Individual Tree Height Research Articles

Direct and automatic measurements of stem curve and volume using a high-resolution airborne laser scanning system

Today, high-quality reference tree measurements, including the position, diameter, height and volume, are cumbersome and slow to carry out, but highly needed for forest inventories based on airborne laser scanning. Mobile laser scanning technologies hold the promise for collecting reference data for forest inventories with an extremely high efficiency. Perhaps, the most efficient approach for reference data collection would be to mount a high-resolution laser scanning system on board an airborne vehicle flying at a low altitude above the forest canopy since this would allow recording reference samples of individual trees with the speed of flight. To demonstrate the potential of this technology, we mounted an in-house developed HeliALS-DW laser scanning system on board a helicopter and collected point cloud data in a boreal forest on three test sites containing a total of 1469 trees. The obtained point clouds incorporated sufficiently many high-quality stem hits for estimating the stem curves and stem volumes of individual trees since the point clouds had a relatively high point density of 2200–3800 echoes/m2, and the scanner had been tilted by 15° from the nadir to increase the possibility of recording stem hits. To automatically estimate the diameters at breast height (DBH) and stem curves of individual trees, we used algorithms designed to tolerate moderate drifts in the trajectory of the laser scanner. Furthermore, the stem volumes of individual trees were computed by using the estimated stem curves and tree heights without any allometric models. Using the proposed methods, we were able to estimate the stem curves with a root-mean-square error (RMSE) of 1.7–2.6 cm (6–9%) while detecting 42–71% of the trees. The RMSE of stem volume estimates was 0.1–0.15 m3 (12–21%). We also showed that the tree detection rate could be improved up to 87–96% for trees with a DBH exceeding 20 cm if slightly larger average errors for the stem attributes were allowed. Our results pave the way for using high-resolution airborne laser scanning for field reference data collection by conducting direct measurements of tree stems with a high efficiency.

Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

Biomass stored in young forests has enormous potential for the reduction of fossil fuel consumption. However, to ensure long-term sustainability, the measurement accuracy of tree height is crucial for forest biomass and carbon stock monitoring, particularly in young forests. Precise height measurement using traditional field measurements is challenging and time consuming. Remote sensing (RS) methods can, however, replace traditional field-based forest inventory. In our study, we compare individual tree height estimation from Light Detection and Ranging (LiDAR) and Digital Aerial Photogrammetry (DAP) with field measurements. It should be noted, however, that there was a one-year temporal difference between the field measurement and LiDAR/DAP scanning. A total of 130 trees (32 Scots Pine, 29 Norway Spruce, 67 Silver Birch, and 2 Eurasian Aspen) were selected for height measurement in a young private forest in south-east Finland. Statistical correlation based on paired t-tests and analysis of variance (ANOVA, one way) was used to compare the tree height measured with the different methods. Comparative results between the remote sensing methods and field measurements showed that LiDAR measurements had a stronger correlation with the field measurements and higher accuracy for pine (R2 = 0.86, bias = 0.70, RMSE = 1.44) and birch (R2 = 0.81, bias = 0.86, RMSE = 1.56) than DAP, which had correlation values of (R2 = 0.71, bias = 0.82, RMSE = 2.13) for pine and (R2 = 0.69, bias = 1.19, RMSE = 2.08) for birch. The correlation of the two remote sensing methods with the field measurements was very similar for spruce: LiDAR (R2 = 0.83, bias = 0.30, RMSE = 1.17) and DAP (R2 = 0.83, bias = 0.44, RMSE = 1.26). Moreover, the correlation was highly significant, with minimum error and mean difference (R2 = 0.79–0.98, MD = 0.12–0.33, RMSD = 0.45–1.67) between LiDAR and DAP for all species. However, the paired t-test suggested that there is a significant difference (p < 0.05) in height observation between the field measurements and remote sensing for pine and birch. The test showed that LiDAR and DAP output are not significantly different for pine and spruce. Presumably, the time difference in field campaign between the methods was the reason for these significant results. Additionally, the ANOVA test indicated that the overall means of estimated height from LiDAR and DAP were not significantly different from field measurements in all species. We concluded that utilization of LiDAR and DAP for estimating individual tree height in young forests is possible with acceptable error and comparable accuracy to field measurement. Hence, forest inventory in young forests can be carried out using LiDAR or DAP for height estimation at the individual tree level as an alternative to traditional field measurement approaches.

