Mangrove above-ground carbon estimation from Sentinel-1A (SAR) and field-based data in Tien Yen district, Quang Ninh province - Hai Hoa, Nguyen; Van Truong, Vu; Thi Thu Hien, Nguyen; Tri Son, Ha; Van Thi, Nguyen; Trong Cuong, Nguyen; Thi Bich Hao, Nguyen; Thi Huong, Tran; Duc Le, Phan; Van Dung, Phan; Thi Thuy An, Thai; Phu Tuan, Le (Journal of Forestry Science and Technology, 2024)
Mangrove forests have been globally recognized as they play a vital role in preventing coastal erosion, mitigating the effects of wave actions, and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. Sentinel-1 (SAR) offers a new opportunity for mangrove cover mapping and biomass estimation, especially in the tropics where mangrove deforestation and degradation are highest and cloud cover is persistent. This study used the Sentinel-1A-derived VV/VH polarizations for mangrove cover mapping with thresholds of -23.5<VH<-9.05 and -17.5<VV<-3.8. Upon using VV and VH polarizations for mangrove cover mapping compared to PlanetScope data, it has been confirmed that these polarizations are suitable for mangrove cover monitoring along the coast of Tien Yen with an overall accuracy of over 90.5% and Kappa coefficient greater than 0.78 in 2022. This study also developed mangrove AGB models based on the field survey data and SAR data for estimating the AGB of mangrove forests in Tien Yen. In fact, we evaluated the capability of using Sentinel-1A for the retrieval and predictive mapping and mangrove AGB through the conventional linear regression models. The findings show that the models based on VV and VH polarization values from Sentinel-1A can be used for mangrove AGB estimation. Overall, selected AGB Model 1 with R2=0.445 (p-value<0.001) has provided an option for carbon estimation. To have more accurate AGB models based on SAR data, this study also suggests that more research should be carried out using more advanced machine learning models based on Sentinel-1A and Sentinel-1B for carbon estimation of mangrove forests in Tien Yen.