Estimating and Mapping Mangrove Biomass Dynamic Change Using WorldView-2 Images and Digital Surface Models

Mapping and quantification of biomass changes is critical to understanding mangrove carbon sequestration, conservation, and restoration. Few previous studies have focused on mangrove biomass changes based on high spatial resolution images, particularly for disturbed and recovering areas. This study developed an effective model to estimate and map mangrove aboveground biomass dynamic change between 2010 and 2016 on Qi'ao Island in South China. The study area includes native Kandelia candel (K. candel) and planted Sonneratia apetala (S. apetala) mangrove species within the largest planted area in China. Models were developed using WorldView-2 images, digital surface models (DSMs), and the random forest algorithm. Accuracies of the model were assessed using multiyear field samples. DSMs were identified as the most important variable for model accuracy, reducing relative error by up to 3.14%. Three models were developed: a model for 2010, another model for 2016, and a combined model for 2010 and 2016. Compared with the 2010 (RMSE = 41.03 t/ha, RMSEr = 24.31%) and 2016 (RMSE = 39.92 t/ha, RMSEr = 23.40%) models, the combined model (RMSE = 50.99 t/ha, RMSEr = 30.48%) only increased the relative error by 6.17% and 7.08%, respectively. Mangrove biomass maps generated from the most accurate models showed total biomass increased from 23270.43 to 39819.03 tons by up to 71.11% over the study period. K. candel total biomass decreased by 36.5% due to Derris trifoliata challenge. S. apetala total biomass increased by 74.79% due to reforestation programs, achieving aboveground biomass accumulation of 4.17 t/ha for stands that existed in 2010. This study provides insights into biomass dynamic change in disturbed and recovering mangrove areas. Future studies should consider using LiDAR techniques to obtain actual tree height applied for biomass estimation instead of DSM.

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