Overdependence on and exploitation of forest resources have significantly transformed the natural reserve forest of Sundarban, which shares the largest mangrove territory in the world, into a great degradation status. By observing these, a most pressing concern is how much degradation occurred in the past, and what will be the scenarios in the future if they continue? To confirm the degradation status in the past decades and reveal the future trend, we took Sundarban Reserve Forest (SRF) as an example, and used satellite Earth observation historical Landsat imagery between 1989 and 2019 as existing data and primary data. Moreover, a geographic information system model was considered to estimate land cover (LC) change and spatial health quality of the SRF from 1989 to 2029 based on the large and small tree categories. The maximum likelihood classifier (MLC) technique was employed to classify the historical images with five different LC types, which were further considered for future projection (2029) including trends based on 2019 simulation results from 1989 and 2019 LC maps using the Markov-cellular automata model. The overall accuracy achieved was 82.30%~90.49% with a kappa value of 0.75~0.87. The historical result showed forest degradation in the past (1989–2019) of 4773.02 ha yr⁻¹, considered as great forest degradation (GFD) and showed a declining status when moving with the projection (2019–2029) of 1508.53 ha yr⁻¹ and overall there was a decline of 3956.90 ha yr⁻¹ in the 1989–2029 time period. Moreover, the study also observed that dense forest was gradually degraded (good to bad) but, conversely, light forest was enhanced, which will continue in the future even to 2029 if no effective management is carried out. Therefore, by observing the GFD, through spatial forest health quality and forest degradation mapping and assessment, the study suggests a few policies that require the immediate attention of forest policy-makers to implement them immediately and ensure sustainable development in the SRF.

Overdependence and cumulative anthropogenic stresses have caused world forests to decrease at an unprecedented rate, especially in Southeast Asia. The Cox’s Bazar–Teknaf Peninsula of Bangladesh is not an exception and follows the global deforestation trend. Despite being one of the country’s richest forest ecosystems with multiple wildlife sanctuaries, reserve forests, and influential wildlife habitats, the peninsula is now providing shelter for nearly one million Rohingya refugees. With the global deforestation trend coupled with excessive anthropogenic stresses from the Rohingya population, the forests in the peninsula are continuously deteriorating in terms of quality and integrity. In response to deforestation, the government invested in conservation efforts through afforestation and restoration programs, although the peninsula faced a refugee crisis in August 2017. The impact of this sudden increase in population on the forest ecosystem is large and has raised questions and contradictions between the government’s conservation efforts and the humanitarian response. Relocation of the refugees seems to be a lengthy process and the forest ecosystem integrity needs to be preserved; therefore, the degree of stresses, level of impacts, and pattern of deforestation are crucial information for forest conservation and protection strategies. However, there are a lack of quantitative analyses on how the forest ecosystem is deteriorating and what future results would be in both space and time. In this study, the impact of the sudden humanitarian crisis (i.e., Rohingya refugees) as anthropogenic stress in Cox’s Bazar–Teknaf peninsula has been spatiotemporally modeled and assessed using Sentinel-2 satellite imagery and other collateral data. Using the density and accessibility of the Rohingya population along with the land cover and other physiographic data, a multi-criteria evaluation (MCE) technique was applied through the Markov cellular automata technique to model the forest vegetation status. The impact of deforestation differs in cost due to variability of the forest vegetation covers. The study, therefore, developed and adopted three indices for assessment of the forest ecosystem based on the variability and weight of the forest cover loss. The spatial severity of impact (SSI) index revealed that out of 5415 ha of total degraded forest lands, 650 ha area would have the highest cost from 2017 to 2027. In the case of the ecosystem integrity (EI) index, a rapid decline in ecosystem integrity in the peninsula was observed as the integrity value fell to 1190 ha (2019) from 1340 ha (2017). The integrity is expected to further decline to 740 ha by 2027, if the stress persists in a similar fashion. Finally, the findings of ecosystem integrity depletion (EID) elucidated areas of 540 and 544 hectares that had a severe EID score of (−5) between 2017 and 2019 and 2017 and 2027, respectively. The displacement and refugee crisis is a recurrent world event that, in many cases, compromises the integrity and quality of natural space. Therefore, the findings of this study are expected to have significant global and regional implications to help managers and policymakers of forest ecosystems make decisions that have minimal or no impact to facilitate humanitarian response.

The economic development of the coastal areas has promoted the construction of the port and led to the expansion of the port city. This study selects 35 port cities along the Maritime Silk Road to develop a Port-City Relationship Index (PCRI) from the analysis of port city development stage and spatial structural change. Three types of port city spatial relationship models are proposed: centralized port city, marginal port city and separated port city. We compared the distribution and variation of PCRI values over four time periods for the 35 port cities. The results show that the ports in West Asia, Africa and Europe are basically mature, and the changes of PCRI values are relatively small. The ports in Southeast Asia and South Asia are still in development stage, and the value of PCRI varies greatly. As the city develops, the dependence of city on the port will gradually decrease. The spatial relationship between the port and the city will generally evolve towards the marginal of the port formation (PCRI=0.6) and eventually remain convergent and stable. In the end, the effect of most ports on urban spatial structure will gradually stabilize.