This study explored the land use land cover (LULC) change over 45 years (1975 -2020) in the world’s largest mangrove forest, Sundarbansusing Landsat imagery. LULC maps were created with same-season imagery with the lowest cloud cover at four intervals: 1975, 1990, 2005, and 2020. Maximum likelihood classification (MLC) was applied to assign five classes: dense forest, moderate forest, sparse forest, barren land, and water body. Accuracy assessment was carried out with 250 control points for each year resulting in overall accuracy and kappa coefficient ranging from 84.8% to 90.0% and 0.81 to 0.87, respectively. Results show dense forest at its highest cover in 1975 and then decreasing by an estimated annual rate of 1.3% from 1975 to 2020, but not consistently. Dense forest class mostly turned moderate and sparse; most of the sparse forest class turned to barren land. Most of the barren lands were located near the boundary between forest and human settlement, and these two classes were more frequent in the Indian part of Sundarbans than in the Bangladesh part. The conclusion is that the time-series of remote sensing data can validly support effective forest management by identifying space and time changes in the biodiversity of Sundarbans.

The sea level rise (SLR) in the Sundarbans areas is higher than the global-average rate of sea rise, and many studies assume that most of the dry land of the Sundarbans will be inundated by the end of the twenty-first century. This study aims to analyze the amount of dry land that can potentially be inundated by SLR in Sundarbans and the impact under different land cover conditions. Four SLR scenarios, a digital elevation data grid, and net subsidence data are used to map areas that will be potentially inundated by 2100. Results for the low (35 cm), mid (52 cm), high (70 cm), and extreme (147 cm) SLR scenarios indicate that the Sundarbans landmass area will be flooded up to 40 km² (1%), 72 km² (1.8%), 136 km² (3.4%), and 918 km² (23%), respectively, under the current net subsidence rate of −2.4 mm/year by 2100. Except for the extreme scenarios, the low, mid, and high SLR will result in riverbank and beach areas to be covered by water. The potential inundation areas of different vegetation cover classes that already exist today (2020) will be nominal for the low, mid, and high SLR scenarios. We also analyzed the sensitivity of the results through station-based SLR data, which fits with the low (35 cm) SLR scenarios under the −2.4 mm/year subsidence rate. This study concluded that the inundation aspect of SLR will not directly affect the Sundarbans; however, indirectly related threats and anthropogenic disturbances can be major drivers of the Sundarbans’ degradation by the end of the twenty-first century. This work discusses reasonable inundation scenarios integrating SLR and subsidence with a custom land-cover map that includes three forest-density categories. The study’s findings contribute to forest management planning and support the UN goals of the Bangladesh Delta Plan.

A systematic literature review was conducted on papers studying coastal socio-ecological systems (SESs) in adapting to climate change to support sustainable coastal management and contribute to achieving the UN SDGs. We selected, analyzed, and synthesized 173 peer-reviewed, English-language scientific publications using the PRISMA method. Firstly, we summarized and compared the selected literature; then, we explored its geographical distribution and respective coastal landscapes, and we identified and classified the adaptation strategies focused on different coastal landscapes. Furthermore, we processed the results obtained to develop a unique conceptual model based upon the DPSIR framework for coastal SESs adapting to climate change. This review shows a gradual increase in the number of published papers, particularly after the Paris Agreement, with an uneven distribution across the world. The number of papers and case studies was lower in highly vulnerable coastal areas, with the exception of Bangladesh. Most of the literature presented a local perspective rather than a national or transnational one, focusing more on vulnerability assessment than adaptation strategies. Recent studies have shown an increasing focus on ecosystem-based adaptation. Institutional and financial support are reported as the main constraints on ensuring long-term monitoring and beneficial impacts.

We explored the state of the art economic and livelihood valuation of ecosystem services (ES) in the Sundarbans mangroves, including a comparative analysis between the Bangladesh and Indian parts of the region. We identified 145 values from 26 studies to estimate the Sundarbans’ economic and livelihood values. The number of ES valuation studies of the Sundarbans is scant, and it has gradually increased over time, focusing mainly on the estimation of provisioning ES (66.2%), followed by regulating and maintenance (25.5%), and cultural (8.3%) ES. However, recently, attention has been paid to estimation, regulating and maintenance, and cultural ES. The number of studies on ES was higher for the Bangladesh (73%) part of the Sundarbans than the Indian (27%) one. The estimated economic values of the Sundarbans’ provisioning, regulating and maintenance, and cultural ES were US $ 713.30 ha⁻¹ yr⁻¹, US $ 2584.46 ha⁻¹ yr⁻¹, and US $ 151.88 ha⁻¹ yr⁻¹, respectively. Except for cultural ES, the identified values for the other two ES categories were about 1.5 to 2.5 times higher for the Bangladesh Sundarbans compared to the Indian ones. The results of the meta-regression model showed that the estimated economic and livelihood values of ES are affected by the associated variables (e.g., type of ES, valuation methods, study area, population, and GDP). Our study also identified some remarkable gaps and limitations in the economic and livelihood valuation of the ES of the Sundarbans, highlighting the need for further research to find out the values of all ES to help with policy decision-making.

The Venice lagoon is the largest in the Mediterranean Sea. The historic city of Venice, located on a cluster of islands in the centre of this lagoon, is an enchanting and iconic destination for national and international tourists. The historical centre of Venice and the other islands of the lagoon, such as Burano, Murano and Torcello, attract crowds of tourists every year. Transportation is provided by boats navigating the lagoon along a network of canals. The lagoon itself attracts visitors who enjoy various outdoor recreational activities in the open air, such as fishing and sunbathing. While statistics are available for the activities targeting the islands, no information is currently available on the spatio-temporal distribution of recreational activities across the lagoon waters. This study explores the feasibility of using Sentinel-2 satellite images to assess and map the spatio-temporal distribution of boats in the Venice Lagoon. Cloud-free Level-2A images have been selected to study seasonal (summer vs. winter) and weekly (weekends vs. weekdays) variabilities in 2023, 2024, and 2025. The RGB threshold filtering and the U-Net Semantic Segmentation were applied to the Sentinel-2 images to ensure reliable results. Two spatial indices were produced: (i) a Water Recreation Index (WRI), identifying standing boats in areas attractive for recreation; and (ii) a Water Transportation Index (WTI), mapping moving boats along the canals. Multi-temporal WRI maps allow areas with recurring recreational activities—that are significantly higher in the summer compared to winter, and on weekends compared to other weekdays—to be identified. The WTI identifies canal paths with higher traffic intensity with seasonal and weekly variations. The latter should be targeted by measures for traffic control to limit wave induced erosion, while the first could be subject to protection or development strategies.