Abstract

Most natural rubber trees (Hevea brasiliensis) are grown on plantations, making rubber an important industrial crop. Rubber plantations are also an important source of household income for over 20 million people. The accurate mapping of rubber plantations is important for both local governments and the global market. Remote sensing has been a widely used approach for mapping rubber plantations, typically using optical remote sensing data obtained at the regional scale. Improving the efficiency and accuracy of rubber plantation maps has become a research hotspot in rubber-related literature. To improve the classification efficiency, researchers have combined the phenology, geography, and texture of rubber trees with spectral information. Among these, there are three main classifiers: maximum likelihood, QUEST decision tree, and random forest methods. However, until now, no comparative studies have been conducted for the above three classifiers. Therefore, in this study, we evaluated the mapping accuracy based on these three classifiers, using four kinds of data input: Landsat spectral information, phenology–Landsat spectral information, topography–Landsat spectral information, and phenology–topography–Landsat spectral information. We found that the random forest method had the highest mapping accuracy when compared with the maximum likelihood and QUEST decision tree methods. We also found that adding either phenology or topography could improve the mapping accuracy for rubber plantations. When either phenology or topography were added as parameters within the random forest method, the kappa coefficient increased by 5.5% and 6.2%, respectively, compared to the kappa coefficient for the baseline Landsat spectral band data input. The highest accuracy was obtained from the addition of both phenology–topography–Landsat spectral bands to the random forest method, achieving a kappa coefficient of 97%. We therefore mapped rubber plantations in Xishuangbanna using the random forest method, with the addition of phenology and topography information from 1990–2020. Our results demonstrated the usefulness of integrating phenology and topography for mapping rubber plantations. The machine learning approach showed great potential for accurate regional mapping, particularly by incorporating plant habitat and ecological information. We found that during 1990–2020, the total area of rubber plantations had expanded to over three times their former area, while natural forests had lost 17.2% of their former area.

Highlights

  • The para rubber tree (Hevea brasiliensis) is the major source of natural rubber for global industrial markets, producing more than 98% of the world’s natural rubber [1,2]

  • Further analysis of the NDVI, Land Surface Water Index (LSWI), and Enhanced Vegetation Index (EVI) between defoliation and refoliation found that rubber plantations could be separated from natural forests and shrublands, while it was difficult to separate natural forests from the shrublands (Figure 4c)

  • While comparing the standard deviation (SD), we found both the maximum likelihood (ML) and random forest method (RF) had a smaller SD than the QUEST decision tree (QDT) classifier, which indicated that the QDT classifier had a lower stability and a lower accuracy, which was consistent with that of Xu et al (2005) and Han et al (2015) who found that the decision tree classifier may suffer from overfitting, but that its accuracy was lower than that of RF [45,46]

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Summary

Introduction

The para rubber tree (Hevea brasiliensis) is the major source of natural rubber for global industrial markets, producing more than 98% of the world’s natural rubber [1,2]. With growing demand for natural rubber, led mainly by the tire industry, rubber plantations. Knowing the spatial distribution of rubber plantation areas at a relatively high resolution (30 m or finer), with a high degree of accuracy, is of great importance to regional planning, sustainable rubber development in the global industry market, and global biogeochemical processes in the carbon and water cycles [4,8]. Mapping with a high accuracy at a high resolution is the basis for land use planning, mapping productivity, carbon finance schemes, conservation policies, and the assessment of economic losses caused by diseases (e.g., powdery mildew disease) or natural disasters (e.g., typhoons). Using remote sensing to map rubber plantations can play an important role for rubber markets at both local and global scales, land use planning, and economic loss assessment

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