Individual-level distance-independent-based growth and yield prediction models for long-term Japanese cedar (Cryptomeria japonica)

Yield prediction has been determined to be vital in sustainable forest management. Recently, research trends have shifted from stand-level to individual-level yield prediction. In this study, we examined the effectiveness of yield prediction models based on a distance-independent approach for Japanese cedar (Cryptomeria japonica (L.f.) D.Don) trees in western Japan. We further examined the accuracy of the models with reference to existing data collected over the long-term. First, we constructed distance-independent height, diameter growth, and survival models. Then, we simulated for approximately 50 years individual tree height, diameter at breast height (DBH), and volume growth using the test data. We then compared the predicted and observed values and calculated root-mean-square error (RMSE) and bias to evaluate the model accuracy. The models were noted to perform well when predicting mean height, DBH, and volume for Japanese cedar trees; in fact, they adequately predicted the diameter distribution. Our results suggest that distance-independent models could adequately predict long-term mean values and diameter distribution. However, RMSE and bias indicated that error propagation occurred over longer time spans. Thus, it is effective to conduct actual measurements at some point in the forest development phase and use the measurements as initial values for short- or medium-term predictions.

Early detection of tree encroachment in high voltage powerline corridor using growth model and UAV-borne LiDAR

UAV-borne LiDAR is an innovative and effective technique for tree encroachment detection in high voltage powerline corridor. However, the periodical inspection of the whole powerline corridor is inefficient, as the powerline segments occurring the tree encroachment only account for a very small part. In this paper, taking one segment of the powerline corridor in Taining County, Fujian province, China as the test site, we acquired the point cloud data using a UAV-borne LiDAR, and then combined the tree growth model and two-phase encroachment detection algorithm to realize efficiently early detection of tree encroachment. First, the points of powerlines and trees were classified from the point cloud, and then the individual tree heights and the belonging tree species were extracted. Based on tree plot data, the relationships between tree heights and tree ages were established using Richards growth model. Secondly, the individual tree heights were predicted at given time points, and the tree encroachments were detected in advance according to the required safe distance between powerlines and trees. To tackle the huge amount of point cloud data and calculation, the two-phase tree encroachment detection algorithm based on bounding boxes was applied to replace the traditional point traversal algorithm. As a result, the exact locations of tree encroachment were early detected and the specific encroaching trees were also pre-identified. Lastly, the pre-detected tree encroachment should be verified through field survey, and treated accordingly. Thus, the inspection efficiency would be greatly improved. In accuracy assessment, the coefficient of determination (R2) and the root mean square error (RMSE) of fitted growth model for Masson pine were 0.812 and 2.308 m, and 0.861 and 2.556 m for Eucalyptus, respectively. Compared with point traversal algorithm, the calculation efficiency of the two-phase tree encroachment detection algorithm was improved by nearly 76 times on average.

Estimating aboveground biomass of urban forest trees with dual-source UAV acquired point clouds

Urban forest is a crucial part of urban ecological environment. The accurate estimation of its tree aboveground biomass (AGB) is of significant value to evaluate urban ecological functions and estimate urban forest carbon storage. It has a high accuracy to estimate the forest AGB with field measured canopy structure parameters, but unsuitable for large-scale operations. Limited by low spatial resolution or spectral saturation, the estimated forest AGBs based on various satellite remotely sensed data have relatively low accuracies. In contrast, Unmanned Aerial Vehicle (UAV) remote sensing provides a promising way to accurately estimate the tree AGB of fragmented urban forest. In this study, taking an artificial urban forest in Ma'anxi Wetland Park in Chongqing City, China as an example, we used UAVs equipped with a digital camera and a LiDAR to acquire two point cloud data. One was produced from overlapping images using Structure from Motion (SfM) photogrammetry, and the other was resolved from laser scanned raw data. The dual point clouds were combined to extract individual tree height (H) and canopy radius (Rc), which were then input to the newly established allometric equation with tree H and Rc as predictor variables to obtain the AGBs of all dawn redwood trees in study area. In accuracy assessment, the coefficient of determination (R2) and Root Mean Square Error (RMSE) of extracted H were 0.9341 and 0.59 m; the R2 and RMSE of extracted Rc were 0.9006 and 0.28 m; the R2 and RMSE of estimated AGB were 0.9452 and 17.59 kg. These results proved the feasibility and effectiveness of applying dual-source UAV point cloud data and the new allometric equation on H and Rc to accurate AGB estimation of urban forest trees.

