Assessing effectiveness of nature-based solution with big earth data: 60 years mangrove plantation program in Bangladesh coast

Abstract

Abstract Background In the climate change context, nature-based solution (NBS) is considered one of the effective tools to increase the resilience of socio-ecological system. The concept coincides with the government’s attempts of afforestation and reforestation programs that have been going on for 60 years in Bangladesh. This study, therefore, envisaged understanding how NBS (mangrove afforestation and reforestation) works to promote climate change resilience through the synthetization of remote sensing-based big earth data, statistical tools, and models. The study took the entire coast of Bangladesh except for Sundarbans Reserve Forest and rolled back to 1962 to work on 60 years’ time series data. Declassified CORONA satellite imagery along with Landsat satellite imagery was used, which is the first-ever attempt in the remote sensing-based ecosystem work in Bangladesh. Results The study's main innovation is to spatially establish the effectiveness of the NBS. The study critically assessed and estimated stable lands and their socio-economic benefits as part of the effectiveness of the NBS. As part of the NBS-derived benefits in the context of climate change, it estimated the sequestrated carbon in mangrove forests. A significant positive relationship was observed between the increase of mangroves and stable lands. Near about 448,011 ha of agricultural land was stabilized due to the NBS intervention whose economic value is 18,837 million USD. In addition, 29,755.71 kt of carbon have been sequestrated due to NBS program. Conclusions The concept of NBS is still in the development stage and very little or no work has been done so far in measuring and labeling the effectiveness of the NBS. Therefore, our study can innovatively contribute to the scientific community to show the effectiveness of the NBS in three domains (social, economic and ecological) in the changing climatic scenario.