Tree Height Distribution Research Articles

Spatial Pattern of Forest Age in China Estimated by the Fusion of Multiscale Information

Forest age is one of most important biological factors that determines the magnitude of vegetation carbon sequestration. A spatially explicit forest age dataset is crucial for forest carbon dynamics modeling at the regional scale. However, owing to the high spatial heterogeneity in forest age, accurate high-resolution forest age data are still lacking, which causes uncertainty in carbon sink potential prediction. In this study, we obtained a 1 km resolution forest map based on the fusion of multiscale age information, i.e., the ninth (2014–2018) forest inventory statistics of China, with high accuracy at the province scale, and a field-observed dataset covering 6779 sites, with high accuracy at the site scale. Specifically, we first constructed a random forest (RF) model based on field-observed data. Utilizing this model, we then generated a spatially explicit forest age map with a 1 km resolution (random forest age map, RF map) using remotely sensed data such as tree height, elevation, meteorology, and forest distribution. This was then used as the basis for downscaling the provincial-scale forest inventory statistics of the forest ages and retrieving constrained maps of forest age (forest inventory constrained age maps, FIC map), which exhibit high statistical accuracy at both the province scale and site scale. The main results included the following: (1) RF can be used to estimate the site-scale forest age accurately (R2 = 0.89) and has the potential to predict the spatial pattern of forest age. However, (2) owing to the impacts of sampling error (e.g., field-observed sites are usually located in areas exhibiting relatively favorable environmental conditions) and the spatial mismatch among different datasets, the regional-scale forest age predicted by the RF model could be overestimated by 71.6%. (3) The results of the downscaling of the inventory statistics indicate that the average age of forests in China is 35.1 years (standard deviation of 21.9 years), with high spatial heterogeneity. Specifically, forests are older in mountainous and hilly areas, such as northeast, southwest, and northwest China, than in southern China. The spatially explicit dataset of the forest age retrieved in this study encompasses synthesized multiscale forest age information and is valuable for the research community in assessing the carbon sink potential and modeling carbon dynamics.

Native species diversity associated with Bosque Palmeras de Ocol in the Amazonas region, Peru

The objective was to evaluate the ecology and dynamics of the native species community in the Bosque Palmeras de Ocol in the district of Molinopampa, Amazonas, Peru. Six sampling points were established, in which 1,569 trees belonging to 47 species were found. Species such as Astrocaryum sp., with a relative density of 10.7 % and an importance value index (IVI) of 23.54 %, and Prunus sp., with a relative density of 8.9 % and an IVI of 22.23 %, stood out. Myrsine coriacea subsp. 2 was identified as key with a high relative frequency and an IVI of 16.21 %. Density per hectare revealed high values for Astrocaryum sp. (560 individuals/ha), Nectandra sp. (526 individuals/ha) and Prunus sp. 2 (470 individuals/ha). The distribution of tree height and diameter showed a predominance of smaller individuals, with most species presenting diameters less than 20 cm and heights between 3 and 4 m. The range/abundance curve highlighted Astrocaryum sp. as the most abundant species. The distribution models showed a good fit to the Null and Preemption distributions, with low values of Akaike's criterion (AIC) 111.56 and 114.60, respectively. These findings provide a detailed understanding of the species community in this forest, fundamental for its conservation and sustainable management.

Height-diameter models for King Boris fir (Abies borisii regis Mattf.) and Scots pine (Pinus sylvestris L.) in Olympus and Pieria Mountains, Greece

In forest science and practice, the total tree height is one of the basic morphometric attributes at the tree level and it has been closely linked with important stand attributes. In the current research, sixteen nonlinear functions for height prediction were tested in terms of their fitting ability against samples of Abies borisii regis and Pinus sylvestris trees from mountainous forests in central Greece. The fitting procedure was based on generalized nonlinear weighted regression. At the final stage, a five-quantile nonlinear height-diameter model was developed for both species through a quantile regression approach, to estimate the entire conditional distribution of tree height, enabling the evaluation of the diameter impact at various quantiles and providing a comprehensive understanding of the proposed relationship across the distribution. The results clearly showed that employing the diameter as the sole independent variable, the 3-parameter Hossfeld function and the 2-parameter Näslund function managed to explain approximately 84.0% and 81.7% of the total height variance in the case of King Boris fir and Scots pine species, respectively. Furthermore, the models exhibited low levels of error in both cases (2.310m for the fir and 3.004m for the pine), yielding unbiased predictions for both fir (−0.002m) and pine (−0.004m). Notably, all the required assumptions for homogeneity and normality of the associated residuals were achieved through the weighting procedure, while the quantile regression approach provided additional insights into the height-diameter allometry of the specific species. The proposed models can turn into valuable tools for operational forest management planning, particularly for wood production and conservation of mountainous forest ecosystems.

