Crown Projection Area Research Articles

Interspecific variation in tree- and stand-scale stemflow funneling ratios in a subtropical deciduous forest in eastern China

Stemflow is an important water flux in forested ecosystems, which is influenced by both biotic and abiotic factors. The stemflow funneling ratio, FP, is a straightforward parameter to express the funneling efficiency of individual trees,FP,Bt, or at the stand-scale, FP,Bs. Nevertheless, no known studies have compared the FP and its influencing factors between tree- and stand-scales in a subtropical deciduous forest. In this study, FP values from Q. acutissima and B. papyrifera trees were computed for a subtropical area of eastern China, and examined in relation to five biotic variables and ten abiotic variables. The relative contribution ratio (RCR) of each biotic and abiotic variable to FP,Bt and FP,Bs were evaluated by relative importance analysis. The median FP,Bs of B. papyrifera (104.2) was significantly higher than that of Q. acutissima (62.2), with the median FP,Bt of Q. acutissima and B. papyrifera both varying greatly among individual trees. Projected crown area (RCR = 29.2%) and diameter at breast height (RCR = 21.4%) were the two most important biotic factors governing FP,Bt of Q. acutissima, while the two critical biotic factors affecting FP,Bt of B. papyrifera were trunk lean (RCR = 62.2%) and tree height (RCR = 15.1%), respectively. At the stand scale, gross precipitation (GP), rainfall duration (DE) and vapor pressure deficit (D) were the three most critical factors affecting FP,Bs of Q. acutissima and B. papyrifera. GP and DE exerted positive effects but D a negative effect on stemflow generation. These findings provide new insights into the FP at both tree- and stand-scales within subtropical ecosystems.

Exploring UAV-imagery to support genotype selection in olive breeding programs

Airborne methodologies based on unmanned aerial vehicles (UAV) are becoming an extraordinary tool for implementing fast, accurate and affordable phenotyping strategies within plant breeding programs. The aim of this paper was to study the potential use of a previously developed UAV-OBIA platform, to fasten and support decision making for olive breeders regarding the selection of the most promising genotypes in terms of tree geometric traits. In particular, we have studied the feasibility of the system to efficiently classify and select olive genotypes according to four architectural parameters: tree height, crown diameter, projected crown area and canopy volume. These vegetative growth traits and their evolution during the first months after planting are key selection criteria in olive breeding programs. On-ground measurements and UAV estimations were recorded over two years (when trees were 15 and 27 months old, respectively) in two olive breeding trials using different training systems, namely intensive open vase and super high-density hedgerows. More than 1000 young trees belonging to 39 olive accessions, including new cross-bred genotypes and traditional cultivars, were assessed. Even though the accuracy in the UAV estimation compared to the on-ground measurements largely improved the second year, both methodologies detected in both years a high variability and significant differences among the studied genotypes, allowing for statistical comparisons among them. Genotype rankings based on the on-ground measures and UAV estimations were compared. The resulting Spearman’s rank coefficient correlations were very high, at above 0.85 in most cases, which highlights that very similar genotype classifications were achieved from either field-measured or airborne-estimated data. Thus, UAV imagery may be used to assess geometric traits and to develop rankings for the efficient screening and selection of genotypes in olive breeding programs.

Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.

Analysis of Canopy Height Model (CHM) Extraction using Quick Terrain Modeller (QTM) for Tropical Forest Area

