Abstract
The largest area of tropical rainforests in China is on Hainan Island, and it is an important part of the world’s tropical rainforests. The structure of the tropical rainforests in Hainan is complex, the biomass density is high, and conducting ground surveys is difficult, costly, and time-consuming. Remote sensing is a good monitoring method for biomass estimation. However, the saturation phenomenon of such data from different satellite sensors results in low forest biomass estimation accuracy in tropical rainforests with high biomass density. Based on environmental information, the biomass of permanent sample plots, and forest age, this study established a tropical rainforest database for Hainan. Forest age and 14 types of environmental information, combined with an enhanced vegetation index (EVI), were introduced to establish a tropical rainforest biomass estimation model for remote sensing that can overcome the saturation phenomenon present when using remote sensing data. The fitting determination coefficient R2 of the model was 0.694. The remote sensing estimate of relative bias was 2.29%, and the relative root mean square error was 35.41%. The tropical rainforest biomass in Hainan Island is mainly distributed in the central mountainous and southern areas. The tropical rainforests in the northern and coastal areas have been severely damaged by tourism and real estate development. Particularly in low-altitude areas, large areas of tropical rainforest have been replaced by economic forests. Furthermore, the tropical rainforest areas in some cities and counties have decreased, affecting the increase in tropical rainforest biomass. On Hainan Island, there were few tropical rainforests in areas with high rainfall. Therefore, afforestation in these areas could maximize the ecological benefits of tropical rainforests. To further strengthen the protection, there is an urgent need to establish a feasible, reliable, and effective tropical rainforest loss assessment system using quantitative scientific methodologies.
Highlights
The rapidly increasing global population, land-use change, and large-area forest fires have resulted in a downward trend in global biomass in recent years [1,2,3,4,5]
Tropical rainforests in low latitudes account for 59% of the global forest biomass carbon sequestration [8,9]
Combined with the forest age, environmental information, and remote sensing factor enhanced vegetation index (EVI), the goodness of fit significantly improved from Model 2 (R2 < 0.100) to Model
Summary
The rapidly increasing global population, land-use change, and large-area forest fires have resulted in a downward trend in global biomass in recent years [1,2,3,4,5]. Forest biomass could effectively delay the increase in carbon dioxide, which has attracted much attention [6,7]. Tropical rainforests in low latitudes account for 59% of the global forest biomass carbon sequestration [8,9]. 2021, 13, 1696 by tropical rainforest biomass is of great significance for accurately assessing the global carbon cycle. Owing to a lack of large-scale traditional sample plot investigations, few studies have monitored tropical rainforest biomass in large-scale spaces. Remote sensing technology can effectively monitor tropical rainforests in large-scale spaces. The use of remote sensing images to determine the distribution, type, growth, and other information of vegetation is a common measure to monitor dynamic changes in forest biomass [13]
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