Tree parameter extraction in Fokienia hodginsii plantation based on airborne LiDAR data.

Accurate and efficient extraction of tree parameters from plantations lay foundation for estimating individual wood volume and stand stocking. In this study, we proposed a method of extracting high-precision tree parameters based on airborne LiDAR data. The main process included data pre-processing, ground filtering, individual tree segmentation, and parameter extraction. We collected high-density airborne point cloud data from the large-diameter timber of Fokienia hodginsii plantation in Guanzhuang State Forestry Farm, Shaxian County, Fujian Province, and pre-processed the point cloud data by denoising, resampling and normalization. The vegetation point clouds and ground point clouds were separated by the Cloth Simulation Filter (CSF). The former data were interpolated using the Delaunay triangulation mesh method to generate a digital surface model (DSM), while the latter data were interpolated using the Inverse Distance Weighted to generate a digital elevation model (DEM). After that, we obtained the canopy height model (CHM) through the difference operation between the two, and analyzed the CHM with varying resolutions by the watershed algorithm on the accuracy of individual tree segmentation and parameter extraction. We used the point cloud distance clustering algorithm to segment the normalized vegetation point cloud into individual trees, and analyzed the effects of different distance thresholds on the accuracy of indivi-dual tree segmentation and parameter extraction. The results showed that the watershed algorithm for extracting tree height of 0.3 m resolution CHM had highest comprehensive evaluation index of 91.1% for individual tree segmentation and superior accuracy with R2 of 0.967 and RMSE of 0.890 m. When the spacing threshold of the point cloud segmentation algorithm was the average crown diameter, the highest comprehensive evaluation index of 91.3% for individual tree segmentation, the extraction accuracy of the crown diameter was superior, with R2 of 0.937 and RMSE of 0.418 m. Tree height, crown diameter, tree density, and spatial distribution of trees were estimated. There were 5994 F. hodginsii, with an average tree height of 16.63 m and crown diameter of 3.98 m. Trees with height of 15-20 m were the most numerous (a total of 2661), followed by those between 10-15 m. This method of forest parameter extraction was useful for monitoring and managing plantations.

Data Acquisition Tree Height and Areal Parameters of Mangrove Vegetation Using UAV

Unmanned aerial vehicles (UAVs) can be used to assess the biophysical parameters of mangroves (particularly tree height and area). UAVs can produce high-resolution imagery, in this study UAV imagery was used to calculate tree height distribution and mangrove area. UAV imagery was taken using a commercial UAV with a planned flight path. The obtained UAV images are then compiled based on coordinate information and z-elevation values, and then 3D modelling is created from the point cloud. The 3D model will be detailed by a dense point cloud. The surface area and texture are obtained from the 3D mesh. Finally, a high-resolution DSM can be generated. DSM is the elevation of the overall surface including vegetation, buildings, and others, while DTM is the elevation data of the ground surface, so the next process is to separate bare ground and above-ground objects. Tree height is obtained from the intersection of DSM and DTM elevation data. The area of mangrove vegetation was obtained using the on-screen digitisation method. Based on the results of the analysis conducted by comparing tree height data from UAV images and in-situ measurement tree height data has a difference with a range of 0.24 m to 0.94 m, with an average of 0.49m. This remote sensing survey research using commercial UAVs can be concluded as one of the solutions to acquire the parameters of tree area and height distribution with good accuracy.

Relationship between carbon stock and the structure of coniferous and broad-leaved mixed forest in Tianmu Mountains, China.

