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

{"title":"Insights into spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity in African terrestrial ecosystems","authors":"Liang Liang,&nbsp;Meng Li,&nbsp;Ziru Huang,&nbsp;Qianjie Wang,&nbsp;Zhen Yang,&nbsp;Shuguo Wang","doi":"10.1016/j.jag.2025.104824","DOIUrl":"10.1016/j.jag.2025.104824","url":null,"abstract":"<div><div>Net Primary Productivity (NPP) is a critical measure of ecosystem vitality. This paper examines the spatiotemporal variation in NPP across Africa during 1981–2018 using Theil-Sen slope estimation and wavelet analysis. Sustainable change characteristics in different regions are analyzed using the Hurst exponent, and the influence of driving factors on African NPP are quantified through a structural equation model (SEM). The analysis revealed that: (1) The annual variation curve of African NPP demonstrated a fluctuating upward trajectory (p = 0.001) throughout the study period. Wavelet analysis revealed a cyclical pattern with a primary period of about 20 years, characterized by two upward and downward transitions during 1981–2018. (2) Spatial analysis indicates the distribution of NPP across Africa is centered around the equator and gradually decreases towards higher latitudes, in which the NPP of tropical rainforest and its adjacent areas increases significantly, covering 40.2 % of Africa’s area. However, Hurst exponent analysis reveals that NPP in Africa generally exhibits anti-sustainability changes, with 52.8 % of the total area potentially shifting from growth to decline in the future. (3) SEM analysis shows that NPP in Africa is mainly regulated by natural factors, particularly cumulative precipitation and temperature extremes, which exhibit the highest impact coefficient of 0.89. While topographic factors also have a substantial overall effect, their impact is primarily indirect through climate, with minimal direct influence. These findings offer a scientific foundation and policy support for sustainable development of environmental and socio-economic systems in Africa.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104824"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048971","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":"Utilization of multi-sensor remote sensing technologies for open pit mine monitoring","authors":"Francesco Falabella ,&nbsp;Antonio Pepe ,&nbsp;Krištof Oštir ,&nbsp;Rushaniia Gubaidullina ,&nbsp;Klemen Kozmus Trajkovski ,&nbsp;Dejan Grigillo ,&nbsp;Veronika Grabrovec ,&nbsp;Veton Hamza ,&nbsp;Polona Pavlovčič Prešeren ,&nbsp;Hannes Blaha ,&nbsp;Ana Cláudia Teodoro ,&nbsp;Fabiana Calò","doi":"10.1016/j.jag.2025.104834","DOIUrl":"10.1016/j.jag.2025.104834","url":null,"abstract":"<div><div>Nowadays, Earth Observation (EO) sensors with different technical characteristics installed on various platforms can provide data for multiple applications. Integrating, combining and processing data for various mining-related applications is required to assist decision-making and process adaptation procedures. Moreover, jointly using different data sets or products created from various sources allows for increasing precision and overcoming inherent measurement uncertainties, thereby enhancing the reliability of the results. Our research shows the potential of an integrated multi-sensor/multi-wavelength SAR data monitoring system that implements an innovative model-aided Phase Unwrapping (PhU) approach for generating ground displacement maps and time series in critical open pit mining areas. This allows us to mitigate the risk associated with rock falls and instabilities that can lead to severe damage to workers and neighbourhoods. Furthermore, the study also points out that jointly using Unmanned Aerial Vehicle (UAV) and spaceborne optical data is valuable to remotely estimate stockpile volume changes with enhanced accuracy and precision, supporting the mining companies’ management.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104834"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049436","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":"Remote sensing monitoring of the SDG indicator mountain green cover index in China from 2000 to 2022","authors":"Jinhu Bian ,&nbsp;Jinping Zhao ,&nbsp;Ainong Li ,&nbsp;Yi Deng ,&nbsp;Guangbin Lei ,&nbsp;Zhengjian Zhang ,&nbsp;Xi Nan ,&nbsp;Amin Naboureh","doi":"10.1016/j.jag.2025.104852","DOIUrl":"10.1016/j.jag.2025.104852","url":null,"abstract":"<div><div>Mountains provide vital ecosystem services that support the livelihoods of billions of people worldwide, playing a crucial role in biodiversity conservation and climate regulation. The United Nations 2030 Agenda for Sustainable Development has established a specific target (SDG 15.4) dedicated to mountain protection. The Mountain Green Cover Index (MGCI) serves as a key indicator for assessing the health of mountain ecosystems. As