The Hinh River Basin is in a mountainous district in the southwest of Phu Yen Province of Vietnam. It features extensive land, majestic mountains and numerous stunning natural landscapes. The land is home to more than 20 ethnic groups occupying nearly half of the total population sharing the territory with the Kinh ethnicity (the majority ethnic group in Vietnam). Hinh River Basin has a diverse range of land covers (LC), including forest landscapes, agroforestry and waterbodies. Hinh River is a primary tributary of the Ba River, flowing through the Hinh River Basin in Phu Yen Province (Fig. 1).

Figure 1.

Hinh River Basin, Phu Yen Province, Vietnam. The map of the study area in Vietnam (a); the map of Phu Yen Province (b); The map of Hinh River Basin, Song Hinh district (c). Landscape types: rice (d), crop (e), scattered trees (f), water (g), annual industrial crop (h), natural forest (i), urban/built-up (j), plantation forest (k).

The four land-cover maps for the years 2010, 2015, 2018 and 2023 were produced by using the Random Forest (RF) algorithm to classify Landsat images from the corresponding years (Fig. 3). The accuracy assessment results showed a mean AUC value of 0.85 and a Kappa value of 0.8. The results indicate the area of 07 LC types (Annual industrial crop; Urban/built-up; Water; Crop/Shrub/Grass; Rice; Scattered trees; Forest (natural and plantation forests) over the years 2010, 2015, 2018 and 2023. The results in Fig. 3 showed that the area of forest and plantation forests and annual industrial crops are the two types of LC that increased in area from 2010 to 2023. Meanwhile, the other LC types (urban/built-up; water, crop/shrub/grass; rice) all decreased in area (Fig. 4). These results indicated a significant change and effectiveness in forest management policies, with an increase in forest area; in contrast, the bare land gradually decreased and the relevance of the RF approach for the accurate classification and analysis of land-cover changes over time, providing valuable insights into environmental and land-use dynamics over more than two decades.

Figure 3.

Map of LC from 2010 to 2023.

Figure 4.

LC dynamics over from 2010 to 2023 and prediction for 2030.

Overall, there are changes in landscape, with an increasing trend in forest and annual industrial crops, while the area of scrattered trees and urban and other types of cultivation gradually decreases. The forest area (both natural and plantation) has significantly increased from around 25,000 ha in 2010 to nearly 40,000 ha in 2030. This is a positive trend for the environment and ecosystems. The area of annual industrial crops has slightly risen from around 15,000 ha in 2010 to below 20,000 ha in 2030. The urban and built-up area has decreased from around 8,000 ha in 2010 to nearly 7,000 ha in 2030. The area of rice cultivation has remained quite stable, fluctuating around 10,000 ha throughout this period from 2010 to present (2023). The area of scattered trees has remained almost unchanged, staying stable at around 5,000 ha. The water area has also remained stable, with slight changes throughout this period. The area of crops, shrubs and grass has significantly decreased from around 26,000 ha in 2010 to 23,000 ha in 2023 and predicted below 20,000 in 2030. (Fig. 4).

The Markov method demonstrates robust analytical capabilities for predicting changes in land-use types up to 2030 by analysing probabilistic transition models from one state to another (Floreano and de Moraes 2021). From these analysis results, we observe significant changes in Forest and plantation forests and Scattered trees; while forests are expanding significantly, Scattered trees are declining. These areas not only hold crucial ecological value, but also harbour substantial potential for developing pure plantation forestry activities, enriching forests in the future. Protecting and restoring Scattered Tree areas not only contributes to environmental conservation, but also opens opportunities for integrated agroforestry projects to enhance LC sustainability (Vo and Hoang 2014).

Forest resource management has not been improving in many parts of Vietnam. The study, utilising census and geographic data from 1990, has clarified the distinction between natural forest regeneration and the increase in plantation forests and has found that policies allocating forest land, the scarcity of forest products and the demand for timber in remote areas have driven the increase in forest area. However, not all areas in Vietnam that are reforested receive equal attention. This means that, while reforestation efforts are taking place, some regions may not be given the same level of focus or resources (Meyfroidt and Lambin 2008). Thien and Phuong (2024) studied LC changes over 20 years in Ba Ria Vung Tau Province (Vietnam) using Landsat imagery, Normalised Difference Vegetation Index (NDVI) and Normalised Difference Water Index (NDWI), revealing a decline in forest area during this period. Different regions exhibit varying increases and decreases in forest and other LC types, which can serve as indicators for land-use management (Gupta and Sharma 2020). The integrated approach of LC modelling and remote sensing (multi-year phases) helps in assessment and analysis, providing a reliable method to recommend solutions to mitigate human-induced disruptions (Piao et al. 2021, Hishe et al. 2021, Delgado-Artés et al. 2022). Results from GEE can be developed into applications for free sharing amongst researchers, managers, ecologists, facilitating ongoing research, improving processes and monitoring LC changes (Hird et al. 2021).

