Diameter At Breast Height Estimation Research Articles

Estimation of Diameter at Breast Height in Tropical Forests Based on Terrestrial Laser Scanning and Shape Diameter Function

Estimating forest carbon content typically requires the precise measurement of the trees’ diameter at breast height (DBH), which is crucial for maintaining the health and sustainability of natural forests. Currently, Terrestrial Laser Scanning (TLS) systems are commonly used to acquire forest point cloud data for DBH estimation. However, traditional circular fitting methods face challenges such as a reliance on forest elevation normalization and underfitting of large trees. This study explores a novel approach, the Shape Diameter Function (SDF) algorithm model, leveraging the advantages of three-dimensional point cloud information to replace traditional circular fitting methods. This study employed a parallel approach, combining the Digital Elevation Model (DEM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to segment tree point clouds at breast height. Additionally, a point cloud SDF algorithm based on an octree structure was proposed to accurately estimate individual tree DBH. The research data were obtained from tropical secondary forests located in Cameroon, Peru, Indonesia, and Guyana, with forest ground point cloud data acquired via TLS. The experimental results demonstrated the superior performance of the SDF algorithm in estimating DBH. Compared with the Random Sample Consensus (RANSAC) and Hough transform methods, the Root Mean Square Error (RMSE) decreased by 28.1% and 47.8%, respectively. Particularly in estimating DBH for large trees, the SDF algorithm exhibited smaller errors, indicating a closer alignment between the estimated individual tree DBH values and those obtained from manual measurements. This study presented a more accurate DBH estimation algorithm, contributing to the exploration of improved forest carbon content estimation methods.

Sampling Protocol for Measuring Mean Diameter at Breast Height of Forked Urban Trees

The sustainable management of urban green areas requires clear and efficient protocols for measuring the biometric properties of tree vegetation. Specifically, operational in situ sampling solutions are essential to inventory forked (multi-stemmed) trees. This study aimed to assess the efficiency of two different sampling protocols for mean tree diameter at breast height (DBH) measurement of forked urban trees. The protocols were tested on a dataset of 76 forked trees, each having more than three stems and sampled in urban areas of Kyiv, Ukraine. First, we tested the efficiency of mean tree DBH estimations using measurements of randomly selected one, two, or three stems (random sampling, or RSM). Second, we examined different combinations of the thinnest, thickest, and average stems (identified visually) for each tree to estimate mean tree DBH (targeted sampling, or TSM). The distributions of mean tree DBH and root mean square errors (RMSE) were utilized to compare the utility of the two approaches. The TSM of three stems (the thinnest, thickest, and average) provided the highest accuracy of mean tree DBH estimation (RMSE% = 6.3% of the mean), compared to the RSM (RMSE% = 12.1%). The TSM of the four thickest stems demonstrated the overestimation of mean tree DBH for forked trees with five or more stems. Accurate mean tree DBH estimates can be derived with negligible systematic errors applying the RSM over a large number of measured trees. However, these estimates will not likely match the measurements from previous inventories due to random stem selection. We recommend using the TSM with measuring three specific stems as a balanced solution in terms of estimation accuracy, bias, and time costs.

Supervised terrestrial to airborne laser scanner model calibration for 3D individual-tree attribute mapping using deep neural networks

Capturing subcanopy forest information from airborne laser scanning (ALS) is constrained by signal occlusion. This study demonstrates the potential of close-range terrestrial laser scanner (TLS) scanning to mitigate the constraints of ALS in acquiring stem-level forest attributes. A transformer-based neural network was adapted to classify and segment 3D individual trees from ALS data. A deep neural network combined with a gaussian process layer was proposed to estimate tree diameter-at-breast-height (DBH) from ALS data. The performance of these methods was compared to other benchmarked methods using the same dataset, including a total of seven classifiers, five segmentors, and six attribute regressors. The study was conducted across four ALS sample areas and ten combined TLS/ALS plots, primarily in montane forests. Manual delineation of TLS trees provided a precise validation reference. The proposed methods demonstrated high accuracies, with a mean intersection-over-union (mIoU) of 0.92 for ALS tree classification, 0.70 for tree segmentation, and a RMSE of 4.2 cm or 18.9 % for DBH estimation on average of the ten plots. Tree detection accuracy was strongly associated with the final segmentation accuracy. Factors such as tree height, overlapping, inclination, and neighboring conditions impacted segmentation accuracy. Our segmentation method effectively mitigated accuracy loss for short and occluded trees. Overall, this study presents scalable and cost-effective solutions for TLS calibration of ALS scans over two meso-scale montane valleys. Leveraging deep neural networks enables scaling of stem attributes to landscape scales, thereby linking fine-scale forest inventory with sustainable management of expansive forest resources. Our codes are available at https://github.com/truebelief/artemis_treescaling.

