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

{"title":"Decadal Earth observation-informed analysis of urban riverbank deformation: InSAR insights into soft ground dynamics in Nanjing, China","authors":"Ruya Xiao ,&nbsp;Xun Wang ,&nbsp;Mi Jiang ,&nbsp;Shanshui Yuan ,&nbsp;Ziyang Li ,&nbsp;Zhou Wu ,&nbsp;Vagner Ferreira ,&nbsp;Xiufeng He","doi":"10.1016/j.jag.2025.104868","DOIUrl":"10.1016/j.jag.2025.104868","url":null,"abstract":"<div><div>The rapid urbanization of urban riverbank soft ground poses significant geotechnical challenges, particularly in densely populated coastal areas like the Yangtze River Delta. This study employs an enhanced multi-temporal Interferometric Synthetic Aperture Radar (InSAR) approach to reveal decadal deformation patterns (2015–2024) in soft ground along the Yangtze River in Nanjing, China. Leveraging 275 Sentinel-1 SAR images and improved data processing strategies, we reconstruct the subsidence dynamics and validate them against leveling measurements with an accuracy of 6.5 mm/a. Results reveal that while the overall Nanjing’s urban riverbank remains stable, localized deformations are concentrated in urban riverbank development zones: Jiangbei New Area (JBA) and Hexi New Town (HXT). The highest intensity of development in JBA shows a cumulative settlement of more than 600 mm over ten years. The soft ground deformation is driven by engineering activities, specifically (i) groundwater drawdown during foundation pit dewatering and (ii) consolidation under surcharge loads from riverbank urban infrastructure. The earlier-developed HXT provides a valuable reference for predicting surface deformation trends in JBA. Furthermore, decadal InSAR deformation analyses of critical urban riverbank infrastructure (river-crossing bridges) confirm the effectiveness of bedrock anchoring in mitigating soft ground settlement. Wavelet analyses reveal that temperature fluctuations primarily drive cyclic displacement in the metal truss main spans of the Beijing-Shanghai High-Speed Railway Nanjing Yangtze River Bridge. This work demonstrates the decisive influence of anthropogenic activities in accelerating subsidence and reveals the efficacy of InSAR in supporting adaptive risk mitigation strategies. The findings provide actionable insights for sustainable urban riverbank development in soft ground regions, emphasizing the need for integrated monitoring and ground improvement measures to enhance infrastructure resilience in global deltaic cities undergoing riverbank urbanization.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104868"},"PeriodicalIF":8.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121242","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":"PointSSM: State space model for large-scale LiDAR point cloud semantic segmentation","authors":"Dilong Li,&nbsp;Jianlong Guan,&nbsp;Ziyi Chen,&nbsp;Jingchen Liao,&nbsp;Jixiang Du","doi":"10.1016/j.jag.2025.104830","DOIUrl":"10.1016/j.jag.2025.104830","url":null,"abstract":"<div><div>LiDAR point cloud semantic segmentation is the foundation of numerous practical applications. Recently, the Mamba, as a promising alternative to Transformer, has been getting intense attention in this field. However, the most of existing Mamba-based methods have to crop the input point clouds into patches, which limits its global modeling ability and hinders its further application in large-scale LiDAR point cloud processing. To this end, we thoroughly investigate the difficulties of Mamba in large-scale LiDAR point cloud learning and resolve this bottleneck by combining Mamba with convolution. Specifically, we introduce convolution as an information propagator to address the long-range collapse issue, which effectively enhances the global modeling ability of Mamba and enables it to handle the large-scale point clouds without patches. Besides, we redesign the bidirectional Mamba and serialization strategy to expand the receptive field of Mamba for point cloud semantic segmentation task. Furthermore, we further investigate the selectivity of Mamba, and exploit Mamba in the down-sampling stage for feature aggregation. To evaluate the effectiveness of our method, extensive experiments are conducted on two indoor and two outdoor public point cloud datasets. The results demonstrate the superiority of our method compared with state-of-the-art networks.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104830"},"PeriodicalIF":8.