Developing an accurate model for estimating the forest structural parameters of planted forests is crucial for forest productivity predictions and can provide a better understanding of the carbon cycle under climate change. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represents a promising active remote sensing technology that has the potential to be used for forest inventories. In addition, the process-based model, physiological principles predicting growth (3-PG), which is based on physiological principles and environmental factors, has been applied to estimate the growth of even-aged, mono-specific forests under the effect of different management levels, site conditions, and climate change. In this study, the performance of UAV-LiDAR metrics was assessed and applied to estimate forest structural parameters using a multivariate linear regression (MLR) method. The 3-PG was parameterized and used to simulate the diameter at breast height, stem density, volume and above-ground biomass of a planted ginkgo forest in eastern China. In addition, a sensitivity analysis was conducted on the 3-PG model's input parameters. The results demonstrated that both the MLR based on UAV-LiDAR data and a progress model of the 3-PG have a promising potential for estimating forest structural parameters (R2 > 0.70, relative root squared error >20%). A sensitivity analysis of the 3-PG parameters also confirmed that the parameter “age at canopy cover” (fullCanAge) is vital for the 3-PG model, and positively correlation with the simulated results. The method presented here represents an improvement on traditional methods for estimating forest structural parameters because it more explicitly accounts for climatic effects included in the 3-PG model.

With the acceleration of urbanization and the increased frequency of extreme rainfall events, flooding has emerged as one of the most serious natural disaster problems, particularly affecting riparian cities. This study conducted a flooding risk assessment and an analysis of the driving factors behind flood disasters in the Songhua River Basin utilizing an improved Soil Conservation Service Curve Number (SCS-CN) model. First, the model was improved by slope adjustments and effective precipitation coefficient correction, with its performance evaluated using the Nash–Sutcliffe efficiency coefficient (NSE) and the Root Mean Square Error (RMSE). Second, flood risk mapping was performed based on the improved model, and the distribution characteristics of the flooding risk were analyzed. Additionally, the Geographical Detector (GD), a spatial statistical method for detecting factor interactions, was employed to explore the influence of natural, economic, and social factors on flooding risk using factor detection and interaction detection methods. The results demonstrated that the improvements to the SCS-CN model encompassed two key aspects: (1) the optimization of the CN value through slope correction, resulting in an optimized CN value of 50.13, and (2) the introduction of a new parameter, the effective precipitation coefficient, calculated based on rainfall intensity and the static infiltration rate, with a value of 0.67. Compared to the original model (NSE = 0.71, rRMSE = 19.96), the improved model exhibited a higher prediction accuracy (NSE = 0.82, rRMSE = 15.88). The flood risk was categorized into five levels based on submersion depth: waterlogged areas, low-risk areas, medium-risk areas, high-risk areas, and extreme-risk areas. In terms of land use, the proportions of high-risk and extreme-risk areas were ranked as follows: water > wetland > cropland > grassland > shrub > forests, with man-made surfaces exacerbating flood risks. Yilan (39.41%) and Fangzheng (31.12%) faced higher flood risks, whereas the A-cheng district (6.4%) and Shuangcheng city (9.4%) had lower flood risks. Factor detection results from the GD revealed that river networks (0.404) were the most significant driver of flooding, followed by the Digital Elevation Model (DEM) (0.35) and the Normalized Difference Vegetation Index (NDVI) (0.327). The explanatory power of natural factors was found to be greater than that of economic and social factors. Interaction detection indicated that interactions between factors had a more significant impact on flooding than individual factors alone, with the highest explanatory power for flood risk observed in the interaction between annual precipitation and DEM (q = 0.762). These findings provide critical insights for understanding the spatial drivers of flood disasters and offer valuable references for disaster prevention and mitigation strategies.