International journal of applied earth observation and geoinformation : ITC journal最新文献_第2页

UAV-based stomatal conductance estimation under water stress using the PROSAIL model coupled with meteorological factors

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-15 DOI: 10.1016/j.jag.2025.104425

Ning Yang , Zhitao Zhang , Xiaofei Yang , Junrui Zhang , Bei Zhang , Pingliang Xie , Yujin Wang , Junying Chen , Liangsheng Shi

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.

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引用次数: 0

Expanding high-resolution sea surface salinity estimation from coastal seas to open oceans through the synergistic use of multi-source data with machine learning

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-15 DOI: 10.1016/j.jag.2025.104427

Taejun Sung , So-Hyun Kim , Seongmun Sim , Daehyeon Han , Eunna Jang , Jungho Im

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.

{"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 , So-Hyun Kim , Seongmun Sim , Daehyeon Han , Eunna Jang , 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 R<sup>2</sup> of 0.80 and an RMSE of 0.92 psu, whereas SMAP achieved an R<sup>2</sup> 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}

引用次数: 0

Near-real-time wildfire detection approach with Himawari-8/9 geostationary satellite data integrating multi-scale spatial–temporal feature

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-15 DOI: 10.1016/j.jag.2025.104416

Lizhi Zhang , Qiang Zhang , Qianqian Yang , Linwei Yue , Jiang He , Xianyu Jin , Qiangqiang Yuan

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: https://github.com/eagle-void/MSSTF.

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引用次数: 0

A difference enhancement and class-aware rebalancing semi-supervised network for cropland semantic change detection

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-15 DOI: 10.1016/j.jag.2025.104415

Anjin Dai , Jianyu Yang , Yuxuan Zhang , Tingting Zhang , Kaixuan Tang , Xiangyi Xiao , Shuoji Zhang

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 https://github.com/DaiAnjin/Semi-DECRNet.

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引用次数: 0

Transformer-based InspecNet for improved UAV surveillance of electrical infrastructure

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-15 DOI: 10.1016/j.jag.2025.104424

Jiangtao Guo, Shu Cao, Tao Wang, Kai Wang, Jingfeng Xiao, Xinxin Meng

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.

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引用次数: 0

A gravity-inspired model integrating geospatial and socioeconomic distances for truck origin–destination flows prediction 一种重力启发模型，整合地理空间和社会经济距离，用于卡车始发目的地流量预测

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104328

Yibo Zhao , Shifen Cheng , Song Gao , Feng Lu

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.

准确预测货车始发地流量对优化物流系统、促进区域协调发展具有重要意义。现有方法通常假设卡车外径流随地理空间距离的增加而单调减小，这过于简化了实践中观察到的复杂的非单调分布模式。此外，这些方法忽略了区域间社会经济距离及其与地理空间距离的相互作用，从而限制了预测的准确性和可靠性。本研究引入了一个重力启发的模型，该模型整合了地理空间和社会经济距离（GSD-DG），以明确表示它们对卡车外径流量的综合影响。具体而言，我们1)利用威布尔函数建立了一个地理空间距离关系图，以模拟不同地理空间距离下卡车外径流的复杂空间分布格局；2)提出了一种基于图注意机制的重力启发表征学习方法，量化社会经济距离对卡车OD流量的影响；3)构建一个整合这些距离及其相互作用的深度重力模型，以捕捉它们与卡车外径流的非线性关系。在不同空间尺度和经济发展水平的4个数据集上进行的大量实验表明，GSD-DG模型在不同空间分布格局下的稳健性和预测精度均有所提高，与6个基线模型相比，RMSE降低14.2% ~ 85.8%，MSE降低23.5% ~ 92.5%。考虑社会经济距离及其与地理空间距离的相互作用，RMSE进一步降低8.5% ~ 36.0%。此外，可解释的人工智能技术强调了这些距离如何影响卡车外径流，为物流规划和协调区域发展提供了有价值的政策见解。

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引用次数: 0

A graph-based multimodal data fusion framework for identifying urban functional zone 基于图的城市功能区多模态数据融合框架

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104353

Yuan Tao , Wanzeng Liu , Jun Chen , Jingxiang Gao , Ran Li , Xinpeng Wang , Ye Zhang , Jiaxin Ren , Shunxi Yin , Xiuli Zhu , Tingting Zhao , Xi Zhai , Yunlu Peng

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 https://github.com/yuantaogiser/G2MF.

