Assesses land use changes in 34 provinces, showing differentiated patterns and intensity post-2020.
Recent land use and land cover (LULC) transformation in Vietnam raises the question of whether recent changes reflect a uniform national trend or differentiated regional patterns. This study assesses provincial LULC dynamics across 34 provinces using nationally consistent remote-sensing data for 2000, 2020, and 2023. We combine annualized intensity analysis, transition matrices, Shannon entropy, dominant transition analysis, and spatial autocorrelation to compare the magnitude, structure, and spatial organization of LULC change before and after 2020. The results show that annualized land-change rates were substantially higher during 2020–2023 than during 2000–2023, with all provinces showing increased rates of transformation. However, this more recent intensification has not been spatially uniform. Higher increases have been concentrated in southern and delta provinces, while several northern and upland provinces showed lower acceleration. Structural responses also varied across provinces: only four of 34 provinces (11.8%) were classified as both accelerated and structurally concentrated, whereas diversified regimes accounted for about two-thirds of the provinces. Population density was moderately associated with post-2020 magnitude of change but only weakly related to structural configuration, indicating that the magnitude and composition of LULC change represent distinct dimensions. By separating change intensity from structural configuration, this study provides a reproducible framework for identifying differentiated provincial land-change regimes. The results show that recent land cover transformation in Vietnam is not a single national process, but a mosaic of spatially and structurally distinct change patterns.
No takes yet. Share an insight, caveat, or question.
An et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: