For centuries, the rapid development of human society has already made human activity the dominant factor in the terrestrial ecosystem. As the city of greatest importance in China, the capital Beijing has experienced eco-environmental changes with unprecedented economic and population growth during the past few decades. To better understand the ecological transition and its correlations in Beijing, Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images were used to investigate vegetation coverage changes using a dimidiate pixel model. Piecewise linear regression, bivariate-partial correlation analysis, and factor analysis were applied to the probing of the relationship between vegetation coverage changes and climatic/human-induced factors. The results showed that from 2000 to 2005, 2005 to 2010, and 2010 to 2015, Beijing experienced both restoration (6.33%, 10.08%, and 12.81%, respectively) and degradation (13.62%, 9.35%, and 9.49%, respectively). The correlation analysis results between climate and vegetation changes demonstrated that from 2000 to 2015, both the multi-year annual mean temperature (r = −0.819, p < 0.01) and the multi-year annual mean precipitation (r = 0.653, p < 0.05) had a significantly correlated relationship with vegetation change. The Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) has shown beneficial spatial effects on vegetation restoration; the total effectiveness in conservation areas (84.94 in 2000–2010) was much better than non-BTSSCP areas (34.34 in 2000–2010). The most contributory socioeconomic factors were the population (contribution = 54.356%) and gross domestic product (GDP) (contribution = 30.677%). The population showed a significantly negative correlation with the overall vegetation coverage (r = −0.684, p < 0.05). The GDP was significantly negatively correlated with vegetation in Tongzhou, Daxing, Central city, Fangshan, Shunyi, and Changping (r = −0.601, p < 0.01), while positively related in Huairou, Miyun, Pinggu, Mentougou and Yanqing (r = 0.614, p < 0.01). These findings confirm that human activity is a very significant factor in impacting and explaining vegetation changes, and that some socioeconomic influences on vegetation coverage are highly spatially heterogeneous, based on the context of different areas.

Forest biodiversity is essential in maintaining ecosystem functions and services. Recently, unmanned aerial vehicle (UAV) remote sensing technology has emerged as a cost-effective and flexible tool for biodiversity monitoring. In this study, we compared the optimal clustering algorithm, classification method (spectral angle mapper, SAM), spectral diversity metric and structural heterogeneity index for forest species diversity estimation in two complex subtropical forests, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves in China, using UAV-borne hyperspectral and LiDAR data. The results showed that the SAM classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (MZL: 0.62 > 0.46 and GGS: 0.55 > 0.46) and Shannon-Wiener index (MZL: 0.64 > 0.58 and GGS: 0.52 > 0.47), while the optimal clustering algorithm had the highest prediction accuracy for the Simpson index, followed by the SAM classification method, spectral diversity metric and structural heterogeneity index (MZL: 0.83>0.44>0.31>0.12, GGS: 0.62>0.44>0.38>0.00). Our study indicated that the SAM classification method had the advantage of identifying rare species and estimating species richness, while the clustering method could capture forest diversity patterns rapidly without distinguishing the specific tree species and predict the Simpson index more accurately. Overall, both clustering and classification methods exhibited superior performance compared to spectral or structural diversity indices. Our findings highlight the applicability of UAV remote sensing in monitoring forest species diversity in complex subtropical forests.

Trait-based approaches, focusing on the functional characteristics of vascular plants in a community, have been increasingly used in plant ecology and biodiversity research. Compared with traditional field survey (which typically samples individual trees), remote sensing enables quantifying functional traits over large contiguous areas, but assigning trait values to biological units such as species and individuals is difficult with pixel-based approaches. We used a subtropical forest landscape in China to compare an approach based on LiDAR-delineated individual tree crowns (ITCs) with a pixel-based approach for assessing functional traits from remote sensing data. We compared trait distributions, trait–trait relationships and functional diversity metrics obtained by the two approaches at changing grain and extent. We found that morphological traits derived from airborne laser scanning showed more differences between ITC- and pixel-based approaches than physiological traits estimated by imaging spectroscopy data. Pixel sizes approximating average tree crowns yielded similar results as ITCs, but 95th quantile height and foliage height diversity tended to be overestimated and leaf area index underestimated relative to ITC-based values. With increasing pixel size, the differences to ITC- based trait values became larger and less trait variance was captured, indicating information loss. The consistency of ITC- and pixel-based functional richness measures also decreased with increasing pixel grain, and changed with the observed extent for functional diversity monitoring. We conclude that whereas ITC-based approaches in principle allow partitioning of variation between individuals, genotypes and species, at high resolution, pixel-based approaches come close to this and can be suitable for assessing ecosystem-scale trait variation by weighting individuals and species according to coverage.