Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.
Extracting the individual tree crowns (ITCs) information is significant to fine forest resource investigation and carbon storage estimation. UAV oblique photogrammetry (UOP) can obtain point cloud data with high density due to higher presence of overlap, which has potential for tree crown information extraction. Many ITCs segmentation methods have been proposed to extract individual tree information. However, accurate ITCs segmentation using UOP data remains a challenge due to the uncertainties in environments with high heterogeneity of forest canopy vertical structure. Here, we proposed a novel approach, adaptive-kernel bandwidth mean-shift algorithm (AMS) considering three-dimensional canopy attributes, to segment ITCs using UOP data in complex forest environment. First, we developed a kernel bandwidth model with automatic adaptive parameter assignment using tree height derived from UOP data and applied it to the mean-shift algorithm. We demonstrated the generality of our algorithm in different tree species plots of a subtropical forest in China with overall precision f ≥ 0.72 and crown width rRMSE ≤ 0.13. Compared with the fixed-kernel bandwidth mean-shift algorithm (FMS) and seed region growth algorithm (SRG), the average f and rRMSE of the AMS algorithm were improved by 0.04 and 0.12, 0.16 and 0.11 respectively. Then, we evaluated the segmentation effect of AMS algorithm with point cloud densities of 25%, 50%, 75% and 100% respectively. We found that the segmentation accuracy decreases with decreasing point cloud density, but 75% of the point cloud density can satisfy most ITCs segmentation needs. In addition, we used LiDAR data (∼45 pts/m2) obtained by UAV to validate generalization ability of our approach, and the average r, p and f reached 0.97, 0.73 and 0.83. These results showed that the AMS algorithm can solve the ITCs segmentation problem in forests with complex structures using UOP and LiDAR point cloud data, which can support the accurate survey and scientific management of forest resources and provide basic data for the accurate estimation of forest carbon sinks.
Microtubules play an indispensable role in numerous cellular processes. Targeting colchicine binding site to destabilize microtubules is a promising strategy for cancer chemotherapy. Our group has developed a SMART analogue, 9, which exhibited moderate anti-proliferative activity. Based on the predicted binding mode of 9, the pocket-based lead optimization strategy was implemented. A series of 9 analogues were designed, synthesized, and subsequently assessed for their anti-proliferative activities against three distinct human tumor cell lines. The compound 12b, featuring a 5-methoxy group on the 1,2,3-triazol ring, demonstrated potent anti-proliferative activity against SGC-7901 cell lines with an IC50 value of 15 nM. The structure-activity relationships suggest that the presence of the steric hindrance at the C5-position on the 1,2,3-triazol ring confers a greater advantageous in terms of inhibitory activity due to its stronger interactions with amino acids. This work presents a pioneering approach to augment their biological activities by optimizing the structure of CBSIs.
Cold-temperate forests (CTFs) are not only an important source of wood but also provide significant carbon storage in China. However, under the increasing pressure of human activities and climate change, CTFs are experiencing severe disturbances, such as logging, fires, and pest infestations, leading to evident degradation trends. Though these disturbances impact both regional and global carbon budgets and their assessments, the disturbance patterns in CTFs in northern China remain poorly understood. In this paper, the Genhe forest area, which is a typical CTF region located in the Inner Mongolia Autonomous Region, Northeast China (with an area of about 2.001 × 104 km2), was selected as the study area. Based on Landsat historical archived data on the Google Earth Engine (GEE) platform, we used the continuous change detection and classification (CCDC) algorithm and considered seasonal features to detect forest disturbances over nearly 30 years. First, we created six inter-annual time series seasonal vegetation index datasets to map forest coverage using the maximum between-class variance algorithm (OTSU). Second, we used the CCDC algorithm to extract disturbance information. Finally, by using the ECMWF climate reanalysis dataset, MODIS C6, the snow phenology dataset, and forestry department records, we evaluated how disturbances relate to climate and human activities. The results showed that the disturbance map generated using summer (June–August) imagery and the enhanced vegetation index (EVI) had the highest overall accuracy (88%). Forests have been disturbed to the extent of 12.65% (2137.31 km2) over the last 30 years, and the disturbed area generally showed a trend toward reduction, especially after commercial logging activities were banned in 2015. However, there was an unusual increase in the number of disturbed areas in 2002 and 2003 due to large fires. The monitoring of potential widespread forest disturbance due to extreme drought and fire events in the context of climate change should be strengthened in the future, and preventive and salvage measures should be taken in a timely manner. Our results demonstrate that CTF disturbance can be robustly mapped by using the CCDC algorithm based on Landsat time series seasonal imagery in areas with complex meteorological conditions and spatial heterogeneity, which is essential for understanding forest change processes.
