on human activities and its broader environmental, economic, and social sustainability. However, optical building shadows pose significant challenges for IS extraction in high-density urban core areas. Even though combining optical and synthetic aperture radar (SAR) imagery makes it easier to identify impervious surfaces, buildings block SAR signals, causing them to mix with optical shadows. Ascending (AS) and descending (DE) SAR imagery offer potential solutions due to their AS/DE characteristics. This study proposes an IS extraction method utilizing hierarchical samples, optical and AS/DE SAR imagery (HS-OAD). Firstly, the AS/DE SAR-based shadowed index (ADSI) is proposed by analyzing the characteristics of building shadowed areas. Then, the OTSU method is adopted to threshold imagery into non-shadowed areas, optically shadowed areas, and SAR shadowed areas. Within each area type, samples representing four urban land covers—IS, vegetation, bare soil, and water bodies—are comprehensively labeled. Subsequently, a random forest classifier is employed to extract IS integrating optical, AS SAR, and DE SAR imagery. The results indicated that the integration of AS and DE SAR imagery significantly reduces the misclassification of IS and water bodies within optically shadowed areas. Furthermore, the hierarchical samples enhance the classification accuracy and reduce the misclassification between IS and vegetation in optically shadowed areas. Moreover, samples labeled in SAR-shadowed areas contribute to the reduction of misclassification between IS and bare soil. Our proposed method performed well in high-density urban core areas with extensive shadows, achieving an overall accuracy of 89.61% and a Kappa coefficient of 0.79.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104595"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative SAR-optical data fusion for reflectance time series reconstruction in vegetation-covered regions","authors":"Shuaijun Liu ,&nbsp;Hui Chen ,&nbsp;Kai Tang ,&nbsp;Yang Chen ,&nbsp;Hongtao Shu ,&nbsp;Tianyu Zan ,&nbsp;Yong Xue ,&nbsp;Jin Chen","doi":"10.1016/j.jag.2025.104567","DOIUrl":"10.1016/j.jag.2025.104567","url":null,"abstract":"<div><div>Frequent cloud cover leads to gaps in remote sensing image time series, posing significant challenges for agriculture, grassland, and forest monitoring applications. This study proposes an innovative method to generate reflectance time series to address this issue, enhancing data reliability and application scope. We adopted a lightweight reconstruction model based on the architecture of deep learning. This model integrates the gapped Sentinel-2 reflectance time series caused by cloud cover with Sentinel-1 synthetic aperture radar (SAR) data to produce continuous high-density time series data. During the model training and evaluation, we selected time series image data from Sentinel-1 and Sentinel-2 in China and the United States, covering various typical geographical and climatic environments. The experimental results indicate that the proposed method performs excellently, significantly improving the completeness and accuracy of reflectance data, especially under conditions of prolonged cloud contamination leading to data gaps. To further validate the practical application of the model, we conducted a test in the grassland region of Mongolia to restore surface burnt areas. The results showed that this method performs excellently in restoring and detecting changes in burnt areas, significantly improving the accuracy and efficiency of detection. In summary, the method proposed in this study provides an effective solution to the problem of gaps in remote sensing image time series caused by cloud cover. This method not only enhances the reliability and practicality of remote sensing data but also demonstrates its broad potential and application prospects in various downstream applications, especially in fields such as agricultural monitoring and grassland disturbance detection.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104567"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A precise estimation framework for individual tree AGB of Pinus kesiya var. Langbianensis utilizing point cloud registration Optimization","authors":"Zhibo Yu ,&nbsp;Yong Wu ,&nbsp;Ziyu Zhang ,&nbsp;Chi Lu ,&nbsp;Hong Wang ,&nbsp;Zhi Liu ,&nbsp;Xiaoli Zhang ,&nbsp;Lei Bao ,&nbsp;Jie Pan ,&nbsp;Guanglong Ou ,&nbsp;Hongbin Luo","doi":"10.1016/j.jag.2025.104612","DOIUrl":"10.1016/j.jag.2025.104612","url":null,"abstract":"<div><div>Accurate estimation of individual tree above-ground biomass (AGB) is crucial for regional forest AGB measurement. In this study, 64 individual trees of <em>Pinus kesiya</em> var. <em>langbianensis</em>, exhibiting a range of diameters, were felled from natural forests in mountainous regions to develop region-specific allometric equations for AGB. To enhance AGB estimation accuracy, we integrated unmanned aerial vehicle laser scanning (ULS) and backpack laser scanning (BLS) point clouds using the Iterative Closest Point (ICP) algorithm for precise registration and fusion under varying slope conditions, enabling 3D tree reconstruction. Furthermore, a height-filtered segmentation strategy was introduced to further enhance registration accuracy by aligning ULS points above 2 m with BLS data. The results showed that: 1) Unnormalized point clouds exhibited better registration performance than normalized ones, indicating that slope has a significant impact on registration accuracy. 2) The segmentation-driven registration method performed best in integrating tree trunks and crowns, with the lowest registration error (RMSE = 0.2581 m) observed in the mid-slope areas (15-25°). In contrast, higher registration errors were found in steep (&gt;25°) and gentle (0-15°) slopes, with RMSE of 0.2976 m and 0.2814 m, respectively. 3) The AGB allometric equation derived from the felled trees demonstrated high accuracy (R² = 0.9879, RMSE = 22.9243 kg). 