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Forest ecosystems play a key role in sustaining life on this planet, given their functions in carbon storage, oxygen production, and the water cycle. To date, calculations of the biomass and carbon absorption capacity of forest ecosystems—especially tropical rainforests—have been quite limited, especially in Vietnam. By applying remote sensing materials, geographic information systems (GIS) facilitate the synchronized estimation of both biomass and ability of forest ecosystems to absorb carbon over large spatial ranges. In this study, we calculated the biomass of tropical rainforest vegetation in the Kon Ha Nung Plateau, Vietnam, according to four regression models based on Sentinel-2 satellite image data, forest reserve maps, and forest survey standard cell data (including 19 standard cells for 2016 and 44 standard cells for 2021). The results of the data comparison for the four biomass computing models (log-log, log-lin, lin-log, and lin-lin) demonstrated that the models with the highest accuracy were the lin-log model for 2016 (with a correlation coefficient of R2 = 0.76) and the lin-log model for 2021 (with a correlation coefficient of R2 = 0.765). Based on the analytical results and the selection of biomass estimation models, biomass maps were developed for the Kon Ha Nung Plateau area, Vietnam, in 2016 and 2021, with a predominant biomass value of 80–180 tons/ha (Mg/ha); furthermore, biomass fluctuations were analyzed for the period 2016–2021. Accordingly, the ability to absorb carbon and CO2 equivalents in this research area for 2016 and 2021 was calculated based on the estimated biomass values. In summary, we present a method for estimating biomass via four basic linear regression models for tropical rainforest areas based on satellite image data. This method can serve as a basis for managers to calculate and synchronize the payment of carbon services, which contributes to promoting the livelihoods of local people.

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Wetlands provide resources, regulate the environment, and stabilize shorelines; however, they are among the most vulnerable ecosystems in the world. Managing and monitoring wetland ecosystems are important for the development and maintenance of ecosystem services and their sustainable use in the context of climate change. We used Phantom 4 multispectral unmanned aerial vehicles (UAVs) to collect data from wetland areas in the Dong Rui Commune, which is one of the most diverse and valuable wetland ecosystems in northern Vietnam. A tree-species classification map was constructed through a combination of the visual classification method and spectral reflectance values of each plant species, and the characteristic distributions of mangrove plants, including Bruguiera gymnorrhiza, Rhizophora stylosa, and Kandelia obovata, were determined with an overall accuracy of 91.11% and a kappa coefficient (K) of 0.87. Universal reflectance graphs of each mangrove plant species were constructed for five wave channels, including blue, green, red, red edges, and near-infrared and the normalized difference vegetation index (NDVI). An experiment was conducted to map plant taxonomy in the same area based only on a graph of spectral reflectance values at five single-spectral bands and constructed NDVI values, resulting in an overall accuracy of 78.22% and a K of 0.67. The constructed map is useful for classifying, monitoring, and evaluating the structure of each group of mangroves, thereby enabling the efficient management and conservation of the Dong Rui Commune wetlands.