Evaluation of Accuracy in Estimating Diameter at Breast Height Based on the Scanning Conditions of Terrestrial Laser Scanning and Circular Fitting Algorithm

A growing societal interest exists in the application of lidar technology to monitor forest resource information and forestry management activities. This study examined the possibility of estimating the diameter at breast height (DBH) of two tree species, Pinus koraiensis (PK) and Larix kaempferi (LK), by varying the number of terrestrial laser scanning (TLS) scans (1, 3, 5, 7, and 9) and DBH estimation methods (circle fitting [CF], ellipse fitting [EF], circle fitting with RANSAC [RCF], and ellipse fitting with RANSAC [REF]). This study evaluates the combination that yields the highest estimation accuracy. The results showed that for PK, the lowest RMSE of 0.97 was achieved when REF was applied to the data from nine scans after noise removal. For LK, the lowest RMSE of 1.03 was observed when applying CF to the data from seven scans after noise removal. Furthermore, ANOVA revealed no significant difference in the estimated DBH from nine scans when more than three scans were used for CF and RCF and more than five for EF and REF. These results are expected to be useful in establishing efficient and accurate DBH estimation plans using TLS for forest resource monitoring.

Estimating Tree Diameter at Breast Height (DBH) Using iPad Pro LiDAR Sensor in Boreal Forests

Traditional Diameter at Breast Height (DBH) mensuration is labor-intensive and costly. This scoping study explored the possibility of using the Apple iPad Pro Light Detection And Ranging (LiDAR) sensor to estimate DBH. Three plots were scanned in a research plantation near Thunder Bay, Canada. Sites consisted of either Black Spruce (Picea mariana) or Red Pine (Pinus resinosa) planted with different initial densities. DBH was manually measured for validation. Point clouds were acquired for each plot using three scanning patterns; circular, figure-8, and transect. Single and five cross sections of 4 or 10 cm in thickness were extracted from each point cloud, centered at 1.3 m above the ground. Two circle fitting algorithms (Pratt, Taubin) and two ellipse fitting algorithms (Taubin, Szpak) were applied to the extracted cross-sections to estimate DBH. Scanning pattern and curve-fitting formula significantly impacted DBH estimate accuracy (p-value ≤ 0.001), while cross-section count and thickness did not. The circular scanning pattern with a single 4 cm cross-section and a combination of circle- and ellipse-fitting formulas was the most accurate DBH estimation method (RMSE = 1.1 cm; 6.17%).

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.

An Automated Pipeline for Extracting Forest Structural Parameters by Integrating UAV and Ground-Based LiDAR Point Clouds

In recent times, airborne and terrestrial laser scanning have been utilized to collect point cloud data for forest resource surveys, aiding in the estimation of tree and stand attributes over hectare-scale plots. In this study, an automated approach was devised to estimate the diameter at breast height (DBH) and tree height across the entire sample area, utilizing information acquired from terrestrial laser scanning (TLS) and airborne laser scanning (ULS). Centered around a meticulously managed artificial forest in Northern China, where Mongolian oak and Chinese Scots pine are the predominant species, both TLS and ULS operations were conducted concurrently on each plot. Subsequent to data collection, a detailed processing of the point cloud data was carried out, introducing an innovative algorithm to facilitate the matching of individual tree point clouds from ULS and TLS sources. To enhance the accuracy of DBH estimation, a weighted regression correction equation based on TLS data was introduced. The estimations obtained for the Chinese Scots pine plots showed a correlation of R2 = 0.789 and a root mean square error (RMSE) of 3.2 cm, while for the Mongolian oak plots, an improved correlation of R2 = 0.761 and a RMSE of 3.1 cm was observed between predicted and measured values. This research significantly augments the potential for non-destructive estimations of tree structural parameters on a hectare scale by integrating TLS and ULS technologies. The advancements hold paramount importance in the domain of large-scale forest surveys, particularly in the calibration and validation of aboveground biomass (AGB) estimations.

