Diameter At Breast Height Measurements Research Articles

Large-scale inventory in natural forests with mobile LiDAR point clouds

Large-scale forest inventory at the individual tree level is critical for natural resource management decision making. Terrestrial Laser Scanning (TLS) has been used for individual tree level inventory at plot scale However, due to the inflexibility of TLS and the complex scene of natural forests, it is still challenging to localize and measure every tree at large scale. In this paper, we present a framework to conduct large-scale natural forest inventory at the individual tree level by taking advantage of deep learning models and Mobile Laser Scanning (MLS) systems. First, a deep learning model, ForestSPG, was developed to perform large-scale semantic segmentation on MLS LiDAR data in natural forests. Then, the forest segmentation results were used for individual stem mapping. Finally, Diameter at Breast Height (DBH) was measured for each individual stem. Two natural forests mapped with backpack and Unmanned Aerial Vehicle (UAV) LiDAR systems were tested. The results showed that the proposed ForestSPG is able to segment large-scale forest LiDAR data into multiple ecologically meaningful classes. The proposed framework was able to localize and measure all 5838 stems at individual tree level in a 20 ha natural forest in less than 20 min using UAV LiDAR. DBH measurement results on trees’ DBH larger than 38.1 cm (15 in) showed that backpack LiDAR was able to achieve 1.82 cm of Root Mean Square Error (RMSE) and UAV LiDAR was able to achieve 3.13 cm of RMSE. The proposed framework can not only segment complex forest components with LiDAR data from different platforms but also demonstrate good performance on stem mapping and DBH measurement. Our research provides and automatic and scalable solution for large-scale natural forest inventory at individual tree level, which can be the basis for large-scale estimation of wood volume and biomass.

Measuring Tree Diameter Using LiDAR Equipped iPad: An Evaluation of ForestScanner and Arboreal Forest Applications

Abstract Forest owners need simple and efficient tools to capture and understand forest metrics. Additionally, they could benefit from emerging technologies in forest analysis. Light detection and ranging (LiDAR) sensors allow for precise measurements of different variables and can be used more easily in forestry settings thanks to their introduction into smartphones and tablets. ForestScanner and Arboreal Forest are two applications (apps) that allow for the measurement of tree diameter at breast height (DBH) on LiDAR-equipped devices. Our study sought to analyze and compare (1) the timing of traditional and LiDAR-based forest inventory methods and (2) the accuracy of traditional and LiDAR-based forest measurements. We established a series of plots to record and compare tree DBH and measurement time between a traditional diameter tape, ForestScanner, and Arboreal Forest. We found that DBH measurements from the apps were in good agreement with diameter tape measurements; however, both apps tended to underestimate DBH. Additionally, measurement time for both apps was found to be significantly shorter than traditional tape measurements. Further improvements in LiDAR apps can present a simple and efficient way for future forest analysis by seasoned foresters and private landowners. Study Implications: LiDAR smartphone applications represent a cost-effective and time-efficient method for wide-spread forest management, especially among landowners who have little access to forestry-specific equipment. As these technologies continue to advance, the incorporation of functions for plot establishment, height measurements, biomass estimations, and ecosystem dynamics could become valuable assets in the forest industry. We encourage individuals to further test LiDAR measurement applications themselves, as further training on these applications will help developers to improve application accuracy.

Tree Diameter at Breast Height Extraction Based on Mobile Laser Scanning Point Cloud

