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Scattered Greek juniper (Juniperus excelsa M. Bieb.) trees have ecological values, such as soil protection and soil erosion reduction in the Irano-Turanian region; however, intensive exploitation puts their habitats at risk. Therefore, knowing the soil characteristics of these habitats plays an important role in their management. The aim of this study was to investigate the soil quality of semi-arid woodlands dominated by a pure Juniperus excelsa community. At three sites in the Irano-Turanian region, soil samples were randomly taken from a depth of 0-20 cm in the eastern direction under the crowns of Greek juniper trees and from bare soil. The evaluated physical properties included bulk density (Bd), moisture, sand percentage, silt percentage and clay. Chemical properties included pH, electrical conductivity (EC), organic carbon (SOC), total nitrogen (TN), and nutrient concentrations of bare soil compared to the below-crown soil. Additionally, we investigated basal respiration (BR) and microbial biomass carbon (MBC) to evaluate the influence of Greek juniper trees on soil microbial activity. The bulk density value in samples taken under crowns was significantly lower by 42% compared to bare soil; however, significantly higher soil moisture (+39.8%) was obtained under tree crowns. Although clay and sand content under tree crowns was significantly higher than that of bare soil, sand content under tree crowns was significantly lower than in bare soil. According to our findings, soil pH under tree crowns and in bare soil was 7.13, which increased to 7.67 in bare soil. Soil EC (+15%), SOC (+76%), and TN (+29%) were significantly higher under tree crowns. The concentrations of magnesium and sodium were similar between bare and below-crown soils, but significantly higher phosphorus (+46%), potassium (+41%), calcium (+31.1%), iron (43.3), and zinc (+56.6%) were observed under Greek juniper crowns. Soil microbial activity was higher under tree crowns compared to bare soil, as evidenced by significantly increased BR (+49.1%) and MBC (+43.5%). Our findings indicate that scattered Greek juniper trees can improve soil properties, and their destruction leads to a significant decrease in soil quality. Generally, planting Greek juniper seedlings in degraded areas can help restore the soil quality.

Dividing a forest point cloud dataset into tiles is a common practice in point cloud processing (e.g., individual tree segmentation), aimed at addressing memory constraints and optimizing processing efficiency. Existing methods typically utilize automatic regular clipping (e.g., rectangular clipping), which tends to result in splitting tree crowns along the cutting lines. To preserve the completeness of tree crowns within predefined clipping boundaries (e.g., rectangles), we develop a tree crown edge-aware (E-A) point cloud clipping algorithm, named E-A algorithm. Firstly, the crown edge and distance features are enhanced and quantified using mathematical morphology and nearest neighbor pixel methods. Then, these two features are linearly weighted and integrated for cutting line detection. Finally, the optimal cutting lines are detected by exploring a set of edges with the minimum sum of integrated feature values. E-A algorithm was tested with airborne LiDAR point clouds collected from China’s Saihanba Forest Farm, comparing it against regular clipping methods. The results indicate that E-A algorithm can automatically and effectively emphasize preserving tree crown completeness within predefined clipping boundaries. It reduces crown fragmentation errors by 73.29% on average and maintains an average area difference of 6.42% compared to regular clippings. This algorithm provides a crucial tool for forest point cloud applications.

Abstract Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation. Herein, we characterised allometric scaling using the dynamic ratio (r) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide. We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D2H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates. These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation.

This paper proposes a new meta-heuristic optimization algorithm, the crown growth optimizer (CGO), inspired by the tree crown growth process. CGO innovatively combines global search and local optimization strategies by simulating the growing, sprouting, and pruning mechanisms in tree crown growth. The pruning mechanism balances the exploration and exploitation of the two stages of growing and sprouting, inspired by Ludvig’s law and the Fibonacci series. We performed a comprehensive performance evaluation of CGO on the standard testbed CEC2017 and the real-world problem set CEC2020-RW and compared it to a variety of mainstream algorithms such as SMA, SKA, DBO, GWO, MVO, HHO, WOA, EWOA, and AVOA. The best result of CGO after Friedman testing was 1.6333/10, and the significance level of all comparison results under Wilcoxon testing was lower than 0.05. The experimental results show that the mean and standard deviation of repeated CGO experiments are better than those of the comparison algorithm. In addition, CGO also achieved excellent results in specific applications of robot path planning and photovoltaic parameter extraction, further verifying its effectiveness and broad application potential in practical engineering problems.