Taking the coniferous and broad-leaved mixed forest of Tianmu Mountain National Nature Reserve in Zhejiang Province as research object, we divided the tree species into three pairs, including evergreen and deci-duous species, broad-leaved and coniferous species, dominant and non-dominant species, to compare the difference of the individual tree carbon stock of each pair and analyze the diameter distribution pattern and tree height distribution pattern of carbon stocks. The relationship between spatial structure and individual tree carbon stock was analyzed by using spatial structure indicators including V_Hegyi competition index, complete mingling and aggregation index, to reveal the relationship between the structure of coniferous and broad-leaved forests and carbon stocks, and provide a theoretical basis for management of forest carbon sequestration. The results showed that the average individual carbon stock for evergreen and deciduous species, broad-leaved and coniferous species, dominant and non-dominant species were 57.7 and 87.4 kg, 54.6 and 74.7 kg, 67.4 and 48.1 kg, respectively. The individual tree carbon stock of evergreen species was significantly lower than that of deciduous species, the individual tree carbon stock of broad-leaved species was significantly lower than that of coniferous species, and the individual tree carbon stock of dominant tree species was significantly higher than that of non-dominant tree species. The diameter distribution and height distribution of carbon stock of each species group obeyed normal distribution. The V_Hegyi competition index was significantly negatively correlated with individual tree carbon stock, and it was consistent with the power function distribution. Both complete mingling and aggregation index were linearly and positively correlated with individual tree carbon stock. The direction of influence of different spatial structures on the individual tree carbon stock was consistent. The structure of coniferous and broad-leaved mixed forest had a significant impact on individual tree carbon stock. In the management of forest carbon sequestration and sink enhancement, it is necessary to regulate the unreasonable forest structure and promote its succession to the climax community in order to improve forest carbon stock.

Analysis of Selection-Cutting Silviculture with Thujopsis dolabrata—A Case Study from Japan Compared to German Plenter Forests

(1) Background: In Japan, single-tree selection-cutting silviculture with hiba (Thujopsis dolabrata var. hondai) is a traditional silvicultural system and is well documented. We evaluated an experimental forest regarding past structural dynamics and future development while comparing it to the German multilayer coniferous “Plenterwald/plenter forest”. (2) Methods: Classical growth and yield data were recorded since 1995 from two hiba-mixed species plots, one managed since Matsukawa’s (the founder) times in 1931 and the other unmanaged since 40–50 years before 1931. (3) Results: Clear structural differences appeared, with the managed plot having a reverse J-shaped DBH and tree height distribution curve, and a higher percentage of hiba, also typical for German plenter forests’ intensely multi-storied structure. The unmanaged plot was composed of trees with large stem diameters, even though they were more evenly distributed, together with less admixed species. In both plots, the growing stock increased in the past. While the managed plot’s growing stock in 2019 was 561 m3 ha−1, the unmanaged plot reached 982 m3 ha−1 with large portions accumulated in DBH classes >60 cm. (4) Conclusions: When compared to today’s clearcutting system in Japan, selection silviculture shows advantages and may thus serve as a valuable inspiration for silviculture in Japan as it was with plenter forestry for Germany.

Tree Height and Crown Extraction From UAV-Based Multispectral Imagery

Conventional methods in measuring tree height and crown diameter are time consuming compared to the advanced technology of unmanned aerial vehicle (UAV) multispectral imagery. UAV multispectral imagery is widely used in vegetation analysis such as crop analysis, vegetation monitoring, precise farming and vegetation health assessment. Thus, the purpose of this research is to extract the individual tree height and crown diameter from UAV- based multispectral imagery using the integration of geospatial techniques. Altogether, the total of 395 individual trees were extracted from the study area of Section U11, Shah Alam using the Support Vector Machine (SVM) classifier. Tree height values were extracted from normalized digital surface model (nDSM) using the zonal statistics tool with the tree height range between 1.568m to 27.850m. The range for derived crown diameters is between 0.919m to 24.506m. The final map shows the distribution of tree height and tree crown extraction from the UAV-based multispectral imagery. The spatial distribution data of tree height and crown diameter are beneficial especially in landscaping and identifying the potential of tree hazard in the urban area.