Predicting the upper stem diameters and volume of a tropical dominant tree species

Predicting upper stem diameters and individual tree volumes is important for product quantification and can provide important information for the sustainable management of forests of important commercial tree species (Shorea robusta) in Nepal. The aim of this study was to develop a taper equation for S. robusta. Fifty-four trees were selected and felled in the southern low land of Nepal. A mixed effect modelling approach was used to evaluate 17 different taper functions. ‘Leave-one-out cross-validation’ was used to validate the fitted taper functions. The variable exponent taper function best fitted our data and described more than 99% of the variation in upper stem diameters. Results also showed significant effects of stand density on tree taper. Individual tree volume prediction using the local volume model developed in this study was more accurate compared to the volume predicted through the taper function and existing volume model. The taper function developed in this study provides the benefit of predicting upper stem diameter and can be used for predicting volume to any merchantable height of individual trees. It will have implications in estimates of volume, biomass, and carbon and thus may be a potential supporting tool in carbon trade and revenue generation.

Individual models for diameter and height projection with contribution of competition index in Pinus taeda L. stands

The aim if this work was to assess and propose projection models for diameter at breast height and total height of individual pine trees and the contribution of competition indexes in these models. Data was obtained from 1056 permanent sampling units of non-thinned Pinus taeda L. stands located in the Telêmaco Borba region, Paraná, Brazil, with ages between 3.4 and 19.5 years. In order to project the diameter and total height of the trees, linear models were tested according to the variable in the current age, the projection age in the future, the basal area, and the site index, as well as their transformations. The Spearman correlation was obtained between competition indexes and dendrometric and stand variables, with the GH (Glover and Hool), LO (Lorimer) and ST (Stage) indexes being selected. These indexes were assessed as independent variables in the models, whose contributions were verified through the “F ci ” test on the incremental contribution. The models were assessed through the adjusted coefficient of determination, standard error of the estimate, and boxplots of residuals. The models provided precise and bias-free projections. The competition indexes statistically contributed to diameter and total height projection according to the “F ci ” test, increasing the precision of projected values for the majority of models tested. The indexes with major contributions to individual tree modelling for diameter and total height projection of Pinus taeda were Glover and Hool (GH) and Lorimer (LO), respectively.

Application of unmanned aerial system structure from motion point cloud detected tree heights and stem diameters to model missing stem diameters

Monitoring of tree spatial arrangement is increasingly essential for restoration of dry conifer forests. The presented method was developed for high-density point clouds, like those from unmanned aerial system imagery, to extract and model individual tree location, height, and diameter at breast height (DBH). Extraction of tree locations and heights uses a variable window function searching point cloud-derived canopy height models. Tree DBH is extracted for a subset of point cloud trees using a slice at 1.32-1.42 m and a least-squares circle fitting algorithm. Extracted heights and DBHs are spatially matched and filtered against each tree's expected DBH predicted using a regional National Forest Inventory height to DBH relationship. Values remaining after filtering are used to create a site-specific height to DBH relationship for predicting missing DBH values. Applying the method in a ponderosa pine-dominated forest found that extracted height values exceeded the precision of field height measurement approaches, while the accuracy of extracted and modeled DBH values had a mean error of 0.79 cm.•Leveraging National Forest Inventory to filter DBH values eliminates the need for in situ observations.•Produces tree list for all extractable stems in the point cloud.•Transferable to high-density point clouds in open-canopy forests.

UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

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

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

Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR.

Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.

Height-diameter allometry for tropical forest in northern Amazonia

Height measurements are essential to manage and monitor forest biomass and carbon stocks. However, accurate estimation of this variable in tropical ecosystems is still difficult due to species heterogeneity and environmental variability. In this article, we compare and discuss six nonlinear allometric models parameterized at different scales (local, regional and pantropical). We also evaluate the height measurements obtained in the field by the hypsometer when compared with the true tree height. We used a dataset composed of 180 harvested trees in two distinct areas located in the Amapá State. The functional form of the Weibull model was the best local model, showing similar performance to the pantropical model. The inaccuracy detected in the hypsometer estimates reinforces the importance of incorporating new technologies in measuring individual tree heights. Establishing accurate allometric models requires knowledge of ecophysiological and environmental processes that govern vegetation dynamics and tree height growth. It is essential to investigate the influence of different species and ecological gradients on the diameter/height ratio.

Biomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information

Improved estimates of aboveground biomass (AGB) are required to improve our understanding of the productivity of mangrove forests to support the long-term conservation of these fragile ecosystems which are under threat from many natural and anthropogenic pressures. To understand how individual species affects biomass estimates in mangrove forests, five species-specific and four genus-specific allometric models were developed. Independent tree inventory data were collected from 140 sample plots to compare the AGB among the species-specific models and seven frequently used pan-tropical and Sundarbans-specific generic models. The effect of individual tree species was also evaluated using model parameters for wood densities (from individual trees to the whole Sundarbans) and tree heights (individual, plot average and plot top height). All nine developed models explained a high percentage of the variance in tree AGB (R 2 = 0.97–0.99) with the diameter at breast height and total height (H). At the individual tree level, the generic allometric models overestimated AGB from 22% to 167% compared to the species-specific models. At the plot level, mean AGB varied from 111.36 Mg ha−1 to 299.48 Mg ha−1, where AGB significantly differed in all generic models compared to the species-specific models (p < 0.05). Using measured species wood density (WD) in the allometric model showed 4.5%–9.7% less biomass than WD from published databases and other sources. When using plot top height and plot average height rather than measured individual tree height, the AGB was overestimated by 19.5% and underestimated by 8.3% (p < 0.05). The study demonstrates that species-specific allometric models and individual tree measurements benefit biomass estimation in mangrove forests. Tree level measurement from the inventory plots, if available, should be included in allometric models to improve the accuracy of forest biomass estimates, particularly when upscaling individual trees up to the ecosystem level.

Variable selection for estimating individual tree height using genetic algorithm and random forest

Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables.

Effects of advance regeneration and germinated seedlings on forest recovery after clearcutting of Chamaecyparis obtusa plantations in Southern Kyushu, Southwestern Japan

We clarified the effect of advance regeneration on natural forest recovery after clearcutting of conifer plantations over the mid-term observation period (12–14 years to allow formation of the forest canopy). We established understory-intact plot (UI-plot), where advance regeneration (AR; DBH more than 1 cm before clearcutting) were retained, and understory-cleared plot (UC-plot), where all ARs were removed at felling of conifer crops. We surveyed individual density, species richness, and tree height with reference to different regeneration origins 1 year after and 12 or 14 years after clearcutting. Then, we compared the structural changes in UI-plot with that in UC-plot. The resprouted AR in UC-plot has provided the same effect as the retained AR in UI-plot in forming their height distribution patterns and stratification over a mid-term recovery process. We also found that individual density and species richness attributed by the individuals of newly established seedlings after clearcutting (SE), have contributed to the stand development in the both plots. Furthermore, SE was found to have a large contribution to forest recovery in terms of tree density and species number, while its significance for the recovery of gravity-dispersal, lucidophyllous trees was limited. We concluded that the advantage of AR retention is limited to the fast recovery of forest structure at the early stage after clearcutting, and mostly disappeared over a mid-term observation period as far as abundant advance regeneration was sufficiently accumulated and surrounding seed sources were effectively secured.

Mangrove Zoning Study Unmanned Aerial Vehicle in Mangrove Forest of Nagari Mandeh, West Sumatra

Mangrove has physical and ecological functions that are very influential on coastal ecosystems. Mandeh area is currently one of the ecotourism areas that has a fairly extensive Mangrove forest. However mangrove condition is being threatened due to various reasons. This study to determine the zoning pattern of the Mandeh mangrove forest using two different methods: (a) field survey using belt transect method with determination of plot points determined by purpose sampling (b) method with unmanned aircraft using 3- dimensional visual analysis by utilizing orthophoto data recorded by unmanned aerial vehicles (UAV). Data were presented in tabulation form, then analyzed qualitatively and quantitatively. The results revealed that the zonation pattern of mangrove forests using the field survey method can provide results in the form of a more detailed zonation pattern, the number of individual trees, tree diameters, and the types of species that dominate. Observation of the zonation pattern of mangrove forests using UAV method providing accurate results in the form of general profiles of mangroves, and individual tree heights. The implication of this research will be useful as a consideration in making policies on mangrove conservation in Nagari Mandeh.

Simulating tree growth response to climate change in structurally diverse oak and beech forests

This study aimed to simulate oak and beech forest growth under various scenarios of climate change and to evaluate how the forest response depends on site properties and particularly on stand characteristics using the individual process-based model HETEROFOR. First, this model was evaluated on a wide range of site conditions. We used data from 36 long-term forest monitoring plots to initialize, calibrate, and evaluate HETEROFOR. This evaluation showed that HETEROFOR predicts individual tree radial growth and height increment reasonably well under different growing conditions when evaluated on independent sites.In our simulations under constant CO2 concentration ([CO2]cst) for the 2071-2100 period, climate change induced a moderate net primary production (NPP) gain in continental and mountainous zones and no change in the oceanic zone. The NPP changes were negatively affected by air temperature during the vegetation period and by the annual rainfall decrease. To a lower extent, they were influenced by soil extractable water reserve and stand characteristics. These NPP changes were positively affected by longer vegetation periods and negatively by drought for beech and larger autotrophic respiration costs for oak. For both species, the NPP gain was much larger with rising CO2 concentration ([CO2]var) mainly due to the CO2 fertilisation effect. Even if the species composition and structure had a limited influence on the forest response to climate change, they explained a large part of the NPP variability (44% and 34% for [CO2]cst and [CO2]var, respectively) compared to the climate change scenario (5% and 29%) and the inter-annual climate variability (20% and 16%). This gives the forester the possibility to act on the productivity of broadleaved forests and prepare them for possible adverse effects of climate change by reinforcing their resilience.

