Under Vietnam’s State land ownership regime, the Government holds supreme authority over compulsory land acquisition. The results show that many improvements in land acquisition policies have been made, but poor implementation measures largely cannot prevent or even mitigate the adverse impacts on displaced persons. In particular, ineffective compensation measures and a lack of production land and livelihood alternatives accelerate the resistance of communities displaced as a result of hydropower development. The close alliance between the local government and the investor, which is considered as an “interest group”, is the main factor that leads to the ignorance of benefits of displaced people within the compulsory land acquisition process.

The study examined the natural and social capital of Bo Hon villagers in central Vietnam, before and after resettlement within Binh Thanh commune due to the building of Binh Dien Hydroelectric dam on the Huu Trach River. (1) Background: The two-fold aim was to develop solutions to the impacts of resettlement on natural and social capital, and strategies for timely intervention and new livelihoods after households were resettled. (2) Methods: Livelihood survey of all 46 households was conducted in 2010, and villagers were asked about 2004, before resettlement, and about 2009, when the occupants of Bo Hon village had been moved to a new location 15 km away from the original one. The research employed mixed-methods by using household surveys, focus group discussions, and key informant interviews. The impacts of displacement and resettlement on production activities and daily life of rural people were examined in the following areas: (i) land resource; (ii) access to common-pool natural resources; (iii) income structure; (iv) agriculturally based livelihoods; (v) material assets; (vi) customary practices; and (vii) social relationships. (3) Results: The most significant impact was on the type of production activities that could be conducted after resettlement and reduction in land area to grow profitable commodities such as Lồ Ô Bamboo. Specifically, land for growing rice and other crops were significantly affected with the land area substantially reduced or flooded. Also harvesting of common pool resources from the forest (NTFPs) were reduced such as honey and rattan, and only 25% of the villagers continued to fish in the river. (4) Conclusions: Strategies were put in place to reduce the level of disruption to the villagers’ livelihoods, but some parts of the compensation package were short-lived or inequitably distributed (e.g., land), while infrastructure developments such as sealed roads have made the village far more accessible to Hue City some 25 km away.

Wetlands are highly productive ecosystems with the capability of carbon sequestration, providing an effective solution for climate change. Recent advancements in remote sensing have improved the accuracy in the mapping of wetland types, but there remain challenges in accurate and automatic wetland mapping, with additional requirements for complex input data for a number of wetland types in natural habitats. Here, we propose a remote sensing approach using the Google Earth Engine (GEE) to automate the extraction of water bodies and mapping of growing lotus, a wetland type with high economic and cultural values in central Vietnam. Sentinel-1 was used for water extraction with the K-Means clustering, whilst Sentinel-2 was combined with the machine learning smile Random Forest (sRF) and smile Gradient Tree Boosting (sGTB) models to map areas with growing lotus. The water map was derived from S-1 images with high confidence (F₁ = 0.97 and Kappa coefficient = 0.94). sGTB outperformed the sRF model to deliver a growth map with a high accuracy (overall accuracy = 0.95, Kappa coefficient = 0.92, Precision = 0.93, and F₁ = 0.93). The total lotus area was estimated at 145 ha and was distributed in the low land of the study site. Our proposed framework is a simple and reliable mapping technique, has a scalable potential with the GEE, and is capable of extension to other wetland types for large-scale mapping worldwide.

“How to gauge the resilience of a household’s livelihood?” and “who is resilience for?”; as the world’s volatility increases, especially with unprecedented changes in climate, interest in these questions continues to rise. While many previous conventional attempts to measure resilience at the household level have largely employed the “objective” top-down framework relying mostly on a macro observed socioeconomic dataset, this present work seeks to estimate household resilience through an alternative bottom-up method, called the “subjective” resilience approach. With specific reference to the context of two ethnic Pa Cô and Tà Ôi minorities living in upland areas of Central Vietnam, this study aims to (1) measure household resilience to climate change by scoring five livelihood capitals, financial, human, social, physical, and natural, by applying the Household Livelihood Resilience (HLR) framework; and (2) provide an intersectional lens of ethnicity and gender in relation to the household’s livelihood resilience. To achieve these goals, in addition to two focus group discussions and eleven in-depth interviews, an empirical survey of 236 households was conducted between September to December 2021. Our findings indicated that there are differences in observed livelihood resilience among the ethnic minority groups and, within that, between gender (sex, status) factors. Ethnic women and poor households have a lower resilience than other groups in society due to their lower human capital and limited access to public and financial resources, which are to some degree linked to the difference in the gender division of labor and the cultural norms of patriarch traditions. This study highlights the importance of considering gender and poverty in resilience-building efforts and offers insights for future programs in multi-ethnic developing countries such as Vietnam.

Bathymetry data is indispensable for a variety of aquatic field studies and benthic resource inventories. Determining water depth can be accomplished through an echo sounding system or remote estimation utilizing space-borne and air-borne data across diverse environments, such as lakes, rivers, seas, or lagoons. Despite being a common option for bathymetry mapping, the use of satellite imagery faces challenges due to the complex inherent optical properties of water bodies (e.g., turbid water), satellite spatial resolution limitations, and constraints in the performance of retrieval models. This study focuses on advancing the remote sensing based method by harnessing the non-linear learning capabilities of the machine learning (ML) model, employing advanced feature selection through a meta-heuristic algorithm, and using image extraction techniques (i.e., band ratio, gray scale morphological operation, and morphological multi-scale decomposition). Herein, we validate the predictive capabilities of six ML models: Random Forest (RF), Support Vector Machine (SVM), CatBoost (CB), Extreme Gradient Boost (XGB), Light Gradient Boosting Machine (LGBM), and KTBoost (KTB) models, both with and without the application of meta-heuristic optimization (i.e., Dragon Fly, Particle Swarm Optimization, and Grey Wolf Optimization), to accurately ascertain water depth. This is achieved using a diverse input dataset derived from multi-spectral Landsat 9 imagery captured on a cloud-free day (19 September 2023) in a shallow, turbid lagoon. Our findings indicate the superior performance of LGBM coupled with Particle Swamp Optimization (R² = 0.908, RMSE = 0.31 m), affirming the consistency and reliability of the feature extraction and selection-based framework, while offering novel insights into the expansion of bathymetric mapping in complex aquatic environments.