The traditional measurement method (e.g., field survey) of tree diameter circumference often has high labor costs and is time-consuming. Mobile laser scanning (MLS) is a powerful tool for measuring forest diameter at breast height (DBH). However, the accuracy of point cloud registration seriously affects the results of DBH measurements. To address this issue, this paper proposes a new method for extracting tree DBH parameters; it achieves the purpose of efficient and accurate extraction of tree DBH by point cloud filtering, single-tree instance segmentation, and least squares circle fitting. Firstly, the point cloud data of the plantation forest samples were obtained by a self-constructed unmanned vehicle-mounted mobile laser scanning system, and the ground point cloud was removed using cloth simulation filtering (CSF). Secondly, fast Euclidean clustering (FEC) was employed to segment the single-tree instances, and the point cloud slices at breast height were extracted based on the point sets of single-tree instances, which were then fitted in two dimensions using the horizontally projected point cloud slices. Finally, a circle fitting algorithm based on intensity weighted least squares (IWLS) was proposed to solve the optimal circle model based on 2D point cloud slices, to minimize the impact of misaligned point clouds on DBH measures. The results showed that the mean absolute error (MAE) of the IWLS method was 2.41 cm, the root mean square error (RMSE) was 2.81 cm, and the relative accuracy was 89.77%. Compared with the random sample consensus (RANSAC) algorithm and ordinary least squares (OLS), the MAE was reduced by 36.45% and 9.14%, the RMSE was reduced by 40.90% and 12.26%, and the relative accuracy was improved by 8.99% and 1.63%, respectively. The R2 value of the fitted curve of the IWLS method was the closest to 1, with the highest goodness of fit and a significant linear correlation with the true value. The proposed intensity weighted least squares circle-fitting DBH extraction method can effectively improve the DBH extraction accuracy of mobile laser scanning point cloud data and reduce the influence of poorly aligned point clouds on DBH fitting.

An Advanced Software Platform and Algorithmic Framework for Mobile DBH Data Acquisition

Rapid and precise tree Diameter at Breast Height (DBH) measurement is pivotal in forest inventories. While the recent advancements in LiDAR and Structure from Motion (SFM) technologies have paved the way for automated DBH measurements, the significant equipment costs and the complexity of operational procedures continue to constrain the ubiquitous adoption of these technologies for real-time DBH assessments. In this research, we introduce KAN-Forest, a real-time DBH measurement and key point localization algorithm utilizing RGB-D (Red, Green, Blue-Depth) imaging technology. Firstly, we improved the YOLOv5-seg segmentation module with a Channel and Spatial Attention (CBAM) module, augmenting its efficiency in extracting the tree’s edge features in intricate forest scenarios. Subsequently, we devised an image processing algorithm for real-time key point localization and DBH measurement, leveraging historical data to fine-tune current frame assessments. This system facilitates real-time image data upload via wireless LAN for immediate host computer processing. We validated our approach on seven sample plots, achieving bbAP50 and segAP50 scores of: 90.0%(+3.0%), 90.9%(+0.9%), respectively with the improved YOLOv5-seg model. The method exhibited a DBH estimation RMSE of 17.61∼54.96 mm (R2=0.937), and secured 78% valid DBH samples at a 59 FPS. Our system stands as a cost-effective, portable, and user-friendly alternative to conventional forest survey techniques, maintaining accuracy in real-time measurements compared to SFM- and LiDAR-based algorithms. The integration of WLAN and its inherent scalability facilitates deployment on Unmanned Ground Vehicles (UGVs) to improve the efficiency of forest inventory. We have shared the algorithms and datasets on Github for peer evaluations.

Development of a Real-Time Continuous Measurement System for Tree Radial Direction

Tree diameter at breast height (DBH) is the most fundamental factor in modelling tree growth, but current DBH measurement instruments mainly focus on instantaneous acquisition, making it difficult to measure tree growth continuously and accurately. In this study, we propose a wireless sensing network that can transmit data in signal-free environments, and combine sensor and computer technologies to develop a real-time continuous measurement system for tree radials, which has the advantages of working in real-time, being low-cost and stable, and enabling high-precision. It can be applied to the DBH measurement of trees in the range of 50 mm–380 mm, with a measurement accuracy of 0.001 mm. Additionally, whole-point sampling, conducted 24 h per day, integrates DBH data measurement, transmission, storage and visualization analysis. After measuring in the field for a year, it initially reveals the change in DBH within the test area within that year. This study provides a scientific basis for researching the microscopic growth pattern of trees and establishing a tree growth model, which will be further optimised and improved in terms of appearance structure, communication and power supply in the future.