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121056","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 high-definition map architecture for transportation digital twin system construction","authors":"Jian Zhou ,&nbsp;Minghao Yu ,&nbsp;Yuan Guo ,&nbsp;Bijun Li ,&nbsp;Shen Ying ,&nbsp;Zhijiang Li","doi":"10.1016/j.jag.2025.104822","DOIUrl":"10.1016/j.jag.2025.104822","url":null,"abstract":"<div><div>Digital twin systems for transportation are widely regarded as a core technology for enabling full life-cycle management of traffic information and providing intelligent decision support, with the goals of improving traffic efficiency and reducing accident risks. However, existing research primarily focuses on simulation and prediction of traffic flow, lacking a unified framework that integrates static infrastructure, dynamic states, and microscopic behaviors. To address this gap, this paper proposes a lightweight behavior-cognitive architecture for high-definition (HD) maps to support multiscale information representation in transportation digital twin. It consists of three layers: (1) a global road network layer that models transportation infrastructure and static geographic features; (2) a dynamic target layer organizing the real time status and trajectory of traffic participants; (3) a behavioral cognition layer for behavior interpretation and understanding. Based on this architecture, a construction method for transportation digital twin systems is developed and validated through experiments conducted in real-world traffic scenarios and simulation environments. The results demonstrate that the proposed approach achieves high adaptability and accuracy, offering effective support for building digital twin systems in complex traffic environments.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104822"},"PeriodicalIF":8.6,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121057","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":"First evaluation of the coherence of LuTan-1 bistatic InSAR system","authors":"Huacan Hu ,&nbsp;Haiqiang Fu ,&nbsp;JianJun Zhu ,&nbsp;Qijin Han ,&nbsp;Aichun Wang ,&nbsp;Kefu Wu ,&nbsp;Dong Zeng ,&nbsp;Yanzhou Xie ,&nbsp;Mingxia Zhang ,&nbsp;Yang Liu","doi":"10.1016/j.jag.2025.104853","DOIUrl":"10.1016/j.jag.2025.104853","url":null,"abstract":"<div><div>The LuTan-1 (LT-1) mission, consisting of two nearly identical synthetic aperture radar (SAR) satellites, LT-1A and LT-1B, conducted unprecedented L-band bistatic data acquisition from June to December 2022, with the objective of generating high-precision digital elevation models (DEMs) and supporting applications in geology, surveying and mapping, and forest-related studies. Therefore, a systematic evaluation of coherence is necessary, as it facilitates a comprehensive understanding of system performance and data quality, thereby supporting the above applications more effectively. This study first analyzes several error sources that can cause LT-1 coherence loss and provides corresponding data processing workflows, calibration procedures, and compensation strategies. Specifically, the evaluation and analysis include the impacts of co-registration errors, baseline decorrelation, spectral shift, radio frequency interference, ambiguity, limited signal-to-noise ratio, and quantization errors. Subsequently, we focus on decorrelation caused by volume scattering and conduct a sensitivity and application analysis of volume decorrelation with respect to forest and building height, as well as penetration depth in desert and snow-/ice-covered areas. This analysis offers a valuable reference for its application in corresponding specific scenarios. Finally, we present the spatial distribution of coherence derived from all LT-1 bistatic InSAR acquisitions over China, demonstrating the excellent interferometric capability of the LT-1 mission and providing the scientific community with a quality overview of this unique dataset.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104853"},"PeriodicalIF":8.