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Automated remote sensing monitoring of cropland non-agricultural and non-grain conversion at parcel scale in complex environments through multi-source data fusion

文献类型：期刊论文

作者Zhang, Junyao1,2; Yang, Xiaomei1,2; Dai, Jianwang3; Wang, Xiaofan3; Fang, Zheng4; Liu, Xiaoliang1,2; Zeng, Xiaowei1,2; Wang, Zhihua1,2
刊名GEO-SPATIAL INFORMATION SCIENCE
出版日期2025-06-15
卷号N/A
关键词Non-agricultural and non-grain remote sensing monitoring parcel scale multi-source data fusion temporal features automatic
ISSN号1009-5020
DOI10.1080/10095020.2025.2514824
产权排序1
文献子类Article ; Early Access
英文摘要Changes in cropland use, particularly the transition from agricultural to non-agricultural and non-food crop production, can diversify rural economies but may also pose challenges to regional food security, especially in densely populated and rapidly developing regions such as China. High-precision monitoring of cropland non-agricultural and non-grain conversion is essential for balance regional food security with rural income enhancement. This study focuses on the monitoring cropland non-agricultural and non-grain conversion in the rainy and cloudy regions of southern China. We aim to develop an automated process framework that accurately extracts parcel boundaries and identifies multiple types of changes. Quantitative experiments assessed the effectiveness of various solutions for key modules in the framework, including multisource data fusion, image segmentation, sample generation, and classification feature strategies. Validation using verification samples obtained through visual interpretation and field surveys revealed the following results: (1). The use of both optical and SAR images improved classification accuracy by 1.30% compared to using optical images alone. (2) Under the constraint of vector patch data, segmentation using high-resolution images outperformed both segmentation using medium-resolution images with the same constraint and segmentation using high-resolution images without the constraint, achieving Mean Intersection over Union (MIOU) improvements of 0.28 and 0.24. (3) Samples automatically generated from vector patch data achieved classification accuracy comparable to that of manually selected samples, with only a 0.64% decrease in overall classification accuracy. (4) Classification utilizing time-series feature extraction from reconstructed data outperformed classification based on temporal feature, with an overall accuracy increase of 1.94%. The optimized automated process framework achieved an overall accuracy of 89.00% in monitoring cropland conversion in the complex planting conditions of southern China. This framework represents an effective approach for the automated monitoring of cropland non-agricultural and non-grain conversion with precise parcel boundaries, providing valuable insights for similar monitoring objectives and application scenarios.
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WOS关键词GOOGLE EARTH ENGINE ; TIME-SERIES ; LANDSAT ; CLASSIFICATION ; SEGMENTATION ; IMAGERY ; MODIS ; SENTINEL-2 ; ACCURACY ; SITES
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:001507933700001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/214614]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Wang, Zhihua
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China;
2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China;
3.Minist Nat Resources Peoples Republ China, China Land Surveying & Planning Inst, Key Lab Land Use, Beijing, Peoples R China;
4.Sichuan Inst Land Sci & Technol, Sichuan Ctr Satellite Applicat Technol, Key Lab Invest Monitoring Protect & Utilizat Culti, Chengdu, Sichuan, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Junyao,Yang, Xiaomei,Dai, Jianwang,et al. Automated remote sensing monitoring of cropland non-agricultural and non-grain conversion at parcel scale in complex environments through multi-source data fusion[J]. GEO-SPATIAL INFORMATION SCIENCE,2025,N/A.
APA Zhang, Junyao.,Yang, Xiaomei.,Dai, Jianwang.,Wang, Xiaofan.,Fang, Zheng.,...&Wang, Zhihua.(2025).Automated remote sensing monitoring of cropland non-agricultural and non-grain conversion at parcel scale in complex environments through multi-source data fusion.GEO-SPATIAL INFORMATION SCIENCE,N/A.
MLA Zhang, Junyao,et al."Automated remote sensing monitoring of cropland non-agricultural and non-grain conversion at parcel scale in complex environments through multi-source data fusion".GEO-SPATIAL INFORMATION SCIENCE N/A(2025).

入库方式： OAI收割

来源：地理科学与资源研究所

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