Spatial predictions of tree density and tree height across Mexico forests using ensemble learning and forest inventory data.

The National Forestry Commission of Mexico continuously monitors forest structure within the country's continental territory by the implementation of the National Forest and Soils Inventory (INFyS). Due to the challenges involved in collecting data exclusively from field surveys, there are spatial information gaps for important forest attributes. This can produce bias or increase uncertainty when generating estimates required to support forest management decisions. Our objective is to predict the spatial distribution of tree height and tree density in all Mexican forests. We performed wall-to-wall spatial predictions of both attributes in 1-km grids, using ensemble machine learning across each forest type in Mexico. Predictor variables include remote sensing imagery and other geospatial data (e.g., mean precipitation, surface temperature, canopy cover). Training data is from the 2009 to 2014 cycle (n > 26,000 sampling plots). Spatial cross validation suggested that the model had a better performance when predicting tree height r 2 = .35 [.12, .51] (mean [min, max]) than for tree density r 2 = .23 [.05, .42]. The best predictive performance when mapping tree height was for broadleaf and coniferous-broadleaf forests (model explained ~50% of variance). The best predictive performance when mapping tree density was for tropical forest (model explained ~40% of variance). Although most forests had relatively low uncertainty for tree height predictions, e.g., values <60%, arid and semiarid ecosystems had high uncertainty, e.g., values >80%. Uncertainty values for tree density predictions were >80% in most forests. The applied open science approach we present is easily replicable and scalable, thus it is helpful to assist in the decision-making and future of the National Forest and Soils Inventory. This work highlights the need for analytical tools that help us exploit the full potential of the Mexican forest inventory datasets.

Improving GEDI Forest Canopy Height Products by Considering the Stand Age Factor Derived from Time-Series Remote Sensing Images: A Case Study in Fujian, China

Forest canopy height plays an important role in forest resource management and conservation. The accurate estimation of forest canopy height on a large scale is important for forest carbon stock, biodiversity, and the carbon cycle. With the technological development of satellite-based LiDAR, it is possible to determine forest canopy height over a large area. However, the forest canopy height that is acquired by this technology is influenced by topography and climate, and the canopy height that is acquired in complex subtropical mountainous regions has large errors. In this paper, we propose a method for estimating forest canopy height by combining long-time series Landsat images with GEDI satellite-based LiDAR data, with Fujian, China, as the study area. This approach optimizes the quality of GEDI canopy height data in topographically complex areas by combining stand age and tree height, while retaining the advantage of fast and effective forest canopy height measurements with satellite-based LiDAR. In this study, the growth curves of the main forest types in Fujian were first obtained by using a large amount of forest survey data, and the LandTrendr algorithm was used to obtain the forest age distribution in 2020. The obtained forest age was then combined with the growth curves of each forest type in order to determine the tree height distribution. Finally, the obtained average tree heights were merged with the GEDI_V27 canopy height product in order to create a modified forest canopy height model (MGEDI_V27) with a 30 m spatial resolution. The results showed that the estimated forest canopy height had a mean of 15.04 m, with a standard deviation of 4.98 m. In addition, we evaluated the accuracy of the GEDI_V27 and the MGEDI_V27 using the sample dataset. The MGEDI_V27 had a higher accuracy (R2 = 0.67, RMSE = 2.24 m, MAE = 1.85 m) than the GEDI_V27 (R2 = 0.39, RMSE = 3.35 m, MAE = 2.41 m). R2, RMSE, and MAE were improved by 71.79%, 33.13%, and 22.53%, respectively. We also produced a forest age distribution map of Fujian for the year 2020 and a forest disturbance map of Fujian for the past 32 years. The research results can provide decision support for forest ecological protection and management and for carbon sink analysis in Fujian.

