Abstract
Rubber (Hevea brasiliensis Muell.) plantations constitute one of the most important agro-ecosystems in the tropical region of China and Southeast Asia, playing an important role in the carbon budget there. Accurately obtaining their biomass over a large area is challenging because of difficulties in acquiring the Diameter at Breast Height (DBH) through remote sensing and the problem of biomass saturation. The stand age, which is closely related to the forest biomass, was proposed for biomass estimation in this study. A stand age map at an annual scale for Hainan Island, which is the second largest natural rubber production base in China, was generated using all Landsat and Sentinel-2 (LS2) data (1987–2017). Scatter plots and the correlation coefficient method were used to explore the relationship (e.g., biomass saturation) between rubber biomass and different LS2-based variables. Subsequently, a regression model fitted with the stand age (R2 = 0.96) and a Random Forest (RF) model parameterizing with LS2-based variables and/or the stand age were respectively employed to estimate rubber biomass for Hainan Island. The results show that rubber biomass was saturated around 65 Mg/ha with all LS2-based variables. The regression model estimated biomass accurately (R2 = 0.79 and Root Mean Square Error (RMSE) = 14.00 Mg/ha) and eliminated the saturation problem significantly. In addition to LS2-based variables, adding a stand age parameter to the RF models was found to significantly improve the prediction accuracy (R2 = 0.82–0.96 and RMSE = 4.08–10.59 Mg/ha, modeling using samples of different biomass sizes). However, all RF models overestimated the biomass of young plantations and underestimated the biomass of old plantations. A hybrid model integrating the optimal results of RF and regression models reduced estimation bias and generated the best performance (R2 = 0.83 and RMSE = 12.48 Mg/ha). The total rubber biomass of Hainan Island in 2017 was about 5.40 × 107 Mg. The northward and westward expansions after 2000 had great impact on the biomass distribution, leading to a higher biomass density for the inland coastal strip from south to northeast and a lower biomass density in the northern and western regions.
Highlights
The rubber tree (Hevea brasiliensis Muell.), which is widely planted in tropical regions such as Southeast Asia and the tropical region of China, is of great importance in producing natural rubber and in providing timber when the rubber latex harvest is finished [1,2]
PC1, PC2, PC3, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI) first increase with the increase in biomass, but there are almost no changes after the biomass is greater than 65 Mg/ha, except for PC2, showing an obvious decrease in a high biomass range
It is of great importance to estimate the biomass of rubber plantations that are widely distributed in tropical regions
Summary
The rubber tree (Hevea brasiliensis Muell.), which is widely planted in tropical regions such as Southeast Asia and the tropical region of China, is of great importance in producing natural rubber and in providing timber when the rubber latex harvest is finished [1,2]. In China, four rubber biomass allometric models have been developed far [8,10,12,13] All these models use DBH or the combination of DBH and H as independent variables, yielding R2 values greater than 0.97. Jia et al.’s [12] model only considers the aboveground biomass but covers biomass variation at low (550–600 m), medium (750–800 m), and high (950–1050 m) elevations above the mean sea level (AMSL), while the other three models consider the belowground biomass [8,10,13] These AE models are very valuable because the purpose of planting rubber trees is to harvest latex for a long period of time (usually 25–30 years) instead of wood. The cost of felling mature rubber trees in a large area to establish AE models is very high
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
