Optimising aboveground biomass estimation in rubber plantations using vegetation indices derived from Landsat 8 OLI-TIRS imagery

Rubber plantations in Southeast Asia have been identified as one of major contributors to deforestation and habitat degradation in the region, primarily when they replace natural forests. However, despite these environmental concerns, well-managed rubber plantations can support sustainability efforts, including climate change mitigation, sustainable land management practices, renewable energy development, environmental conservation, and economic viability. Among the key components, aboveground biomass (AGB) is particularly important in regulating potential carbon emissions from rubber plantations. Therefore, this study aimed to develop an optimal statistical model for estimating the AGB of rubber plantations by utilising several vegetation indices (VIs) derived from Landsat 8 OLI-TIRS satellite imagery. To achieve this, both linear and non-linear regression tests were conducted. Furthermore, vegetation and landscape properties were included as inputs to differentiate the variance of Vis’ influence on AGB prediction. The developed model serves as a fundamental algorithm for generating a map that predicts the AGB within the study area. Additionally, the distribution of biomass on the surface was estimated based on models A and B, which were further divided into three age groups and two landscape categories. The statistical analysis showed a strong similarity between the estimated and observed AGB, with a difference of less than 1%. The results indicated that the mathematical model for these variables aligns more closely with non-linear regression rather than its linear counterpart. This was evidenced by the R2 value, which increased fivefold for non-linear regression.

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