A Novel Framework for Stratified-Coupled BLS Tree Trunk Detection and DBH Estimation in Forests (BSTDF) Using Deep Learning and Optimization Adaptive Algorithm

Diameter at breast height (DBH) is a critical metric for quantifying forest resources, and obtaining accurate, efficient measurements of DBH is crucial for effective forest management and inventory. A backpack LiDAR system (BLS) can provide high-resolution representations of forest trunk structures, making it a promising tool for DBH measurement. However, in practical applications, deep learning-based tree trunk detection and DBH estimation using BLS still faces numerous challenges, such as complex forest BLS data, low proportions of target point clouds leading to imbalanced class segmentation accuracy in deep learning models, and low fitting accuracy and robustness of trunk point cloud DBH methods. To address these issues, this study proposed a novel framework for BLS stratified-coupled tree trunk detection and DBH estimation in forests (BSTDF). This framework employed a stratified coupling approach to create a tree trunk detection deep learning dataset, introduced a weighted cross-entropy focal-loss function module (WCF) and a cosine annealing cyclic learning strategy (CACL) to enhance the WCF-CACL-RandLA-Net model for extracting trunk point clouds, and applied a (least squares adaptive random sample consensus) LSA-RANSAC cylindrical fitting method for DBH estimation. The findings reveal that the dataset based on the stratified-coupled approach effectively reduces the amount of data for deep learning tree trunk detection. To compare the accuracy of BSTDF, synchronous control experiments were conducted using the RandLA-Net model and the RANSAC algorithm. To benchmark the accuracy of BSTDF, we conducted synchronized control experiments utilizing a variety of mainstream tree trunk detection models and DBH fitting methodologies. Especially when juxtaposed with the RandLA-Net model, the WCF-CACL-RandLA-Net model employed by BSTDF demonstrated a 6% increase in trunk segmentation accuracy and a 3% improvement in the F1 score with the same training sample volume. This effectively mitigated class imbalance issues encountered during the segmentation process. Simultaneously, when compared to RANSAC, the LSA-RANCAC method adopted by BSTDF reduced the RMSE by 1.08 cm and boosted R2 by 14%, effectively tackling the inadequacies of RANSAC’s filling. The optimal acquisition distance for BLS data is 20 m, at which BSTDF’s overall tree trunk detection rate (ER) reaches 90.03%, with DBH estimation precision indicating an RMSE of 4.41 cm and R2 of 0.87. This study demonstrated the effectiveness of BSTDF in forest DBH estimation, offering a more efficient solution for forest resource monitoring and quantification, and possessing immense potential to replace field forest measurements.

Examining the Effect of Beech Bark Disease on Radial Growth in American Beech

Beech bark disease (BBD) is a significant threat to forests of North America and the impact of BBD on radial growth in the American beech is substantial. We developed a novel hierarchical Bayesian (HB) model to simultaneously model disease dynamics, tree growth, and the interaction of the two. Our model can be adapted to both emerging and more mature forest–pathogen systems to aid in ecosystem loss predictions. Long-term data from a single site minimized potential confounding variables such as climate change, precipitation, land use history, and soil conditions that may influence radial growth. Here, 206 beech trees were monitored over 15 years at an 85-acre site in southwestern Vermont, measuring diameter at breast height (DBH) and progression of BBD. Our model allows us to accurately estimate error rates in disease severity estimation and DBH measurements, and estimate the true state based on environmental variables. As disease poses significant threats to many tree species around the world, researchers can obtain more value and information from their datasets utilizing an adapted HB model.