Forest biomass or above-ground carbon stock is the mass of carbon that stored in trees which requires a continuous monitoring in order to predict the amount of potential carbon accumulation of the forest. Therefore, the forest has an important role at absorbing carbon Dioxide (CO2) from the atmosphere. This research aims to measure the capability of Quick Terrain Modeller software at estimating above-ground carbon stock by single tree segmentation combining ground inventory, Light Detection and Ranging (LiDAR), and by using allometric equations. In particular, to achieve the aim, there are three (3) objectives were outlined. Canopy Height Models (CHM) was generated via Quick Terrain Modeller (QTM) and ArcGIS. Non-linear Regression analyses were performed for both surface models to ensure the models were fit to estimate carbon stock. Secondly, tree contours were delineated using watershed transformation. Local maxima were determined at the raster as a pour point for watershed and also represent the highest peak of the tree crown. In addition, flow direction, drop output, and flow accumulation of the raster were also determined to generate contour from the watershed transformation. Manual tree crown projection was performed by watershed tree contour to generate Crown Projection Area (CPA). Then, from the digitized CPA, carbon stock and above-ground biomass was calculated using equations from [1] and [2]. Thirdly, tree species on the selected area were extracted and finally a map of tree carbon stock by species was produced. From the generated map, total carbon stock according to species and total carbon stock in single tree according to species information were extracted. As a result, Hopea sulcata; the endangered tree species appeared to be the highest appearance in the map followed by Dipterocarpus verrucosus, Shorea macroptera, Endospermum diadenum, and the other less appeal species. Also from the map, Hopea Sulcata has the highest carbon stock which is 23% compared to the other species. However, for a single tree, Dipterocarpus verrucosus held the highest carbon stock which is 1565.401 kg/tree.

Crown plasticity of five pine species in response to competition along an aridity gradient

Crown dimensions such as diameter or crown projection area are important indicators for tree vigour, growing stage or production efficiency and can be used as proxies for the assessment of wood quality, leaf area and leaf biomass, biodiversity, wildlife habitat and disturbance risk to forest stability. Crown allometry can vary with site and competition conditions, but variation patterns are not still well understood.The main aim of this study was to analyze this allometry variation in the most common pine species along the aridity gradient of the Iberian Peninsula, studying the crown response to aridity and to competition, and determining whether the presence of inter-specific competition modifies crown allometry.A set of 40,402 sample trees from 12,283 sample plots, along with the Martonne aridity index as an indicator of climatic conditions, were used to develop allometric models, the parameters of which were generalized by including aridity and competition indicators. The plots were located in monospecific or mixed stands of Pinus sylvestris, Pinus nigra, Pinus pinaster, Pinus pinea and Pinus halepensis.According to our results, both aridity conditions and competition status are important factors modifying the crown allometry of the studied species. Moreover, we found that both size-symmetric competition and size-asymmetric competition should be taken into account when modelling the crown size, both types leading to smaller crown diameter. The crown plasticity of the studied pine species in response to competition presented a low range of variation, the greatest being that of maritime pine, while Scots pine and black pine showed the smallest. The way in which aridity and competition influence crown size differs among the studied species, indicating differences in crown plasticity patterns. Moreover, in humid sites, where competition for light is more important, the crown allometry response to changes in competition is greater, being most evident in P. pinea and P. halepensis.The species mixing effects had less influence on pine crown allometry than the total competition effect, although the effect was always significant. The effect of size-symmetric competition from admixed species was positive or neutral, except for the effect of P. sylvestris on P. nigra crowns. Size-asymmetric competition also plays an important role in the species mixing effects, these effects on intermediate or suppressed trees varying depending on the species mixture. The models developed reveal significant species-specific effects on crown allometry, pointing to the ability of pine species to adapt their crowns to different growing conditions.

High-Resolution Mapping of Forest Carbon Stock Using Object-Based Image Analysis (OBIA) Technique

This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)—WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. Object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.

Extracting apple tree crown information from remote imagery using deep learning

Manual measurement and visual inspection is a common practice for acquiring crop data in orchards and is a labor-intensive, time-consuming, and costly task. Accurate and rapid acquisition of crop data is vital for monitoring the dynamics of tree growth and optimizing farm management. In this work, we present a technique for orchard data acquisition and analysis that uses remote imagery acquired from unmanned aerial vehicles (UAVs) combined with deep learning convolutional neural networks to automatically detect and segment individual trees and measure the crown width, perimeter, and crown projection area of apple trees. By using an UAV platform, 50 high-resolution images of apple trees were collected from an orchard during dormancy (bare branches), and then each apple tree was detected by using a Faster R-CNN object detector. Based on these results, each tree was segmented by using a U-Net deep learning network. After convex tree boundaries were extracted from the semantic segmentation results by using an efficient pruning strategy, the crown parameters were automatically calculated, and the accuracy was compared with that obtained by manual delineation. The results show that the proposed remote sensing technique can be used to detect and count apple trees with precision and recall of 91.1% and 94.1%, respectively, segment their branches with an overall accuracy of 97.1%, and estimate crown parameter with an overall accuracy exceeding 92%. We conclude that this method not only saves labor by avoiding field measurements but also allows growers to dynamically monitor the growth of orchard trees.

