Tree Crown Shape Research Articles

Simulation of Ku-Band Profile Radar Waveform by Extending Radiosity Applicable to Porous Individual Objects (RAPID2) Model

Similar to light detection and ranging (lidar), profile radar can detect forest vertical structure directly. Recently, the first Ku-band profile radar system designed for forest applications, called Tomoradar, has been developed and evaluated in boreal forest. However, the physical relationships between the waveform and forest structure parameters such as height, leaf area index (LAI), and aboveground biomass are still unclear, which limits later forestry applications. Therefore, it is necessary to develop a theoretical model to simulate the relationship and interpret the mechanism behind. In this study, we extend the Radiosity Applicable to Porous IndiviDual objects (RAPID2) model to simulate the profile radar waveform of forest stands. The basic assumption is that the scattering functions of major components within forest canopy are similar between profile radar and the side-looking radar implemented in RAPID2, except several modifications. These modifications of RAPID2 mainly include: (a) changing the observation angle from side-looking to nadir-looking; (b) enhancing the ground specular scattering in normal direction using Fresnel coefficient; (c) increasing the timing resolution and recording waveform. The simulated waveforms were evaluated using two plots of Tomoradar waveforms at co- and cross- polarizations, which are collected in thin and dense forest stands respectively. There is a good agreement (R2 ≥ 0.80) between the model results and experimental waveforms in HH and HV polarization modes and two forest scenes. After validation, the extended RAPID2 model was used to explore the sensitivity of the stem density, single tree LAI, crown shape, and twig density on the penetration depth in the Ku-band. Results indicate that the backscattering of the profile radar penetrates deeper than previous studies of synthetic aperture radar (SAR), and the penetration depth tends to be several meters in Ku-band. With the increasing of the needle and twig density in the microwave propagation path, the penetration depth decreases gradually. It is worth noting that variation of stem density seems to have the least effect on the penetration depth, when there is no overlapping between the single tree crowns.

Characterizing variations in growth characteristics between Douglas-fir with different genetic gain levels using airborne laser scanning

In British Columbia, Canada, Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco] selective breeding is used to develop genetically improved regeneration stock. To evaluate realized performance of improved stock, breeders take frequent measurements in genetic gain trials to determine whether yield gains are being met. Currently, variables collected focus on individual tree yield attributes; however, information generated from progeny test trials may not directly reflect plantation performance. Realized yield trials help to bridge the gap between progeny test estimated gain and plantation setting performance. High density airborne laser scanning (ALS) has the potential to identify variables that could improve the selection and validation process. We utilized ALS collected from an unmanned aerial system to assess performance of genetic improvement across three genetic levels and three stand spacings. ALS derived metrics were used to test three hypotheses: (1) tree height is correlated with the level of genetic superiority, (2) tree crown shape varies across genetic level, and (3) tree crown density is associated with genetic level. Random forest algorithms were used to identify candidate ALS metrics. To account for interaction effects, a two-way analysis of variance was conducted for each metric, followed by a post-hoc test to investigate significant differences between genetic level and spacing. The scale and shape parameters of Weibull probability density functions, vertical complexity index, and the fraction of euphotic voxels were found to be important metrics. Results show that genetically superior trees are typically taller, with higher, shorter and denser crowns. In addition, variation across genetic level may be indicative of greater phenotypic plasticity, as superior trees possess the ability to respond to tighter stand spacing.

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

Apple breeding process duration and ways of its reduction

A careful 63-year-old work on apple breeding, resulting in the creation of 54 its varieties, included in the State Register of breeding achievements admitted for use, makes it possible to speak about the ways of reduction in the time of the actual breeding process and the subsequent stages of accelerating the introduction of new varieties into production. According to the experience of the Russian Research Institute of Fruit Crop Breeding, an average of 19 years is spent from hybridization to the adoption of a variety for State trials, and from hybridization to inclusion in the State Register it is spent 27 years. The requirements for new apple varieties are changing and becoming tougher to a large extent during this period. We off er the possibility of reducing the time to create a new variety by combining in time the actual breeding process and the primary variety study. For this purpose already during the selection and transfer of biannual seedlings to the breeding orchard it is necessary to multiply and include the best seedlings on morphological features with a culture of 5 points (no more than 1 seedling per thousand selected ones) in the primary study. In this case the actual breeding process is combined with the primary study. When transferring the variety to the state trial, it is necessary to lay orchards of a small production testing of 20-40 trees of a new variety in each of the three replications. The time of single breeders is a thing of the past. To create varieties that meet high requirements of production (convenient for cultivation, highly adapted to local conditions, with a certain shape of tree crowns, with the fruits of high commodity and consumer qualities), interdisciplinary teams are needed, which include not only breeders, but also pomologists, geneticists, cytoembryologists, biochemists, physiologists, phytopathologists, and agricultural technicians. Usually years, and sometimes decades are spent on the creation of well-coordinated teams of professionals of diff erent specialties, but only such teams are able to create varieties that meet all main requirements. At Russian Research Institute of Fruit Crop Breeding such a team consists of 22 people.