Development and Testing of a New UWB Positioning Measurement Tool to Assist in Forest Surveys

Forest resource inventory is a significant part of the sustainable management of forest ecosystems. Finding methods to accurately estimate the diameter at breast height (DBH), tree height and tree position is a significant part of forest resource inventory. The traditional methods of forest resource inventory are expensive, difficult, laborious and time-consuming; the existing systems are not convenient to carry, resulting in low working efficiency. In addition, it is usually necessary to rely on a forest compass, DBH taper and RTK or handheld GPS to set up the plot. These instruments each have a single function and cannot achieve accurate positioning under the forest canopy. Therefore, it is necessary to update the existing equipment and technology. This study aimed to design. a multi-functional, high-precision, real-time. positioning intelligent tree-measuring instrument that integrates plot the set-up, DBH measurement, tree height measurement and tree position measurement. The instrument is based on the ultra-wideband positioning principle, sensor technology, image processing technology, trigonometric functions, tree surveying and other related theories and realizes the functions of plot set-up, tree position measurement, DBH measurement, tree height measurement and other functions. The device was tested in four square plots. The results showed that the root mean squared. error (RMSE). of the tree position estimates ranged from 0.07 m to 0.16 m, while the relative root mean squared error (rRMSE) of the DBH estimates of individual trees ranged from 3.01 to 6.43%, which is acceptable for practical applications in traditional forest inventory. The rRMSE of the tree height estimates ranged from 3.47 to 5.21%. Furthermore, the cost of this instrument is only about one-third that of traditional forestry survey tools, while the work efficiency is three times that of the traditional measurement methods. Overall, the results confirmed that the tree measuring instrument is a practical tool for obtaining. accurate measurements of the tree position, DBH and tree height for forest inventories.

Estimating the performance of multi-rotor unmanned aerial vehicle structure-from-motion (UAVsfm) imagery in assessing homogeneous and heterogeneous forest structures: a comparison to airborne and terrestrial laser scanning

The implementation of Unmanned Aerial Vehicles (UAVs) and Structure-from-Motion (SfM) photogrammetry in assessing forest structures for forest inventory and biomass estimations has shown great promise in reducing costs and labour intensity while providing relative accuracy. Tree Height (TH) and Diameter at Breast Height (DBH) are two major variables in biomass assessment. UAV-based TH estimations depend on reliable Digital Terrain Models (DTMs), while UAV-based DBH estimations depend on reliable dense photogrammetric point cloud. The main aim of this study was to evaluate the performance of multi-rotor UAV photogrammetric point cloud in estimating homogeneous and heterogeneous forest structures, and their comparison to more accurate LiDAR data obtained from Aerial Laser Scanners (ALS), Terrestrial Laser Scanners (TLS), and more conventional means like manual field measurements. TH was assessed using UAVSfM and LiDAR point cloud derived DTMs, while DBH was assessed by comparing UAVSfM photogrammetric point cloud to LiDAR point cloud, as well as to manual measurements. The results obtained in the study indicated that there was a high correlation between UAVSfM TH and ALSLiDAR TH (R2 = 0.9258) for homogeneous forest structures, while a lower correlation between UAVSfM TH and TLSLiDAR TH (R2 = 0.8614) and UAVSfM TH and ALSLiDAR TH (R2 = 0.8850) was achieved for heterogeneous forest structures. A moderate correlation was obtained between UAVSfM DBH and field measurements (R2 = 0.5955) for homogenous forest structures, as well as between UAVSfM DBH and TLSLiDAR DBH (R2 = 0.5237), but a low correlation between UAVSfM DBH and UAVLiDAR DBH (R2 = 0.1114). The study demonstrated that UAV acquired imagery can be used to accurately estimate TH in both forest types, but has challenges estimating DBH. The research does not suggest that UAVSfM serves as a replacement for more high-cost and accurate LiDAR data, but rather as a cheaper adequate alternative in forestry management depending on accuracy requirements.

