Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.

Knowledge of how different drivers affect tree responses to drought is unprecedentedly imperative in the context of increasing frequency and severity of climatic droughts. Here, to fully understand the drought response complexity of trees, we assessed drought resilience (resistance and recovery) for Chinese fir (Cunninghamia lanceolata) in Southeast China based on tree ring from 324 trees, and used mixed effects model and machine learning (ML) to examine the roles of tree size, predrought growth performances, multiple drought dimensions, and microtopography in affecting tree drought responses. ML were interpreted using a novel of SHapley Additive exPlanations (SHAP) method. Tree responses to drought were primarily driven by tree characteristics (tree size and predrought growth), rather than drought dimensions (intensity, duration and occurrence Timing) and microtopography (elevation and slope aspect). Resistance and resilience increased with tree size and pre-drought growth variability but decreased with drought intensity- quantified by negative climate water balance. Recovery increased with predrought growth rates but decreased with drought duration. The drought intensity threshold for trees fully recovery of tree growth was about -80 mm. Higher elevations and shady slopes favored resistance (resilience) and recovery respectively, which combined with a greater impact of drought in the dry season suggested that the trees suffered more from droughts that only occurred in the dry season, especially at low- and medium-elevation sunny slopes. This study provided a comprehensive insight into tree growth response to drought, and contributed to the understanding of the mechanisms underlying the complexity of drought response. Increasing size diversity in Chinese fir plantations at sunny lower-elevation slopes is a promising measure to cope with the negative effects of drought.

Legacy effects following drought are widely detected across worldwide forests, significantly affecting the growth recovery and susceptibility of trees after droughts. Thinning is a common forest management practice used to alter tree growth and climate-growth relationships. Although the effects of thinning on tree response during drought have been investigated, how thinning modulates post-drought legacy effects remains largely unknown. In this study, based on tree-ring data of 140 trees, we examined the effects of thinning on post-drought legacy effects using the quantile mixed effect model for Chinese fir (Cunninghamia lanceolata) in Southeastern China. The tree-ring data were stratified sampling from a thinning experiment applied 10 years ago in 8-year-old plantations, and included four thinning intensities (20 %, 25 %, 33 %, and 50 % reduction of tree number) and an unthinned control treatment. Drought legacy effects of tree growth positively depended on tree social status with the magnitudes and variations larger in higher status classes. Dominant large trees without thinning management had the greatest drought legacy effects. Although the effects of thinning varied slightly at different quantiles, they all indicated that thinning mitigated the growth legacies after drought and the reduction effect was more pronounced with increasing thinning intensity. Thinning could also reduce post-drought climate sensitivities, but only after moderate thinning (20 % and 25 % thinning intensity). Heavier thinning (33 % and 50 % thinning intensity) instead enhanced tree growth responses to climate changes following drought. Thinning intensity needs to be carefully considered to really reap the post-drought benefits of forest thinning management. Our findings suggested that mild thinning offered an alleviation of climate dependency following drought in addition to reducing drought legacy effects on growth, benefiting tree recovery from drought. The results of this study are useful to inform management adaptive strategies for drought-vulnerable plantations under increasingly frequent droughts.

As one of the important timber species in China, Cunninghamia lanceolata is widely distributed in southern China. The information of tree individuals and crown plays an important role in accurately monitoring forest resources. Therefore, it is particularly significant to accurately grasp such information of individual C. lanceolata tree. For high-canopy closed forest stands, the key to correctly extract such information is whether the crowns of mutual occlusion and adhesion can be accurately segmented. Taking the Fujian Jiangle State-owned Forest Farm as the research area and using the UAV image as the data source, we developed a method to extract crown information of individual tree based on deep learning method and watershed algorithm. Firstly, the deep learning neural network model U-Net was used to segment the coverage area of the canopy of C. lanceolata, and then the traditional image segmentation algorithm was used to segment the individual tree to obtain the number and crown information of individual tree. Under the condition of maintaining the same training set, validation set and test set, the extraction results of the canopy coverage area by the U-Net model and traditional machine learning methods [random forest (RF) and support vector machine (SVM)] were compared. Then, two individual tree segmentation results were compared, one using the marker-controlled watershed algorithm, and the other using the combination of the U-Net model and marker-controlled watershed algorithm. The results showed that the segmentation accuracy (SA), precision, IoU (intersection over union) and F1-score (harmonic mean of precision and recall) of the U-Net model were higher than those of RF and SVM. Compared with RF, the value of those four indicators increased by 4.6%, 14.9%, 7.6% and 0.05, respectively. Compared with SVM, the four indicators increased by 3.3%, 8.5%, 8.1% and 0.05, respectively. In terms of extracting the number of trees, the overall accuracy (OA) of the U-Net model combined with the marker-controlled watershed algorithm was 3.7% higher than that of the marker-controlled watershed algorithm, with the mean absolute error (MAE) being decreased by 3.1%. In terms of extracting crown area and crown width of individual tree, R2 increased by 0.11 and 0.09, mean squared error decreased by 8.49 m2 and 4.27 m, and MAE decreased by 2.93 m2 and 1.72 m, respectively. The combination of deep learning U-Net model and watershed algorithm could overcome the challenges in accurately extracting the number of trees and the crown information of individual tree of high-density pure C. lanceolata plantations. It was an efficient and low-cost method of extracting tree crown parameters, which could provide a basis for developing intelligent forest resource monitoring.