The Impact of Street Tree Height on PM2.5 Concentration in Street Canyons: A Simulation Study

With the rapid development of cities and the rapid increase in automobile ownership, traffic has become one of the main sources of PM2.5 pollution, which can be reduced by road greening through sedimentation, blocking, adhesion, and absorption. Using the method of combining field monitoring and ENVI-met simulation, the influence of the tree height on the PM2.5 concentration on both sides of the city streets was discussed. The influence of tree height on PM2.5 under five conditions was analyzed, including 10 m tall trees (i), 15 m tall trees (ii), alternating distribution of 15 and 10 m tall trees (iii), 5 m tall trees (iv), no trees on either side of the road (v). The results show that: Roadside trees can increase the concentration of PM2.5 in the narrow space of street canyons. However, without roadside trees, PM2.5 from traffic sources is not reduced in time, it is more easily spread to the distance. When the height of the roadside trees is 5 m and their crown widths are smaller than those of other trees, there is a relatively wide space between them. Compared with the higher roadside tree models with larger crown widths, the concentration of PM2.5 on the roadway and the downwind sidewalk is relatively low. In the three models (i–iii) with tree height above or equal to 10 m, the PM2.5 concentration around the trees do not show regular change with the change in tree height. Due to the tree height of 10 and 15 m, the crown width is large enough, and the alternate distribution of tree height of 15 and 10 m fails to make the PM2.5 concentration in the models lower than the models with tree height of 15 m or 10 m. The reasonable height of roadside trees in street canyons helps improve the wind circulation to promote the diffusion of PM2.5 pollution. There is no optimal height of roadside trees for PM2.5 pollution in street canyons, thus it is necessary to select the height reasonably according to the specific situation.

Direct use of large-footprint lidar waveforms to estimate aboveground biomass

Many studies have established the strong connections between aboveground biomass and lidar height metrics; however, these relationships are site-specific. Field data required to derive these relationships are not readily available in many cases. We developed a model to estimate plot-level aboveground biomass density (AGBD) directly from large-footprint lidar waveform measurements. An individual tree-based aboveground biomass (AGB)-height allometric relationship was scaled up to the plot level using lidar-waveform sensed tree height and crown size distribution characteristics. The AGBD was estimated based on a waveform/foliage profile-weighted height-based allometric equation. The AGBD-height scaling exponent was then built on the allometric relationships of tree height with stem diameter and crown volume with tree height. Global vegetation structure data analysis demonstrated that one general model (scaling exponent ~ 1.6–1.8) works reasonably well across all global forest biomes except boreal forests (scaling exponent ~ 0.9). We applied the model to estimate aboveground biomass in two distinct geographic regions: temperate deciduous/conifer forests in the northeastern USA and a montane conifer forest in Sierra National Forest in California. Local vegetation structural data analysis leads to a consistent height scaling exponent for these two distinct biomes, slightly different from the global data analysis results. This model produced optimal AGBD estimates using the local height scaling exponent value. Adequate AGBD estimates with the general height scaling exponent value were also provided by our model. Our analysis suggests one general allometric relationship between plot-level AGBD and large-footprint lidar waveforms. Integrating local structure allometric relationships improve the predictive accuracy of the model. Our model outperformed the lidar height metrics-based approach for AGBD estimates and overcame the biomass underestimation problem using height metrics for high biomass regions. This model could potentially serve as a general and robust model for monitoring forest carbon stocks using large-footprint lidar waveform measurements such as the Global Ecosystem Dynamics Investigation (GEDI) mission at the continental and global scales. The model could be a framework for integrating a demography-based terrestrial ecosystem model and GEDI global mission measurements to improve global carbon stock and flux estimates.

Detection of Forest Tree Losses in Côte d’Ivoire Using Drone Aerial Images

The fight against deforestation and forest degradation is now a major challenge for the preservation of global forest ecosystems. The remote sensing forest monitoring methods that are currently deployed are not always adapted to the Ivorian context because of the high cloud cover, diversity of shaded crops, and land clearing techniques. This study proposes a drone-based approach to assess intra-annual tree losses in the Bossématié classified forest. The method used is based on a detection analysis of tree losses in forest areas from a time series of aerial images acquired by drones from November 2018 to April 2019 on five sites in the studied forest. Based on photogrammetric models and photointerpretation, tree heights and tree crown sizes were estimated. Then, tree losses were detected based on the variation of tree heights during the study period. An analysis of the distribution of tree heights in Bossématié classified forest reveals that the maximum tree height was 65.06 m in November 2018 and 64.07 m in April 2019 with an average tree height of 34.29–37.00 m in November 2018 and 34.63–36.88 m in April 2019. The average tree crown area, meanwhile, was estimated to be 152 m². With an estimation accuracy of about 97%, these tree structural data indicate a minimum loss of 107 trees corresponding to a clearing area of 2 ha across all the surveyed sites from November 2018 to April 2019. This forest monitoring approach shows a considerable local loss of biodiversity and should be involved in the implementation of preservation, rehabilitation, and deployment strategies in an operational deforestation monitoring system in Côte d’Ivoire.