Long-Term Growth of Highway Rights-of-Way Trees

Background: Highway rights-of-ways (ROWs, or verges) contain multiple stressors which can influence tree growth, including compacted soils, soils with little topsoil, poor drainage, air and soil pollutants, construction activities, and de-icing salts in cold climates. Yet highway ROWs often provide ample planting space for growing trees, which can contribute to the mitigation of negative environmental impacts associated with highways. Methods: For this study, we assessed the trunk diameter of 1,058 trees from 11-, 22-, and 31-year-old planting cohorts along a highway in the Chicago metropolitan region (Illinois, USA) to examine factors which could influence long-term growth. We analyzed the impact of location factors within the ROW (e.g., distance and elevation relative to highway, slope, and aspect) on trunk diameter at breast height (DBH), since these factors are relevant to the landscape design process. Using estimates from i-Tree, we compared carbon sequestration, carbon storage, runoff reduction, and air-pollution removal within and among the 3 cohorts. Results: Of the 6 site location characteristics we evaluated, no single characteristic consistently impacted DBH, though some characteristics were significant within a single cohort. DBH measurements of most species were smaller than model predictions based on existing urban tree models. Since all cohorts included large- and small-statured trees, and even within species DBH could be highly variable, the range in per-tree ecosystem services varied substantially within cohorts, especially the 31-year-old cohort. Conclusions: These findings highlight both the potential for and challenges of growing trees alongside highways.

Exploring the Potential of UAV LiDAR Data for Trunk Point Extraction and Direct DBH Measurement

The accurate measurement of diameter at breast height (DBH) is one of the essential tasks for biomass estimation at an individual tree scale. This paper aims to explore the potential of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) for trunk point extraction and direct DBH measurement. First, the trunk point cloud for each tree is extracted based on UAV LiDAR data by the multiscale cylindrical detection method. Then, the DBH is directly measured from the point cloud via the multiscale ring fitting. Lastly, we analyze the influence of scanning angle and mode on trunk point extraction and DBH measurement. The results show that the proposed method can obtain high accuracy of trunk point extraction and DBH measurement with real (R2 = 0.708) and simulated (R2 = 0.882) UAV LiDAR data. It proves that the UAV LiDAR data is feasible to directly measure the DBH. The highest accuracy was obtained with the scanning angles ranging from 50 to 65 degrees. Additionally, as the number of routes increases, the accuracy increases. This paper demonstrates that the UAV LiDAR can be used to directly measure the DBH, providing the scientific guidance for UAV path planning and LiDAR scanning design.

Automatic Forest DBH Measurement Based on Structure from Motion Photogrammetry

Measuring diameter at breast height (DBH) is an essential but laborious task in the traditional forest inventory; it motivates people to develop alternative methods based on remote sensing technologies. In recent years, structure from motion (SfM) photogrammetry has drawn researchers’ attention in forest surveying for its economy and high precision as the light detection and ranging (LiDAR) methods are always expensive. This study explores an automatic DBH measurement method based on SfM. Firstly, we proposed a new image acquisition technique that could reduce the number of images for the high accuracy of DBH measurement. Secondly, we developed an automatic DBH estimation pipeline based on sample consensus (RANSAC) and cylinder fitting with the Least Median of Squares with impressive DBH estimation speed and high accuracy comparable to methods based on LiDAR. For the application of SfM on forest survey, a graphical interface software Auto-DBH integrated with SfM reconstruction and automatic DBH estimation pipeline was developed. We sampled four plots with different species to verify the performance of the proposed method. The result showed that the accuracy of the first two plots, where trees’ stems were of good roundness, was high with a root mean squared error (RMSE) of 1.41 cm and 1.118 cm and a mean relative error of 4.78% and 5.70%, respectively. The third plot’s damaged trunks and low roundness stems reduced the accuracy with an RMSE of 3.16 cm and a mean relative error of 10.74%. The average automatic detection rate of the trees in the four plots was 91%. Our automatic DBH estimation procedure is relatively fast and on average takes only 2 s to estimate the DBH of a tree, which is much more rapid than direct physical measurements of tree trunk diameters. The result proves that Auto-DBH could reach high accuracy, close to terrestrial laser scanning (TLS) in plot scale forest DBH measurement. Our successful application of automatic DBH measurement indicates that SfM is promising in forest inventory.

Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.

