Previous research studies have demonstrated that the relationship between remote sensing-derived parameters and aboveground biomass (AGB) could vary across different species types. However, there are few studies that calibrate reliable statistical models for mangrove AGB. This study quantifies the differences of accuracy in AGB estimation between the results obtained with and without the consideration of species types using Worldview-2 images and field surveys. A Back Propagation Artificial Neural Network (BP ANN) based model is developed for the accurate estimation of uneven-aged and dense mangrove forest biomass. The contributions of the input variables are further quantified using a “Weights” method based on BP ANN model. Two types of mangrove species, Sonneratia apetala (S. apetala) and Kandelia candel (K. candel), are examined in this study. Results show that the species type information is the most important variable for AGB estimation, and the red edge band and the associated vegetation indices from WorldView-2 images are more sensitive to mangrove AGB than other bands and vegetation indices. The RMSE of biomass estimation at the incorporation of species as a dummy variable is 19.17% lower than that of the mixed species level. The results demonstrate that species type information obtained from the WorldView-2 images can significantly improve of the accuracy of the biomass estimation.

To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

Background and Objectives: The rapid spread of invasive Spartina alterniflora Loisel. in the mangrove ecosystems of China was reduced using Sonneratia apetala Buch.-Ham. as an ecological replacement. Here, we studied the effects of invasion and ecological replacement using S. apetala on soil organic carbon fractions and stock on Qi’ao Island. Materials and Methods: Seven sites, including unvegetated mudflat and S. alterniflora, rehabilitated mangroves with different ages (one, six, and 10 years) and mature native Kandelia obovata Sheue, Liu, and Yong areas were selected in this study. Samples in the top 50 cm of soil were collected and then different fractions of organic carbon, including the total organic carbon (TOC), particulate organic carbon (POC), soil water dissolved carbon (DOC) and microbial biomass carbon (MBC), and the total carbon stock were measured and calculated. Results: The growth of S. alterniflora and mangroves significantly increased the soil TOC, POC, and MBC levels when compared to the mudflat. S. alterniflora had the highest soil DOC contents at 0–10 cm and 20–30 cm and the one-year restored mangroves had the highest MBC content. S. alterniflora and mangroves both had higher soil total carbon pools than the mudflat. Conclusions: The invasive S. alterniflora and young S. apetala forests had significantly lower soil TOC and POC contents and total organic carbon than the mature K. obovata on Qi’ao Island. These results indicate that ecological replacement methods can enhance long term carbon storage in Spartina-invaded ecosystems and native mangrove species are recommended.

Mapping and quantification of biomass changes is critical to understand mangrove carbon sequestration, conservation, and restoration. This study developed an effective model to estimate and map mangrove aboveground biomass dynamic changes 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 multi-year field samples. DSMs was 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 from the entire period from 2010 to 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 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 changes in disturbed and recovering mangrove areas.