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

{"title":"UAV-based stomatal conductance estimation under water stress using the PROSAIL model coupled with meteorological factors","authors":"Ning Yang ,&nbsp;Zhitao Zhang ,&nbsp;Xiaofei Yang ,&nbsp;Junrui Zhang ,&nbsp;Bei Zhang ,&nbsp;Pingliang Xie ,&nbsp;Yujin Wang ,&nbsp;Junying Chen ,&nbsp;Liangsheng Shi","doi":"10.1016/j.jag.2025.104425","DOIUrl":"10.1016/j.jag.2025.104425","url":null,"abstract":"<div><div>Leaf stomatal conductance (Gs) is an important indicator for measuring crop water stress. Influenced by variation of environmental conditions and growth stages of crops, achieving the reliable and accurate Gs estimation by UAV image is of challenge. Therefore, this study aimed to explore the potential of Gs estimation of winter wheat by UAV-based multispectral imagery based on coupling meteorological factors with the PROSAIL model. Firstly, we set up field experiments with different moisture treatments, acquired the canopy images of winter wheat at different fertility stages using the UAV equipped with a multispectral camera, and acquired meteorological factors (MFs) synchronously. Next, we collected leaf chlorophyll content (Cab), leaf area index (LAI), canopy chlorophyll content (CCC) and Gs. Then, we used PROSAIL model and machine learning models to estimated Gs from UAV-based multispectral images, and the estimation results of Gs at different growth stages were evaluated by coupling MFs. The results showed that, (1) the PROSAIL model successfully retrieved Cab, LAI, and CCC from UAV-based multispectral images, with rRMSE of 0.109, 0.136, and 0.191 respectively, (2) the Cab, LAI and CCC retrieved by PROSAIL model performed well to estimate Gs, with rRMSE of 0.166, 0.150 and 0.130, respectively, (3) the coupling of meteorological factors with the retrieved Cab, LAI, and CCC further enhanced the estimation accuracy of Gs, which is comparable to the results obtained with machine learning models, importantly. The proposed method also enhanced the robustness of estimating Gs at different growth stages. In conclusion, the potential of the Gs estimation with UAV-based multispectral images was proved through the PROSAIL model coupled with meteorological factors, which also provided a technical reference and idea for the assessment of crop water stress.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104425"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding high-resolution sea surface salinity estimation from coastal seas to open oceans through the synergistic use of multi-source data with machine learning","authors":"Taejun Sung ,&nbsp;So-Hyun Kim ,&nbsp;Seongmun Sim ,&nbsp;Daehyeon Han ,&nbsp;Eunna Jang ,&nbsp;Jungho Im","doi":"10.1016/j.jag.2025.104427","DOIUrl":"10.1016/j.jag.2025.104427","url":null,"abstract":"<div><div>High-spatiotemporal-resolution sea surface salinity (SSS) estimations are essential for understanding marine phenomena in both coastal seas and open oceans. Although studies have enhanced the resolution of SSS estimations using ocean color (OC) satellite data, the limited variance of OC signals and weak correlation with SSS in open oceans have confined these advancements to coastal seas. To overcome this limitation and broaden the scope of research, a machine learning-based approach is proposed that combines multi-source data. Geostationary Ocean Color Imager (GOCI) remote sensing reflectance (Rrs) was used as an input variable for a multilayer perceptron (MLP) model along with Hybrid Coordinate Ocean Model (HYCOM) SSS and multi-scale ultra-high-resolution sea surface temperature (MURSST) to simulate corrected and gap-filled Soil Moisture Active Passive (SMAP) SSS for East Asia. The high-quality SSS data generated by the proposed approach, with fine spatial (500–m) and temporal (hourly) resolutions, simulated detailed seasonal and spatial variations in SSS across both coastal seas and open oceans. In validation with in situ observations, the MLP model performed better than SMAP, achieving an R² of 0.80 and an RMSE of 0.92 psu, whereas SMAP achieved an R² of 0.76 and an RMSE of 1.05 psu. Shapley additive explanations analysis revealed that the contributions of input variables to SSS estimations varied by region and season. In the open ocean, HYCOM SSS and MURSST made significant contributions, compensating for the weaker relationship with Rrs. In coastal areas, Rrs412 and Rrs555 showed a positive correlation with SSS. This integration enabled the detection of high-resolution SSS, including changes driven by cold-water masses near the coastline of the East Sea. The findings of this study advance the generation of high-resolution SSS data for East Asia and also enhance our understanding of the relationship between OC properties and SSS.