RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations
文献类型：期刊论文
| 作者 | Xiao, Chiwei1,2,3; Yue, Zilong2,3; Feng, Zhiming2,3; Dong, Jinwei2,3; Lu, Juliet4; Pyone, Khin Htet Htet4; Boudmyxay, Khampheng5 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2026-03-01 |
| 卷号 | 334页码:115241 |
| 关键词 | Rubber plantations Rubber plantation index (RPI) Sentinel-1/Sentinel-2 Gaussian mixture model (GMM) Laos |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2026.115241 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurate and updated maps of rubber plantations are beneficial to eco-environmental and socio-economic impact assessment and sustainable agroforestry management. However, existing remotely-sensed approaches to identifying rubber plantations primarily rely on phenological signals from time-series optical data, which are limited by persistent cloud cover, regional phenological variability or inconsistency, and high data demands. To address these challenges, here, we propose an innovative phenology-independent framework that integrates a rubber plantation index (RPI) with an unsupervised Gaussian Mixture Model (GMM) classifier. The RPI is a structure sensitive index derived from dual-polarized Sentinel-1 SAR backscatter (VV/VH) and Sentinel-2 SWIR reflectance (Band 11), capturing plantation regularity and canopy moisture characteristics. We evaluated the RPI-GMM framework across six diverse sample areas of rubber plots in tropics representing variations in phenology, topography, and plantation structure. Results demonstrated high classification accuracy, with F1 scores over 0.87 under both phenologically strong and weak conditions, as well as across mountainous and fragmented landscapes. Our RPI-GMM method achieved an overall accuracy of 87.0% in Laos, and estimated 234,206 ha of rubber plots in 2024. Spatial analysis revealed that approximately 70% of rubber plantations are located in Laotian border areas near China and Vietnam, 90% are situated at elevations below 1000 m, and 80% are found degrees degrees on slopes with gradients ranging from 3 to 16. Notably, our simple and integrated method of RPI-GMM requires no temporal or labeled data, ensuring robustness, cost-efficiency, and transferability. The results highlight valuable insights of structure-based SAR-optical fusion for future global or tropical monitoring of tree-plantation dynamics and support broader applications in agroforestry management. |
| URL标识 | 查看原文 |
| WOS关键词 | TIME-SERIES ; HEVEA-BRASILIENSIS ; INTEGRATING PALSAR ; TROPICAL FORESTS ; TREE GROWTH ; STAND AGES ; AREAS ; PIXEL |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001669681200001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/220978]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Xiao, Chiwei |
| 作者单位 | 1.Minist Nat Resources Peoples Republ China, Key Lab China ASEAN Satellite Remote Sensing Appli, Beijing, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China; 4.Univ British Columbia, Dept Forest Resources Management, Vancouver, BC, Canada; 5.Natl Univ Laos, Fac Educ, Res & Acad Serv Div, Viangchan, Laos |
| 推荐引用方式 GB/T 7714 | Xiao, Chiwei,Yue, Zilong,Feng, Zhiming,et al. RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations[J]. REMOTE SENSING OF ENVIRONMENT,2026,334:115241. |
| APA | Xiao, Chiwei.,Yue, Zilong.,Feng, Zhiming.,Dong, Jinwei.,Lu, Juliet.,...&Boudmyxay, Khampheng.(2026).RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations.REMOTE SENSING OF ENVIRONMENT,334,115241. |
| MLA | Xiao, Chiwei,et al."RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations".REMOTE SENSING OF ENVIRONMENT 334(2026):115241. |
入库方式： OAI收割
来源：地理科学与资源研究所
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