Digital aerial photograph (DAP) data is processed based on Structure from Motion (SfM) algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging (LiDAR) and digital orthophoto mosaic (DOM) similar to optical remote sensing image. In this study, we obtained high-resolution images of mature forests of Chinese fir by unmanned aerial vehicle (UAV) flying through cross-route flight, and then reconstructed the three-dimensional point clouds in the UAV aerial area by SfM technique. The point cloud segmentation (PCS) algorithm was used for the individual tree segmentation, and the F-score of the three sample plots were 0.91, 0.94, and 0.94, respectively. Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented. The relative root mean squared error (rRMSE) values of random forest (RF), bagged tree (BT) and support vector regression (SVR) were 34.48%, 35.74% and 40.93%, respectively. Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

The continued loss of unfragmented intact forest landscapes (IFLs) despite numerous global conservation initiatives indicates the need for improved knowledge of proximate and underlying drivers. Yet the role of non-agricultural activities in forest degradation and fragmentation has not received adequate attention. We focus on IFL loss caused by various economic activities and investigate the influence of global consumption and trade via the multi-regional input-output model. For IFL loss associated with the 2014 world economy, over 60% was related to final consumption of non-agricultural products. More than one-third of IFL loss was linked to export, primarily from Russia, Canada, and tropical regions to mainland China, the EU, and the United States. Of IFL loss associated with export, 51% and 26% was directly caused by logging and mining or energy extraction, respectively. The dispersed nature of IFL loss drivers and their indirect links to individual final consumers call for stronger government engagement and supply chain interventions.

An emerging poplar canker caused by the gram-negative bacterium, Lonsdalea populi, has led to high mortality of hybrid poplars Populus × euramericana in China and Europe. The molecular bases of pathogenicity and bark adaptation of L. populi have become a focus of recent research. This study revealed the whole genome sequence and identified putative virulence factors of L. populi. A high-quality L. populi genome sequence was assembled de novo, with a genome size of 3,859,707 bp, containing approximately 3434 genes and 107 RNAs (75 tRNA, 22 rRNA, and 10 ncRNA). The L. populi genome contained 380 virulence-associated genes, mainly encoding for adhesion, extracellular enzymes, secretory systems, and two-component transduction systems. The genome had 110 carbohydrate-active enzyme (CAZy)-coding genes and putative secreted proteins. The antibiotic-resistance database annotation listed that L. populi was resistant to penicillin, fluoroquinolone, and kasugamycin. Analysis of comparative genomics found that L. populi exhibited the highest homology with the L. britannica genome and L. populi encompassed 1905 specific genes, 1769 dispensable genes, and 1381 conserved genes, suggesting high evolutionary diversity and genomic plasticity. Moreover, the pan genome analysis revealed that the N-5-1 genome is an open genome. These findings provide important resources for understanding the molecular basis of the pathogenicity and biology of L. populi and the poplar-bacterium interaction.

This paper reports the preliminary results of a study that aims at designing an intelligent navigation system providing automated scene descriptions and effective directions for a wayfinder. In general, there are two ways for humans to communicate about a route and its surroundings, which are verbal descriptions and graphic depictions (for instance sketch maps). Spatial preposition in language and spatial arrangement of depicted objects reflect how humans select and localise spatial objects relating to a route in navigation. This information is critical to the development of an effective cognitive model for an intelligent navigation system. An experiment including two different sizes of environments was carried out. Participants were asked to provide route instructions using both sketch maps and verbal directions. The comparisons of the two types of route instructions focus on spatial disposition and are based on the linguistic conceptual framework proposed by Talmy. The results demonstrate the basic distinctions of geometry and spatial relation in structuring a walking space.