Estimating tree Diameter at Breast Height (DBH) using an iPad Pro LiDAR sensor

ABSTRACT Diameter at Breast Height (DBH) plays an important role in forest management. New technologies such as airborne and terrestrial Light Detection and Ranging (LiDAR) are expensive and time-consuming. We explored the iPad LiDAR sensor to measure DBH considering affordability and availability. The study was carried out at a research forest plantation near Thunder Bay, Ontario, Canada. Five plots were selected for differences in stem spacing, average DBH and species, and made 360° scans separately. A manual circle fitting method was used to produce circular features for the estimation of DBH. The coefficient of determination (R 2) between estimated DBH and field measurements was 0.52, indicating a moderate accuracy. The Root Mean Square Error (RMSE) of DBH estimation ranged from 2.82 cm to 8.24 cm. The accuracy of smaller red pine trees was found to be relatively higher than that of larger red pine and white spruce trees. These accuracies did not change with the distance from the scanning location to trees. Therefore, results showed the potential use of an iPad LiDAR Scanner for DBH estimation. We recommend developing an application combining the iPad’s location and LiDAR sensors and incorporating a precise positioning method for fieldwork, and further broad field testing.

Quantifying tropical forest structure through terrestrial and UAV laser scanning fusion in Australian rainforests

Accurately quantifying tree and forest structure is important for monitoring and understanding terrestrial ecosystem functioning in a changing climate. The emergence of laser scanning, such as Terrestrial Laser Scanning (TLS) and Unoccupied Aerial Vehicle Laser Scanning (UAV-LS), has advanced accurate and detailed forest structural measurements. TLS generally provides very accurate measurements on the plot-scale (a few ha), whereas UAV-LS provides comparable measurements on the landscape-scale (>10 ha). Despite the pivotal role dense tropical forests play in our climate, the strengths and limitations of TLS and UAV-LS to accurately measure structural metrics in these forests remain largely unexplored. Here, we propose to combine TLS and UAV-LS data from dense tropical forest plots to analyse how this fusion can further advance 3D structural mapping of structurally complex forests. We compared stand (vertical point distribution profiles) and tree level metrics from TLS, UAV-LS as well as their fused point cloud. The tree level metrics included the diameter at breast height (DBH), tree height (H), crown projection area (CPA), and crown volume (CV). Furthermore, we evaluated the impact of point density and number of returns for UAV-LS data acquisition. DBH measurements from TLS and UAV-LS were compared to census data. The TLS and UAV-LS based H, CPA and CV measurements were compared to those obtained from the fused point cloud. Our results for two tropical rainforest plots in Australia demonstrate that TLS can measure H, CPA and CV with an accuracy (RMSE) of 0.30 m (Haverage =27.32 m), 3.06 m2 (CPAaverage =66.74 m2), and 29.63 m3 (CVaverage =318.81 m3) respectively. UAV-LS measures H, CPA and CV with an accuracy (RMSE) of <0.40 m, <5.50 m2, and <30.33 m3 respectively. However, in dense tropical forests single flight UAV-LS is unable to sample the tree stems sufficiently for DBH measurement due to a limited penetration of the canopy. TLS can determine DBH with an accuracy (RMSE) of 5.04 cm, (DBHaverage =45.08 cm), whereas UAV-LS can not. We show that in dense tropical forests stand-alone TLS is able to measure macroscopic structural tree metrics on plot-scale. We also show that UAV-LS can be used to quickly measure H, CPA, and CV of canopy trees on the landscape-scale with comparable accuracy to TLS. Hence, the fusion of TLS and UAV-LS, which can be time consuming and expensive, is not required for these purposes. However, TLS and UAV-LS fusion opens up new avenues to improve stand-alone UAV-LS structural measurements at the landscape-scale by applying TLS as a local calibration tool.