The determination of some stand parameters using SfM-based spatial 3D point cloud in forestry studies: an analysis of data production in pure coniferous young forest stands

Benefiting from current unmanned air vehicle (UAV) and remote sensing techniques, the present study aims to estimate tree count (TC), tree height (TH), and tree crown cover area (TCCA) in a young Calabrian pine stand via canopy height model (CHM). Overlay images obtained using Quadcopter were used to generate two spatial three-dimensional (3D) cloud points in two different qualities. Point clouds were processed using R program in order to produce tree data using CHM. The sensitivity of CHM-based tree data was revealed using 318 tree measurements in 32 different sampling units. Estimation and measurement values were classified based on their structure from motion (SfM) quality and cover classes, and the statistical relationships among them were analyzed. Without any classification, R2 was calculated for TC, THMean, and TCCATotal estimations and field measurements. R2 values were calculated as 0.865, 0.778, and 0.869, respectively, for SfMHighest CHM, while they were calculated as 0.863, 0.736, and 0.843, respectively, for SfMMedium CHM. In addition, sensitivity and performance ranking in different groups were determined based on root mean square error (RMSE) and mean absolute percentage error (MAPE) values. A significant difference was observed among groups in terms of quality and cover for TH, while no significant differences were observed for TCCA. Therefore, it is possible to estimate the properties of SfM CHM-based young coniferous stand. It was understood that tree density, crown shape, and branching influenced the accuracy of the present study. The developed UAV (Drone)-SfM is a promising technique for further small-scale forestry studies.

L1-NORM FITTING OF ELLIPTIC PARABOLOIDS WITH PRIOR INFORMATION FOR ENHANCED CONIFEROUS TREE LOCALIZATION IN ALS POINT CLOUDS

Abstract. Airborne laser scanning (ALS) is an established tool for deriving various tree characteristics in forests. In some applications, an accurate pointwise estimate of the tree position is required. For dense data acquired by TLS or UAV-mounted scanners, this can be achieved by locating the stem, whose center coordinates are then used for deriving the planimetric tree position. However, in case of standard ALS data this is often not an option due to the low probability of obtaining stem hits in operational scenarios of forest mapping campaigns. This paper presents an alternative, indirect approach where the tree position is approximated as the center of a quadric surface which best represents the tree crown shape. The study targets coniferous trees due to their distinct crown shape which may be approximated by an elliptic paraboloid. It is assumed that individual tree point clusters are given and the task is to find the tree center for each cluster. We first consider the general problem of fitting an elliptic paraboloid with a known axis and an L1 residual norm error criterion, which is more robust to outliers compared to least-squares fitting. We formulate this problem as a quadratically constrained quadratic program (QCQP), and show how prior knowledge on the crown shape and center position can be incorporated. Next, a computationally simpler problem is considered where the paraboloid semiaxis lengths are constrained to be equal, and a corresponding linear program is constructed. Experiments on ALS datasets of forest plots from Bavaria, Germany and Oregon, USA reveal that a reduction in median tree position error of up to 20% can be attained compared to both least-squares fitting and other baseline techniques, resulting in an absolute error of ca. 22 cm on both datasets.