This paper introduces a novel method for accurately calculating the upper biomass of single trees using Light Detection and Ranging (LiDAR) point cloud data. The proposed algorithm involves classifying the tree point cloud into two distinct ones: the trunk point cloud and the crown point cloud. Each part is then processed using specific techniques to create a 3D model and determine its volume. The trunk point cloud is segmented based on individual stems, each of which is further divided into slices that are modeled as cylinders. On the other hand, the crown point cloud is analyzed by calculating its footprint and gravity center. The footprint is further divided into angular sectors, with each being used to create a rotating surface around the vertical line passing through the gravity center. All models are represented in a matrix format, simplifying the process of minimizing and calculating the tree’s upper biomass, consisting of crown biomass and trunk biomass. To validate the proposed approach, both terrestrial and airborne datasets are utilized. A comparison with existing algorithms in the literature confirms the effectiveness of the new method. For a tree dimensions estimation, the study shows that the proposed algorithm achieves an average fit between 0.01 m and 0.49 m for individual trees. The maximum absolute quantitative accuracy equals 0.49 m, and the maximum relative absolute error equals 0.29%.

Individual tree detection from airborne laser scanning (ALS) point clouds is the basis for forestry inventory and further applications. In the past decade, many methods have been developed to localize tree instances in ALS point clouds. These methods rely on empirical rules and field measurements that may change from plot to plot. Besides, most existing methods cannot consider multiple clues (e.g., shape priors and neighboring trees) under the same framework, which makes them not flexible and extensible. In this letter, we devise a new point-based and model-driven framework named “crown guess and selection”. This framework first generates crown candidates automatically, and then the qualities of candidates and their neighboring information are both considered. Finally, expected crowns are selected from candidates simultaneously. The proposed framework is tested and evaluated in a benchmark dataset. We also compare the new framework with several existing methods, and it turns out that the proposed framework outperforms others in terms of model flexibility and detection accuracy.

Striving for climate neutrality and wider implementation of climate change mitigation measures including tree introduction in agricultural land, request for approaches and general allometric models for estimating carbon (C) stock in tree above-ground biomass (AGB) based on relatively easily obtainable remote sensing data is increasing. Here, we present estimates of individual trees’ crown diameters (CDs) for Betula pendula Roth (B. pendula) and Populus tremuloides Michx. × Populus tremula L. (P. tremuloides × P. tremula) in 11-year-old tree plantations (tree height ranged up to 12.8 and 18.1 m, respectively) in the hemiboreal region of Europe (in Latvia). Individual trees’ CDs were measured using a drone orthophoto map. Afterwards, linear equations were developed to predict individual trees’ stem diameters at breast height (DBHs) and, consequently, tree AGB (which was then converted to C stock) from remote-sensed tree CD data. RMSEs of the prediction models of tree stem DBH were in the ranges of 1.87–2.12 cm for B. pendula and 2.50–3.12 cm for P. tremuloides × P. tremula. This demonstrated approach is applicable to carry out, for instance, a self-assessment and approximate C stock in the AGB of selected tree species by land owners, managers, and other implementers of climate change mitigation measures.

Inter-tree competition can be assessed using relatively simple indices derived from tree diameters, heights and locations, but they have often been found to be deficient for predicting tree growth. To better understand these linkages, we measure dimensions of Scots pine and Norway spruce crowns, which are assumed to be affected by competition pressure. We extract these features from terrestrial laser scanning point clouds and model their dependencies on competition. Our results indicate that while competition is a major determinant for crown morphology, the characteristics and most applicable indices of the two species are contrasting. We interpret our results primarily by light competition: pines are seeking for light and invest their resources on widening the crown only in suitable conditions, while spruce may grow large despite of shortage of light. We conclude that shade tolerance affects strongly on the identification of actual competitors, which should be addressed when modelling competition.