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104427"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-real-time wildfire detection approach with Himawari-8/9 geostationary satellite data integrating multi-scale spatial–temporal feature","authors":"Lizhi Zhang ,&nbsp;Qiang Zhang ,&nbsp;Qianqian Yang ,&nbsp;Linwei Yue ,&nbsp;Jiang He ,&nbsp;Xianyu Jin ,&nbsp;Qiangqiang Yuan","doi":"10.1016/j.jag.2025.104416","DOIUrl":"10.1016/j.jag.2025.104416","url":null,"abstract":"<div><div>Wildfires pose a great threat to the ecological environment and human safety. Therefore, rapid and accurate detection of wildfires holds significant importance. However, existing wildfire detection methods neglect the full integration of spatial–temporal relationships across different scales, and thus suffer from issues of low robustness and accuracy in varying wildfire scenes. To address this, we propose a deep learning model for near-real-time wildfire detection, where the core idea is to integrate multi-scale spatial–temporal features (MSSTF) to efficiently capture the dynamics of wildfires. Specifically, we design a multi-kernel attention-based convolution (MKAC) module for extracting spatial features representing the differences between fire and non-fire pixels within multi-scale receptive fields. Moreover, a long short-term Transformer (LSTT) module is used to capture the temporal differences from the image sequences with different window lengths. The two modules are combined into multiple streams to integrate the multi-scale spatial–temporal features, and the multi-stream features are then fused to generate the fire classification map. Extensive experiments on various fire scenes show that the proposed method is superior to JAXA Wildfire products and representative deep learning models, achieving the best accuracy scores (i.e., average fire accuracy (FA): 88.25%, average false alarm rate (FAR): 20.82%). The results also show that the method is sensitive to early-stage fire events and can be applied in the task of near-real-time wildfire detection with 10-minute Himawari-8/9 satellite data. The data and codes used in the study are detailed in: <span><span>https://github.com/eagle-void/MSSTF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104416"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A difference enhancement and class-aware rebalancing semi-supervised network for cropland semantic change detection","authors":"Anjin Dai ,&nbsp;Jianyu Yang ,&nbsp;Yuxuan Zhang ,&nbsp;Tingting Zhang ,&nbsp;Kaixuan Tang ,&nbsp;Xiangyi Xiao ,&nbsp;Shuoji Zhang","doi":"10.1016/j.jag.2025.104415","DOIUrl":"10.1016/j.jag.2025.104415","url":null,"abstract":"<div><div>Changes in cropland are among the most widespread transitions on the Earth surface, significantly impacting food security, ecological conservation, and social stability. Compared to conventional change events, cropland changes involve complex dynamic transformations of semantic representations within the land system, requiring the identification of both the locations and categories of changes. Despite numerous remote sensing change detection methods have been proposed in previous studies, two challenges in cropland semantic change detection (SCD) still deserve further discussion: 1) transition confusions between similar categories and 2) under-labeling and class imbalance related to semantic labels. To address these challenges, we propose a difference enhancement and class-aware rebalancing semi-supervised network (Semi-DECRNet) for cropland SCD. The proposed Semi-DECRNet is implemented in a multi-task three-branch architecture, incorporating a multi-scale semantic aggregation difference enhancement module to couple the semantic and initial differential features at both global and local levels to model the temporal and causal relationships among the binary change detection and semantic segmentation branches. Additionally, a class-aware rebalancing self-training strategy is developed to adaptively calibrate the pseudo-label thresholds and further mine the semantic knowledge in unchanged areas. Experiments and analysis on three benchmark datasets demonstrate the effectiveness and superiority of the proposed Semi-DECRNet method for the cropland SCD task. Code is available at <span><span>https://github.com/DaiAnjin/Semi-DECRNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104415"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based InspecNet for improved UAV surveillance of electrical infrastructure","authors":"Jiangtao Guo,&nbsp;Shu Cao,&nbsp;Tao Wang,&nbsp;Kai Wang,&nbsp;Jingfeng Xiao,&nbsp;Xinxin Meng","doi":"10.1016/j.jag.2025.104424","DOIUrl":"10.1016/j.jag.2025.104424","url":null,"abstract":"<div><div>Surveillance is crucial for maintaining critical infrastructure integrity and disaster risk reduction. Unmanned Aerial Vehicles have emerged as vital tools for aerial inspections, offering flexibility, efficiency, and cost-effectiveness. A significant challenge in UAV surveillance is the precise detection of damaged electrical components, particularly in complex environments where numerous objects are in close proximity that may cause hazards. Traditional methods often fall short under these demanding conditions, leading to notable monitoring deficiencies. To address these challenges, we introduce a novel detection method utilizing the Transformer architecture, named InspecNet. This approach leverages the architecture’s proficiency in understanding contextual information, which significantly enhances the accuracy of identifying key damaged components: damaged ceramic insulators, burned ceramic insulators, and loose U-bolts. These components are particularly challenging to detect due to their subtle and variable damage signatures. Through extensive data augmentation, we have created a new and diverse sample set to train our model and improve its detection capabilities. Our experimental evaluation, conducted with an extended set of UAV image data, demonstrates a detection accuracy increase of 20 % over conventional methods, achieving a precision of 95.7 %, recall of 93.1 %, and a mean average precision (mAP) of 92.9 %. These results underscore InspecNet potential to deliver accurate and reliable infrastructure monitoring, setting a new standard in automated UAV surveillance technology to reduce hazards.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"137 ","pages":"Article 104424"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin–destination flows prediction","authors":"Yibo Zhao ,&nbsp;Shifen Cheng ,&nbsp;Song Gao ,&nbsp;Feng Lu","doi":"10.1016/j.jag.2024.104328","DOIUrl":"10.1016/j.jag.2024.104328","url":null,"abstract":"<div><div>Accurately predicting truck origin–destination (OD) flows is essential for optimizing logistics systems and promoting coordinated regional development. Existing methods typically assume a monotonic decrease in truck OD flows with increasing geospatial distance, which oversimplifies the complex non-monotonic distribution patterns observed in practice. Moreover, these methods overlook interregional socioeconomic distances and their interaction with geospatial distances, thereby limiting the prediction accuracy and reliability. This study introduces a gravity-inspired model that integrates both geospatial and socioeconomic distances (GSD-DG) to explicitly represent their combined influence on truck OD flows. Specifically, we 1) develop a geospatial distance relation graph using the Weibull function to model the complex spatial distribution patterns of truck OD flows with varying geospatial distances; 2) propose a gravity-inspired representation learning method based on graph attention mechanism to quantify the influence of socioeconomic distance on truck OD flows; and 3) construct a deep gravity model that integrates these distances and their interactions to capture their non-linear relationship with truck OD flows. Extensive experiments on four datasets with varying spatial scale and economic development levels demonstrate that the GSD-DG model improves the robustness and prediction accuracy across diverse spatial distribution patterns, reducing RMSE by 14.2%–85.8% and MSE by 23.5%–92.5% compared to the six baseline models. Incorporating socioeconomic distance and its interaction with geospatial distance further reduces RMSE by 8.5%–36.0%. Additionally, explainable artificial intelligence techniques highlight how these distances affect truck OD flows, providing valuable policy insights for logistics planning and coordinated regional development.