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.

Towards accurate individual tree parameters estimation in dense forest: optimized coarse-to-fine algorithms for registering UAV and terrestrial LiDAR data

ABSTRACT Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R2: 0.9, RMSE <1.85 cm), and tree height (R2: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R2: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R2: 0.84, RMSE: 0.37 m) and broadleaf (R2: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

Assessing the Performance of a Handheld Laser Scanning System for Individual Tree Mapping—A Mixed Forests Showcase in Spain

The use of mobile laser scanning to survey forest ecosystems is a promising, scalable technology to describe the 3D structure of forests at a high resolution. We use a structurally complex, mixed-species Mediterranean forest to test the performance of a mobile Handheld Laser Scanning (HLS) system to estimate tree attributes within a forest patch in central Spain. We describe the different stages of the HLS approach: field position, ground data collection, scanning path design, point cloud processing, alignment between detected trees and measured reference trees, and finally, the assessment of main tree structural attributes diameter at breast height (DBH) and tree height considering species and tree size as control factors. We surveyed 418 reference trees to account for omission and commission error rates over a 1 ha plot divided into 16 sections and scanned using two different scanning paths. The HLS-based approach reached a high of 88 and 92% tree detection rate for the best combination of scanning path and point cloud processing modes for the HLS system. The root mean squared errors for DBH estimates varied between species: errors for Pinus pinaster were below 2 cm for Scan 02. Quercus pyrenaica, and Alnus glutinosa showed higher error rates. We observed good agreement between ALS and HLS estimates for tree height, highlighting differences to field measurements. Despite the complexity of the mixed forest area surveyed, our results show that HLS is highly efficient at detecting tree locations, estimating DBH, and supporting tree height measurements as confirmed with airborne laser data used for validation. This study is one of the first HLS-based studies conducted in the Mediterranean mixed forest region, where variability in tree allometries and spacing and the presence of natural regeneration pose challenges for the HLS approach. HLS is a feasible, time-efficient, scalable technology for tree mapping in mixed forests with potential to support forest monitoring programmes such as national forest inventories lacking three-dimensional, remote sensing data to support field measurements.

Estimation of individual stem volume and diameter from segmented UAV laser scanning datasets in Pinus taeda L. plantations

ABSTRACT The competitive neighbourhood surrounding an individual tree can have a significant influence on its diameter at breast height (DBH) and individual tree stem volume (SV). Distance dependent competition index metrics are rarely recorded in traditional field campaigns because they are laborious to collect and are spatially limited. Remote sensing data could overcome these limitations while providing estimation of forest attributes over a large area. We used unoccupied aerial vehicle laser scanning data to delineate individual tree crowns (ITCs) and calculated crown size and distance-dependent competition indices to estimate DBH and SV. We contrasted two methods: (i) Random Forest (RF) and (ii) backwards-stepwise, linear multiple regression (LMR). We utilized an existing experiment in Pinus taeda L. plantations including multiple planting densities, genotypes and silvicultural levels. While the tree planting density did affect the correct delineation of ITCs, between 61% and 99% (mean 86%) were correctly linked to the planting location. The most accurate RF and LMR models all included metrics related to ITC size and competitive neighbourhood. The DBH estimates from RF and LMR were similar: RMSE 3.05 and 3.13 cm (R2 0.64 and 0.62), respectively. Estimates of SV from RF were slightly better than for LMR: RMSE 0.06 and 0.07 m3 (R2 0.77 and 0.70), respectively. Our results provide evidence that ITC size and competition index metrics may improve DBH and SV estimation accuracy when analysing laser-scanning data. The ability to provide accurate, and near-complete, forest inventories holds a great deal of potential for forest management planning.