Testate amoebae (TA) are important components of soil ecosystems, where they play an essential role in belowground food webs. In this study, we examined species composition, diversity, and structure of TA assemblages in soils beneath tree crowns (microscale) at three slope positions (mesoscale) in subtropical urban parks in Shenzhen, China. Forty-two species of TA belonging to 18 genera were identified in 81 samples. TA assemblages were the most diverse and abundant in the center of the tree crown comparing with other under-tree habitats. Foot of the hills harbored higher TA abundance and diversity comparing with upper locations along the hillslopes. The distribution of TA was mostly driven by under-crown and hillside positions but not by elementary environmental conditions such as pH, moisture content and thickness of leaf litter layer. None of later factors were sufficient in shaping TA assemblage composition. The findings of our study suggest that in regional studies of soil microbial eukaryotes both micro- (i.e., under-crown) and mesoscale (i.e., the slope position) heterogeneity should be considered.

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

The accurate assessment of tree crowns is important for agriculture, for example, to adjust spraying rates, to adjust irrigation rates or even to estimate biomass. Among the available methodologies, there are the traditional methods that estimate with a three-dimensional approximation figure, the HDS (High Definition Survey), or TLS (Terrestrial Laser Scanning) based on LiDAR technology, the aerial photogrammetry that has re-emerged with unmanned aerial vehicles (UAVs), as they are considered low cost. There are situations where either the cost or location does not allow for modern methods and prices such as HDS or the use of UAVs. This study proposes, as an alternative methodology, the evaluation of images extracted from Google Maps (GM) for the calculation of tree crown volume. For this purpose, measurements were taken on orange trees in the south of Spain using the four methods mentioned above to evaluate the suitability, accuracy, and limitations of GM. Using the HDS method as a reference, the photogrammetric method with UAV images has shown an average error of 10%, GM has obtained approximately 50%, while the traditional methods, in our case considering ellipsoids, have obtained 100% error. Therefore, the results with GM are encouraging and open new perspectives for the estimation of tree crown volumes at low cost compared to HDS, and without geographical flight restrictions like those of UAVs.

Tree species classification using hyperspectral imagery is a challenging task due to the high spectral similarity between species and large intra-species variability. This paper proposes a solution using the Multiple Instance Adaptive Cosine Estimator (MI-ACE) algorithm. MI-ACE estimates a discriminative target signature to differentiate between a pair of tree species while accounting for label uncertainty. Multi-class species classification is achieved by training a set of one-vs-one MI-ACE classifiers corresponding to the classification between each pair of tree species and a majority voting on the classification results from all classifiers. Additionally, the performance of MI-ACE does not rely on parameter settings that require tuning resulting in a method that is easy to use in application. Results presented are using training and testing data provided by a data analysis competition aimed at encouraging the development of methods for extracting ecological information through remote sensing obtained through participation in the competition. The experimental results using one-vs-one MI-ACE technique composed of a hierarchical classification, where a tree crown is first classified to one of the genus classes and one of the species classes. The species-level rank-1 classification accuracy is 86.4% and cross entropy is 0.9395 on the testing data, provided by the competition organizer, without the release of ground truth for testing data. Similarly, the same evaluation metrics are computed on the training data, where the rank-1 classification accuracy is 95.62% and the cross entropy is 0.2649. The results show that the presented approach can not only classify the majority species classes, but also classify the rare species classes.

This illustrated review is aimed to provide succinct account of different methods applicable for obtaining objective information about the functional ecology of forest sites and stands (and also, e.g., orchards, watersheds, parks or alleys) for the purposes of phytotechnology or precise forestry. Management strategies must consider preservation of many important functions of forests in the landscape. Management methodologies include classical forestry approaches based on classification of the natural environment according to climatic zones, soil characteristics and composition of herbaceous as well as woody vegetation. One possibility for obtaining objective information came with the development of ecologically applicable instrumentation, allowing studies on the whole tree and stand levels to be undertaken anywhere in the field. Information may be derived at sites that are not permanently observed and are not equipped with additional constructions, such as towers, masts, or greenhouses. These field applicable methods are focused especially on tree and stand macrostructure, water relations and functional parameters of trees. In addition, problems associated with interactions between trees and different organisms are also discussed. A series of methods based on different principles is useful to measure mostly quantitative, operative or effective tree parameters as tree crowns and leaf distribution, stems (or large tree trunks) and root systems including skeleton and absorptive fine roots. This is sometimes possible to do with rather simple methods, but instrumental methods prevail especially for root studies. In addition to several classical measurement principles including anatomical or mechanical sensing, the methods work with sound speed (acoustic tomography) using electromagnetic rays of different wavelength (from radar to optical), electric conductivity or impedance, supersonic air stream and thermodynamics for example. Methods for data evaluation and examples of application are also included. The whole complex system approach should serve for different scientific fields (including hydrology, landscape care, horticulture, forensic engineering, etc.) and especially to support precise forestry.

