Objective: The objective of the research is to estimate the cost of ecosystem service value (ESV) due to the Rohingya refugee influx in Ukhiya and Teknaf upazilas of Bangladesh.
Methods: Artificial neural network (ANN) supervised classification technique was used to estimate land use/land cover (LULC) dynamics between 2017 (ie, before the Rohingya refugee influx) and 2021. The ESV changes between 2017 and 2021 were assessed using the benefit transfer approach.
Results: According to the findings, the forest lost 54.88 km2 (9.58%) because of the refugee influx during the study. Around 47.26 km2 (8.25%) of settlement was increased due to the need to provide shelter for Rohingya refugees in camp areas. Due to the increase in Rohingya refugee settlements, the total ESV increased from US $310.13 million in 2017 to US $332.94 million in 2021. Because of the disappearance of forest areas, the ESV for raw materials and biodiversity fell by 13.58% and 14.57%, respectively.
Conclusion: Natural resource conservation for long-term development will benefit from the findings of this study.
Keywords: LULC; Rohingya refugees; artificial neural network; ecosystem; ecosystem service value.