Prediction of Individual Tree Diameter Using a Nonlinear Mixed-Effects Modeling Approach and Airborne LiDAR Data

Rapidly advancing airborne laser scanning technology has become greatly useful to estimate tree- and stand-level variables at a large scale using high spatial resolution data. Compared with that of ground measurements, the accuracy of the inferred information of diameter at breast height (DBH) from a remotely sensed database and the models developed with traditional regression approaches (e.g., ordinary least square regression) may not be sufficient. Thus, this regression approach is no longer appropriate to develop accurate models and predict DBH from remotely sensed-related variables because DBH is subject to the random effects of forest stands. This study developed a generalized nonlinear mixed-effects DBH estimation model from remotely sensed imagery data. The light detection and ranging (LiDAR)-derived stand canopy density, crown projection area, and tree height were used as predictors in the DBH estimation model. These variables can be more readily measured over an extensive forest area with higher accuracy compared to the conventional field-based methods. The airborne LiDAR data for a total of 402 Picea crassifolia Kom trees on a sample plot that were divided into 16 sub-sample plots and located in the most important distribution region of western China were used. The leave-one sub-sample plot-out cross-validation method was applied to evaluate the model’s prediction accuracy. The results indicated that the random effects of the sub-sample plot on the prediction of DBH were large and their inclusion into the DBH model significantly improved the prediction accuracy. The prediction accuracy of the proposed model at the mean (M) response was also substantially improved relative to the accuracy obtained from the base model. Among several tree selection alternatives evaluated, a sample size of the two largest trees per sub-sample plot used in estimating the random effects showed a significantly higher accuracy compared to other sampling alternatives. This sample size would balance both the measurement cost and potential prediction errors. The nonlinear mixed-effects DBH estimation model at the M response can also be applied if obtaining the estimates of individual tree DBH with a relatively lower cost, and a lower prediction accuracy was the purpose of the study.

Quantifying urban forest structure with open-access remote sensing data sets

Future cities are set to face ever increasing population and climate pressures, ecosystem services offered by urban forests have been recognised as providing significant mitigation for these pressures. Therefore, the ability to accurately quantify the extent and structure of urban forests, across large and highly dynamic cities, is vital for determining the value of services provided and to assess the effectiveness of policy to promote these important assets. Current inventory methods used in urban forestry are mostly reliant on plot networks measuring a range of structural and demographic metrics; however, limited sampling (spatially and temporally) cannot fully capture the dynamics and spatial heterogeneity of the urban matrix. The rapid increase in the availability of open-access remote sensing data and processing tools offers an opportunity for monitoring and assessment of urban forest structure that is synoptic and at high spatial and temporal resolutions. Here we present a framework to estimate urban forest structure that uses open-access data and software, is robust to differences in data sources, is reproducible and is transferable between cities. The workflow is demonstrated by estimating three metrics of 3D forest structure (canopy cover, canopy height and tree density) across the Greater London area (1577 km2). Random Forest was trained with open-access airborne LiDAR or iTree Eco inventory data, with predictor variables derived from Sentinel 2, climatic and topography data sets. Output were maps of forest structure at 100 m and 20 m resolution. Results indicate that forest structure can be accurately estimated across large urban areas; Greater London has a mean canopy cover of ∼16.5% (RMSE 11-17%), mean canopy height of 8.1–15.0 m (RMSE 4.9–6.2 m) m and is home to ∼4.6 M large trees (projected crown area >10 m2). Transferability to other cities is demonstrated using the UK city of Southampton, where estimates were generated from local and Greater London training data sets indicating application beyond geographic domains is feasible. The methods presented here can augment existing inventory practices and give city planners, urban forest managers and greenspace advocates across the globe tools to generate consistent and timely information to help assess and value urban forests.

