Predicting Future Diameter Distributions Given Current Stand Attributes

This study discussed four methods to project a diameter distribution from age A1 to age A2. Method 1 recovers parameters of the distribution at age A2 from stand attributes at that age. Method 2 uses a stand-level model to grow the quadratic mean diameter, and then recovers the distribution parameters from that prediction. Method 3 grows the diameter distribution by assuming tree-level survival and diameter growth functions. Method 4 first converts the diameter distribution at age A1 into a list of individual trees before growing these trees to age A2. In a numerical example employing the Weibull distribution, methods 3 and 4 produced better results based on two types of error indices and the relative predictive error for each diameter class. Method 4 is a novel method that converts a diameter distribution into a list of individual-trees, and in the process, successfully links together diameter distribution, individual-tree, and whole stand models.

Comparison of Classical Methods and Mask R-CNN for Automatic Tree Detection and Mapping Using UAV Imagery

Detecting and mapping individual trees accurately and automatically from remote sensing images is of great significance for precision forest management. Many algorithms, including classical methods and deep learning techniques, have been developed and applied for tree crown detection from remote sensing images. However, few studies have evaluated the accuracy of different individual tree detection (ITD) algorithms and their data and processing requirements. This study explored the accuracy of ITD using local maxima (LM) algorithm, marker-controlled watershed segmentation (MCWS), and Mask Region-based Convolutional Neural Networks (Mask R-CNN) in a young plantation forest with different test images. Manually delineated tree crowns from UAV imagery were used for accuracy assessment of the three methods, followed by an evaluation of the data processing and application requirements for three methods to detect individual trees. Overall, Mask R-CNN can best use the information in multi-band input images for detecting individual trees. The results showed that the Mask R-CNN model with the multi-band combination produced higher accuracy than the model with a single-band image, and the RGB band combination achieved the highest accuracy for ITD (F1 score = 94.68%). Moreover, the Mask R-CNN models with multi-band images are capable of providing higher accuracies for ITD than the LM and MCWS algorithms. The LM algorithm and MCWS algorithm also achieved promising accuracies for ITD when the canopy height model (CHM) was used as the test image (F1 score = 87.86% for LM algorithm, F1 score = 85.92% for MCWS algorithm). The LM and MCWS algorithms are easy to use and lower computer computational requirements, but they are unable to identify tree species and are limited by algorithm parameters, which need to be adjusted for each classification. It is highlighted that the application of deep learning with its end-to-end-learning approach is very efficient and capable of deriving the information from multi-layer images, but an additional training set is needed for model training, robust computer resources are required, and a large number of accurate training samples are necessary. This study provides valuable information for forestry practitioners to select an optimal approach for detecting individual trees.

How Bark Beetle Attack Changes the Tensile and Compressive Strength of Spruce Wood (Picea abies (L.) H. Karst.)

Since 2014, forestry in the Czech Republic has been significantly affected by a bark beetle outbreak. The volume of infested trees has exceeded processing capacity and dead standing spruce (Picea abies) remain in the forest stands, even for several years. What should be done with this bark beetle wood? Is it necessary to harvest it in order to preserve the basic mechanical and physical properties? Is it possible to store it under standard conditions, or what happens to it when it is “stored” upright in the forest? These are issues that interested forest owners when wood prices were falling to a minimum (i.e., in 2018–2019) but also today, when the prices of quality wood in Central European conditions are rising sharply. To answer these questions, we found out how some of the mechanical properties of wood change in dead, bark beetle-infested trees. Five groups of spruce wood were harvested. Each of these groups was left upright in the forest for a specified period of time after bark beetle infestation, and one group was classified as a reference group (uninfested trees). Subsequently, we discovered what changes occurred in tensile and compressive strength depending on the time left in the stand and the distance from the center of the trunk. When selecting samples, we eliminated differences between individual trees using a CT scanning technique, which allowed us to separate samples, especially with different widths of annual rings and other variations that were not caused by bark beetle. The results showed the effect of log age and radial position in the trunk on tensile and compressive strength. The values for tensile strength in 3-year infested trees decreased compared to uninfested trees by 14% (from 93.815 MPa to 80.709 MPa); the values for compressive strength then decreased between the same samples by up to 25.6% (from 46.144 MPa to 34.318 MPa). A significant decrease in values for compressive strength was observed in the edges of the trunks, with 44.332 MPa measured in uninfested trees and only 29.750 MPa in 3-year infested trees (a decrease of 32.9%). The results suggest that the use of central timber from bark beetle-infested trees without the presence of moulds and fungi should not be problematic for construction purposes.

Aboveground Biomass Stockpile of Trees in Vallanadu Blackbuck Sanctuary, Peninsular India

