MODELLING ABOVE-GROUND BIOMASS OF MANGROVE FORESTS USING INTEGRATED SENTINEL-1 AND SENTINEL-2 DATA IN NORTHERN VIETNAM
DOI:
https://doi.org/10.70169/VJFS.1285Keywords:
Biomass estimation, biomass model, combined optical and Radar, remote sensing indices, mangrovesAbstract
Mangrove forests are coastal ecosystems that play an important role in shoreline protection, biodiversity conservation, and carbon accumulation. Above-ground biomass (AGB) is a fundamental indicator for assessing forest carbon stocks; however, field-based biomass surveys are often time-consuming, costly, and difficult to scale up over large spatial extents. This study aims to develop and compare models for estimating mangrove AGB in Northern Vietnam based on three remote sensing indices, namely RVI (Radar Vegetation Index), SWIRS (Short-Wave Infrared), and CORVI (Combined Optical and Radar Vegetation Index). Specifically, RVI is derived from the VH and VV polarizations of Sentinel-1; SWIRS is calculated as the sum of the shortwave infrared bands B11 and B12 of Sentinel-2; and CORVI is a combined index integrating RVI and SWIRS, defined as . For each independent variable, four functional forms are tested, including linear, power, logarithmic, and exponential models. The models were developed based on 322 sample plots, with observed AGB ranging from 15.4 to 282.8 Mg/ha, and were evaluated using the coefficient of determination R2, root mean square error (RMSE), and bias. The results showed that the power regression model using the CORVI index was the most suitable model for estimating mangrove AGB, achieving the highest validation accuracy (R2 = 0.5240), the lowest validation RMSE (28.77 Mg/ha), and a validation Bias of 2.11 Mg/ha; therefore, this model is recommended for mapping the spatial distribution of mangrove above-ground biomass in the study area.
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