A comparison of longitude–latitude and Euclidean distance shape descriptors for determining tree crown shapes derived from LiDAR data

ABSTRACTPrevious studies demonstrate the effectiveness of the Euclidean distance shape descriptor for characterizing individual tree crowns derived from Light Detection and Ranging (LiDAR) data. This paper evaluates the effectiveness of the longitude–latitude shape descriptor for tree crown shape characterization in comparison with the Euclidean distance descriptor using LiDAR data for 43 individual tree crowns. The three-dimensional (3D) shape signatures obtained from longitude–latitude and Euclidean distance measures of the tree crowns are compared with 3D shape signatures of simulated ellipsoid and hemisphere tree crowns. Kolmogorov–Smirnov test (K–S test) and Mean Square Error (MSE) are used to measure the similarity of longitude–latitude and Euclidean distance signatures. The experimental results from all 43 examined trees suggest that the longitude–latitude shape descriptor can determine tree crown shapes with different leaf traits, while Euclidean distance is only able to characterize the shape of evergreen trees. Furthermore, the longitude–latitude shape descriptor, unlike the Euclidean distance shape descriptor, is not sensitive to the number of LiDAR points from the tree crown.

Scale dependence shapes how plant traits differentially affect levels of pre- and post-dispersal seed predation in Scots pine

To understand the demographic consequences of seed predation, it is essential to scale-up losses over space and time. We tested how individual tree characteristics, forest attributes, and the local environment affected rates of both pre- and post-dispersal seed predation in Scots pine Pinus sylvestris, in Scotland’s ancient native pinewoods. The sampling strategy was hierarchical, allowing assessment of how the influence of these factors differed with spatial scale. This approach revealed that the fate of a tree’s seeds depended on many factors, at multiple scales. Levels of seed predation were highly variable over space and time. Seed predation by local post-dispersal seed predators (invertebrates and small mammals) varied at the finest spatial scales, whereas mobile avian pre-dispersal seed predators discriminated among seeds at every spatial scale. Tree crown shape affected removal of seed by all seed predators; both crossbills Loxia spp. and generalist avian granivores selected trees with broad crowns. However, trees with narrow crowns were selected by red squirrels Sciurus vulgaris, and the associated tall understory vegetation supported the highest levels of post-dispersal seed predation. Seed chemistry was an effective seed defence that reduced seed loss to all seed predators. In contrast, increases in physical cone defence were ineffective at reducing seed loss and showed a potential trade-off with investment in seed viability. Large seeds carried a survival cost, being preferentially consumed by squirrels and post-dispersal seed predators. All post-dispersal seed predators consumed a higher proportion of seeds at the times and locations where seed fall was greatest.

Influence of 3D Spruce Tree Representation on Accuracy of Airborne and Satellite Forest Reflectance Simulated in DART

Advances in high-performance computer resources and exploitation of high-density terrestrial laser scanning (TLS) data allow for reconstruction of close-to-reality 3D forest scenes for use in canopy radiative transfer models. Consequently, our main objectives were (i) to reconstruct 3D representation of Norway spruce (Picea abies) trees by deriving distribution of woody and foliage elements from TLS and field structure data and (ii) to use the reconstructed 3D spruce representations for evaluation of the effects of canopy structure on forest reflectance simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Data for this study were combined from two spruce research sites located in the mountainous areas of the Czech Republic. The canopy structure effects on simulated top-of-canopy reflectance were evaluated for four scenarios (10 × 10 m scenes with 10 trees), ranging from geometrically simple to highly detailed architectures. First scenario A used predefined simple tree crown shapes filled with a turbid medium with simplified trunks and branches. Other three scenarios used the reconstructed 3D spruce representations with B detailed needle shoots transformed into turbid medium, C with simplified shoots retained as facets, and D with detailed needle shoots retained as facets D. For the first time, we demonstrated the capability of the DART model to simulate reflectance of complex coniferous forest scenes up to the level of a single needle (scenario D). Simulated bidirectional reflectance factors extracted for each scenario were compared with actual airborne hyperspectral and space-borne Sentinel-2 MSI reflectance data. Scenario A yielded the largest differences from the remote sensing observations, mainly in the visible and NIR regions, whereas scenarios B, C, and D produced similar results revealing a good agreement with the remote sensing data. When judging the computational requirements for reflectance simulations in DART, scenario B can be considered as most operational spruce forest representation, because the transformation of 3D shoots in turbid medium reduces considerably the simulation time and hardware requirements.