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094013","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":"Deep learning tools to support deforestation monitoring in Ivory Coast using SAR and optical satellite imagery","authors":"Gabriele Sartor,&nbsp;Matteo Salis,&nbsp;Stefano Pinardi,&nbsp;Özgür Saracik,&nbsp;Rosa Meo","doi":"10.1016/j.jag.2025.104849","DOIUrl":"10.1016/j.jag.2025.104849","url":null,"abstract":"<div><div>Deforestation is gaining increasing importance due to its strong influence on the surrounding environment, especially in developing countries where the population has a disadvantaged economic condition and agriculture is the main source of income. In Ivory Coast, for instance, where the cocoa production is the most remunerative activity, it is not rare to assist the replacement of portions of ancient forests with new cocoa plantations. To monitor this type of deleterious activity, satellites can be employed to recognize the disappearance of the forest. In this study, Forest-Non-Forest map (FNF) has been refined (from 25m/px to 10m/px) to be used as target for models based on Sentinel images input. State-of-the-art models U-Net, Attention U-Net, Segnet and FCN32 are compared over different years, combining Sentinel-1, Sentinel-2, and cloud probability to create forest/non-forest segmentation. Although Ivory Coast lacks of local forest coverage datasets and is partially covered by Sentinel images, it is demonstrated the feasibility of creating models classifying forest and non-forest pixels over the area using open datasets to predict where deforestation could have occurred. Although a significant portion of the deforestation research is carried out on visible bands, SAR acquisitions and the cloud probability layer are employed to overcome the limits of RGB images over areas often covered by clouds. Finally, the most promising models are employed to estimate the forest that has been cut between 2019 and 2020.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104849"},"PeriodicalIF":8.6,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094012","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":"TerrainFloodSense: Improving seamless flood mapping with cloudy satellite imagery via water occurrence and terrain data fusion","authors":"Zhiwei Li ,&nbsp;Shaofen Xu ,&nbsp;Qihao Weng","doi":"10.1016/j.jag.2025.104855","DOIUrl":"10.1016/j.jag.2025.104855","url":null,"abstract":"<div><div>Extreme flood disasters are intensified by climate change, exposing an increasing share of the global population to flood hazards. Accurate monitoring of inundation extents during floods is crucial for disaster management and impact assessment. While remote sensing can provide strong support for flood monitoring, optical satellite images often face significant challenges due to weather conditions and infrequent revisits, particularly in cloudy and rainy regions. To address this limitation and achieve seamless flood mapping with cloudy satellite images, this paper proposes TerrainFloodSense, a novel method that fuses water occurrence with terrain data to enhance the reconstruction of cloud-covered flooding areas, especially under extreme and unprecedented flood scenarios. Specifically, TerrainFloodSense first generates enhanced water occurrence data by Bayesian fusion of terrain indices, including Digital Surface Model (DSM) along with Height Above the Nearest Drainage (HAND), and historical water occurrence data. Then, enhanced water occurrence data are used to fill gaps caused by clouds in water maps derived from optical satellite images, guided by the submaximal stability assumption. The basic idea is that prior terrain information can be incorporated into the initial water occurrence data to enhance the ability to predict the inundation probabilities for both regular pre-flood water and extreme floodwater and to help reconstruction of cloud-covered flooding areas even under extreme flooding scenarios. Simulated experiments and applications in large-area flood mapping cases confirmed that TerrainFloodSense significantly outperformed existing methods, achieving absolute accuracy improvements of 2.95%–8.86% in overall accuracy and 0.038–0.087 increases in F1-Score under extreme flooding scenarios. This study demonstrated that the fusion of water occurrence and terrain data can effectively improve seamless flood mapping by using optical satellite images, supporting disaster monitoring and impact assessment in cloudy and rainy environments. The code associated with this study has been made publicly accessible via <span><span>https://github.com/RCAIG/TerrainFloodSense</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104855"},"PeriodicalIF":8.6,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094015","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 cervid forage in Sweden using remote sensing and national forest inventory data","authors":"Lukas Graf ,&nbsp;Inka Bohlin ,&nbsp;Per-Ola Hedwall ,&nbsp;Jonas Dahlgren ,&nbsp;Annika M. Felton","doi":"10.1016/j.jag.2025.104850","DOIUrl":"10.1016/j.jag.2025.104850","url":null,"abstract":"<div><div>Cervid