Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R² = 0.92, rRMSE = 3.5%), followed by the CHMM (R² = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R² = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

Bilobalide, the only sesquiterpene compound from Ginkgo biloba leaf, exhibits various beneficial pharmaceutical activities, such as antioxidant, anti-inflammation, and protective effects for the central nervous system. Several bioactive components extracted from Ginkgo biloba extract reportedly have the potential to attenuate lipid metabolism. However, the effect of bilobalide on lipid metabolism remains unclear. In this study, we used 3T3-L1 cells as the cell model to investigate the effect of bilobalide on adipogenesis. The results showed that bilobalide inhibited 3T3-L1 preadipocyte differentiation and intracellular lipid accumulation. Quantitative real-time PCR and western blotting results indicated that several specific adipogenic transcription factors and a few important adipogenesis-related genes were significantly down regulated on both mRNA and protein levels in bilobalide treatment groups. By contrast, the expression of some lipolytic genes, such as adipose triglyceride lipase, hormone-sensitive lipase (HSL), and carnitine palmitoyltransferase-1α, were all up-regulated by bilobalide treatment, and the phosphorylation of AMP-activated protein kinase (AMPK), acetyl-CoA carboxylase 1, and HSL were stimulated. Furthermore, bilobalide treatment partially restored AMPK activity following its blockade by compound C (dorsomorphin). These results suggested that bilobalide inhibited adipogenesis and promoted lipolysis in 3T3-L1 cells by activating the AMPK signaling pathway.

Ginkgo tea is a kind of health food produced from Ginkgo biloba leaves. The market of Ginkgo tea encountered many difficulties because of its bad palatability and vague function statement. In this study, two kinds of glycosidase were used to improve the flavor of Ginkgo tea, and three kinds of bioactivities were selected to investigate the health care function of the tea infusion. The aroma components extracted by headspace absorb (HSA) method during the making of Ginkgo tea were analyzed by GC-MS. The flavonoids and ginkgolides released into the tea infusion were studied by HPLC. A combination of β-glucosidase (β-G) and α-rhamnosidase (α-R) was applied during the making of the tea. The contents of characteristic aroma components and the release of total flavonoids and ginkgolides were increased significantly by adding β-G and α-R. The composition of flavone glycosides was changed greatly. The free radical scavenging, inhibition of inflammatory cell activation, and tumor cytotoxicity activities of the tea were demonstrably improved. According to the release of active components, Ginkgo tea can be brewed repeatedly for at least three times. The enzymes used here show potential application prospects in the making of Ginkgo tea or tea drink to get higher contents of flavonoids, ginkgolides, and aroma components.

The Asian elephant (Elephas maximus Linnaeus) is a globally endangered species, an internationally protected species, and a first-class protected animal in China. However, future climate change and human activities exacerbate the instability of its habitat range, leading to a possible reduction in the range. By using multi-source remote sensing data and products, as well as climate change models, including ASTER GDEM v3, Landsat8 OLI image and ClimateAP, we examined the effects of ecological factors related to climate and natural and anthropogenic influences on the distribution of Asian elephants in Sipsongpanna. Multiyear elephant field tracking data were used with a MaxEnt species distribution model and the climate model. First, the distribution of Asian elephants in potentially suitable areas in Sipsongpanna was simulated under current climatic conditions without considering human activities. The predicted distribution was verified by existing Asian elephant migration trajectories. Subsequently, the distribution of potentially suitable areas for Asian elephants in Sipsongpanna was simulated under two climate change scenarios (RCP4.5, RCP8.5) in three periods (2025, 2055, and 2085). The changes in potentially suitable areas for Asian elephants in Sipsongpanna were analyzed under multiple climate change scenarios for the current (2017) and different future periods by considering the effects of human activities. The results show the following: (1) under anthropogenic interference (AI), the optimal MaxEnt model has a high prediction accuracy with the area under the curve (AUC) of 0.913. The feature combination (FC) includes linear, quadratic, and threshold features, and the regularization multiplier (RM) is 2.1. (2) Jackknife analyses of the non-anthropogenic interference (NAI) and anthropogenic interference (AI) scenarios indicate that topography (altitude (Alt)), temperature (mean warmest month temperature (MWMT)), and precipitation (mean annual precipitation (MAP)) are the top three factors influencing the distribution of Asian elephants. (3) The total area suitable for Asian elephants under current climate conditions and AI accounts for 46.35% of the total area. Areas of high suitability (occurrence probability >0.5) are located in Jinghong City in central Sipsongpanna and Mengla County in southeastern Sipsongpanna. Among them, the minimum habitat range and ecological corridors are mainly located in Mengman Town, Mohan Town, Mengla Town, Mengban Township, Dadugang Township, and Mengwang Township. (4) The change in potentially suitable areas for Asian elephants between current and future conditions is small under AI and large under undisturbed conditions.

