The removal of low vegetation is still challenging in UAV photogrammetry. According to the different topographic features expressed by point-cloud data at different scales, a vegetation-filtering method based on multiscale elevation-variation coefficients is proposed for terrain modeling. First, virtual grids are constructed at different scales, and the average elevation values of the corresponding point clouds are obtained. Second, the amount of elevation change at any two scales in each virtual grid is calculated to obtain the difference in surface characteristics (degree of elevation change) at the corresponding two scales. Third, the elevation variation coefficient of the virtual grid that corresponds to the largest elevation variation degree is calculated, and threshold segmentation is performed based on the relation that the elevation variation coefficients of vegetated regions are much larger than those of terrain regions. Finally, the optimal calculation neighborhood radius of the elevation variation coefficients is analyzed, and the optimal segmentation threshold is discussed. The experimental results show that the multiscale coefficients of elevation variation method can accurately remove vegetation points and reserve ground points in low- and densely vegetated areas. The type I error, type II error, and total error in the study areas range from 1.93 to 9.20%, 5.83 to 5.84%, and 2.28 to 7.68%, respectively. The total error of the proposed method is 2.43–2.54% lower than that of the CSF, TIN, and PMF algorithms in the study areas. This study provides a foundation for the rapid establishment of high-precision DEMs based on UAV photogrammetry.

Vegetation serves as an indicator of ecological change and phenology is an important indicator for evaluating vegetation growth and development. Analyzing the spatiotemporal characteristics of vegetation phenology and its driving factors is of great significance for analyzing the carbon, water, and energy balance of terrestrial ecosystems. To better assess the impact of external environmental changes on vegetation phenology and comprehend the changing trends of phenology, this study employed phenological parameters as response variables and incorporated meteorological factors and urbanization factors as explanatory variables. A generalized additive model (GAM) was constructed meticulously to investigate how vegetation phenology responds to climate change and urbanization in Beijing, as well as to predict vegetation phenology. The results showed that there were non-linear relationships between the vegetation phenological parameters SOS (start of season) and EOS (end of season) and external environmental changes. The R² of the GAM predicted SOS and EOS with respect to the observed data increased to 0.622 and 0.756, respectively. The prediction effects in each region of the urban-rural gradient zone were better with RMSE of approximately 4-7 d. This study demonstrates that GAM considers the non-linear relationships between vegetation phenology and external environmental factors. The validation results based on observed data show that the models are reliable, and the findings can provide theoretical references for urban development planning and ecological environmental protection in Beijing.