Modelling the Crown Profile of Western Hemlock (Tsuga heterophylla) with a Combination of Component and Aggregate Measures of Crown Size

Research Highlights: We present statistical methods for using crown measurement data from multiple destructive sampling studies to model crown profiles in the Tree and Stand Simulator (TASS) and evaluate it using component (branch-level) and aggregate (tree-level) predictions. Combining data collected under different sampling protocols offered unique challenges. Background and Objectives: The approach to modelling crown profiles was based on Mitchell’s monograph on Douglas-fir growth and simulated dynamics. The functional form defines the potential crown size and shape and governs the rate of crown expansion. With the availability of additional data, we are able to update these functions as part of ongoing TASS development and demonstrate the formulation and fitting of new crown profile equations for stand-grown western hemlock (Tsuga heterophylla (Raf.) Sarg. Materials and Methods: Detailed measurements on 1616 branches from 153 trees were collected for TASS development over a 40-year period. Data were collected under two different sampling protocols and the methods were designed to allow the use of data from both protocols. Data collected on all branches were then introduced through the application of the ratio of length of each of the selected branches to the largest branch in the internode (RL). Results: A mixed-effects model with two random effects, which accounted for tree-level variation, provided the best fit. From that, a model that expressed one parameter as a function of another with one random effect was developed to complement the structure of the Tree and Stand Simulator (TASS). The models generally over-estimated crown size when compared to the projected crown area recorded from field measurements, and a scalar adjustment factor of 0.89 was applied that minimised mean-squared error of the differences. The new model is fit from direct measures of crown radius and predicts narrower crown shapes than previous functions used in TASS.

Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner

Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two vegetative shoots. Very few studies were carried out in evaluating how new remote sensing technologies such as terrestrial laser scanners (TLS) can be used in assessing cone production, or in trying to explain the tree-to-tree variability within a given stand. Using data from 129 trees in 26 plots located in the Spanish Northern Plateau, the gain observed by using TLS data when compared to traditional inventory data in predicting the presence, the number, and the average weight of the cones in an individual tree was evaluated. The models using TLS-derived metrics consistently showed better fit statistics, when compared to models using traditional inventory data pertaining to site and tree levels. Crown dimensions such as projected crown area and crown volume, crown density, and crown asymmetry were the key TLS-derived drivers in understanding the variability in inter-tree cone production. These results underline the importance of crown characteristics in assessing cone production in stone pine. Moreover, as cone production (number of cones and average weight) is higher in crowns with lower density, the use of crown pruning, abandoned over 30 years ago, might be the key to increasing production in combination with stand density management.

Stand structure, regeneration and seed dispersal patterns of Nothofagus glauca (Hualo) in central Chile

Growing interest in functional silviculture is promoting new approaches to the study of seed dispersal and regeneration, two of the key processes that determine the structure and dynamics of plant populations. Nothofagus glauca is an obligate-seeding tree species typical of Mediterranean forests in central Chile. We studied stand structure, regeneration, seed release (quantity and quality) and seed dispersal in stands of N. glauca. Seed release started in mid- to late summer or in early autumn, and continued for a period of four to five months. Seed release reached a number of up to 2 million seeds per hectare. Seed shadow was characterised by a peak of density beneath the plant canopy and a very short tail of much lower densities, indicating that seeds are concentrated beneath mother plants when dispersed. The number of individuals who produced seeds during the season was estimated at 100 trees per hectare, and the highest frequency of seeding trees was observed in the diameter class of 28 cm to 48 cm, with the highest production of seeds in the 43 cm individuals (70 years old). Ninety percent of the regeneration was concentrated within the seed tree crown radius. Only a marginal presence of seedlings was observed outside the crown projection area and decreased as the distance to the base of the tree increased, until reaching a maximum distance of 12 m. It can be concluded that adult N. glauca forests rarely disturbed, tend to form multi-age stand structures.