browsing influences forest ecosystems worldwide, stressing the need for wildlife management founded in accurate estimates of available forage. In this study, we developed the first national-scale models for Sweden to estimate the abundance of cervid forage by combining data from the National Forest Inventory (NFI) and different remote sensing (RS) datasets. We focused on six key forage tree species for cervids in Sweden: Scots pine (<em>Pinus sylvestris</em>), birch (<em>Betula</em> spp.), European aspen (<em>Populus tremula</em>), rowan (<em>Sorbus aucuparia</em>), oak (<em>Quercus</em> spp.), and goat willow (<em>Salix caprea</em>).</div><div>We combined airborne laser scanning and other auxiliary RS data with NFI data from 2016 to 2022 to model small tree abundance from 19 461 plots across Sweden in an area-based approach. We fitted generalized linear mixed models using likelihood-ratio tests to predict species-specific forage availability. Models were validated using an independent dataset of NFI data collected in 2023. Our models demonstrated moderate to strong predictive performance, with marginal R² values ranging from 0.226 to 0.973. Model validation suggested higher RMSE and rRMSE values for tree species that are scarce throughout the country than for more abundant species.</div><div>We provide maps for all six modelled tree species, both at a 1 ha and a 1 km² spatial scale, with the aim for them to be used in wildlife management, forestry planning, and ecological research. Our map products can for example help stakeholders assess a region’s spatial distribution of cervid forage and thus inform habitat management and potentially mitigate browsing-related economic losses in forestry.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104850"},"PeriodicalIF":8.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094190","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":"Deep Learning and Multi Source 2D and 3D Geospatial Data for Urban Quality of Life Assessment","authors":"Ayush Dabra ,&nbsp;Pyare Lal Chauhan ,&nbsp;Vaibhav Kumar","doi":"10.1016/j.jag.2025.104838","DOIUrl":"10.1016/j.jag.2025.104838","url":null,"abstract":"<div><div>Urban Quality of Life (UQoL) assessment is essential for improving well-being and guiding urban planning. While most studies focus on developed countries using household surveys and satellite imagery, this study addresses the gaps pertaining to the quantification of UQoL at a very microscale by utilizing multi-modal 2D and 3D data, viz. elevation-stacked Unmanned Aerial Vehicle (UAV) imagery, Google Street View (GSV) imagery, and amenities information derived from crowdsourced OpenStreetMap (OSM). An Unsupervised Domain Adaptation (UDA) based Deep Learning (DL) pipeline is implemented to segment UAV imagery, extracting indicators like built-up and green cover from the segmentation maps. Additionally, DeepLabV3 is used to segment GSV imagery to compute the sky-view factor, while OSM is employed to extract the location information of amenities. The UDA-based ResiDualGAN, equipped with a convolutional resizer model and integrated with the OSA method, trained on the RGB-nDSM dataset, achieved an IoU of 60.48%. Principal Component Analysis (PCA) is applied to create a weighted UQoL index. The results reveal disparities in UQoL across the study area, with higher UQoL in green, amenity-rich regions, emphasizing the importance of utility access. Notably, informal settlements located near essential services exhibited high UQoL despite having limited green cover and higher built-up density, which clearly emphasizes the importance of proximity as a key indicator of UQoL.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104838"},"PeriodicalIF":8.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093975","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":"Feasibility of using Sentinel-2 images to detect decline in kiwifruit orchards","authors":"Marianne Avignon ,&nbsp;Maxime Garnault ,&nbsp;Claire Marsden ,&nbsp;Adeline Gachein ,&nbsp;Lionel Alletto ,&nbsp;Yvan Capowiez ,&nbsp;Claire Marais-Sicre","doi":"10.1016/j.jag.2025.104846","DOIUrl":"10.1016/j.jag.2025.104846","url":null,"abstract":"<div><div>Tree decline affects many perennial orchards with potentially heavy impacts on production. In five years 3 % of national production of kiwifruit orchards were lost in France, leading to economic issues and jeopardizing the value chain. Some specific management practices could help to mitigate kiwifruit decline, but rapid and simple tools are needed to assess the development of the decline in response to these practices. As kiwifruit decline is characterized by a low-vigor canopy along with changes in canopy color and density, Sentinel-2 images were used to detect vine decline over large areas. We first selected 28 orchards, with varying characteristics (e.g. row grass cover, hail protection nets, fertilization practices) and characterized each vine (14000 in total) according to its agronomic status (<em>i.e.