Pinus densata: Farjon, A.

Author(s):  
Keyword(s):  
2012 ◽  
Vol 44 ◽  
pp. 79-82 ◽  
Author(s):  
Bo Li ◽  
Yun-Heng Shen ◽  
Chang-Xia Li ◽  
Yi-Ren He ◽  
Wei-Dong Zhang
Keyword(s):  

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250073
Author(s):  
Liu Shu-Qin ◽  
Bian Zhen ◽  
Xia Chao-Zong ◽  
Bilal Ahmad ◽  
Zhang Ming ◽  
...  

According to the forest resources inventory data for different periods and the latest estimation parameters of forest carbon reserves in China, the carbon reserves and carbon density of forest biomass in the Tibet Autonomous Region from 1999 to 2019 were estimated using the IPCC international carbon reserves estimation model. The results showed that, during the past 20 years, the forest area, forest stock, and biomass carbon storage in Tibet have been steadily increasing, with an average annual increase of 1.85×104 hm2, 0.033×107 m3, and 0.22×107 t, respectively. Influenced by geographical conditions and the natural environment, the forest area and biomass carbon storage gradually increased from the northwest to the southeast, particularly in Linzhi and Changdu, where there are many primitive forests, which serve as important carbon sinks in Tibet. In terms of the composition of tree species, coniferous forests are dominant in Tibet, particularly those containing Abies fabri, Picea asperata, and Pinus densata, which comprise approximately 45% of the total forest area in Tibet. The ecological location of Tibet has resulted in the area being dominated by shelter forest, comprising 68.76% of the total area, 64.72% of the total forest stock, and 66.34% of the total biomass carbon reserves. The biomass carbon storage was observed to first increase and then decrease with increasing forest age, which is primarily caused by tree growth characteristics. In over-mature forests, trees’ photosynthesis decreases along with their accumulation of organic matter, and the trees can die. In addition, this study also observed that the proportion of mature and over-mature forest in Tibet is excessively large, which is not conducive to the sustainable development of forestry in the region. This problem should be addressed in future management and utilization activities.


2019 ◽  
Vol 11 (23) ◽  
pp. 2750
Author(s):  
Ou ◽  
Lv ◽  
Xu ◽  
Wang

Uncertainties in forest aboveground biomass (AGB) estimates resulting from over- and underestimations using remote sensing data have been widely studied. The uncertainties may occur due to the spatial effects of the plot data. In this study, we collected AGB data from a total of 147 Pinus densata forest sample plots in Yunnan of southwestern China and analyzed the spatial effects on the estimation of AGB. An ordinary least squares (OLS) and four spatial regression methods were compared for the estimation using Landsat 8-OLI images. Through the spatial analysis of AGB and residuals of model predictions, it was found that the spatial autocorrelation and heterogeneity of the plot data could not be ignored. Compared with the OLS, the impact of the spatial effects on AGB estimation could be reduced slightly by the spatial lag model (SLM) and the spatial error model (SEM) and greatly reduced by the linear mixed effects model (LMM) and geographically weighted regression (GWR) based on the distributions of prediction residuals, global Moran’s I, and Z score. The spatial regression models had better performance for model fitting and prediction because of the reduction in overestimations and underestimations for the forests with small and large AGB values, respectively. However, the reductions in the overestimations and underestimations varied depending on the spatial regression models. The GWR provided the most accurate predictions with the largest R2 (0.665), the smallest root mean square error (34.507), and mean relative error (−9.070%) by greatly reducing the AGB interval for overestimations occurring and significantly increasing the threshold of AGB from 150 Mg/ha to 200 Mg/ha for underestimations. Thus, GWR offered the greatest potential of improving the estimation of Pinus densata forest AGB in Yunnan of southwestern China.


2009 ◽  
Vol 185 (1) ◽  
pp. 204-216 ◽  
Author(s):  
Fei Ma ◽  
Changming Zhao ◽  
Richard Milne ◽  
Mingfei Ji ◽  
Litong Chen ◽  
...  

2003 ◽  
Vol 12 (11) ◽  
pp. 2995-3001 ◽  
Author(s):  
Bao‐Hua Song ◽  
Xiao‐Quan Wang ◽  
Xiao‐Ru Wang ◽  
Kai‐Yu Ding ◽  
De‐Yuan Hong

2020 ◽  
Author(s):  
Jianpeng Zhang ◽  
Jinliang Wang ◽  
Weifeng Ma ◽  
Yicheng Liu ◽  
Qianwei Liu ◽  
...  

Abstract Background: Aiming at the problems of low accuracy of tree stem extraction from point cloud data of natural forest and poor universality, a method for batch extraction of tree stem from natural forest point cloud data based on terrestrial laser scanning is proposed.Methods: First, the principal component analysis method is used to calculate the point cloud eigenvalues and eigenvectors, and the information entropy is minimized as a constraint to achieve the best neighborhood scale selection; Then combined with the spatial distribution features of the three-dimensional forest, using the Z-axis component of normal vector as the feature variable, the threshold method is used to filter out a large number of non-stem point clouds, and the 3D features are used for rough extraction of tree stem point cloud; Finally, density clustering is used to realize the precise extraction of tree stem point cloud. Results: Select the two typical representative natural forest sample plots of Pinus densata Mast. and Picea asperata Mast. in Shangri-La as the experimental data to extract stem. All the stem of the two natural forest sample plots were detected and extracted. Using the extracted individual tree stem point cloud and the true tree stem point cloud for correlation analysis, the R2 of the Pinus densata Mast. sample plot was 0.990, and the R2 of the Picea asperata Mast. sample plot with a more complex growth environment was 0.982. Conclusions: The results show that this method can well achieve batch extraction of tree stem point cloud from natural forest, and has the characteristics of high extraction accuracy and strong adaptability.


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