Yield and must composition of grapevines subjected to phosphate fertilization in Southern Brazil

Abstract: The objective of this work was to evaluate the yield and must composition of 'Cabernet Sauvignon' and 'Chardonnay' grapevines subjected to phosphorus applications to a soil from a high-altitude region of Southern Brazil, during three crop seasons. Experiments 1 and 2 were carried out in 'Cabernet Sauvignon' and 'Chardonnay' commercial vineyards, respectively, in the municipality of Água Doce, located in the Midwestern region of the state of Santa Catarina. The soil from the two vineyards was classified as a Typic Humicryept. The used source of P was triple superphosphate (45% P2O5). From 2011 to 2013, at flowering, a total of 0, 10, 20, 40, and 80 kg ha-1 P2O5 were applied in the crown projection area, to the soil surface, without incorporation, in both vineyards. Soil and leaf P concentration, yield parameters, and must quality were evaluated. The increase in P availability decreased total titratable acidity and tartaric acid in the must of the 'Cabernet Sauvignon' grapevine in the 2013/2014 crop season and increased pH and total soluble solids in the must of the 'Chardonnay' grapevine in the 2011/2012 crop season. Phosphorus application to the soil increases the levels of available phosphorus, but does not result in higher yields for 'Cabernet Sauvignon' and 'Chardonnay' grapevines.

Fertilization Effects on Early Growth, Aboveground Biomass, Carbon Storage, and Leaf Characteristics of Eucalyptus pellita F. Muell. in South Sumatera

Fertilization is one of the nutrient management efforts that play an important role in improving the productivity of plantation forests. It was conducted to provide adequate nutrients for the plant at the initial growth period when the availability of soil nutrients is very limited. The optimum dose of fertilization for each plant is different depending on its requirement, climate, and soil properties. This study investigated the influence of fertilization on early growth, aboveground biomass, carbon storage, and leaf characteristics in Eucalyptus pellita in the sixth months after field establishment. An experiment comprising four treatments of fertilization, i.e., 0, 75, 133, and 167 kg ha -1 of triple superphosphate 46% P2O5 was set up using randomized complete block design with five replications. Four indicators were selected to evaluate the early growth of E. pellita, namely survival rate, height, the collar of diameter, and crown projection area. Aboveground biomass and carbon storage of E. pellita were estimated in each component, covering stem, bark, branches, and leaves. The leaf characteristics of E. pellita were described by individual leaf area, individual leaf dry weight, specific leaf area, and leaf area index. Results showed that the different treatment of fertilization did not have a meaningful effect on survival rate, individual leaf area, and specific leaf area. The treatments presented a substantial effect on other indicators such as height, diameter, crown projection area, aboveground biomass, carbon storage, individual leaf dry weight, and leaf area index. Despite the fact that the highest mean of height and collar of diameter were observed in the dose of 167 kg ha -1, the greatest average of aboveground biomass and carbon storage were noted in the rate of 133 kg ha -1. Nevertheless, this study did not find a highly different performance of E. pellita between both treatments statistically.

ESTIMATION OF ABOVE GROUND FOREST BIOMASS USING ULTRA HIGH RESOLUTION UAV IMAGES: A CASE STUDY FROM BARANDABHAR FOREST, NEPAL

Abstract. Forest biomass is the sum of above ground living organic material contained in trees which is expressed as dry weight per unit area. Forest biomass acts as substantial terrestrial carbon sinks, they are estimated to absorb 2.7 Petagrams of carbon per year, as such accurate estimation of forest carbon stock is very important. The estimation of biomass is also important because of its application in commercial exploitation as well as in global carbon cycle. Particularly in the latter context, the estimation of the total above-ground biomass (TAGB) with sufficient accuracy is vital in reporting the spatial and temporal state of forest under the United Nations Framework Convention on Climate Change (UNFCCC), Reducing Emissions from Deforestation in Developing Countries (REDD). In this research, tree height, DBH and crown cover were measured using field instruments. Individual ultra-high-resolution UAV images acquired using customized Visible-NIR, were georeferenced and tree crown were extracted using multi-resolution segmentation. A regression equation between field measured biomass and Crown Projection Area (CPA) was developed. The paper presents results from Barandabhar Forest of Chitwan District, Nepal. RMSE of ortho-mosaic was found to be 18 cm. While R2 value of 89% was obtained for relationship between DBH and biomass, that of 61% was attained for relationship between CPA and biomass.

