Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha⁻¹. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R² of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R² of 0.62 and an RMSE of 50.36 Mg ha⁻¹) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.

Accessibility is a major method for evaluating the distribution of service facilities and identifying areas in shortage of service. Traditional accessibility methods, however, are largely model-based and do not consider the actual utilization of services, which may lead to results that are different from those obtained when people’s actual behaviors are taken into account. Based on taxi GPS trajectory data, this paper proposed a novel integrated catchment area (ICA) that integrates actual human travel behavior to evaluate the accessibility to healthcare facilities in Shenzhen, China, using the enhanced two-step floating catchment area (E2SFCA) method. This method is called the E2SFCA-ICA method. First, access probability is proposed to depict the probability of visiting a healthcare facility. Then, integrated access probability (IAP), which integrates model-based access probability (MAP) and data-based access probability (DAP), is presented. Under the constraint of IAP, ICA is generated and divided into distinct subzones. Finally, the ICA and subzones are incorporated into the E2SFCA method to evaluate the accessibility of the top-tier hospitals in Shenzhen, China. The results show that the ICA not only reduces the differences between model-based catchment areas and data-based catchment areas, but also distinguishes the core catchment area, stable catchment area, uncertain catchment area and remote catchment area of healthcare facilities. The study also found that the accessibility of Shenzhen’s top-tier hospitals obtained with traditional catchment areas tends to be overestimated and more unequally distributed in space when compared to the accessibility obtained with integrated catchment areas.