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104328"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142874813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A graph-based multimodal data fusion framework for identifying urban functional zone","authors":"Yuan Tao ,&nbsp;Wanzeng Liu ,&nbsp;Jun Chen ,&nbsp;Jingxiang Gao ,&nbsp;Ran Li ,&nbsp;Xinpeng Wang ,&nbsp;Ye Zhang ,&nbsp;Jiaxin Ren ,&nbsp;Shunxi Yin ,&nbsp;Xiuli Zhu ,&nbsp;Tingting Zhao ,&nbsp;Xi Zhai ,&nbsp;Yunlu Peng","doi":"10.1016/j.jag.2024.104353","DOIUrl":"10.1016/j.jag.2024.104353","url":null,"abstract":"<div><div>Accurately mapping urban functional zone (UFZ) provides crucial foundational geographic information services for urban sustainable development, territorial spatial planning, and public resource allocation. UFZs are blocks within urban environments that serve specific functions, typically comprising physical objects with specific spatial distribution patterns and semantic objects of various types. However, previous studies for identifying UFZs have focused on physical or semantic aspects of UFZs, overlooking the spatial relationships and connectivity among objects. Furthermore, few have leveraged the constructed graphs by heterogeneous geospatial data to identify functional zones by street block-based mapping units. To bridge this gap, we developed a graph-based multimodal data fusion framework (G2MF) to identify UFZs. It is a fully graph-based identification framework with a feature-level fusion strategy that integrates very high-resolution remote sensing images and point of interest data. Firstly, physical objects within a UFZ unit are classified using semantic segmentation technology; then, the two independent graph structures are constructed for both physical and semantic objects within the UFZ unit; finally, the graphs are input into the proposed graph-based multimodal fusion network for UFZ identification. Experimental results show that the proposed G2MF achieves an overall identification accuracy of 88.5 % on test data from four Chinese cities and also exhibits good generalization ability on test data with geographic isolation. This study not only promotes the development of automatic UFZ identification technology but also provides new directions and methodologies for future urban big data analysis. Our source codes are released at <span><span>https://github.com/yuantaogiser/G2MF</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104353"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping Spatio-Temporal dynamics of irrigated agriculture in Nepal using MODIS NDVI and statistical data with Google Earth Engine: A step towards improved irrigation planning","authors":"Pramit Ghimire ,&nbsp;Saroj Karki ,&nbsp;Vishnu Prasad Pandey ,&nbsp;Ananta Man Singh Pradhan","doi":"10.1016/j.jag.2024.104345","DOIUrl":"10.1016/j.jag.2024.104345","url":null,"abstract":"<div><div>The importance of water resources in supporting food production is ever increasing, especially in the face of climate change, urbanization and population growth. This study aims to map and analyze the spatio-temporal dynamics of irrigated agricultural areas to support improved planning of irrigation water and irrigation sector in Nepal. Using the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) employing Google Earth Engine (GEE) platform, this study classifies and analyzes change in irrigated and rainfed areas over the past two decades. NDVI time series analysis across different physiographic regions uncovered two cropping cycles annually in the Terai and Siwalik regions. In contrast, predominantly a single cropping cycle was observed in the Middle and High Mountain regions. The k-means clustering algorithm was applied to NDVI time series within the agriculture land use database of the International Centre for Integrated Mountain Development (ICIMOD) for Nepal. The obtained irrigated areas distribution were also analyzed across different provinces of Nepal as provinces are the main functional administrative divisions after federal level that are responsible for irrigation development. The produced irrigation areas distribution showed reasonable accuracy as compared to the statistical irrigation areas database of the Department of Water Resources and Irrigation (DWRI), Nepal. The results showed that, on average, approximately 60% (2.18 million hectares) of agricultural land was irrigated annually over the past decade. The findings will provide valuable insights for sustainable irrigation and water resource management, crop productivity enhancement, and strategy formulation to ensure food and water security in Nepal.