Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R² decreased as the moving window size increased: the highest R² values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.

Canopy cover is an important vegetation attribute used for many environmental applications such as defining management objectives, thinning and ecological modeling. However, the estimation of canopy cover from high spatial resolution imagery is still a difficult task due to limited spectral information and the heterogeneous pixel values of the same canopy. In this paper, we compared the capacity of two high spatial resolution sensors (SPOT6 and GF2) using three ensemble learning models (Adaptive Boosting (AdaBoost), Gradient Boosting (GDBoost), and random forest (RF)), to estimate canopy cover (CC) in a Chinese northern subtropics forest. Canopy cover across 97 plots was measured across 41 needle forest plots, 24 broadleaf forest plots, and 32 mixed forest plots. Results showed that (1) the textural features performed more importantly than spectral variables according to the number of variables in the top ten predictors in estimating canopy cover (CC) in both SPOT6 and GF2. Moreover, the vegetation indices in spectral variables had a lower relative importance value than the band reflectance variables. (2) GF2 imagery outperformed SPOT6 imagery in estimating CC when using the ensemble learning model in our data. On average across the models, the R² was almost 0.08 higher for GF2 over SPOT6. Likewise, the average RMSE and average MAE were 0.002 and 0.01 lower in GF2 than in SPOT6. (3) The ensemble learning model showed good results in estimating CC, yet the different models performed a little differently in the results. Additionally, the GDBoost model performed the best of all the ensemble learning models with R² = 0.92, root mean square error (RMSE) = 0.001 and mean absolute error (MAE) = 0.022.

Green spaces in subtropical cities are important for providing ecological services that support human well-being and serve as reservoirs for diverse microbial communities, which in turn support ecosystem functions. However, studies on the characteristics of the phyllosphere microbial community and public health risks associated with putative pathogens in various urban green spaces exposed to anthropogenic stress remain limited. To address this gap, we collected leaf samples from forests, greenbelts, parklands, and wetlands across Wuhan, China, and analyzed the bacterial and fungal communities via next-generation sequencing (NGS) techniques. For bacterial and fungal communities, alpha diversity was significantly greater in low-traffic zones than in high-traffic zones. Beta diversity analysis revealed distinct clustering of bacterial and fungal communities according to the urban green space type. Anthropogenic factors (foot traffic) influence green space type to shape microbial community structure, function, and stability, with shifts significantly associated with soil physicochemical properties via Mantel tests and redundancy analysis. The relative abundance of Enterobacter and Enterococcus was significantly greater in high-intensity parklands (HIPS) and high-intensity greenbelts (HIGS) (41.84, 38.32%), respectively. Our findings provide important information for the sustainable management of urban green spaces by regulating microbial communities, offering new insights into ecosystem health and human well-being.