4) The individual tree AGB estimation based on breast height (DBH) and height (H) extracted from the fused point clouds had R² values ranging from 0.8721 to 0.9730, with RMSE between 20.0242 kg to 48.6254 kg. This framework provides valuable insights for accurate forest resource surveys and management in complex terrain and highly heterogeneous regions.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104612"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New tourism route planning for the Anyue grottoes in Sichuan, China: Fusion of personalization and data analysis","authors":"Yanping Deng ,&nbsp;Miaole Hou ,&nbsp;Beibei Zhao ,&nbsp;Su Yang ,&nbsp;Hongchao Fan ,&nbsp;Yang Xie","doi":"10.1016/j.jag.2025.104603","DOIUrl":"10.1016/j.jag.2025.104603","url":null,"abstract":"<div><div>With the surge in cultural heritage tourism, personalized tourism route planning has emerged as a crucial strategy to enrich tourists’ experiences. Our objective is to establish a personalized tourism route recommendation and evaluation system. This system will leverage the Analytic Hierarchy Process (AHP) and Delphi method to assess the factors influencing the travel decisions of city-life lovers, cultural-attraction seekers, and selective sightseers. Subsequently, we will gather Points of Interest (POI) data related to these influencing factors and overlay with 147 Anyue grottoes point data, to get the grotto temples in the core area of each type of POIs, and then these grotto temples latitude and longitude coordinates into the Ant Colony Optimization (ACO) to meet the above three categories of the target group of tourists, respectively, the distance of the shortest tour path. Ultimately, this process will lead to the recommendation of personalized Anyue grottoes tour routes, offering tourists customized travel experiences. Based on this approach, we aim to explore and optimize new tourism paths at Anyue grottoes. Our endeavor in personalized tour route recommendation not only enhances tourist satisfaction but also provides data and theoretical support for the tourism industry at Anyue grottoes. It contributes to the construction of the digital display center at Anyue grottoes and offers a viable solution for personalized tourism services at other cultural heritage sites.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104603"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144108133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing coal fire detection model for large-scale areas based on RS indices and machine learning","authors":"Jinglong Liu ,&nbsp;Feng Zhao ,&nbsp;Yunjia Wang ,&nbsp;Yanan Wang ,&nbsp;Sen Du ,&nbsp;Libo Dang ,&nbsp;Jordi J. Mallorqui","doi":"10.1016/j.jag.2025.104587","DOIUrl":"10.1016/j.jag.2025.104587","url":null,"abstract":"<div><div>Coal fires present significant global environmental and energy challenges, posing substantial barriers to achieving carbon-neutral goals. Thermal Infrared Remote Sensing (TIRS) technology, which is used to retrieve land surface temperatures, plays a crucial role in detecting coal fires. However, its accuracy suffers from solar radiation interference. In addition, there is limited research focused specifically on detecting coal fires over large areas. In this paper, thermal anomaly indices (TAIs), derived from short-wave infrared and near-infrared data, were selected for coal fire detection due to their relatively low sensitivity to solar radiation. Using these TAIs alongside other remote sensing (RS) indices, a coal fire detection model (CFDM) was developed and trained using the AutoGluon machine learning (ML) framework. The model is capable of identifying large-scale coal fire target areas without relying on deformation associated with coal fires. CFDM outperformed other ML algorithms, achieving Recall, Precision, F1-score, and Kappa coefficient values of 0.89, 0.94, 0.93, and 0.92, respectively. Shapley Additive Explanations (SHAP) were used to evaluate the importance of different features, validating the model’s reliability and interoperability. The model’s robustness has been further demonstrated using observed coal fire points over Xinjiang, China, and Jharkhand, India. A T-test confirms that the proposed CFDM is significantly superior to TAIs-based methods, offering better differentiation of coal fires from other thermal anomalies and reducing commission errors.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104587"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing urban connectivity measurements for SDG 11.a with SDGSAT-1 nighttime light data in urban agglomerations","authors":"Zhuoran Lv ,&nbsp;Huadong Guo ,&nbsp;Lu Zhang ,&nbsp;Dong Liang ,&nbsp;Lingxuan Gong ,&nbsp;Yiming Liu","doi":"10.1016/j.jag.2025.104611","DOIUrl":"10.1016/j.jag.2025.104611","url":null,"abstract":"<div><div>Urban agglomerations, formed by multiple cities connected through transportation and information networks, play a pivotal role in the sustainable development of human settlements and communities. This study introduces a data-driven approach to analyze urban connectivity using high-precision 10-meter panchromatic nighttime light (NTL) data from SDGSAT-1, integrated with OpenStreetMap (OSM) road data, to support SDG 11.a for sustainable cities and communities. We develop image enhancement and network extraction methods to accurately detect urban road networks and assess connectivity. By incorporating geographical adjustment factors and integrating urban geographic information, we construct a series of indicators to evaluate the resource flow capacities of urban agglomerations using social network models. The study focuses on three major urban agglomerations: the Chengdu-Chongqing Urban Agglomeration (CCUA), the Shandong Peninsula Urban Agglomeration (SPUA), and Liaoning Province (Liaoning). Through these case studies, we extract urban connectivity networks and analyze their resource flow capabilities. This approach provides valuable insights into the intensity and efficiency of urban resource circulation, offering data-driven support for fostering sustainable urban development in alignment with SDG 11.a.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"140 ","pages":"Article 104611"},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}