A reliable assessment of forest carbon sequestration depends on our understanding of wood ecophysiology. Within a forest, trees exhibit different timings and rates of growth during wood formation. However, their relationships with wood anatomical traits remain partially unresolved. This study evaluated the intra-annual individual variability in growth traits in balsam fir [Abies balsamea (L.) Mill.]. We collected wood microcores weekly from April to October 2018 from 27 individuals in Quebec (Canada) and prepared anatomical sections to assess wood formation dynamics and their relationships with the anatomical traits of the wood cells. Xylem developed in a time window ranging from 44 to 118 days, producing between 8 and 79 cells. Trees with larger cell production experienced a longer growing season, with an earlier onset and later ending of wood formation. On average, each additional xylem cell lengthened the growing season by 1 day. Earlywood production explained 95% of the variability in xylem production. More productive individuals generated a higher proportion of earlywood and cells with larger sizes. Trees with a longer growing season produced more cells but not more biomass in the wood. Lengthening the growing season driven by climate change may not lead to enhanced carbon sequestration from wood production.

Because of the physical character of soil from granite weathering and the typhoon rains in South China, the model for early warnings of existing shallow landslides cannot be well applied in that area. This study developed a new shallow landslide model based on the effect of soil macropores for determining the rainfall threshold to post an early warning of the possibility of a shallow landslide in South China. We studied the hydrological effects of macropore flow and proposed a mechanistic model of the formation of shallow landslides by introducing the macropore coefficient of granite residual soil. The rainfall threshold of each slope unit was calculated by combining the hydrological model with the proposed shallow landslide model. Lastly, we tested the calculated result in the Maguihe watershed, Guangdong province, South China, which experienced a group of massive shallow landslides on 21 September 2010. The study results showed that the macropores of vegetation roots had a significant effect by increasing the permeability of granite residual soil. Coniferous forest land has the highest initial infiltration rate and stable infiltration rate, followed by shrub forest land, and then bare land. Statistical verification showed that the accurate prediction rate of the proposed model was 80.65%, which is adequate for early warning of shallow landslides in South China. We discuss the application conditions and parameter calibrations of the proposed model, and offer recommendations for future research.

Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation.

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.

Landslides are frequent in granite areas during rainfall. The hydrological response and deformation damage mechanisms of granite-weathered crust slopes were investigated by physical model tests. The variability of the material with respect to the thickness of the residual layer was taken into account. The results show that the three types of slopes exhibit distinct disaster mechanisms. For the E1 slope (10 cm residual layer), the residual layer was rapidly saturated, and seepage was formed. The residual soil was susceptible to severe suffosion, and an overall flow-slip failure occurred with no obvious sliding surface. For the E2 slope (20 cm residual layer), rainfall infiltrated the soil–rock interface, forming a temporary water table at this location. The slope tended to slide along the soil–rock interface at the foot of the slope under the traction and drag of water flow. For the E3 slope (30 cm residual layer), rainfall failed to infiltrate the interface, and no seepage was observed within the slope. The slope gradually slides within the residual layer under hydrostatic pressure and self-weight, with a circular arc sliding surface. This study fills the gap in granite-weathered crust landslides with different residual layer thicknesses and has certain theoretical significance.