scholarly journals Transformation of even-aged spruce stands at the School Forest Enterprise Kostelec nad Černými lesy: Structure and final cutting of mature stand

2012 ◽  
Vol 52 (No. 4) ◽  
pp. 158-171 ◽  
Author(s):  
J. Remeš
Keyword(s):  
Forest Stand ◽  
Growth Potential ◽  
Main Criterion ◽  
Individual Tree ◽  
Main Method ◽  
Single Tree ◽  
Mature Stand ◽  
Spruce Stands ◽  
Individual Trees ◽  
Very High

This paper deals with the transformation of pure even-aged forest stands to mixed and more uneven-aged stands on an example of selected even-aged Norway spruce stands in the School Forest Enterprise (SFE) in Kostelec nad Černými lesy. A forest stand where individual tree felling was used as the main method of forest stand regeneration was chosen as a conversion example. The main criterion of tree maturity is the culmination of mean volume increment of a single tree. The analyses confirmed a very high variability in the growth potential of individual trees. The potential and actual increment was strongly influenced by the stand position of tree and by crown release. These results show a high potential level of tree growth even at the age of 120 years. From 30% to 9% of all trees on particular experimental plots achieved felling maturity.

scholarly journals Presence of Root Rot Reduces Stability of Norway Spruce (Picea abies): Results of Static Pulling Tests in Latvia

Forests ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 416 ◽  
Author(s):  
Oskars Krisans ◽  
Roberts Matisons ◽  
Steffen Rust ◽  
Natalija Burnevica ◽  
Lauma Bruna ◽  
...  
Keyword(s):  
Norway Spruce ◽  
Root Rot ◽  
Rotation Period ◽  
Wind Damage ◽  
Individual Tree ◽  
Study Region ◽  
Secondary Failure ◽  
The Future ◽  
Spruce Stands ◽  
Individual Trees

Storms are the main abiotic disturbance in European forests, effects of which are expected to intensify in the future, hence the importance of forest stand stability is increasing. The predisposition of Norway spruce to wind damage appears to be enhanced by pathogens such as Heterobasidion spp., which reduce stability of individual trees. However, detailed information about the effects of the root rot on the stability of individual trees across diverse soil types is still lacking. The aim of the study was to assess the effect of root rot on the individual tree stability of Norway spruce growing on drained peat and mineral soils. In total, 77 Norway spruce trees (age 50–80 years) growing in four stands were tested under static loading. The presence of Heterobasidion spp. had a significant negative effect on the bending moment at primary and secondary failure of the tested trees irrespectively of soil type. This suggests increased legacy effects (e.g., susceptibility to pathogens and pests due to fractured roots and altered water uptake) of storms. Damaged trees act as weak spots increasing the susceptibility of stands to wind damage, thus forming a negative feedback loop and contributing to an ongoing decline in vitality of Norway spruce stands following storms in the study region in the future. Accordingly, the results support the importance of timely identification of the decayed trees, lowering stand density and/or shortening rotation period as the measures to counteract the increasing effects of storms on Norway spruce stands.

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343 ◽  
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

scholarly journals Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

2021 ◽  
Author(s):  
Qianwei Liu ◽  
Weifeng Ma ◽  
Jianpeng Zhang ◽  
Yicheng Liu ◽  
Dongfan Xu ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Plant Morphology ◽  
Natural Forest ◽  
Normal Vector ◽  
Natural Forests ◽  
Individual Tree ◽  
Single Tree ◽  
Point Cloud Segmentation ◽  
Individual Trees

AbstractForest resource management and ecological assessment have been recently supported by emerging technologies. Terrestrial laser scanning (TLS) is one that can be quickly and accurately used to obtain three-dimensional forest information, and create good representations of forest vertical structure. TLS data can be exploited for highly significant tasks, particularly the segmentation and information extraction for individual trees. However, the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness, and hence do not lead to satisfactory results for natural forests in complex environments. In this paper, we propose a trunk-growth (TG) method for single-tree point-cloud segmentation, and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan, China. First, the point normal vector and its Z-axis component are used as trunk-growth constraints. Then, the points surrounding the trunk are searched to account for regrowth. Finally, the nearest distributed branch and leaf points are used to complete the individual tree segmentation. The results show that the TG method can effectively segment individual trees with an average F-score of 0.96. The proposed method applies to many types of trees with various growth shapes, and can effectively identify shrubs and herbs in complex scenes of natural forests. The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

Wood density in Norway spruce: changes with thinning intensity and tree age

2005 ◽  
Vol 35 (7) ◽  
pp. 1767-1778 ◽  
Author(s):  
Tuula Jaakkola ◽  
Harri Mäkinen ◽  
Pekka Saranpää
Keyword(s):  
Norway Spruce ◽  
Wood Density ◽  
Basal Area ◽  
Tree Age ◽  
Individual Tree ◽  
Thinning Intensity ◽  
Individual Tree Growth ◽  
Spruce Stands ◽  
Individual Trees

The effect of thinning intensity on growth and wood density in Norway spruce (Picea abies (L.) Karst.) was investigated in two long-term thinning experiments in southeastern Finland. The stands were approaching maturity, and their development had already been studied for 30 years. The intensities of thinning were low, normal, and high (i.e., the stand basal area after the thinning was, on average, 40, 27, and 24 m2·ha–1, respectively, in Heinola, and 30, 28, and 17 m2·ha–1 in Punkaharju, respectively). Compared with the low thinning intensity, the normal and high thinning intensities increased the basal-area increment of individual trees by 52% and 68%, respectively. Normal and high thinning intensities resulted in a relatively small reduction (1%–4%) of mean ring density compared with low thinning intensity. The random variation in wood density between and within trees was large. About 27% of the total variation in wood density was related to variation between rings. Our results indicate that the prevailing thinning intensities in Norway spruce stands in Fennoscandia cause no marked changes in wood density. At least, the possible reduction in wood density is low compared with the increase in individual tree growth.