the 2030 Agenda passes its midpoint, the mid-term assessment of the MGCI is essential for adjusting implementation strategies and ensuring the realization of the 2030 Agenda for the protection of mountain ecosystems. However, existing country-level MGCI values fail to account for the three-dimensional characteristics unique to mountains. Additionally, quantifying the detailed mechanisms of change and dynamics in highly heterogeneous mountain areas within countries remains challenging. In this study, we developed a high-resolution grid-based MGCI model for China and estimated MGCI values from 2000 to 2022 using 30 m annual land cover data and the true surface area of mountains. We analyzed the spatiotemporal patterns of the MGCI and quantified the impacts of anthropogenic and natural factors on MGCI dynamics during the 2022 mid-term assessment. The results show that from 2000 to 2022, China’s overall MGCI increased from 78.15 % to 82.23 %, with an average annual growth rate of 0.18 %. Notably, 8.48 % of mountains experienced an MGCI increase within the (0, 0.5) range, while only 0.03 % of areas saw a decrease greater than 0.5, primarily concentrated on the Qinghai–Tibetan Plateau. Spatial pattern analysis revealed clear variations in MGCI along elevation and hydrothermal gradients. Driving factor analysis indicated that water-related variables explain MGCI spatial distribution more effectively than thermal conditions. Furthermore, the interaction between grazing intensity and water factors demonstrated a strong synergistic effect on MGCI distribution. This research enhances the understanding of MGCI dynamics and its driving factors in China’s mountain ecosystems, offering valuable reference for the timely achievement of mountain sustainable development goals.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104852"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094016","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":"Large-scale remote sensing based methods for glyphosate usage detection: A case study in the Netherlands","authors":"Yongjin Wang ,&nbsp;Collin van Rooij ,&nbsp;Julian Helfenstein ,&nbsp;Wouter Meijninger ,&nbsp;Maciej J. Soja ,&nbsp;Arno Timmer ,&nbsp;Gerbert Roerink","doi":"10.1016/j.jag.2025.104836","DOIUrl":"10.1016/j.jag.2025.104836","url":null,"abstract":"<div><div>Glyphosate is a widely used herbicide, and given its potential threats to health and the environment, it is necessary to monitor its actual use. Currently there is no publicly available remote sensing method for detecting glyphosate use on a large scale. This study developed two glyphosate detection methods based on Sentinel-2 data and compared them in a case study over the entire Netherlands. The NDVI-method detects glyphosate use based on parcel-level NDVI variation, employing multiple constraints. The Color-method detects glyphosate use by analyzing parcel-level changes in spectral values, combining a random forest classifier with multi-temporal constraints. Training and validation data consisted of citizen-science observations from waarneming.nl platform and random parcels, both manually validated. Validation showed that the NDVI-method achieved median precision 0.51 and recall 0.52, and was more sensitive to phenomena gradually reducing NDVI in actual detection (e.g., multiple shallow tillage). The Color-method demonstrated better overall performance, with median precision 0.84 and recall 0.77. The areas of glyphosate use detected by the two methods were 52,682 and 38,923 ha, respectively. Analysis based on crop type changes and soil types revealed that for Dutch agricultural land in spring, glyphosate is mainly used on sandy soils, to destroy cover crops in cropland, and to remove grass or weeds entirely in grasslands for conversion to cropland. This study demonstrates the potential of remote sensing for quantifying glyphosate use at large spatial scales, making direct detection of glyphosate use possible. However, factors including regional climate and ploughing patterns affect data availability, remaining a limitation.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104836"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094017","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":"Evaluating the effectiveness of forest type stratification for aboveground biomass inference","authors":"Ziqiang Wu ,&nbsp;Xin Liu ,&nbsp;Shoumin Cheng ,&nbsp;Chenhui Yang ,&nbsp;Zongquan Wang ,&nbsp;Yongshuai Liu ,&nbsp;Lihu Dong ,&nbsp;Fengri Li ,&nbsp;Yuanshuo Hao","doi":"10.1016/j.jag.2025.104829","DOIUrl":"10.1016/j.jag.2025.104829","url":null,"abstract":"<div><div>Accurate quantification of aboveground biomass (AGB) in heterogeneous forest ecosystems is critical for reliable carbon cycle modeling and the effective climate policy development. Although remote sensing-assisted methods have significantly enhanced estimation efficiency, the