Farmland shelterbelt plays an important role in protecting farmland and ensuring stable crop yields, and it is mainly distributed in the form of bands and patches; different forms of distribution have different impacts on farmland, which is an important factor affecting crop yields. Therefore, high-precision classification of banded and patch farmland shelterbelt is a prerequisite for analyzing its impact on crop yield. In this study, we explored the effectiveness and transferability of an improved Prototypical Network model incorporating data augmentation and a convolutional block attention module for extracting banded and patch farmland shelterbelt in Northeast China, and we analyzed the potential of applying it to the production of large-scale farmland shelterbelt products. Firstly, we classified banded and patch farmland shelterbelt under different sample window sizes using the improved Prototypical Network in the source domain study area to obtain the optimal sample window size and the optimal classification model. Secondly, fine-tuning transfer learning and learning from scratch directly were used to classify the banded and patch farmland shelterbelt in the target domain study area, respectively, to evaluate the extraction model’s migratability. The results showed that classification of farmland shelterbelt using the improved Prototypical Network is very effective, with the highest extraction accuracy under the 5 × 5 sample window; the accuracies of the banded and patch farmland shelterbelt are 92.16% and 90.91%, respectively. Using the fine-tuning transfer learning method in the target domain can classify the banded and patch farmland shelterbelt with high accuracy, above 95% and 89%, respectively. The proposed approach can provide new insight into farmland shelterbelt classification and farmland shelterbelt products obtained from freely accessible Sentinel-2 multispectral images.
The connotation of personal knowledge management model for network learning and social software mash-up technology has been analyzed firstly in this paper.Then,the author elaborates the relationship between social software and personal knowledge management while introducing the concept of mash-up in order to construct personal knowledge management model suitable for network learning based on social software mash-up technology.This mash-up technology can provide a kind of new conception about personal knowledge management and give users excellent experience coming from Web 2.0 era.
Due to increasing service duration and geological decline, the precise requirements for adjusting the line of Shanghai Maglev are more urgent and frequent. In this paper, the application results of GMS are applied in the adjustment of track beam shape, and the overall effect of the line maintenance of the Maglev Shanghai demonstration line is analyzed, which including the distribution and the maximum value of the long-wave deviation of the position of the buttocks in the current situation, and compared the results before and after the route maintenance. The track line could be adjusted while deviation exceeded 5 mm on suspended surface and 8 mm on guide surface, in particular subtle changes (>2 mm) also could be modified. It shows that the GMS measurement method is accurate use and high efficiency application in the maintenance.
<p>Forest aboveground biomass (AGB) plays an important role in measuring forest carbon reserves. Accurate mapping AGB is important for monitoring carbon stocks and will contribute to achieve the goal of sustainable development. In this study, we explored the potential of mapping AGB in north China using a three-year monthly time series of Senitinel-1 (S1) and Sentinel-2 (S2) data. The backscattering and indices of SAR S1 combined with spectral reflectance, vegetation indices and biophysical parameters from multispectral S2 imagery were evaluated for AGB prediction in a Random Forest regression.&#160;Three scenarios were conducted with different datasets to determine:&#160;(1) the potential of using S1 and S2 to estimate AGB, (2)&#160;optimal variables selection for AGB mapping, (3)&#160;contribution of time series datasets to improving the accuracy of AGB mapping. Random forest regression was used to develop forest AGB estimation models, which was divided into three types of modeling using only S1, only S2, and a combination of S1 and S2. Compared to S1 (RMSE&#160;= 65.7 Mg/ha), S2 achieved better prediction accuracy (RMSE = 58.4 Mg/ha), although the combination of S1 and S2 time series datasets estimated&#160;the best AGB results (RMSE&#160;= 42.3 Mg/ha).&#160;The research implied that incorporation of SAR and multispetral data considerably improved AGB mapping performance when compared with the use of SAR or multispectral data alone.&#160;This proposed approach provides a new insight in improving the estimation accuracy of forest AGB in north&#160;China.</p>
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