Microclimate estimation under different coffee-based agroforestry systems using full-sun weather data and shade tree characteristics

In Central America, coffee is mainly grown in agroforestry systems. This practice modifies the microclimate, which, in turn, influences coffee growth and development. However, modeling these microclimate modifications is a challenge when trying to predict the development of a disease in the understory crop, based on variables usually monitored in weather stations exposed to full sunlight. Furthermore, critical variables for plant disease development, such as leaf wetness duration and leaf temperatures, are generally not measured by weather stations. In our study, we sought to build models explaining daily minimum and maximum coffee leaf temperatures, daily coffee leaf wetness duration, and minimum and maximum air temperatures in agroforestry systems with a single shade tree species, which are common in Central America, and which were characterized by shade tree height, canopy openness and light gap distribution. The modeled variables were mainly explained by one or more meteorological variables provided by reference weather stations exposed to full sunlight. The presence of shade trees resulted in a buffer effect, reducing daily maximum air and leaf temperatures, and increasing daily minimum air and leaf temperatures. Moreover, except for the daily minimum air temperature under shade, shade tree characteristics affected these microclimatic variables. Indeed, the buffer effect on the daily maximum air temperature increased with shade trees 7 m tall or over, whereas for extreme leaf temperatures, this effect seemed to be further intensified by a dense and homogeneous canopy. The tallest shade trees also tended to provide conditions that reduced coffee leaf wetness duration. The coffee leaf stratum affected the daily maximum leaf temperature, with a top layer intercepting radiation for the lower strata, but had no effect on the daily minimum leaf temperature, detected at night. The models developed were simple equations allowing interpretation of shade tree height, the effects of canopy characteristics on the microclimate and were therefore useful for designing and managing agroforestry system. The more accurate models could be incorporated into an early warning system for coffee pests and diseases in the region.

New approach to calculating tree height at the regional scale

BackgroundDetermining the spatial distribution of tree heights at the regional area scale is significant when performing forest above-ground biomass estimates in forest resource management research. The geometric-optical mutual shadowing (GOMS) model can be used to invert the forest canopy structural parameters at the regional scale. However, this method can obtain only the ratios among the horizontal canopy diameter (CD), tree height, clear height, and vertical CD. In this paper, we used a semi-variance model to calculate the CD using high spatial resolution images and expanded this method to the regional scale. We then combined the CD results with the forest canopy structural parameter inversion results from the GOMS model to calculate tree heights at the regional scale.ResultsThe semi-variance model can be used to calculate the CD at the regional scale that closely matches (mainly with in a range from − 1 to 1 m) the CD derived from the canopy height model (CHM) data. The difference between tree heights calculated by the GOMS model and the tree heights derived from the CHM data was small, with a root mean square error (RMSE) of 1.96 for a 500-m area with high fractional vegetation cover (FVC) (i.e., forest area coverage index values greater than 0.8). Both the inaccuracy of the tree height derived from the CHM data and the unmatched spatial resolution of different datasets will influence the accuracy of the inverted tree height. And the error caused by the unmatched spatial resolution is small in dense forest.ConclusionsThe semi-variance model can be used to calculate the CD at the regional scale, together with the canopy structure parameters inverted by the GOMS model, the mean tree height at the regional scale can be obtained. Our study provides a new approach for calculating tree height and provides further directions for the application of the GOMS model.