Determining an Accurate and Cost-Effective Individual Height-Diameter Model for Mongolian Pine on Sandy Land

Height-diameter (H-D) models are important tools for forest management practice. Sandy Mongolian pine plantations (Pinus sylvestris var. mongolica) are a major component of the Three-North Afforestation Shelterbelt in Northern China. However, few H-D models are available for Mongolian pine plantations. In this paper we compared different equations found in the literature for predicting tree height, using diameter at breast height and additional stand-level predictor variables. We tested if the additional stand-level predictor variable is necessary to produce more accurate results. The dominant height was used as a stand-level predictor variable to describe the variation of the H-D relationship among plots. We found that the basic mixed-effects H-D model provided a similar predictive accuracy as the generalized mixed-effects H-D model. Moreover, it had the advantage of reducing the sampling effort. The basic mixed-effects H-D model calibration, in which the heights of the two thickest trees in the plot were included to calibrate the random effects, resulted in accurate and reliable individual tree height estimations. Thus, the basic mixed-effects H-D model with the above-described calibration design can be an accurate and cost-effective solution for estimating the heights of Mongolian pine trees in northern China.

Target-based automated matching of multiple terrestrial laser scans for complex forest scenes

Terrestrial laser scanners are widely used to derive unbiased and non-destructive estimates of the vertical distribution of the plant area index and plant area volume density at plot-level scales, as well as the above-ground biomass, height, and diameter at breast height of individual trees. Multiple scans are often employed to capture and register data so that all of the stems can be detected and their complete forms can be analyzed. Researchers have traditionally preferred target-less strategies to register scans because of their low cost and convenience. However, in complex forest scenes, even state-of-the-art approaches cannot guarantee the success of any pairwise registration. In this study, we present an automated target-based processing approach for the registration of unordered scans in complex forest scenes. In contrast to previous studies, the proposed registration method automatically detects the artificial targets and builds a geometric network to judge their connectivity. A pose graph is then exploited to combine these data with the corresponding pairwise transformation, and then the scans are integrated into a unified coordinate system. This method is more robust and efficient than target-less approaches because it is independent of the characteristics of individual trees and does not require ground information. In an experimental scenario, we use an extremely complex wild bamboo forest scene to evaluate the performance of the proposed approach in terms of robustness, accuracy, and efficiency.

Under-Canopy UAV Laser Scanning Providing Canopy Height and Stem Volume Accurately

The automation of forest field reference data collection has been an intensive research objective for laser scanning scientists ever since the invention of terrestrial laser scanning more than two decades ago. In this study, we demonstrated that an under-canopy UAV laser scanning system utilizing a rotating laser scanner can alone provide accurate estimates of canopy height and stem volume for the majority of trees in a boreal forest. We mounted a rotating laser scanner based on a Velodyne VLP-16 sensor onboard a manually piloted UAV. The UAV was commanded with the help of a live video feed from the onboard camera. Since the system was based on a rotating laser scanner providing varying view angles, all important elements such as treetops, branches, trunks, and ground could be recorded with laser hits. In an experiment including two different forest structures, namely sparse and obstructed canopy, we showed that our system can measure the heights of individual trees with a bias of −20 cm and a standard error of 40 cm in the sparse forest and with a bias of −65 cm and a standard error of 1 m in the obstructed forest. The accuracy of the obtained tree height estimates was equivalent to airborne above-canopy UAV surveys conducted in similar forest conditions or even at the same sites. The higher underestimation and higher inaccuracy in the obstructed site can be attributed to three trees with a height exceeding 25 m and the reduced point density of these tree tops due to occlusion and the limited ranging capacity of the scanner. Additionally, we used our system to estimate the stem volumes of individual trees with a standard error at the level of 10%. This level of error is equivalent to the error obtained when merging above-canopy UAV laser scanner data with terrestrial point cloud data. The results show that we do not necessarily need a combination of terrestrial point clouds and point clouds collected using above-canopy UAV systems in order to accurately estimate the heights and the volumes of individual trees in reference data collection.