Detection of Fir Trees (Abies sibirica) Damaged by the Bark Beetle in Unmanned Aerial Vehicle Images with Deep Learning

Invasion of the Polygraphus proximus Blandford bark beetle causes catastrophic damage to forests with firs (Abies sibirica Ledeb) in Russia, especially in Central Siberia. Determining tree damage stage based on the shape, texture and colour of tree crown in unmanned aerial vehicle (UAV) images could help to assess forest health in a faster and cheaper way. However, this task is challenging since (i) fir trees at different damage stages coexist and overlap in the canopy, (ii) the distribution of fir trees in nature is irregular and hence distinguishing between different crowns is hard, even for the human eye. Motivated by the latest advances in computer vision and machine learning, this work proposes a two-stage solution: In a first stage, we built a detection strategy that finds the regions of the input UAV image that are more likely to contain a crown, in the second stage, we developed a new convolutional neural network (CNN) architecture that predicts the fir tree damage stage in each candidate region. Our experiments show that the proposed approach shows satisfactory results on UAV Red, Green, Blue (RGB) images of forest areas in the state nature reserve “Stolby” (Krasnoyarsk, Russia).

Improving Individual Tree Crown Delineation and Attributes Estimation of Tropical Forests Using Airborne LiDAR Data

Individual tree crown (ITC) segmentation is an approach to isolate individual tree from the background vegetation and delineate precisely the crown boundaries for forest management and inventory purposes. ITC detection and delineation have been commonly generated from canopy height model (CHM) derived from light detection and ranging (LiDAR) data. Existing ITC segmentation methods, however, are limited in their efficiency for characterizing closed canopies, especially in tropical forests, due to the overlapping structure and irregular shape of tree crowns. Furthermore, the potential of 3-dimensional (3D) LiDAR data is not fully realized by existing CHM-based methods. Thus, the aim of this study was to develop an efficient framework for ITC segmentation in tropical forests using LiDAR-derived CHM and 3D point cloud data in order to accurately estimate tree attributes such as the tree height, mean crown width and aboveground biomass (AGB). The proposed framework entails five major steps: (1) automatically identifying dominant tree crowns by implementing semi-variogram statistics and morphological analysis; (2) generating initial tree segments using a watershed algorithm based on mathematical morphology; (3) identifying “problematic” segments based on predetermined set of rules; (4) tuning the problematic segments using a modified distance-based algorithm (DBA); and (5) segmenting and counting the number of individual trees based on the 3D LiDAR point clouds within each of the identified segment. This approach was developed in a way such that the 3D LiDAR points were only examined on problematic segments identified for further evaluations. 209 reference trees with diameter at breast height (DBH) ≥ 10 cm were selected in the field in two study areas in order to validate ITC detection and delineation results of the proposed framework. We computed tree crown metrics (e.g., maximum crown height and mean crown width) to estimate aboveground biomass (AGB) at tree level using previously published allometric equations. Accuracy assessment was performed to calculate percentage of correctly detected trees, omission and commission errors. Our method correctly identified individual tree crowns with detection accuracy exceeding 80 percent at both forest sites. Also, our results showed high agreement (R2 > 0.64) in terms of AGB estimates using 3D LiDAR metrics and variables measured in the field, for both sites. The findings from our study demonstrate the efficacy of the proposed framework in delineating tree crowns, even in high canopy density areas such as tropical rainforests, where, usually the traditional algorithms are limited in their performances. Moreover, the high tree delineation accuracy in the two study areas emphasizes the potential robustness and transferability of our approach to other densely forested areas across the globe.

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

English

Farmers have a detailed local knowledge about different tree species which are either retained or planted on their farms. Thus, it is possible to learn from farmers’ observations to enhance understanding of local agro-ecological knowledge. This study aimed to investigate coffee growers’ local knowledge on shade tree species. The study was conducted at Adola Rede District, in Guji Zone, Southern Ethiopia. To address the objectives of this study, necessary data were collected through key informant interview and questionnaire survey. A total of 30 key informants and 90 households participated in the household interview. The study results revealed that coffee growers preferred shade grown coffee plants for better coffee yields, to protect coffee plants from unsuitable environmental stress, for soil fertility improvement and for longer life span of coffee plants. Coffee growers also encountered wilting and stunted growth of coffee plants, coffee yield reduction, poor soil fertility, less coffee stems and branches, besides coffee plants need more management when grown open. In the study district, eleven commonly used coffee shade tree species were identified. Based on their criteria of suitability identification, coffee growers preferred compatible shade tree species such as Ficus sur, Millettia ferruginea, Cordia africana, Albizia gummifera, Croton macrostachyus and Vernonia amygdalina, in this order. In the study area, the scale of shade tree species preferences for coffee growers varies. However, their main preferences of shade tree characteristics were mainly based on shade tree height, crown shape and evergreen or deciduous quality of the shade tree species. Coffee growers of the study area managed their owned shade tree species through pruning, thinning, pollarding and coppicing tending operations. They practiced various shade tree managements such as to cud dead or over grown branches, to collect wood used for various uses and to reduce of the shade for coffee plants. Therefore, based on the finding of this study, if the knowledge of local farmers is recorded and effectively used with scientific findings, it can provide valuable information that can give feedback synergistically to channel the direction of conventional science to meet the needs of local people. Key words: Adola Rede District, coffee growers, local knowledge, coffee shade tree species.