The University of West Florida Campus Ecosystem Study: age-diameter and growth relationships of longleaf pine using hurricane-induced windthrows

The campus of the University of West Florida (UWF) was constructed among second-growth longleaf pine (Pinus palustris) stands that survived extensive logging in the Florida Panhandle. Previous studies on longleaf pine on the main UWF campus have estimated, based on a model from old-growth longleaf in southern Georgia, that oldest stems were just under 200 yr old and that > 80% of stems on the main UWF are between 50 and 125 years old. More accurate age data can be obtained from disks collected locally from recently fallen trees. On 16 September 2020, Hurricane Sally impacted UWF as a Category 2 storm, with winds reaching 49 m/sec. Our study took advantage of longleaf pine blowdowns by Sally to obtain cross-sections for age determinations. Two natural areas of the UWF campus were chosen for sampling: the Edward Ball Nature Trail and the Baars-Firestone Wildlife Sanctuary. For each sampled section, diameter at breast height (DBH) and number and width of annual rings were recorded. Based on a total of 50 sampled trees, linear regression revealed a statistically significant (P < 0.00001; R2 = 0.82) relationship between DBH and age. Applying this to DBH measurements of 2,165 stems on the main campus indicates that the oldest longleaf pines are ~ 130 years old (mean age = 63.9 ± 0.4 yr), consistent with cessation of historically widespread harvesting in the region. Mean age for the Trails site (55.7 ± 1.6 yr) was significantly lower than that of the Sanctuary (66.7 ± 2.0 yr), suggesting that they represent sites of contrasting land-use history. Annual growth rates of older pines were mostly negatively correlated with temperature. Directions of stem windthrows did not vary between natural areas and were consistent with characteristics of the eyewall of Hurricane Sally with strongest wind gusts moving from a southeast to northwest direction. This study confirms that college/university campuses can be used as a units of ecological study in a way that takes advantage of stochastic events such as tropical cyclones.

Estimation of breast height diameter and trunk curvature with linear and single-photon LiDARs

Key messageNew technologies can take us towards real precision forestry: the terrestrial single-photon avalanche diode (SPAD) light detection and ranging (LiDAR) has a great potential to outperform conventional linear mode LiDARs in measuring tree parameters at the stand level.ContextPrecision forestry together with new sensor technologies implies Digital Forest Inventories for estimation of volume and quality of trees in a stand.AimsThis study compared commercial LiDAR, new prototype SPAD LiDAR, and manual methods for measuring tree quality attributes, i.e., diameter at breast height (DBH) and trunk curvature in the forest stand.MethodsWe measured 7 Scots pine trees (Pinus sylvestris) with commercial LiDAR (Zeb Horizon by GeoSLAM), prototype SPAD LiDAR, and manual devices. We compared manual measurements to the DBH and curvature values estimated based on LiDAR data. We also scanned a densely branched Picea abies to compare penetrability of the LiDARs and detectability of the obstructed trunk.ResultsThe DBH values deviated 1–3 cm correlating to the specified accuracies of the employed devices, showing close to acceptable results. The curvature values deviated 1–6 cm implying distorted range measurements from the top part of the trunks and inaccurate manual measurement method, leaving space for improvement. The most important finding was that the SPAD LiDAR outperformed conventional LiDAR in detecting tree stem of the densely branched spruce.ConclusionThese results represent preliminary but clear evidence that LiDAR technologies are already close to acceptable level in DBH measurements, but not yet satisfactory for curvature measurements. In addition, terrestrial SPAD LiDAR has a great potential to outperform conventional LiDARs in forest measurements of densely branched trees.

A handheld device for measuring the diameter at breast height of individual trees using laser ranging and deep-learning based image recognition

BackgroundAccurate and efficient measurement of the diameter at breast height (DBH) of individual trees is essential for forest inventories, ecological management, and carbon budget estimation. However, traditional diameter tapes are still the most widely used dendrometers in forest surveys, which makes DBH measurement time-consuming and labor-intensive. Automatic and easy-to-use devices for measuring DBH are highly anticipated in forest surveys. In this study, we present a handheld device for measuring the DBH of individual trees that uses digital cameras and laser ranging, allowing for an instant, automated, and contactless measurement of DBH.ResultsThe base hardware of this device is a digital camera and a laser rangefinder, which are used to take a picture of the targeted tree trunk and record the horizontal distance between the digital camera and the targeted tree, respectively. The core software is composed of lightweight convolutional neural networks (CNNs), which includes an attention-focused mechanism for detecting the tree trunk to log the number of pixels between the edges. We also calibrated the digital camera to correct the distortion introduced by the lens system, and obtained the normalized focal length. Parameters including the horizontal distance between the digital camera and the targeted tree, number of pixels between the edges of the tree trunk, and normalized focal length were used to calculate the DBH based on the principles of geometrical optics. The measured diameter values, and the longitudes and latitudes of the measurement sites, were recorded in a text file, which is convenient to export to external flash disks. The field measurement accuracy test showed that the BIAS of the newly developed device was − 1.78 mm, and no significant differences were found between the measured diameter values and the true values (measured by the conventional tape). Furthermore, compared with most other image-based instruments, our device showed higher measurement accuracy.ConclusionsThe newly developed handheld device realized efficient, accurate, instant, and non-contact measurements of DBH, and the CNNs were proven to be successful in the detection of the tree trunk in our research. We believe that the newly developed device can fulfill the precision requirement in forest surveys, and that the application of this device can improve the efficiency of DBH measurements in forest surveys.