Bilobalide exhibits numerous beneficial bioactivities, including neuroprotective, anti-inflammatory, and antioxidant activity. Our previous study demonstrated that bilobalide inhibits adipogenesis and promotes lipolysis. The dose-dependent cytotoxicity was found to be specific to the mature adipocytes only, indicating the potential for regulating apoptosis in them. Herein, we aimed to investigate the apoptotic effects of bilobalide on 3T3-L1 mature adipocytes and elucidate the underlying mechanisms thereof. Flow cytometry analysis (FACS) revealed the pro-apoptotic effects of bilobalide on these cells. Bilobalide induced early apoptosis by reducing the mitochondrial membrane potential (MMP). DNA fragmentation was confirmed using TUNEL staining. Additionally, bilobalide increased the intracellular reactive oxygen species (ROS) levels and activities of Caspases 3/9. Pre-treatment with NAC (an ROS scavenger) confirmed the role of ROS in inducing apoptosis. Moreover, bilobalide up- and down-regulated the expression of Bax and Bcl-2, respectively, at the mRNA and protein expression levels; upregulated the Bax/Bcl-2 ratio; triggered the release of cytochrome c from the mitochondria; and increased the protein expression of cleaved Caspase 3, cleaved Caspase 9, and PARP cleavage. These results support the conclusion that bilobalide induces apoptosis in mature 3T3-L1 adipocytes through the ROS-mediated mitochondrial pathway, and offers potential novel treatment for obesity.

Regarded as a marked category of global forests, forest plantations not only have great significance for the development of the global economy, but also contribute ecological and social benefits. The accurate acquisition of the multi-scale (from individual tree to landscape level) and near-real-time information of structural parameters in plantations is the premise of decision-making in sustainable management for the whole forest farm, and it is also the basis for the evaluation of forest productivity in stands. The development and synergetic applications of multi-source and multi-platform remote sensing technology provide a technical basis for the highly accurate estimation of multi-scale forest structural parameters. In this study, we developed an advanced framework for estimating these parameters of forest plantations in multiple scales (individual tree, plot and landscape levels) based on the Unmanned Aircraft System Light Detection and Ranging (UAS-LiDAR) transects and wall-to-wall Sentinel-2 imagery, combined with the sample plot data in a typical forest farm plantation (mainly Larch, Chinese pine) of Northern China. The position and height of individual trees within the plots were extracted by the LiDAR-based point cloud segmentation (PCS) algorithm, and then different approaches to the extrapolation of forest structural parameters from the plot to landscape level were assessed. The results demonstrate that, firstly, the individual tree height obtained by PCS was of relatively high accuracy (rRMSE = 1.5–3.3%); secondly, the accuracy of the forest structure parameters of the sample plot scale estimated by UAS-LiDAR is rRMSE = 4.4–10.6%; and thirdly, the accuracy of the two-stage upscaling approach by UAS-LiDAR transects as an intermediate stage (rRMSE = 14.5–20.2%) performed better than the direct usage of Sentinel-2 data (rRMSE = 22.9–27.3%). This study demonstrated an advanced framework for creating datasets of multi-scale forest structural parameters in a forest plantation, and proved that the synergetic usage of UAS-LiDAR transects and full coverage medium-resolution satellite imagery can provide a high-precision and low-cost technical basis for the multi-level estimation of forest structural parameters.