Modeling the Directional Clumping Index of Crop and Forest

The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP). Meanwhile, the parameterization of Ω is challenging partly due to the varying observations of canopy gaps from different view angles. Many previous studies have shown the increase of Ω with view zenith angle through samples of gap size distribution from in situ measurements. In contrast, remote sensing retrieval algorithms only assign a constant value for each biome type to roughly correct the clumping effect as a compromise between the accuracy and efficiency. In this paper, analytical models are proposed that estimate the directional clumping index (Ω(θ)) of crop and forest at canopy level. The angular variation trend and magnitude of crop Ω(θ) was analyzed within row structure where vegetation elements are randomly spaced along rows. The forest model predicts Ω(θ) with tree density, distribution pattern, crown shape, trunk size, and leaf area and angle distribution function. The models take into account the main directional characteristics of clumping index using easy-to-measure parameters. Test cases showed that Ω(θ) magnitude variation for black spruce forest was 102.3% of the hemispherical average clumping index ( Ω ˜ ), whereas the Larch forest had 48.7% variation, and row crop variation reached 32.4%. This study provided tools to assess Ω(θ) of discontinuous canopies.

How management intensity and neighborhood composition affect the structure of beech (Fagus sylvatica L.) trees

The intensity of silvicultural interventions and the neighborhood composition determine branching patterns, crown shape, and trunk attributes of beech ( Fagus sylvatica L.) trees. The intensity of silvicultural interventions and the composition of tree species are important forest management decisions. Both determine tree shape and thus influence the value of a tree, be it in terms of economy (trunk form, branchiness), or in terms of ecology (microhabitats). However, our knowledge on the distinct changes in tree architecture due to silvicultural management intensity or different neighborhood diversities is still limited, especially if the focus is on single tree attributes, e.g., branching patterns or crown shapes. We used terrestrial laser scanner data to calculate 25 structural measures for 55 European beech (Fagus sylvatica L.) trees that grew either in pure stands along a gradient of management intensity or in intra or interspecific neighborhoods in unmanaged stands. We found a lower height of maximal horizontal crown extension, a higher crown surface area, and straighter trunks with increasing management intensity. Moreover, our study revealed that beech trees surrounded by valuable hardwoods showed a lower height of maximal horizontal crown extension, a lower height–diameter ratio, and longer branches with flatter branch angles than beech trees surrounded by conspecific neighbors. Our findings provide evidence of phenotypic plasticity of European beech to diverse environmental conditions. The differences in tree structure indicate an increasing crown competition with decreasing management intensity and stronger competitive pressure for beech surrounded by conspecific neighbors in comparison to alien neighbors.

Influence of changing trees locations on thermal comfort on street parking lot and footways

Locations of trees in street parking lots (SPL) impact outdoor thermal comfort and should be considered during the urban planning process. In this paper we developed a procedure for changing trees locations in order to improve outdoor thermal comfort on SPL and associated footways. Furthermore, a sensitivity test on the effect of different tree crown shapes on outdoor thermal comfort was carried out. We applied theprocedure on real-world SPL design in the City of Novi Sad (Serbia). A temporal analysis is performed for the heat wave period using Universal Thermal Climate Index (UTCI) calculations in the Ladybug software.The results showed improvement of outdoor thermal comfort on 77 % of all body locations in proposed SPL design with predetermined number of trees. The largest outdoor thermal comfort improvement was noticed in the afternoon hours with up to 3.3°C UTCI decrease on single body location. By adding trees to the SPL, heat stress was reduced on 84 % of all body locations with maximal UTCI decrease of 3.7°C on single body location. Furthermore, heat stress reduction by cylinder-shaped tree crowns showed to be more pronounced compared to the sphere-shaped and the cone-shaped tree crowns. Proposed procedure showed that the locations of trees as well as tree crown shapes are very important for the improvement of outdoor thermal comfort and creation of environmentally conscious SPL design.

