Assessing the influence of landscape pattern changes on ecosystem service value (ESV) is critical for developing land-use polies and increasing ecosystem services. The data sources include remote-sensing image data and statistical yearbooks from 2000, 2010, and 2020. This study employs the patch-generating land-use simulation model, landscape pattern index, and ecological service value estimation to analyse the changes in landscape patterns and ESV in Fujian coastal areas over the last 20 years. The landscape pattern and ESV in the future (2050) are then simulated under the low-carbon scenario (LCS), with the natural development scenario (NDS) serving as a comparison. The results show that: (1) the most noticeable changes from 2000 to 2020 are the reduced cultivated land area and the rapid expansion of construction land area. By 2050, construction land will account for 7.67% of the total land area under LCS, whereas NDS will account for 9.45%, and changes in the landscape pattern indices all indicate there will be greater variety and fragmentation of the landscape, with the NDS being more serious than the LCS; (2) From 2000–2020, the total ESV value showed a decreasing trend. In 2050, the ESV under the LCS will be 122.387 billion yuan, which is higher than the 121.434 billion yuan under the NDS. Regulating services contribute the most to the total ESV, followed by support services; and (3) In the past 20 years, except for a slight increase in water area, the ESV of other landscapes has decreased, with a net decrease of 3.134 billion yuan in total. The R² fitting between the area change of cultivated and construction land and the total ESV reached 0.9898 and 0.9843, respectively. The correlations between ESV and landscape indices indicate that landscape pattern changes significantly impact ESV. Simulating ESV in LCS can provide guidance for optimising landscape patterns, promoting the benign operation of the regional ecosystem, and achieving sustainable ecological development.

Forest biomass dynamics are important indicators of forest productivity and carbon sinks, which are useful for evaluating forest ecological benefits and management options. Rapid and accurate methods for monitoring forest biomass would serve this purpose well. This study aimed at measuring aboveground biomass (AGB) and stand growth from tree crown parameters derived using unmanned aerial vehicle–light detection and ranging (UAV–LiDAR). We focused on 17-year-old Chinese fir plantations in a subtropical area in China and monitored them using UAV–LiDAR from February 2019 to February 2020. Two effective crown height (ECH) detection methods based on drone discrete point clouds were evaluated using ground survey data. Based on the evaluation results, the voxel method based on point cloud segmentation (root-mean-squared error (RMSE) = 0.62 m, relative RMSE (rRMSE) = 4.26%) was better than the tree crown boundary pixel sum method based on canopy height segmentation (RMSE = 1.26 m, rRMSE = 8.63%). The effective crown area (ECA) of an individual tree extracted using ECH was strongly correlated with the annual biomass growth (coefficient of determination (R²) = 0.47). The estimation of annual growth of individual tree crowns based on annual tree height increase (ΔH) derived from LiDAR was statistically significant (R² = 0.33, p < 0.01). After adding the crown projection area or ECA, the model accuracy R² increased to 0.57 or 0.63, respectively. As the scale increased to the plot level, the direct model with ECA (RMSE = 1.59 Mg∙ha⁻¹∙a⁻¹, rRMSE = 15.02%) had a better performance than the indirect model using tree height and crown diameter (RMSE = 1.81 Mg∙ha⁻¹∙a⁻¹, rRMSE = 17.10%). The mean annual growth rate of AGB per middle-aged Chinese fir tree was determined to be 8.45 kg∙a⁻¹ using ECA and ΔH, and the plot-level growth rate was 11.47 Mg∙ha⁻¹∙a⁻¹. We conclude that the rapid and accurate monitoring of the annual growth of Chinese fir can be achieved based on multitemporal UAV–LiDAR and effective crown information.