Sustainable forest management in Zagros Mountains entails accurate information on tree crown density, which could be possibly derived from remote sensing data. Moreover, those remote sensing products need to be objectively evaluated. In this study, the results of three pixel-based classifiers of UltraCam-D aerial imagery were evaluated for classifying Brant`s oak (Quercus brantii Lindl.) crowns in Zagros forests in western Iran. This was carried out by means of receiver operating characteristic (ROC) curve. Therefore, a 30 ha plot was selected in pure Brant`s oak stand, in which the location and crown area of all trees were mapped. The UltraCam-D aerial imagery was classified by maximum likelihood (ML), artificial neural networks (ANNs) and support vector machines (SVMs) classifiers. The classification results were then evaluated by ROC curve and were presented by overall accuracy and Kappa coefficient. Results showed that the ML classified returned the largest area under ROC curve of "tree crowns" (0.894), whereas the lowest rate was found for SVM classifier (0.819). Sensitivity and specificity of "tree crowns" in ML classifier (0.999 and 0.999, respectively) were higher than those in two other classifiers. Although the precision of SVM classifier was the highest in discriminating "tree crowns" (1.000), the achieved accuracy of tree “crown class” was higher for ML classifier (0.999). This study concluded that using ROC curve enables an evaluation accuracy and precision of common pixel-based classifiers of such aerial imagery to discriminate tree crowns.

Transport caused air pollution is one of the most topical problems in the country. One of the pollutants emitted by the vehicles into the environment is nitrogen dioxide, which adversely affects the plant foliage. At nitrogen dioxide concentration of 30 µg/m3, defoliation process becomes more intense, the growth of trees becomes slower and non-specific leaf choruses symptoms become evident. The aim of research is to investigate of NO2 concentrations and assess the intensity of defoliation process within traffic areas of different intensity. To determine nitrogen dioxide content, the diffusion samplers, which are exhibited after 2 weeks and analyzed in a laboratory, are used. Crown defoliation is determined by comparing the tree to be researched with a reference tree that has all leaves (100%). The main source of nitrogen dioxide in the city is passing vehicles; therefore, research results will reflect the current situation. Santrauka Transporto sukeliama oro tarša yra viena iš aktualiausių problemų šalyje. Vienas iš transporto išmetamų į aplinką teršalų yra azoto dioksidas, kuris neigiamai veikia augalų lapiją. Esant 30 µg/m3 koncentracijai azoto dioksido, suintensyvėja defoliacijos procesas, sulėtėja medžių augimas, atsiranda nespecifinės lapų chlorozės požymių. Tyrimo tikslas − ištirti NO2 koncentracijas ir įvertinti defoliacijos proceso intensyvumą skirtingo intensyvumo eismo zonose. Azoto dioksido kiekiui nustatyti naudojami difuziniai ėmikliai, kurie eksponuojami po 2 savaites ir tiriami laboratorijoje. Lajos defoliacija nustatoma lyginant tiriamą medį su etaloniniu medžiu, kuris turi visus (100 %) lapus. Mieste pagrindinis azoto dioksido šaltinis yra pravažiuojantis transportas, todėl tyrimo rezultatai atspindės esamą situaciją. Reikšminiai žodžiai: azoto dioksidas, defoliacija, oro tarša, automobilių transportas, eismo intensyvumas, medžių laja.

