A Study on the Analysis of Timber Utilization by Grade Using Stand Yield Table of Pinus koraiensis

2020 ◽  
Vol 54 (1) ◽  
pp. 27-33
Author(s):  
Soon-Duk Kwon ◽  
Doo-Ahn Kwak
Keyword(s):  
Pinus Koraiensis ◽  
Yield Table ◽  
Stand Yield

scholarly journals A Simple Technique for Estimating Board Foot Volume Yields in Appalachian Hardwoods

2006 ◽  
Vol 23 (3) ◽  
pp. 211-214
Author(s):  
John R. Brooks ◽  
Harry V. Wiant
Keyword(s):  
Simple Technique ◽  
Basal Area ◽  
Site Index ◽  
Parameter Estimates ◽  
Separate Model ◽  
Yield Table ◽  
Input Variables ◽  
Table Data ◽  
Stand Yield ◽  
Appalachian Hardwoods

Abstract A simple whole stand yield equation based solely on basal area per acre for sawtimber-sized trees and average dominant height was found to provide reasonably accurate estimates of board foot (International) yield in Appalachian hardwoods. Separate parameter estimates were obtained for mesophytic hardwood and mixed oak remeasurement plot data and the yield tables of Schnur (Schnur, G.L. 1937. Yield, stand and volume tables for even-aged upland oak forests. USDA Tech. Bull. 560). Estimates of board foot yield per acre for the remeasurement plot data were within 10% of observed values for stands over 30 years old and within 5% for stands over 45 years old. A separate model form based on the same two input variables was developed for Schnur’s yield table data for site index classes 50–80. Estimates of board foot yield were within 10% of tabular values for stands over 45 years old, regardless of site index class.

APPLICATION OF THE GROWTH STIMULATOR BERES?4 FOR GROWING SEEDLINGS OF THE CEDAR KOREAN (PINUS KORAIENSIS)

2018 ◽  
pp. 119-123
Author(s):  
V.G. SHVEDOV ◽  
E.V. STELMAH ◽  
I.L. REVUTSKAYA ◽  
I.S. GUMENNY
Keyword(s):  
Pinus Koraiensis ◽  
Growth Stimulator

Stand-yield prediction for managed Ocala sand pine

1997 ◽  
Author(s):  
D.L. Rockwood ◽  
B. Yang ◽  
K.W. Outcalt
Keyword(s):  
Yield Prediction ◽  
Stand Yield ◽  
Sand Pine

Effects of nitrogen deposition on litter decomposition and nutrient release mediated by litter types and seasonal change in a temperate forest

2020 ◽  
Vol 100 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Guoyong Yan ◽  
Xiongde Dong ◽  
Binbin Huang ◽  
Honglin Wang ◽  
Ziming Hong ◽  
...  
Keyword(s):  
Mass Loss ◽  
Litter Decomposition ◽  
Nutrient Release ◽  
N Deposition ◽  
Pinus Koraiensis ◽  
N Addition ◽  
Entire Study ◽  
Mixed Litter ◽  
Negative Effect ◽  
Four Levels

We conducted a field experiment with four levels of simulated nitrogen (N) deposition (0, 2.5, 5, and 7.5 g N m−2 yr−1, respectively) to investigate the response of litter decomposition of Pinus koraiensis (PK), Tilia amurensis (TA), and their mixture to N deposition during winter and growing seasons. Results showed that N addition significantly increased the mass loss of PK litter and significantly decreased the mass loss of TA litter throughout the 2 yr decomposition processes, which indicated that the different responses in the decomposition of different litters to N addition can be species specific, potentially attributed to different litter chemistry. The faster decomposition of PK litter with N addition occurred mainly in the winter, whereas the slower decomposition of TA litter with N addition occurred during the growing season. Moreover, N addition had a positive effect on the release of phosphorus, magnesium, and manganese for PK litter and had a negative effect on the release of carbon, iron, and lignin for TA litter. Decomposition and nutrient release from mixed litter with N addition showed a non-additive effect. The mass loss from litter in the first winter and over the entire study correlated positively with the initial concentration of cellulose, lignin, and certain nutrients in the litter, demonstrating the potential influence of different tissue chemistries.