impact of forest type stratification on estimation accuracy remains insufficiently investigated, especially when classified forest types from remote sensing data are used. In this study, we conducted a comprehensive comparison between model-assisted (MA) and model-based (MB) estimators and conventional simple random sampling (SRS) estimators under three different stratified or nonstratified scenarios: (A) a nonstratified estimation framework; (B) stratified estimation employing error-free forest type maps; and (C) stratified estimation predicated on classification results from remote sensing. Additionally, we assessed the effect of model specification—whether using a general model or strata-specific models—on estimation accuracy within stratified frameworks. The results showed that both the MA and MB estimators outperformed the SRS estimator. Stratification with ground truth reference maps significantly enhanced estimation accuracy, especially for the variance of the MB estimator employing strata-specific models is reduced from 13.65 t/ha to 10.42 t/ha, with the highest relative efficiency (RE = 2.95) achieved by the error-free stratified MA estimator using a general model. However, classification errors in remote sensing-derived maps substantially reduced these benefits, often leading to estimation variances exceeding those of the unstratified approach. Specifically, the variances of estimators MA and MB have increased from 8.89 t/ha to 24.17 t/ha, and from 10.42 t/ha to 23.65 t/ha, respectively. The predominant source of error was model misassignment due to misclassified forest types. This study provides a practical framework for estimating regional forest AGB using remote sensing data and offers decision support for the scientific formulation of forest management and sustainable utilization plans.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104829"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996793","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":"Multi-dataset-integrated Coral-Lab segmentation with enhanced towed camera array for rapid large-scale coral reef monitoring and mapping","authors":"Jiaqi Wang ,&nbsp;Katsunori Mizuno ,&nbsp;Shigeru Tabeta ,&nbsp;Tetsushi Matsuoka ,&nbsp;Tomo Odake ,&nbsp;Satoshi Igei ,&nbsp;Taro Uejo ,&nbsp;Takashi Nakamura","doi":"10.1016/j.jag.2025.104819","DOIUrl":"10.1016/j.jag.2025.104819","url":null,"abstract":"<div><div>Highly efficient and reliable monitoring of coral reef ecosystems is imperative for effective conservation and management under increasing anthropogenic and climatic pressures. However, current survey techniques either offer limited coverage and low efficiency or incur substantial manual costs for data processing. In this study, we propose a highly efficient towed optical camera array system, Speedy Sea Scanner version 2.0 (SSSv2), with an advanced electrical system supporting a stable power supply, reliable communications, and underwater illumination, which enables continuous video data collection and real-time monitoring. We also develop a semantic segmentation model, Coral-Lab, with high accuracy and robustness in coral identification task, which enables fully automated coral reef identification and coral coverage calculation. Coral-Lab model achieved an F-score of 0.802 and an mIoU of 0.665 on our test set. Leveraging SSSv2, we conducted field surveys off the northern coast of Kumejima Island, Okinawa, Japan, on July 14, 2024 and July 15, 2024 across seven sampling areas comprising 29 transect lines. Over two days survey, we collected video data covering a total seafloor area of 47,950 m², which was converted into a georeferenced orthomosaic at an average spatial resolution of 2.5 mm via Structure-from-Motion (SfM) and Multi-View Stereo (MVS) techniques. This approach achieved an effective survey efficiency of approximately 7200 m² per hour. Applying Coral-Lab model to 25,658 orthomosaic tiles at a 0.25 m grid resolution, we generated detailed coral-cover distribution maps in under 75 min of inference time, processing each 512 × 512-pixel tile in <span><math><mo>∼</mo></math></span>0.17 s. These results demonstrate the synergistic potential of integrating advanced imaging hardware with deep learning algorithms, enabling rapid, large-scale coral reef monitoring and assessments.