Single Tree Segmentation and Diameter at Breast Height Estimation With Mobile LiDAR

The tree diameter at breast height (DBH) is one of the most important variables for monitoring the forest ecology. Mobile laser scanning (MLS), which has been widely applied in the forestry field, makes DBH measurement fast and convenient. However, there are many shrubs and deadwood in the neutral forest environment and the point clouds quality from MLS are easily affected by the environment which results in low single tree segmentation and DBH estimation accuracy. To improve the accuracy in a complex forest environment and low point cloud quality, we propose a relative density segmentation method for the single tree segmentation and DBH estimation method based on multi-height diameters for the DBH estimation. The relative density segmentation method calculates the relative density according to the ratio of density in two different scales, and segments the tree trunks by the higher relative density of trunk point clouds compared with their surroundings points. In the natural forest plot, the precision and recall of the proposed segmentation method reached 0.966 and 0.946, respectively; In the urban forest plot, the precision and recall reached 1 and 0.966, respectively. The proposed DBH estimation method was used to estimate the DBH of trees using multi-height diameters. The multi-height diameters combined with the outlier detection algorithm were able to improve the accuracy and robustness when the trunk point clouds have large noise. For the DBH estimation results, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) were 2.5 cm, 11.54%, and 3.17 cm, respectively, in the natural forest plot and 1.65 cm, 6.31%, and 1.97 cm, respectively, in the urban forest plot. The good experiment results indicate that the proposed method can achieve accurate and robust DBH extraction and provide fundamental data for supervision and sustainable development of forest resources.

Aboveground Wood Production Is Sustained in the First Growing Season after Phloem-Disrupting Disturbance

Carbon (C) cycling processes are particularly dynamic following disturbance, with initial responses often indicative of longer-term change. In northern Michigan, USA, we initiated the Forest Resilience Threshold Experiment (FoRTE) to identify the processes that sustain or lead to the decline of C cycling rates across multiple levels (0, 45, 65 and 85% targeted gross leaf area index loss) of disturbance severity and, in response, to separate disturbance types preferentially targeting large or small diameter trees. Simulating the effects of boring insects, we stem girdled &gt; 3600 trees below diameter at breast height (DBH), immediately and permanently disrupting the phloem. Weekly DBH measurements of girdled and otherwise healthy trees (n &gt; 700) revealed small but significant increases in daily aboveground wood net primary production (ANPPw) in the 65 and 85% disturbance severity treatments that emerged six weeks after girdling. However, we observed minimal change in end-of-season leaf area index and no significant differences in annual ANPPw among disturbance severities or between disturbance types, suggesting continued C fixation by girdled trees sustained stand-scale wood production in the first growing season after disturbance. We hypothesized higher disturbance severities would favor the growth of early successional species but observed no significant difference between early and middle to late successional species’ contributions to ANPPw across the disturbance severity gradient. We conclude that ANPPw stability immediately following phloem disruption is dependent on the continued, but inevitably temporary, growth of phloem-disrupted trees. Our findings provide insight into the tree-to-ecosystem mechanisms supporting stand-scale wood production stability in the first growing season following a phloem-disrupting disturbance.