Assessing Legacy Effects of Wildfires on the Crown Structure of Fire-Tolerant Eucalypt Trees Using Airborne LiDAR Data

The fire-tolerant eucalypt forests of south eastern Australia are assumed to fully recover from even the most intense fires; however, surprisingly, very few studies have quantitatively assessed that recovery. The accurate assessment of horizontal and vertical attributes of tree crowns after fire is essential to understand the fire’s legacy effects on tree growth and on forest structure. In this study, we quantitatively assessed individual tree crowns 8.5 years after a 2009 wildfire that burnt extensive areas of eucalypt forest in temperate Australia. We used airborne LiDAR data validated with field measurements to estimate multiple metrics that quantified the cover, density, and vertical distribution of individual-tree crowns in 51 plots of 0.05 ha in fire-tolerant eucalypt forest across four wildfire severity types (unburnt, low, moderate, high). Significant differences in the field-assessed mean height of fire scarring as a proportion of tree height and in the proportions of trees with epicormic (stem) resprouts were consistent with the gradation in fire severity. Linear mixed-effects models indicated persistent effects of both moderate and high-severity wildfire on tree crown architecture. Trees at high-severity sites had significantly less crown projection area and live crown width as a proportion of total crown width than those at unburnt and low-severity sites. Significant differences in LiDAR -based metrics (crown cover, evenness, leaf area density profiles) indicated that tree crowns at moderate and high-severity sites were comparatively narrow and more evenly distributed down the tree stem. These conical-shaped crowns contrasted sharply with the rounded crowns of trees at unburnt and low-severity sites and likely influenced both tree productivity and the accuracy of biomass allometric equations for nearly a decade after the fire. Our data provide a clear example of the utility of airborne LiDAR data for quantifying the impacts of disturbances at the scale of individual trees. Quantified effects of contrasting fire severities on the structure of resprouter tree crowns provide a strong basis for interpreting post-fire patterns in forest canopies and vegetation profiles in Light Detection and Ranging (LiDAR) and other remotely-sensed data at larger scales.

Combining unmanned aerial vehicle and multispectral Pleiades data for tree species identification, a prerequisite for accurate carbon estimation

Forest carbon estimation currently largely relies on remote sensing techniques in combination with field measurement. High-resolution images, which are commonly utilized for carbon estimation, are not readily available, and their cost prohibits communities from reaping the benefits of maintaining their forest under the UN reducing emissions from deforestation and forest degradation program. Our study explores the combination of readily available and relatively cheaper unmanned aerial vehicle (UAV) (4-cm resolution) and multispectral Pleiades (50-cm resolution) images for species classification robustness in view for carbon estimation through object-based image analysis. The images are resampled and used to evaluate the effect of combining multispectral Pleiades image on the accuracies of segmenting UAV images for tree crown projection area (CPA) estimation and species classification. RGB images from a UAV platform are processed in a photogrametric software and combined with the near-infrared band of a Pleiades image to get a UAV-Pleiades image composite. The images are segmented using the ESP 2 tool and the segmentation accuracy compared using a paired t -test. The segmented tree crowns are classified using random trees (RT), support vector machines (SVM), and maximum likelihood (ML) classifiers, and the classification accuracies of the three classifiers are compared using the McNemar’s chi-squared test. Our study demonstrates a 93.5% accuracy of segmenting UAV-Pleiades image composite, which is significantly higher than the 84.8% accuracy of segmenting UAV images ( p p p

Influence of foliar feeding on productivity and the quality of apple fruit in conditions of Krasnodar Territory