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104819"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093976","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":"Mapping forest fine-grained soil particle size distributions: a holistic GeoAI approach via graph neural networks, LiDAR, and Sentinel-2","authors":"Omid Abdi,&nbsp;Ville Laamanen,&nbsp;Jori Uusitalo","doi":"10.1016/j.jag.2025.104807","DOIUrl":"10.1016/j.jag.2025.104807","url":null,"abstract":"<div><div>Fine-grained soils are crucial for assessing forest diversity and soil disturbances. Existing models for predicting particle size distributions (PSDs) often rely heavily on soil samples or lack necessary spatial dependencies, scalability and flexibility. This study introduces a holistic GeoAI model using graph neural networks (GNNs), LiDAR, and Sentinel-2 data to address these limitations. We collected 330 soil samples from 47 forest stands with a random-stratified method in southwestern Finland. The samples were pre-processed and analyzed for PSDs using a laser diffraction method, and classified into four groups: &lt;2 µm, 2–6 µm, 6–20 µm, and 20–60 µm. To increase the number of annotations, we predicted soil PSDs at unmeasured locations using CoKriging within stands. The forests were segmented into small homogeneous polygons to construct the graph layer. We mapped 61 covariates using LiDAR and Sentinel-2 based on <em>scorpan</em> model, which were then summarized into the graph layer. Subsequently, we established the pipelines of five GNN models regarding the top covariates. The results indicate that geomorphometry and organisms covariates accounted for the majority of importance. The graph attention network (GAT) recorded high stability during training and remarkable prediction accuracy after testing with R² values above 0.98 in predicting fine-grained soil PSDs across all four soil groups. Conversely, the relational graph convolutional networks (RGCN) also achieved R² values above 0.97, but with lower stability and longer training times. However, the high accuracy of the predictive models is partly due to the large number of annotations derived from CoKriging, which may introduce uncertainties. Our GAT model demonstrated strong transferability when applied to an independent test stand using CoKriging-derived data (R²: 0.98–0.99) and showed robust performance when evaluated against real ground-truth samples (R²: 0.88–0.95). The observed prediction errors (RMSE: 0.68–2.82) reflect a combination of uncertainties originating from the CoKriging training data (RMSE: 0.34–2.46) and model-induced errors during training (RMSE: 0.37–1.46). Nevertheless, the consistently high R² values indicate a strong agreement between predicted and measured soil PSDs. Future studies should focus on training the model with a larger number of ground-truth soil samples and evaluating its transferability across diverse boreal forest landscapes.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104807"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925318","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":"Predicting Household Income with Sentinel and Street View Imagery: A Comparative Study across Amsterdam, Sydney, and New York","authors":"Oleksandr Karasov ,&nbsp;Evelyn Uuemaa ,&nbsp;Olle Järv ,&nbsp;Monika Kuffer ,&nbsp;Tiit Tammaru","doi":"10.1016/j.jag.2025.104828","DOIUrl":"10.1016/j.jag.2025.104828","url":null,"abstract":"<div><div>In the context of urbanisation and growing disparities, timely and detailed spatial data on income inequality in cities is essential. We combined satellite imagery with streetlevel photographs provided by Google Street View to reveal the spatial distribution of household income. For this, we suggest a harmonised framework for median household income modelling based on deconstructing landscape patterns using a machine-learning approach, applied across three ’global cities’: Amsterdam, New York, and Sydney. First, we classified Sentinel-1 and Sentinel-2 mosaics and Google Street View scenes to detect functional elements of the built environment. Second, we calculated spatial indices for Sentinel imagery and visual indices for Google Street View scenes to characterise the urban landscape. Third, by combining various indicators, we trained city-specific income prediction models according to ground truth census data. The correlation between actual and predicted income in New York and Sydney reached 0.76 and 0.78, respectively. The accuracy of income prediction in Amsterdam reached 51.13%. We revealed relationships between spatial indicators of landscape patterns and spatial income distribution and recommend using Sentinel-1 and Sentinel-2 imagery as the primary data choice for income modelling in datarestricted regions. Google Street View data can be used complementarily when available.