The refugee influx from Myanmar, known as Rohingya refugees, is a serious concern for global refugee issues. Bangladesh currently hosts one million Rohingya refugees in the coastal district of Cox’s Bazar. Considering the number of the refugees, in addition to the humanitarian concerns, they are also creating pressure on the local host communities. This study explored the socioeconomic changes of the host communities after the refugee influx. In order to fulfill this study’s objectives, 35 villages near the Rohingya refugee camps from the coastal district of Bangladesh were surveyed. In the villages, 10% of households were surveyed in 2016 and also in 2020, covering 1924 and 2265 households, respectively. A temporal comparison of the host community’s socioeconomic status between 2016 and 2020 was conducted in order to determine the changes after the recent refugee influx. This study found that the local community’s socioeconomic status degraded. The annual income decreased by 24%, which is unusual for a country with over 6% gross domestic product (GDP) growth in recent times. The income decreased from all livelihood options except farming, which could be related to the availability of cheap labor and the high demand for commodities. The villages were clustered using k-means, and 20 villages were found to be affected after the refugee influx with degraded socioeconomic status. The host community’s general perception was initially positive, but later turned negative toward the refugees. This study will be important for the government and donor agencies to develop strategies to properly manage the refugee camps and adjacent host communities.

The Rohingya refugee influx to Bangladesh in 2017 was a historical incident; the number of refugees was so massive that significant impacts to local communities was inevitable. The Bangladesh government provided land in a preserved area for constructing makeshift camps for the refugees. Previous studies have revealed the land cover changes and impacts of the refugee influx around campsites, especially with regard to local forest resources. Our aim is to establish a convenient approach of providing up-to-date information to monitor holistic local situations. We employed a classic unsupervised technique—a combination of k-means clustering and maximum likelihood estimation—with the latest rich time-series satellite images of Sentinal-1 and Sentinal-2. A combination of VV and normalized difference water index (NDWI) images was successful in identifying built-up/disturbed areas, and a combination of VH and NDWI images was successful in differentiating wetland/saltpan, agriculture /open field, degraded forest/bush, and forest areas. By doing this, we provided annual land cover classification maps for the entire Teknaf peninsula for the pre- and post-influx periods with both fair quality and without prior training data. Our analyses revealed that on-going impacts were still observed by May 2021. As a simple estimation of the intervention consequence, the built-up/disturbed areas increased 6825 ha (compared with the 2015–17 period). However, while the impacts on the original forest were not found to be significant, the degraded forest/bush areas were largely degraded by 4606 ha. These cultivated lands would be used for agricultural activities. This is in line with the reported farmers’ increased income, despite local people with other occupations that are all equally facing the decreases in income. The convenience of our unsupervised classification approach would help keep accumulating a time-series land cover classification, which is important in monitoring impacts on local communities.

In Japan, rural areas are grappling with population decline and aging, leading to a shortage of labor for farmland maintenance. This has resulted in the abandonment of farmland or its conversion for solar photovoltaic (PV) use. However, this unplanned conversion raises concerns about agricultural productivity decline, landscape degradation, biodiversity loss, water resource maintenance, and disaster prevention. This study focuses on the Kushida watershed, examining (1) accurate farmland classification using remote sensing data, (2) the geographical distribution of farmland converted to PV systems from 2016 to 2021 and concentrated along the river, especially on north-facing slopes, (3) the highest conversion rates in wheat fields, followed by legume fields, tea fields, and paddy fields, and (4) no clear correlation between farmland conversions and changes in the number of farmers, but associations with farmland geography and solar radiation levels. These findings contribute to a nuanced understanding of sustainable rural development in Japan, emphasizing the importance of considering geographical factors in the conversion of farmland to PV.