No abstract

This study aimed at evaluating the spatiotemporal patterns of mangrove forest variations for three ecological zones of the Can Gio biosphere reserve (i.e., core, buffer, and transition zones) and its relation to land use/land cover changes. Time series Sentinel-2 Imagery—which presents the Normalized Different Vegetation Index (NDVI), obtained through the Google Earth Engine and Overlap Similarity Algorithm—was used to characterize vegetation cover in the study area. Furthermore, the Cohen’s Kappa agreement was applied to examine the accuracy of mangrove classification, and the Mann–Kendal (MK) significance was used to analyze the spatiotemporal trends of mangrove forests. The results showed that an NDVI value greater than 0.3 recorded the reflected signal of mangrove population in the study area with an O-index greater than 0.85. A Cohen’s Kappa statistic of agreement of 0.7 and an overall classification accuracy of 83% was obtained. Regarding the trend in mangrove forest patterns, an increase in area of 669 ha and 579 ha explored at the buffer and core zones, respectively, while the largest declined mangrove area of 350 ha was investigated at the buffer zone, followed by a transition at 314 ha during the study period due to the interconversion of shrimp farming and the expansion of built-up areas. Moreover, the study also described the negative impacts of the sea-encroached urban-tourism zone on mangrove patterns in the foreseeable future. The results from this study will act as a basic fundamental authentic report for local governments in proposing strategies for the shielding of mangrove forests and economic development from negative consequences in foreseeable future.

Saltwater intrusion risk assessment is a foundational step for preventing and controlling salinization in coastal regions. The Vietnamese Mekong Delta (VMD) is highly affected by drought and salinization threats, especially severe under the impacts of global climate change and the rapid development of an upstream hydropower dam system. This study aimed to apply a modified DRASTIC model, which combines the generic DRASTIC model with hydrological and anthropogenic factors (i.e., river catchment and land use), to examine seawater intrusion vulnerability in the soil-water-bearing layer in the Ben Tre province, located in the VMD. One hundred and fifty hand-auger samples for total dissolved solids (TDS) measurements, one of the reflected salinity parameters, were used to validate the results obtained with both the DRASTIC and modified DRASTIC models. The spatial analysis tools in the ArcGIS software (i.e., Kriging and data classification tools) were used to interpolate, classify, and map the input factors and salinization susceptibility in the study area. The results show that the vulnerability index values obtained from the DRASTIC and modified DRASTIC models were 36–128 and 55–163, respectively. The vulnerable indices increased from inland districts to coastal areas. The Ba Tri and Binh Dai districts were recorded as having very high vulnerability to salinization, while the Chau Thanh and Cho Lach districts were at a low vulnerability level. From the comparative analysis of the two models, it is obvious that the modified DRASTIC model with the inclusion of a river or canal network and agricultural practices factors enables better performance than the generic DRASTIC model. This enhancement is explained by the significant impact of anthropogenic activities on the salinization of soil water content. This study’s results can be used as scientific implications for planners and decision-makers in river catchment and land-use management practices.

No abstract

Chhukha, a southern district of Bhutan remains susceptible to landslides due to excessive temporal rainfall variability and land instability aggravated by anthropogenic factors. This has led to multiple fatalities, substantial financial losses, and damages to infrastructure, farmland, and transportation networks. This study developed the district scale Landslide susceptibility index (LSI) by a bivariate statistical approach called Probabilistic Frequency Ratio (FR) and logistic regression (LR) with the help of a geospatial technology system. A total of 236 historical landslide inventories were identified through field deputation and google earth interpretation with the rationing of 70:30. 70% of the existing landslides were used to train the models, while the remaining 30% of them were used for model validation. The FR model outperformed the LR model with an accuracy of 88.3% and 83.2% respectively. The AUC model verification shows satisfactory agreement to predict landslide susceptibility at the district scale in the Himalayan region. Both models indicated that the central and northern parts of the district account for the least susceptibility, while the southern portion of the Chhukha district accounts for the highest susceptibility to landslides. These authentic findings of the research enable the local government and other decision-making bodies in developing policies, implement innovative measures, and disseminate awareness and preparedness for the consequences of landslide disasters.