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.

Remote sensing imagery is the most suitable tool for monitoring, managing, and evaluating land-use overlay fluctuations, especially forest cover for large areas. Free- and medium-resolution satellite imagery is a useful tool that allows scientific researchers and management organizations to monitor forest development in developing countries, such as Vietnam. In this study, we used SPOT 4 and Planet remote sensing data to assess land-use status fluctuations in the Kon Ha Nung Plateau area, Vietnam, between 2000 and 2021 (the overall accuracy was 90.52%, Kappa value = 0.89). The results showed that from 2000 to 2010, the rate of natural forest loss in this area was 0.32%/year, of which, more than 6500 ha were converted to other uses. Between 2010 and 2021, the rate of natural forest loss gradually decreased (0.09%/year) instead of fluctuating between different types of land use. The area of forests, perennial crop land, and annual crop land tended to increase from 2000 to 2010; however, from 2010 to 2021, the area of plantation forests decreased markedly, while the area of perennial crop land and annual crop land continued to expand. The analysis of the policies on forest management, exploitation, and protection was applied locally, to explain the causes of the change in spatiotemporal aspects of the types of land-use cover in the Kon Ha Nung Plateau. Restoring forest areas during 2010-2021 initially improved effectiveness in forest management and protection. Furthermore, the results provide a better understanding of the current position and role of the government apparatus, cadres, and ethnic minorities in socioeconomic development associated with forest protection and development on the Kon Ha Nung Plateau. The results of this study can help managers monitor annual forest-cover fluctuations based on free remote sensing imagery to reduce both the cost of management and surveying, yielding relatively accurate results.

How does the activity of the ecosystem of monsoon tropical forest decrease during the dry season of more than 4 months? To answer this question, the dynamics of evapotranspiration is analyzed in the national park of Cát Tiên (Southern Vietnam) using continuous eddy covariance and meteorological dataset for 2012–2013. A comparison of evapotranspiration between Cát Tiên and 21 sites in different tropical forests over the world is performed. It is shown that the abundance of resources of energy and moisture in the annual cycle, as well as the storage of water from the rainy season allows monsoon ecosystem to implement the proactive vegetation during the dry season and make specificity of ecosystem energy and water fluxes in Cát Tiên forest similar to the rainforests.

Long-term and accurate daily and monthly streamflow data play a crucial role in understanding hydrological regimes and managing water resources; however, such records are often incomplete, particularly for reservoir inflows. This study addresses this gap by evaluating and comparing the performance of a process-based model (SWAT) and a data-driven model (Light Gradient Boosting Machine – LGBM) in simulating daily and monthly inflows to six major reservoirs in the Ba River Basin, Vietnam – a basin strongly influenced by reservoir regulation for irrigation and hydropower. Observed meteorological, hydrological, and satellite-derived datasets from 2016 to 2023 were utilized, with the period from 2017 to 2022 allocated for calibration and training, and 2016 and 2023 reserved for independent validation. SWAT calibration was performed automatically with SWAT-CUP, while the hyperparameters of LGBM were optimized using Bayesian Optimization (BOA). The results demonstrate the superior performance of LGBM in most cases, achieving higher Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE) scores, and reproducing key hydrological signatures more accurately than SWAT. Nevertheless, SWAT exhibited comparatively better performance in simulating monthly inflows to the An Khe reservoir, highlighting the potential advantages of process-based models under specific conditions.

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