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104828"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996792","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":"Frequently updating DEMs based on multi-track repeat-pass InSAR observations using robust variance component estimation","authors":"Zhanpeng Cao ,&nbsp;Zefa Yang ,&nbsp;Cui Zhou ,&nbsp;Zhiwei Li","doi":"10.1016/j.jag.2025.104821","DOIUrl":"10.1016/j.jag.2025.104821","url":null,"abstract":"<div><div>Space-borne interferometric synthetic aperture radar (InSAR) is a useful technique to generate or update digital elevation models (DEMs) over large regions. Specifical InSAR missions for DEM generation/update currently work in bistatic mode. The bistatic InSAR satellites have a low temporal coverage, causing the difficulty to keep DEM products up to date. InSAR satellites working in a repeat-pass mode can offer numerous data sources with a short temporal coverage, offering a great potential to frequently update DEMs to keep DEM valid with time. However, the accuracy of repeat-pass InSAR DEMs using the existing algorithms is too low for practical uses currently. To circumvent this, we proposed a new method to frequently update DEMs from repeat-pass InSAR datasets, in order to improve update accuracy. Firstly, multi-track repeat-pass InSAR datasets were utilized to offer more redundant observations to mitigate InSAR noises. A new quantitative model was then developed to scientifically guide the exclusion of multi-track interferograms with very short spatial baselines, in order to further reduce the propagation of InSAR errors into DEM products. Thirdly, a robust variance component estimation (RVCE) algorithm, which can adaptively weight multi-track InSAR observations and automatically exclude outliers, was used to dynamically update the DEMs. The proposed method was tested over the Hambach open-pit mine in Germany. The results show that the mean accuracy of the updated DEMs is about 8.7 m, demonstrating a 60 % improvement over classical single-track repeat-pass InSAR techniques. The proposed method offers a new option to frequently update DEMs, especially over areas with changes of surface terrain.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104821"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144925317","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":"Geological hazard susceptibility assessment and forecasting analysis based on InSAR and C-L-A model","authors":"Jie Hu,&nbsp;Zhihua Zhang,&nbsp;Xinyu Zhu,&nbsp;Xinxiu Zhang,&nbsp;Shuwen Yang,&nbsp;Chunlin Huang,&nbsp;Wei Wang,&nbsp;Xuhui Li,&nbsp;Li Hou,&nbsp;Lujia Zhao","doi":"10.1016/j.jag.2025.104840","DOIUrl":"10.1016/j.jag.2025.104840","url":null,"abstract":"<div><div>Subsidence along expressways and railways poses significant risks to transportation infrastructure safety and environmental stability. Predicting ground settlement enables enhanced understanding of deformation characteristics along transportation corridors and facilitates timely warnings for high risk areas. This study focuses on the area within 50 km of the Jishishan earthquake epicenter, employing InSAR technology to obtain preseismic and postseismic surface deformation maps. Utilizing 35 preseismic and 21 postseismic Sentinel-1A descending orbit images from 2021 to 2024, we derived surface deformation rates through PS-InSAR and SBAS-InSAR time series analysis. The preseismic annual deformation rate was incorporated with 12 influencing factors including PGA for coseismic geological hazard susceptibility evaluation. A novel Convolutional Neural Network and Long Short-Term Memory model with attention mechanism (C-L-A) was developed for settlement forecasting by integrating deformation rate data. The results show, integrated analysis incorporating InSAR derived deformation velocities and seismic dynamic factors such as PGA significantly enhances geological hazard susceptibility assessment precision. Compared to conventional static evaluation models, the novel methodology achieves a 21 % reduction in the spatial extent of very high susceptibility zones while elevating the hazard occurrence frequency ratio by 33 %, effectively mitigating false alarm risks. This approach particularly highlight extreme hazard vulnerability in areas exhibiting annual deformation rates exceeding 30 mm. Time series InSAR monitoring unequivocally delineates regional deformation patterns: significant preseismic subsidence (reaching 118 mm/year) prevailed across the study area, while the coseismic deformation field (maximum uplift: 7.85 cm) confirms a thrust type earthquake with strike slip components, tectonically linked to the South Margin Fault of the Lajishan Mountains. Persistent postseismic uplift within a 25-km radius of the epicenter reflects ongoing stress adjustment processes. The proposed C-L-A land subsidence forecasting model demonstrates superior performance across critical metrics, including Δx(MAX) = 2.94 mm, MAE = 1.74 mm, MSE = 3.39 mm², and RMSE = 1.84 mm, outperforming benchmark models (RF, CNN, LSTM, CNN-LSTM). This architecture effectively captures spatiotemporal deformation characteristics along transportation corridors, with its high accuracy short term forecasts (about 5 months) providing reliable foundations for infrastructure risk early warning systems and disaster mitigation decision support.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104840"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048975","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}