A forest tree inventory study was conducted in Vallanadu Black buck sanctuary, Tuticorin. The current study was conducted to assess tree density, species richness, basal area (BA) and aboveground biomass (AGB) stockpile. The study area has been classified as Southern Thorn Forest (SFT). One hundred square plots (total area 1 ha), each 10m × 10m (100 m2 each) laid randomly across study area. All live trees with ≥5 cm diameter at breast height (DBH) measured at 137 cm above the ground. As the whole, 1335 individual trees ≥5cm DBH recorded. A total number of 18 species recorded from 14 genera and 11 families in study area. The family Mimosaceae has maximum number of species (7 species) followed by Rhamnaceae (2 species), while 9 families had just single species’ each. The total basal area recorded was 22.046 m2 ha-1, while, the mean wood density (WD) of trees estimated as 0.70±0.093 g cm-3. Total amount of 50.065 Mg ha-1 present in STF. The contribution of different species in terms of total AGB varied significantly. Commiphora berryi stocked 45.13% (22.588 Mg ha-1) of AGB followed by A. planifrons (23.31%, 11.669 Mg ha-1), A. mellifera (7.233%, 3.621 Mg ha-1), whereas remaining 15 species collectively stocked 24.327% (12.187 Mg ha-1) AGB. The STF had a large number of trees compared to some dry forests within Tamil Nadu. Southern Thorn Forest endowed with a moderate number of trees species. Aboveground biomass stockpile of trees is comparable with the range recorded from Indian dry forests. The study area experiences lesser mean annual rainfall and >6 months dry season. Further, endowed with short-bole and smaller leaved trees, hence stocked a relatively lesser AGB in trees.

STUDIES ON THE STIMULATION OF CATALPA BIGNONIOIDES WALT. SEED GERMINATION

Catalpa is a tree with an attractive ornamental value and compact shape. Catalpa bignonioides Walt., is a tree with heights of up to 35 meters and a large trunk. It is an exotic species in North America areal. It grows well in a warm and humid climate, on alluvial, fertile, deep, temperate soils. It has a light temperament, withstands winter frosts well, but is sensitive to late frosts. The degree of germination varies both between species and within them. Within batches of seeds of the same species varies depending on the origin, year of harvest and individual trees. There are different methods and techniques for overcoming drowsiness depending on the seeds. Various pre-treatments are used such as scarification and hot and cold aeration to stimulate the embryo. The paper presents the stimulation of seed germination at catalpa, using different concentrations of Nitragin: 0.1%, 0.3%, 0.5%, 0.7%, 0.9% and following the seed germination interval.

Methodology of Calculating the Number of Trees Based on ALS Data for Forestry Applications for the Area of Samławki Forest District

Airborne Laser Scanning (ALS) is a technology often used to study forest areas. The main area of application of ALS in forests is collecting data to determine the height of individual trees and entire stands, tree density and stand biomass. The content of the ALS data is also classified, i.e., registered objects are identified, including the species affiliation of individual trees. Important information for forest districts includes other parameters related to the structure and share of stands and the number of trees in the forest district. The main goal of this study was to propose the new ALS data processing methodology for detecting single trees in the Samławki Forest District. The idea of the proposed methodology is to indicate a free and accessible solution for any user (at least in Poland). This new ALS data processing methodology contributes to research on the use of ALS data in forest districts to maintain up-to-date and accurate stand statistics. This methodology was based on free data from the geoportal.gov.pl portal and free software, which allowed to minimize the costs of preparing data for the needs of forestry activities. In cooperation with the Samławki Forest District, the proposed methodology was used to detect the number and heights of trees for two forest addresses 13b and 30a, and then to calculate the volume of stands. As a result, the volume of the analyzed stands was calculated, obtaining values differing from the nominal ones included in the FMP (Forest Management Plan) by about 25% and 5%, respectively, for larch and oak.

Application of ImageJ Software in the assessment of flowering Intensity and growth Vigor of pear Trees

The study evaluated the possibility of using the image acquisition and processing method with ImageJ software for estimating growth vigor and flowering intensity of ‘Conference’ pear trees. For assessing flowering intensity, manual counting of flower clusters and taking of photographs of the trees were conducted at full bloom. Tree vigor was estimated by manually measuring the total length of the central leader and shoots of individual trees. The trees were photographed from the same distance using a hand-held camera. The calibration model for assessing the vigor or flowering of trees by image analysis was based on measurements and photographs taken for nine selected trees differing in the total length of shoots or in the number of flower clusters. Then, a quality assessment of the model was carried out on 26 nonselected trees. Image processing was performed using ImageJ software. High regression coefficients were obtained between the surface area of petals measured on the photographs and the number of inflorescences counted (r2 = 0.98); however, observations carried out in the following year indicate the need for individual calibration of estimation models in each evaluation season. Subsequently, the quality of estimating the flowering intensity of pear trees was assessed using a previously determined calibration model. Mean absolute percentage error (MAPE) values ranged from 14.0% to 21.8%, depending on the measurement time. In the assessment of tree growth vigor, a high correlation (r2 = 0.98) was also obtained between the actual length of shoots measured individually for each tree and the values obtained by analyzing the photographic image, where the MAPE error was 12.9%.

Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation.

Directional Variability in Response of Pinus koraiensis Radial Growth to Climate Change

Climate change affects forest ecosystems at a variety of scales, from the composition of landscapes to the growth of individual trees. Research across regions and tree species has produced contradictory findings on the effects of climate variables on radial growth. Here, we examine tree ring samples taken from four directions of a tree to determine whether there is directional variability in tree growth in relation to climate trends. The results showed directional differences in the temporal growth processes of Pinus koraiensis, with more commonalities between the west and north directions and between the east and south directions. The contemporaneous June maximum temperature was the main climate factor associated with the difference between the growth of tree rings toward the east or west. Annual tree ring growth toward the east was more affected by the year’s temperature while growth toward the south was more sensitive to the year’s precipitation. Our research demonstrates that diverse response of tree growth to climate may exist at intra-individual scale. This contributes to understanding the sensitivity of tree growth to climate change at differ scales.