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104345"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142929500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network","authors":"Haoyi Wang ,&nbsp;Weitao Chen ,&nbsp;Xianju Li ,&nbsp;Qianyong Liang ,&nbsp;Xuwen Qin ,&nbsp;Jun Li","doi":"10.1016/j.jag.2025.104383","DOIUrl":"10.1016/j.jag.2025.104383","url":null,"abstract":"<div><div>Multibeam sounding is a high-precision remote sensing method for seabed detection. Seabed topography classification is crucial for marine science research, resource exploration and engineering. When using multibeam data for seabed topography automatic classification, the fuzzy boundaries of different topographic entities, redundancy of multimodal data, and the lack of geological knowledge guidance have led to low classification accuracy. Thus, a knowledge graph-guided vision mamba seabed topography classification network (CUG-STCN) was constructed, consisting of three modules: (1) The long sequence modeling mamba-based encoder addresses the fuzzy seabed topography boundary. It uses 2D-selective-scan to create image blocks in different scanning directions. By combining with the selective state space model to capture long-range dependencies and ensure transmission of spatial context information while maintaining linear computational complexity. (2) The cross-modal information interaction and fusion module addresses the redundancy of multimodal information. By employing a bidirectional information interaction mechanism, it captures the correlations of seabed topography between different modalities and achieving feature fusion. (3) The seabed topography knowledge graph-guided semantic perception module guides the geological knowledge. It constructs seabed topography knowledge vectors through entity query and word embedding, using the similarity between vectors to create a similarity measurement matrix. It provides geological knowledge, enhancing the modeling capability of complex seabed topography relationship. CUG-STCN achieves OA of 90.11% and mIOU of 48.50%, outperforming six mainstream networks, which at most, achieve the OA and mIOU improvements of 5.37% and 14.18%. Notably, the application of CUG-STCN in other regions demonstrates its strong generalization performance.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104383"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143050023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine","authors":"Ningsang Jiang ,&nbsp;Peng Li ,&nbsp;Zhiming Feng","doi":"10.1016/j.jag.2025.104403","DOIUrl":"10.1016/j.jag.2025.104403","url":null,"abstract":"<div><div>Swidden agriculture, widely practiced by impoverished ethnic groups, continues to undergo rapid transition and transformation in tropical highlands. Exploring universal approaches for accurate mapping of newly-opened swiddens and fallows of different ages has not yet been stopped. The development of data-, information-, and knowledge-based algorithms for monitoring swidden agriculture requires integration of multi-dimensional features. The first part of the Continuous Change Detection and Classification (CCDC) algorithm holds promising potential in capturing abrupt changes. However, the CCD-derived temporal attributes and other multi-dimension features are seldom utilized to monitor swidden agriculture. Here, a combined algorithm integrating CCD and Support Vector Machine (SVM) is firstly developed to comprehensively highlight fundamental characteristics of swidden agriculture for maximumly and effectively mapping freshly opened swiddens. Local experimental results demonstrate that the CCD-SVM algorithm significantly enhances the performance of SVM in newly-opened swidden identification, with an average accuracy of over 85% (around a 10–20% improvement) under different land cover conditions. Next, CCD-SVM is applied to generate the 2019 map of newly-opened swidden in Laos using Landsat-8 dry-season (February to April) imagery. Comparisons with the same year results obtained from the CCDC-Spectral Mixture Analysis (SMA) show that CCD-SVM (94.69%) outperforms CCDC-SMA (87.52%) primarily due to less commission errors. Features inclusion of terrain and fire greatly improves classification accuracy. Additionally, over 60% of Laotian swiddens cross-validated by the 375-meter Visible Infrared Imaging Radiometer Suite active fires demonstrate CCD-SVM’s reliability and fidelity. The integration CCDC with SVM represents a novelty in combining time series analysis and machine learning techniques and helps monitor annual swidden agriculture in the tropics.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"136 ","pages":"Article 104403"},"PeriodicalIF":7.6,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143211675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}