Airborne LiDAR and Photogrammetric Point Cloud Fusion for Extraction of Urban Tree Metrics According to Street Network Segmentation

This paper provides a practical procedure for fusing LiDAR and photogrammetric point clouds for the extraction of tree metrics. Aerial photogrammetric point clouds are first generated using the structure-from-motion and dense-matching methods. Registration of the LiDAR and photogrammetric point clouds is then performed using an onboard global positioning system and inertial measurement unit. However, due to systematic deviations, it is impossible to directly merge the two types of point cloud. Therefore, an urban street network obtained from the OpenStreetMap digital mapping system is utilized for point cloud segmentation. After segmentation, each chunk is finely registered and merged based on the iterative closest point algorithm, allowing the two types of data to be accurately co-registered and a fused point cloud obtained. Finally, we conducted experiments to extract stand and individual tree metrics from fused point clouds created for two study plots. The height distributions of the fused point clouds were highly consistent with LiDAR data, with the 5%, 10%, 25%, 50%, 75%, 90%, and 95% height percentiles showing acceptable similarities. The height distribution of individual trees was also consistent with that of field measurements. Furthermore, the fused point clouds contain a high point density and RGB color information, which allow shape delineation and estimation of tree health status. This comprehensive analysis demonstrates that this procedure provides a practical way to inventory tree stands and individuals in urban areas.

Relationships between tree height and tree species richness at small scales

Tree height and tree species richness are both essential to characterize forest structure. Previous research showed that maximum tree height is a strong predictor of tree species richness at forests on a large scale. However, it is unknown whether this relationship exists in a forest at small scales. Here the intensively surveyed data from 1 ha of forest plot in northern Alabama was used to exam this relationship at small scales (e.g., 1 m × 1 m, 2 m × 2 m, …, and 50 m × 50 m). The results indicated that there was no significant correlation between the maximum tree height and tree species richness across small scales. Majority trees with the highest tree height at the small scales were oaks and hickories, which are dominant trees in this forest community through succession. A weak negative spatial autocorrelation existed in tree heights. Tree height distribution across the small scales followed power-laws but with varied scaling exponents. These scaling exponents were significantly correlated with tree species richness when the scales were no less than 1000 m2, which was equivalent to have about 15 tree species in the study area. The results may explain the complicated relationship between tree species diversity and tree height. This study may provide implications for forest structure management and conservation.

Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning

In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.

Orientational Ordering of Tree Elements in the Dielectric Permittivity Model of a Forest Stand

The dielectric characteristics of trees are studied for a dielectric permittivity model of a forest stand of L1 band signals of navigation satellites. The contribution of the orientational ordering of tree elements and of the wood anisotropy to the effective wood dielectric permittivity e has been estimated. The crown layer has the height-depend anisotropy ∆e and the orientational ordering of branch tensor elements. The anisotropy gradients in the layer of trunks are determined by the height-dependent wood volume of a single tree and the tree height distribution in a large forest.

Spatial distribution pattern and scale effect of secondary forests in Daxing'anling, China

We examined the spatial distribution patterns and their scale effects of different tree species (Larix gmelinii, Betula platyphylla and others) and different size classes of trees (1-5) of natural L. gmelinii secondary forest (LF), natural B. platyphylla secondary forest (BF) and the mixed secondary forest of both species (MF) in Daxing'anling. The results showed that among the three forest types, LF was the only one type reaching a good state of regeneration, while other two forest types were poorly regenerated. For different forest types, the abundance of seedlings and saplings in the regeneration layer were significantly different from that of the tree layer, and the diameter distribution (except for LF and BF) and height distribution of trees in each forest type were not reasonable, indicating that all the three forest types belonged to unstable communities. At species level, the spatial distributions of main species in each plot were mainly clumped. The five indicators used in this study varied significantly with the scales, which mainly focused on the linear increases (40%), the power increases (22%) and the negative quadratic polynomials (20%), respectively. For different size classes, significant clumped distributions were observed for the regeneration levels (1-3), while the spatial distribution of tree layers (4-5) usually fluctuated distinctly among various distribution patterns. The scale effects of different size classes were mainly dominated by the linear increases (44%), the power increases (15%) and the negative quadratic polynomials (12%). For each forest type and sampling scale, the cluster degrees of trees decreased significantly with increasing tree sizes. Within each forest type, the pattern size of non-dominant species was significantly larger than that of dominant species, while the pattern size of regeneration layers was significantly larger than that of tree layers.