</em> vigor and presence of decline symptoms). Sentinel-2 images are made up of 10 x 10 m pixels. To classify them we fitted two models using a random forest procedure. The spectral model (SM) used only spectral inputs, while the agronomic and spectral model (ASM) used both spectral inputs from satellite images and agronomic inputs obtained from orchard characteristics. Spectral inputs included raw spectral bands (e.g., red, near-infrared, green) and vegetation indices (e.g., NDVI, GNDVI). Results show that vigorous and dead areas were well detected (more than 80 % of correct predictions). Declining areas were correctly detected when patches of decline were larger than 500 m². Mixed pixels containing vines with different agronomic status were poorly predicted. Accuracy improves when agronomic information, such as soil texture, is incorporated into the model. Both models (SM and ASM) could enable growers to adjust their practices in real time according to the health status of their vines.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"144 ","pages":"Article 104846"},"PeriodicalIF":8.6,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093973","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":"Early Deforestation Detection in the Tropics using L-band SAR and Optical multi-sensor data and Bayesian Statistics","authors":"Africa I. Flores-Anderson ,&nbsp;Jeffrey A. Cardille ,&nbsp;Josef Kellndorfer ,&nbsp;Franz J. Meyer ,&nbsp;Pontus Olofsson","doi":"10.1016/j.jag.2025.104831","DOIUrl":"10.1016/j.jag.2025.104831","url":null,"abstract":"<div><div>The growing availability of medium-resolution optical and radar satellite observations has prompted the development of synergistic change detection methodologies. Timely forest change detection, particularly early deforestation, is crucial for preventing illegal activities. This study proposes and evaluates an innovative model that integrates ALOS-2 PALSAR-2 L-band data with optical data from Landsat and Sentinel-2 to detect early deforestation, defined as the initial transition from stable to logged forest. Our model employs a 2-tier approach, combining harmonic curve fitting and z-scores to calculate differences between the time series. Bayesian updating statistics are then used to derive change probabilities. We comprehensively assessed the spatial and temporal detection accuracy of early deforestation maps generated by each sensor type, both individually and in combination. The integrated L-band Synthetic Aperture Radar (SAR) and optical method demonstrated the best performance, achieving a user’s accuracy of 99.19 ± 0.0081% (<span><math><mo>±</mo></math></span> 95 confidence interval) and a mean detection time lag of just 16 days. For comparison, L-band SAR data alone yielded a user’s accuracy of 93.70% (<span><math><mo>±</mo></math></span> 0.0333) with a mean time lag of 67 days, primarily due to ALOS-2’s lower repeat frequency. Optical-derived detections achieved a user’s accuracy of 98.39% (<span><math><mo>±</mo></math></span> 0.0113) and a mean time lag of 20 days. These findings confirm that combining radar and optical datasets significantly improves both detection accuracy and timeliness. Furthermore, detections were consistently captured shortly after logging activities, well before subsequent forest disturbances, underscoring true early deforestation. The high detection accuracies validate that both individual and combined L-band SAR and optical data can reliably detect early deforestation in this tropical region. We anticipate that the longer detection time lags observed with ALOS-2 PALSAR-2 will substantially improve with upcoming L-band SAR missions, such as NISAR and ALOS-4 PALSAR-3, which promise significantly enhanced global temporal sampling.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"143 ","pages":"Article 104831"},"PeriodicalIF":8.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048969","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}