RPI-GMM: A novel structure-based and phenology-independent algorithm for mapping latest 10-m resolution national-level rubber plantations

IF 11.4 1区地球科学 Q1 ENVIRONMENTAL SCIENCES

Remote Sensing of Environment Pub Date : 2026-03-01 Epub Date: 2026-01-14 DOI:10.1016/j.rse.2026.115241

Chiwei Xiao , Zilong Yue , Zhiming Feng , Jinwei Dong , Juliet Lu , Khin Htet Htet Pyone , Khampheng Boudmyxay

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引用次数: 0

Abstract

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 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.

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RPI-GMM：一种基于结构和物候无关的新型算法，用于绘制最新10米分辨率的国家级橡胶种植园

准确和更新的橡胶园地图有利于生态环境和社会经济影响评价以及可持续农林业管理。然而，现有的识别橡胶种植园的遥感方法主要依赖于时间序列光学数据的物候信号，这些数据受到持续云层覆盖、区域物候变化或不一致以及高数据需求的限制。为了解决这些挑战，本文提出了一个创新的物候独立框架，该框架将橡胶种植指数（RPI）与无监督高斯混合模型（GMM）分类器集成在一起。RPI是基于Sentinel-1双偏振SAR后向散射（VV/VH）和Sentinel-2 SWIR反射率（波段11）的结构敏感指数，用于捕获人工林的规律性和冠层水分特征。我们评估了热带地区六个不同橡胶样地的RPI-GMM框架，这些橡胶样地代表了物候、地形和种植园结构的变化。结果表明，无论是物候强还是物候弱，无论是山地还是破碎化景观，分类精度均在0.87以上。我们的RPI-GMM方法在老挝实现了87.0%的总体精度，并在2024年估计了234,206公顷的橡胶地块。空间分析显示，约70%的橡胶种植园位于靠近中国和越南的老挝边境地区，90%的橡胶种植园位于海拔低于1000米的地区，80%的橡胶种植园位于坡度为3°至16°的斜坡上。值得注意的是，我们的简单和集成的RPI-GMM方法不需要时间或标记数据，确保鲁棒性，成本效益和可移植性。这些结果突出了基于结构的sar -光学融合对未来全球或热带人工林动态监测的有价值的见解，并支持在农林业管理中的更广泛应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing of Environment 环境科学-成像科学与照相技术

CiteScore

25.10

自引率

8.90%

发文量

455

审稿时长

53 days

期刊介绍： Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.