Non-linear response of stomata in Pinus koraiensis to tree age and elevation

Trees ◽  
2012 ◽  
Vol 26 (4) ◽  
pp. 1389-1396 ◽  
Author(s):  
Yumei Zhou ◽  
Marcus Schaub ◽  
Lianxuan Shi ◽  
Zhongling Guo ◽  
Anan Fan ◽  
...  
Keyword(s):  
Linear Response ◽  
Tree Age ◽  
Pinus Koraiensis ◽  
Non Linear

scholarly journals Effects of ontogenetic stage and leaf age on leaf functional traits and the relationships between traits in Pinus koraiensis

2021 ◽  
Author(s):  
Meng Ji ◽  
Guangze Jin ◽  
Zhili Liu
Keyword(s):  
Significant Positive Correlation ◽  
Leaf Age ◽  
Leaf Traits ◽  
Ontogenetic Stage ◽  
Pinus Koraiensis ◽  
Mature Trees ◽  
N Content ◽  
Ontogenetic Stages ◽  
Leaf N Content ◽  
Leaf N

AbstractInvestigating the effects of ontogenetic stage and leaf age on leaf traits is important for understanding the utilization and distribution of resources in the process of plant growth. However, few studies have been conducted to show how traits and trait-trait relationships change across a range of ontogenetic stage and leaf age for evergreen coniferous species. We divided 67 Pinus koraiensis Sieb. et Zucc. of various sizes (0.3–100 cm diameter at breast height, DBH) into four ontogenetic stages, i.e., young trees, middle-aged trees, mature trees and over-mature trees, and measured the leaf mass per area (LMA), leaf dry matter content (LDMC), and mass-based leaf nitrogen content (N) and phosphorus content (P) of each leaf age group for each sampled tree. One-way analysis of variance (ANOVA) was used to describe the variation in leaf traits by ontogenetic stage and leaf age. The standardized major axis method was used to explore the effects of ontogenetic stage and leaf age on trait-trait relationships. We found that LMA and LDMC increased significantly and N and P decreased significantly with increases in the ontogenetic stage and leaf age. Most trait-trait relationships were consistent with the leaf economic spectrum (LES) at a global scale. Among them, leaf N content and LDMC showed a significant negative correlation, leaf N and P contents showed a significant positive correlation, and the absolute value of the slopes of the trait-trait relationships showed a gradually increasing trend with an increasing ontogenetic stage. LMA and LDMC showed a significant positive correlation, and the slopes of the trait-trait relationships showed a gradually decreasing trend with leaf age. Additionally, there were no significant relationships between leaf N content and LMA in most groups, which is contrary to the expectation of the LES. Overall, in the early ontogenetic stages and leaf ages, the leaf traits tend to be related to a "low investment-quick returns" resource strategy. In contrast, in the late ontogenetic stages and leaf ages, they tend to be related to a "high investment-slow returns" resource strategy. Our results reflect the optimal allocation of resources in Pinus koraiensis according to its functional needs during tree and leaf ontogeny.

scholarly journals Using U-Net-Like Deep Convolutional Neural Networks for Precise Tree Recognition in Very High Resolution RGB (Red, Green, Blue) Satellite Images

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Kirill A. Korznikov ◽  
Dmitry E. Kislov ◽  
Jan Altman ◽  
Jiří Doležal ◽  
Anna S. Vozmishcheva ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Images ◽  
Russian Far East ◽  
Pinus Koraiensis ◽  
Accuracy Score ◽  
Learning Approaches ◽  
Individual Tree ◽  
Populus Suaveolens ◽  
Very High

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.

scholarly journals Trade-Offs among Release Treatments in Jack Pine Plantations: Twenty-Five Year Responses

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 370
Author(s):  
Holly D. Deighton ◽  
Frederick Wayne Bell ◽  
Nelson Thiffault ◽  
Eric B. Searle ◽  
Mathew Leitch ◽  
...  
Keyword(s):  
Plant Diversity ◽  
Boreal Forests ◽  
Pinus Banksiana ◽  
Vegetation Management ◽  
Jack Pine ◽  
Forest Diversity ◽  
Trade Offs ◽  
Jack Pine Forests ◽  
Stand Yield ◽  
Forest Managers

We assessed 27 indicators of plant diversity, stand yield and individual crop tree responses 25 years post-treatment to determine long-term trade-offs among conifer release treatments in boreal and sub-boreal forests. This research addresses the lack of longer-term data needed by forest managers to implement more integrated vegetation management programs, supporting more informed decisions about release treatment choice. Four treatments (untreated control, motor-manual brushsaw, single aerial spray, and complete competition removal) were established at two jack pine (Pinus banksiana Lamb.) sites in Ontario, Canada. Our results suggest that plant diversity and productivity in boreal jack pine forests are significantly influenced by vegetation management treatments. Overall, release treatments did not cause a loss of diversity but benefitted stand-scale yield and individual crop tree growth, with maximum benefits occurring in more intensive release treatments. However, none of the treatments maximized all 27 indicators studied; thus, forest managers are faced with trade-offs when choosing treatments. Research on longer term effects, ideally through at least one rotation, is essential to fully understand outcomes of different vegetation management on forest diversity, stand yield, and individual crop tree responses.

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