The article presents the results of a field experiment to study the effectiveness of the special complex fertilizers of various compositions in the fruiting plantings of dwarf apple trees Renet Kubanskiy and Prikubanskoe varieties on rootstock SK4 in the Central zone of Krasnodar Territory. Analysis of the results of the study for 2013-2018 revealed a significant advantage of the option with the systematic use of fertilizing in terms of productivity of apple tree. The number of fruits per square meter of crown projection area on the background of leaf feeding annually exceeded the control variant (without fertilizers) by 8-30 % (Renet Kubanskiy variety), 10-60 % (Prikubanskoe variety). The materiality of exceeding the indicator is proved statistically at the 5 % level of significance. Increased reproductive function throughout the study period was due to an increase in the number of green pigments in the leaves of shoots. On the example of the Prikubanskaya apple varieties was determined that the total content of chlorophylls (a+b) in the first decade of June was higher than in the control variant 1.9- 27.5 %, and in the third decade of August, in the range of 2.0 to 44.5 % . Leaf feeding of trees with water solutions of special fertilizers provided an increase in the quality of apples, determined during the period of removable maturity. Weight of the fruit on background of fertilizing annually exceeded the control variant (without fertilizers) was 1.1 to 3.8 % (Renet Kubanskiy variety), 1.1-3.9 % (Prikubanskoe variety). The most stable significant excess of this parameter was in Prikubanskoe apple varieties and in Renet Kubanskiy apple varieties at planting scheme 4.5×1.2 m. In terms of commercial quality, apples were characterized as the highest and first commercial grades, since against the background of foliar applications at the main mass the coloring of fruits had an area ranging from ½ to ¾ of the surface (Prikubanskoe variety). The output of high-quality products (the highest and first commercial grades) in the variant with the use of dressings significantly exceeds the control option. At the intensity of hydrothermal factors in the summer, the receiving of foliar feeding provides a higher level of adaptation of the pigment apparatus to environmental conditions in apple plants and increases the yield of high-quality fruits.

Sustainable Land Use Management Needed to Conserve the Dragon’s Blood Tree of Socotra Island, a Vulnerable Endemic Umbrella Species

Unsustainable overgrazing is one of the most important threats to the endemic and endangered population of dragon’s blood tree (Dracaena cinnabari) on Socotra Island (Republic of Yemen). However, there is a lack of information about the exact population size and its conservation status. We estimated the population size of D. cinnabari using remote sensing data. The age structure was inferred using a relationship between crown projection area and the number of branch sections. The conservation importance of each sub-population was assessed using a specially developed index. Finally, the future population development (extinction time) was predicted using population matrices. The total population size estimated consists of 80,134 individuals with sub-populations varying from 14 to 32,196 individuals, with an extinction time ranging from 31 to 564 years. Community forestry controlled by a local certification system is suggested as a sustainable land management approach providing traditional and new benefits and enabling the reforestation of endemic tree species on Socotra Island.

Параметри крон дерев експериментальних соснових культур, створених з різною густотою у Житомирському Поліссі

Дослідження проведено у Житомирському Поліссі на стаціонарному досліді № 1, закладеному у 1976 р. Проаналізовано деякі параметри крон дерев (площу проекції, діаметр і протяжність крон дерев), а також зімкнутість пологу насаджень сосни звичайної (Pinus sylvestris L.) різної густоти. Встановлено, що за густоти насадження 4000–1000 шт.·га-1 середній діаметр крони становить 2,4–4,0 м, різниця значень цього показника змінюється у межах 9–40 %. Підтверджено достовірну різницю між середніми діаметрами крон дерев однофакторним дисперсійним аналізом на 95% довірчому рівні, де Fф = 7,82…126,03 > Fт(0,95) = 3,89. Виявлено, що за густоти насаджень 4000–2000 шт.·га-1 форма крони є овальною, тоді як за густоти насадження 1000 шт.·га-1 – майже округлою. Встановлено, що середня проекція площ крон дерев за густоти насадження 4000 і 1000 шт.·га-1 коливається у межах 5,5–6,1 і 10,6–13,1 м2, різниця між цими параметрами становить 42–58 %. Достовірну різницю між величинами проекції площ крон дерев підтверджено однофакторним дисперсійним аналізом, де Fф = 8,0…108,7 > Fт(0,95) = 3,89. Простежено, що середня протяжність крони за густоти насадження 4000 і 1000 шт.·га-1 знаходиться у межах 4,1–5,1 і 5,1–8,1 м, різниця між цими величинами становить 2–43 %. Зроблено висновок про те, що густота впливає на середній діаметр, проекцію площ і протяжність крон дерев, тоді як впливу густоти на зімкнутість намету не виявлено.