Effects of Urban Forestry on the Local Climate in Cotonou, Benin Republic

The effect of urban green on meteorological parameters such as temperature and relative humidity was assessed on one major city street in Cotonou called “Boulevard de Missèbo-Zongo” (BMZ). Tree inventory and field measurement of meteorological parameters were performed on the roadside and central median of the BMZ. Dendrometric data collected were the number of tree species, the number of tree individuals per species, the diameter of trees at breast height, the tree heights, the tree crown shape, shade form on the ground when the sun is at Zenith; while the meteorological data were the air temperature and the relative humidity inside and outside of urban green at 1.5, 2 and 3 meters height measured from 7am to 6pm at one hour interval. Data were analyzed using Mixed Generalized Linear Model under R 3.3.1 software. Results showed that street alignment trees were poorly diversified and dominated by <i>Khaya senegalensis</i>. This species appeared to be highly pruned and threatened because of its numerous medicinal virtues. Moreover it was found that air temperature and relative humidity were influenced by the time and the place of measurement. The coolness effect of urban green was evidenced by the decrease in temperature under alignment trees compared with that recorded on roadside free of trees. These results suggested that urban green could be adopted as adaptation strategy to address the issue of global warming in the city.

Distribución y estructura de los bosques de galería en dos ríos del centro sur de Nuevo León

Los bosques de galería se presentan como comunidades exuberantes en relación con su entorno. Se desarrollan en los márgenes de los cauces de los ríos, arroyos y canales, formando una estrecha franja que funciona en muchas ocasiones como corredores de fauna al comunicar comunidades vegetales aisladas. Para la evaluación de estas áreas en dos ríos se utilizó la interpretación de los materiales obtenidos por la percepción remota para actualizar la información cartográfica, misma que sirvió de base para la planeación y ejecución de un inventario forestal y florístico. Basado en esta información se analizaron las comunidades arbóreas de ambos ríos. Se observó una reducción de la vegetación natural y un incremento de las áreas agrícolas; la tasa anual de cambio de estas superficies en 19 años fue de 1.14% y 1.64% para cada río, afectando aproximadamente un 0.4% de la superficie ocupada por los bosques de galería. De las 25 especies arbóreas registradas, se consideran dominantes 4 en cada río debido a su valor de importancia en la comunidad. En cuanto a la diversidad de especies en las comunidades estudiadas se presentan valores mayores de diversidad y equitatividad (considerando el índice de Shannon) en el río Cabezones en comparación con el río Ramos. Considerando los valores obtenidos utilizando el índice de Sörensen, la similitud entre ambos alta. Las especies arbóreas más altas presentaron de la misma manera una conformación de la copa balanceada y una proporción baja de carga con respecto al fuste.

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

Abstract. Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.

ACCURACY ASSESSMENT OF CROWN DELINEATION METHODS FOR THE INDIVIDUAL TREES USING LIDAR DATA

Forest canopy density and height are used as variables in a number of environmental applications, including the estimation of biomass, forest extent and condition, and biodiversity. The airborne Light Detection and Ranging (LiDAR) is very useful to estimate forest canopy parameters according to the generated canopy height models (CHMs). The purpose of this work is to introduce an algorithm to delineate crown parameters, e.g. tree height and crown radii based on the generated rasterized CHMs. And accuracy assessment for the extraction of volumetric parameters of a single tree is also performed via manual measurement using corresponding aerial photo pairs. A LiDAR dataset of a golf course acquired by Leica ALS70-HP is used in this study. Two algorithms, i.e. a traditional one with the subtraction of a digital elevation model (DEM) from a digital surface model (DSM), and a pit-free approach are conducted to generate the CHMs firstly. Then two algorithms, a multilevel morphological active-contour (MMAC) and a variable window filter (VWF), are implemented and used in this study for individual tree delineation. Finally, experimental results of two automatic estimation methods for individual trees can be evaluated with manually measured stand-level parameters, i.e. tree height and crown diameter. The resulting CHM generated by a simple subtraction is full of empty pixels (called "pits") that will give vital impact on subsequent analysis for individual tree delineation. The experimental results indicated that if more individual trees can be extracted, tree crown shape will became more completely in the CHM data after the pit-free process.