We studied old beech trees individually dispersed in spruce monocultures after having found wildlings in their vicinity. The goal was to define stem and crown dimensions of the trees in dependence on their position in the primary spruce stand and to find out what kind of development they went through before reaching the current condition. We made an inventory of 883 trees in about 800 ha of stands growing in the fir-beech forest altitudinal zone (FAZ). A detailed biometric analysis conducted on two research plots of a total area 19 ha included 110 trees. Their age is 140-180 years as indicated by the analysis of annual rings. Thus, they grow in the second generation of the spruce stand. The fact gives them an absolute competitive advantage, which can be documented by their size (dbh = 49-93 cm as compared with the spruce = 15-66 cm, crown width 8-17 m as compared with the spruce = 1-10 m) as well as by the relation of their disposable (ADISP) and social (ASOC) areas to the size of horizontal crown projection. Regarding the size, the good condition of the crown and the expected recurrent fructification, we can consider these trees suitable for use in the systematic conversion of spruce monocultures into mixed forests for a long time.

In the past, many algorithms have been applied for three-dimensional (3-D) single tree extraction using Airborne Laser Scanner (ALS) data. Clustering based algorithms are widely used in different applications but rarely being they used in the field of forestry using ALS data as an input. In this paper, a comparative qualitative study was conducted using the iterative partitioning and hierarchical clustering based mechanisms and full waveform ALS data as an input to extract the individual trees/tree crowns in their most appropriate shape. The full waveform LIght Detection And Ranging (LIDAR) data was collected from the Waldkirch black forest area in the south-western part of Germany in August 2005 with density of 4–5 points/m2. Both the clustering algorithms were used in their original and modified form for a comparative qualitative analysis of the results obtained in the form of individual clusters containing 3-D points for each tree/tree crown. A total of 378 trees were found in all the 1.2 ha area with height ranging from 15 m to 50.9 m. The forest contains mainly older trees with deciduous, coniferous and mixed stands. The findings showed that among the three kind of clustering methods applied (normal k-means, modified k-means and hierarchical clustering), the modified k-means algorithm using external seed points and scaling down the height for initialization of the clustering process was the most promising method for the extraction of clusters of individual trees/tree crowns. A 3-D reconstruction of extracted individual clusters was carried out using QHull algorithm. In this study, the result was not possible to validate quantitatively due to lack of the field inventory data.

Data pits commonly appear in lidar-derived canopy height models (CHMs) owing to the penetration ability of airborne light detection and ranging (lidar) into tree crowns. They have a seriously negative effect on the quality of tree detection and subsequent biophysical measurements. In this study, we propose an algorithm based on robust locally weighted regression and robust z-scores for the construction of a pit-free CHM. A significant advantage of the new algorithm is that it is parameter free, which makes it efficient and robust for practical applications. Simulated and airborne lidar-derived data sets are employed to assess the performance of the new method for CHM construction, and its results are compared to those of three classical methods, namely the natural neighbor (NN) interpolation of the highest point method (HPM), mean filter, and median filter. The results from the simulated data set demonstrate that our algorithm is more accurate compared to the three classical methods for generating pit-free CHMs in the presence of data pits. CHM construction using the lidar-derived data set shows that, compared to the classical methods, the new method has a better ability to remove data pits as well as preserving the edges, shapes, and structures of canopy gaps and crowns. Moreover, the proposed method performs better compared to the classical methods in deriving plot-level maximum tree heights from CHMs. Thus, the new method shows high potential for pit-free CHM construction.

Before the availability of digital photography resulting from the invention of charged couple devices in 1969, the measurement of plant architecture was a manual process either on the plant itself or on traditional photographs. The introduction of cheap digital imaging devices for the consumer market enabled the wide use of digital images to capture the shape of plant networks such as roots, tree crowns, or leaf venation. Plant networks contain geometric traits that can establish links to genetic or physiological characteristics, support plant breeding efforts, drive evolutionary studies, or serve as input to plant growth simulations. Typically, traits are encoded in shape descriptors that are computed from imaging data. Skeletons are one class of shape descriptors that are used to describe the hierarchies and extent of branching and looping plant networks. While the mathematical understanding of skeletons is well developed, their application within the plant sciences remains challenging because the quality of the measurement depends partly on the interpretation of the skeleton. This article is meant to bridge the skeletonization literature in the plant sciences and related technical fields by discussing best practices for deriving diameters and approximating branching hierarchies in a plant network.