Landslide susceptibility depends on various causal factors such as geology, land use/land cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time, LULC is a dynamic factor associated with human activities. This study evaluates the impact of LULC change on landslide susceptibility in the Rangamati municipality of Rangamati district, Bangladesh, based on three LULC scenarios—the existing (2018) LULC, the proposed LULC (proposed in 2010, but not yet implemented), and the simulated LULC of 2028—using artificial neural network (ANN)-based cellular automata. The random forest model was used for landslide susceptibility mapping. The model showed good accuracy for all three LULC scenarios (existing: 82.7%; proposed: 81.4%; and 2028: 78.3%) and strong positive correlations (>0.8) between different landslide susceptibility maps. LULC is either the third or fourth most important factor in these scenarios, suggesting that is has a moderate impact on landslide susceptibility. Future LULC changes will likely increase landslide susceptibility, with up to 14.5% increases in the high susceptibility zone for both the proposed and simulated LULC scenarios. These findings may help policymakers carry out proper urban planning and highlight the importance of considering landslide susceptibility in LULC planning.

Changes in land cover are a major driving force behind habitat change, which significantly impacts the distribution of wildlife and ecological systems. However, there is a substantial lack of information on the effects of land cover changes on wildlife habitat and local conservation. Therefore, it is essential to understand how land cover changes may threaten future land cover trends and wildlife habitat loss, especially in protected areas. Landsat satellite imagery uses a geographic information system and remote sensing techniques to determine the spatiotemporal pattern of land cover change and its impact on the human–elephant conflict in the Fashiakhali Wildlife Sanctuary. We found that within the sanctuary (1994–2005), settlements, agricultural land, and bare land increased by 69.8 ha (2.3%), 991.6 ha (32.3%), and 39.5 ha (1.3%), and forest areas and water areas decreased by 1094.1 ha (35.7%) and 6.9 ha (0.2%), respectively. On the other hand (2005–2015), settlements, agricultural land, and water areas increased by 11.7 ha (0.4%), 264.7 ha (8.6%), and 36.2 ha (1.2%), and forest areas and bare land decreased by 308.9 ha (10.1%) and 3.7 ha (0.1%), respectively. Our findings have shown that increased agriculture and settlements have become a severe threat to the ecological sustainability of elephant habitat, resulting in habitat fragmentation and human encroachment of elephant habitats, as well as extreme pressure and competition on resources.

The primary purpose of this study is to find out and discuss the characteristics, causes, and consequences of the landslides of June 13, 2017, in the Rangamati district Bangladesh. Since rainfall triggered the landslides, debris flow accounts for 40.45% of the landslides. Most of the landslides are small (mean 274. 2 m²with a standard deviation of 546.1 m²). Size of 62.30% of the landslides was < 100 m². The probability density of 50–100 m²landslides is the highest and with the increase of the size of landslides, probability density decreases. It indicates the chance of large landslides (> 1000 m²) is low. Frequency ratio, logistic regression, and Spearman’s rank correlation were used to find out the relationship between 15 landslide causal factors including elevation, slope, rainfall, aspect, land use/land cover, land use/land cover change and distance to the road network with the occurrences and size of landslides. Among the land use/land cover types built-up areas [frequency ratio (FR) = 5.67], among land-use land-cover change types: vegetation to built-up (FR = 5.31) are the most prone areas to landslides. Logistic regression models found six causal factors were statistically significant, including slope (Coefficient, ß = 1.05), and distance to the road network (ß = 0.44). The size of the landslides had a significant relationship with five causal factors, including annual rainfall (ρ = 0.52), and elevation (ρ = 0.24). Paired sample t-test on pre-event and post-event monthly incomes revealed that landslides had a significant impact on different occupations of the local people. People involved in primary economic activities like the slash and burn agriculture (locally known as jhum cultivation) and fishing are the worst sufferers of landslides as they experienced a significant fall of income after the landslides. The findings of the study would help the policymakers to mitigate landslide hazards in the Rangamati district.

This study evaluates and compares three machine learning models: K-Nearest Neighbour (KNN), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) for landslide susceptibility mapping for part of areas in Rangamati District, Bangladesh. The performance of these methods has been assessed by employing statistical methods such as the area under the curve (AUC) for success rate (SR) and prediction rate (PR), Kappa index, Qs index and Friedman's test. Results show that XGBoost had the best performance with the highest AUC for both SR (95.27%) and PR (90.63%), followed by RF (SR: 89.26%; PR: 84.74%) and KNN models (SR: 85.54%; PR: 81.02%). This study provides a useful analysis for the selection of the best model for landslide susceptibility mapping and that it will be helpful for disaster planning and risk reduction.

Identifying the flooding risk hotspot is crucial for aiding a rapid response and prioritizes mitigation efforts over large disaster impacted regions. While climate change is increasing the risk of floods in many vulnerable regions of the world, the commonly used crisis map is inefficient and cannot rapidly determine the spatial variation and intensity of flooding extension across the affected areas. In such cases, the Local Indicators of Spatial Association (LISA) statistic can detect heterogeneity or the flooding hotspot at a local spatial scale beyond routine mapping. This area, however, has not yet been studied in the context of the magnitude of the floods. The present study incorporates the LISA methodology including Moran’s I and Getis–Ord Gi* to identify the spatial and temporal heterogeneity of the occurrence of flooding from super cyclone Amphan across 16 coastal districts of Bangladesh. Using the Synthetic Aperture Radar (SAR) data from Sentinel-1 and a Support Vector Machine (SVM) classification, “water” and “land” were classified for the pre-event (16 May 2020) and post-events (22 May, 28 May, and 7 June 2020) of the area under study. A Modified Normalized Difference Water Index (MNDWI), and visual comparison were used to evaluate the flood maps. A compelling agreement was accomplished between the observed and predicted flood maps, with an overall precision of above 95% for all SAR classified images. As per this study, 2233 km² (8%) of the region is estimated to have been inundated on 22 May. After this point, the intensity and aerial expansion of flood decreased to 1490 km² by 28 May before it increased slightly to 1520 km² (2.1% of the study area) on 7 June. The results from LISA indicated that the main flooding hotspots were located in the central part, particularly in the region off the north-east of the mangrove forest. A total of 238 Unions (smallest administrative units) were identified as high flooding hotspots (p < 0.05) on 22 May, but the number of flooding hotspots dropped to 166 in the second week (28 May) after Amphan subsided before it increased to a further 208 hotspots (p < 0.05) on 7 June due to incessant rainfall and riverbank failure in the south-west part of the study area. As such, an appropriate, timely, and cost-effective strategy would be to assess existing flooding management policies through the identified flooding hotspot regions. This identification would then allow for the creation of an improved policy to help curtail the destructive effects of flooding in the future.