scholarly journals Potential for Noninfectious Bud-failure in `Carmel' Almond Orchards in California

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 762C-762
Author(s):  
Dale E. Kester ◽  
K.H Shackel ◽  
T.M. Gradziel ◽  
M. Viveros ◽  
W.C. Micke
Keyword(s):  
Spatial Analysis ◽  
Source Material ◽  
Economic Threshold ◽  
Individual Tree ◽  
Single Tree ◽  
Almond Orchards ◽  
Progeny Tests ◽  
Selection For ◽  
The Individual ◽  
Individual Trees

The potential for noninfectious bud-failure in propagation source material for `Carmel' almond in California has been determined in progeny tests from commercial nursery sources. Percentage BF increased with time (temporal), but decreased in severity (spatial). Analysis of variability in nursery sources showed that the key to successful selection for low BF potential is the individual tree, although variability exists among nurseries, budsticks (within trees), and individual buds (within budsticks). One-half of the individual trees of the nursery population tested have produced BF progeny so far within the test period. Future BF from the remainder was project by a BF model to be beyond the critical economic threshold. Two low BF-potential single tree sources were identified for commercial usage and progeny tests have started on an additional 19.

scholarly journals INDIVIDUAL TREE OF URBAN FOREST EXTRACTION FROM VERY HIGH DENSITY LIDAR DATA

2016 ◽  
Vol XLI-B3 ◽  
pp. 337-343
Author(s):  
A. Moradi ◽  
M. Satari ◽  
M. Momeni
Keyword(s):  
Urban Forest ◽  
Accuracy Assessment ◽  
Airborne Lidar ◽  
Light Detection And Ranging ◽  
Individual Tree ◽  
Point Density ◽  
Peak Points ◽  
3D Information ◽  
Individual Trees ◽  
Very High

Airborne LiDAR (Light Detection and Ranging) data have a high potential to provide 3D information from trees. Most proposed methods to extract individual trees detect points of tree top or bottom firstly and then using them as starting points in a segmentation algorithm. Hence, in these methods, the number and the locations of detected peak points heavily effect on the process of detecting individual trees. In this study, a new method is presented to extract individual tree segments using LiDAR points with 10cm point density. In this method, a two-step strategy is performed for the extraction of individual tree LiDAR points: finding deterministic segments of individual trees points and allocation of other LiDAR points based on these segments. This research is performed on two study areas in Zeebrugge, Bruges, Belgium (51.33° N, 3.20° E). The accuracy assessment of this method showed that it could correctly classified 74.51% of trees with 21.57% and 3.92% under- and over-segmentation errors respectively.

On the photoreactivity of some hardwood species studied by their leaf characteristics

1970 ◽  
Vol 23 ◽  
Author(s):  
M. Van Miegroet
Keyword(s):  
Morphological Characteristics ◽  
Vegetation Period ◽  
Forest Stand ◽  
Light Environment ◽  
Light Radiation ◽  
Light Conditions ◽  
Site Factors ◽  
Leaf Characteristics ◽  
Single Tree ◽  
Changing Light

A  certain number of measurable characteristics of tree leaves (morphological  characteristics, absorption of light radiation, intensity of respiration and  photosynthesis) are clearly linked with the presence of physiologically  active pigments in the leaves.     Leaf characteristics are highly and inequally influenced by changing  conditions of light environment, especially those related to light intensity,  light quality and duration of the daily illumination period. These  modifications do not only apply to light radiation as created under  laboratory conditions, but also to light conditions ensuing from the place in  the crown of a single tree, the social position of the tree in a forest stand  and the site factors in general.     There are also changes taking place due to the progression of the  vegetation period, at the end of which all species are less tolerant or more  light demanding. The reaction of the leaves towards light radiation out of  different regions of the spectrum is also different. The so-called blue light  radiation (λmax = 440 nm) seems to be of the greatest importance in this  relation, as species react quite different to its action.     The biggest variation in leaf characteristics due to changing light  environment was measured for oak and beech, which both react quickly and are  qualified as 'photolabile species'. No important variations occur in leaves  of ash and maple, which therefore are qualified as 'photostable species'.      As a consequence of variable reactions to changing light conditions, the  relationships between the species are continually modified, even in such a  way that their potential for dominance is not constant.     The classical division into tolerant and intolerant species or  classification of the species based upon the degree of light demand, is  highly inaccurate and it seems preferable to speak of relative light demands  and relative tolerance. All these observations and conclusions bring about a  clear confirmation of the necessity to recognize the individuality of the  single tree, the special character of each growth condition, the own  structure of each forest stand, the specific reaction to one sided  modifications of environmental factors. This is especially important for an  intensive sylvicultural practice.     They also prove the necessity for more physiological and biochemical  research to arrive at a better understanding of growth and its mechanism.      Sylviculture in fact must try to regulate, on an expanded scale, the  phenomens of growth, which is the exchange, absorption and transformation of  energy.     A practical interpretation and regulation of fundamental laws of physiology  and growth will be possible as soon as a clinical form of sylviculture is  created and the adequate instrumentarium developed.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

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 Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

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