The score of each factor and sub-factor in the AHP model was prioritised. The results from the FGD process helped identify the types of important sub-factors (Table 3). Results showed the LC is the most influential factor on PEF, with a weight of 40%, followed by slope (27.5%), precipitation (14%); fluvial hierarchy (11%); percentage of sand in soil (4.5%) and geological type (3%). The consistency index (CI) and consistency ratio (CR) were < 0.10. The MCA evaluation process resulted in the PEF Map, dividing the area into five different zoning of EF (Fig. 5). Generally, areas with low PEF are distributed in the northeast or southwest parts of the study area, characterised by extensive natural and plantation forests. Areas with very high PEF are located in the northwest or around large reservoirs. Specifically, dark-green-coded areas indicate environments with low fragility and high resilience to external impacts (covering approximately 19% of the total area). These are typically stable areas less affected by human or natural activities. Light-green-coded areas indicate moderately fragile environments (covering approximately 20% of the total area). These areas still exhibit good resilience, but are beginning to show susceptibility to damaging factors. Yellow-coded areas represent moderately fragile environments (covering approximately 27% of the total area). The environment here is less prone to damage from human and natural impacts. Orange-coded areas indicate highly fragile environments (covering approximately 17.7% of the total area). Environments in these areas are very susceptible to damage from human and natural impacts. Red-coded areas represent extremely high fragility (covering approximately 16.3% of the total area). These are the most sensitive areas, highly susceptible to severe damage from human and natural impacts. Priority should be given to protection and management measures to safeguard these areas.

Figure 5.

Map of potential environmental fragility (PEF).

The EEF was generated through integrated PEF and LC using the AHP-MCA analysis, offering a comprehensive view of the environmental impacts of human activities in the region (Fig. 6). Areas categorised as requiring extremely high protection (Extremely high EEF) are predominantly characterised by low-lying terrain, extensive river networks, lakes and scattered trees that have not yet matured into forests, covering 26% of the territorial area. Areas necessitating high protection (High EEF) are primarily located in high mountainous regions with extensive natural forests and well-established plantations, encompassing 29% of the territorial area, predominantly situated in the southern and eastern parts of the study area. Areas requiring moderate to low protection (Medium and Slightly low EEF) are associated with medium and low mountainous areas, featuring numerous small farms and annual industrial crop trees. These areas exhibit relatively stable environmental conditions and, thus, require less intensive protection. Areas classified as requiring low protection (Low EEF) are mainly found in low-lying areas with extensive rice paddies and flower fields.

Figure 6.

Zoning of Emergent Environmental Fragility (EEF).

The EEF map indicated the high sensitivity areas to EF, with Water LC (WT) being the most sensitive, followed by Annual industrial crop (AIC), Forest (FST), Scattered trees (SCT); Rice (RC), Crop/Shrub/Grass (CR) and Urban/Built-up (UB) (Fig. 7). Water LC often depends on multiple environmental factors to maintain ecological balance (França et al. 2022). Factors such as water quality, pollution levels and changes in these factors can significantly impact the ecological health and development of aquatic LCs. Water pollution, agricultural practices and industrial activities can degrade water quality, alter flow regimes and affect species distribution within aquatic LCs, leading to a decline in their quality (Gayathri et al. 2021). Aquatic areas are typically sensitive and vulnerable to changes from the surrounding environment, especially when fragmented or under pressure from human activities (Jumani et al. 2020). Small fragmented aquatic areas or those not connected to each other may become less sustainable and more vulnerable to external threats. Plant and animal species within aquatic environments are often sensitive to changes in their habitats (Mayeda and Boyd 2020). When environments are disrupted, these species may struggle to adapt or migrate to other areas, increasing the risk of extinction or population decline. These factors make Water LC more sensitive and prone to damage from environmental changes and human activities, resulting in a high EF index. The evaluation results accurately reflect that areas with high PEF indices are primarily Water LC. This recommendation could assist local authorities in enhancing solutions to protect river, lake, irrigation and canal ecosystems, optimising land resource use and improving the economic efficiency of local agriculture and forestry production. Forecasting changes in aquatic, forest and agricultural LC using a Markov-CA model in GEE cloud technology has proven highly useful in guiding the early detection of urban development trends linked with environmental protection.

Figure 7.

The percentage of LC in total area of extremely high EEF. UB: urban/built-up LC; CR: Crop/shrub/grass LC; RC: Rice LC; SCT: Scattered tree LC; FST: Forest LC (natural and plantation forests); AIC: annual industrial crop LC; WT: water LC.