准确绘制城市功能区地图，为城市可持续发展、国土空间规划和公共资源配置提供重要的基础地理信息服务。ufz是城市环境中具有特定功能的街区，通常由具有特定空间分布模式的物理对象和各种类型的语义对象组成。然而，以往识别ufz的研究主要集中在ufz的物理或语义方面，忽视了物体之间的空间关系和连通性。此外，很少有人利用异构地理空间数据构建的图形，通过基于街道块的测绘单元来识别功能区。为了弥补这一差距，我们开发了一个基于图的多模态数据融合框架（G2MF）来识别ufz。它是一个完全基于图形的识别框架，具有特征级融合策略，集成了高分辨率遥感图像和感兴趣点数据。首先，利用语义分割技术对UFZ单元内的物理对象进行分类；然后，为UFZ单元内的物理对象和语义对象分别构建两个独立的图结构；最后，将图像输入到基于图像的多模态融合网络中进行UFZ识别。实验结果表明，G2MF对中国4个城市的测试数据的总体识别准确率达到88.5%，对具有地理隔离的测试数据也具有良好的泛化能力。本研究不仅促进了UFZ自动识别技术的发展，也为未来城市大数据分析提供了新的方向和方法。我们的源代码发布在https://github.com/yuantaogiser/G2MF。

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引用次数: 0

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 利用MODIS NDVI和谷歌Earth Engine的统计数据绘制尼泊尔灌溉农业的时空动态：朝着改善灌溉规划迈出的一步

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2024.104345

Pramit Ghimire , Saroj Karki , Vishnu Prasad Pandey , Ananta Man Singh Pradhan

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.

水资源在支持粮食生产方面的重要性日益增加，特别是在面对气候变化、城市化和人口增长的情况下。本研究旨在绘制和分析灌溉农业区的时空动态，以支持尼泊尔灌溉用水和灌溉部门的改进规划。利用谷歌Earth Engine （GEE）平台的MODIS中分辨率植被指数（NDVI），对近20年来中国灌区和雨牧区的变化进行了分类分析。不同地理区域的NDVI时间序列分析发现，Terai和Siwalik地区每年有两个种植周期。而在中、高山地区，主要是单作周期。将k-means聚类算法应用于国际山地综合发展中心（ICIMOD）尼泊尔农业用地数据库内的NDVI时间序列。获得的灌溉区分布也在尼泊尔不同省份之间进行了分析，因为省份是联邦一级之后负责灌溉发展的主要职能行政区划。与尼泊尔水资源和灌溉部（DWRI）的统计灌溉区数据库相比，生产灌溉区分布显示出合理的准确性。结果表明，在过去十年中，平均每年约有60%（218万公顷）的农业用地得到灌溉。研究结果将为尼泊尔的可持续灌溉和水资源管理、提高作物生产力和制定战略提供有价值的见解，以确保粮食和水安全。

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引用次数: 0

CUG-STCN: A seabed topography classification framework based on knowledge graph-guided vision mamba network

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104383

Haoyi Wang , Weitao Chen , Xianju Li , Qianyong Liang , Xuwen Qin , Jun Li

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.

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引用次数: 0

Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine

IF 7.6 Q1 REMOTE SENSING

International journal of applied earth observation and geoinformation : ITC journal

Pub Date : 2025-02-01 DOI: 10.1016/j.jag.2025.104403

Ningsang Jiang , Peng Li , Zhiming Feng

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.

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引用次数: 0