An improved area-based approach for estimating plot-level tree DBH from airborne LiDAR data

The diameter at breast height (DBH) of trees and stands is not only a widely used plant functional trait in ecology and biodiversity but also one of the most fundamental measurements in managing forests. However, systematically measuring the DBH of individual trees over large areas using conventional ground-based approaches is labour-intensive and costly. Here, we present an improved area-based approach to estimate plot-level tree DBH from airborne LiDAR data using the relationship between tree height and DBH, which is widely available for most forest types and many individual tree species. We first determined optimal functional forms for modelling height-DBH relationships using field-measured tree height and DBH. Then we estimated plot-level mean DBH by inverting the height-DBH relationships using the tree height predicted by LiDAR. Finally, we compared the predictive performance of our approach with a classical area-based method of DBH. The results showed that our approach significantly improved the prediction accuracy of tree DBH (R2 ​= ​0.85–0.90, rRMSE ​= ​9.57%–11.26%) compared to the classical area-based approach (R2 ​= ​0.80–0.83, rRMSE ​= ​11.98%–14.97%). Our study demonstrates the potential of using height-DBH relationships to improve the estimation of the plot-level DBH from airborne LiDAR data.

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.

Towards real-time forest inventory using handheld LiDAR

While mobile LiDAR sensors are increasingly used to scan in ecology and forestry applications, reconstruction and characterization are typically carried out offline. Motivated by this, we present an online LiDAR system which is capable of running on a handheld device. Our system is capable of creating 3D point cloud reconstructions of large forest areas, segment and track individual trees, and create an inventory for the detected trees. Segments relating to each tree are accumulated over time, and tree models are completed as more scans are captured from different perspectives. The LiDAR scans are processed in an online fashion, and feedback can be provided to the operator via a screen mounted on the device. This allows the operator to ensure the desired area is mapped satisfactorily without any gaps or missing sections. We employ a pose-graph based SLAM system with loop closures to correct for drift errors allowing us to map large areas accurately. Our mapping system also provides multi-session capability where data captured during different runs can be automatically merged in a post-processing step. In this work, we estimate the Diameter at Breast Height (DBH) of individual trees as an example parameter for the forest inventory. The DBH is estimated online by fitting a cylinder to each tree trunk through a least-squares optimization within a RANSAC loop. We demonstrate our mapping approach operating in two different forests (both ecological and commercial) with the total travel distance spanning several kilometres. Further, we also provide experimental results comparing our DBH estimation to ground-truth measurements recorded manually in an ecological forest (Wytham Woods, Oxford). We demonstrate that our DBH estimates are within ∼7 cm accuracy for 90% of individual trees detected in the dataset.

Comparing Mobile Laser Scanner and manual measurements for dendrometric variables estimation in a black pine (Pinus nigra Arn.) plantation

The growing demand of ecosystem services provided by forests increased the need for fast and accurate field survey. The recent technological innovations fostered the application of geomatic tools and processes to different fields of the forestry sector. In this study we compared the efficiency and the accuracy of Mobile Laser Scanner (MLS), combined with Simultaneous Localization and Mapping (SLAM) technology, and traditional field survey for the mensuration of main forest dendrometric variables like stem diameter at breast height (DBH), individual tree height (H), crown base height (CBH) and branch-free stem volume (VOL). With ground truth measurements taken from 50 felled trees, we tested the applicability of MLS technology for individual tree parameters estimation in a conifer plantation in central Italy. Our results showed no bias of DBH estimates and the corresponding RMSE was equal to 10.8% (2.7 cm). H and CBH measured with MLS were underestimated compared to the ground truth (bias of −8.6% for H and −13.3% for CBH). VOL values showed a bias and a RMSE of −4.1% (−0.01 m3) and 12.4% (0.04 m3) respectively. Tree height is not perfectly estimated due to laser obstruction by crowns layer, but the acquisition speed of this survey, joined with a suitable accuracy of parameters extraction, suggests sufficient suitability of the method for operational applications in simple forest structures (e.g. one-layered stands).

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.

Evaluation of UAS LiDAR data for tree segmentation and diameter estimation in boreal forests using trunk- and crown-based methods

Very high point density laser scanning data from unmanned aerial systems (UAS) can be used to segment the trunks of individual trees. Such segmentation (e.g., individual trunk segmentation (ITS)) is useful, for example, in the estimation of diameter at breast height (DBH), which in turn is needed for the estimation of other tree and stand attributes, such as stem volume and diameter of the basal area median tree (DGM). In this paper, we assess the estimation of DBH directly from UAS LiDAR data in open and closed canopy conditions that represent the range of operational conditions encountered in boreal forests in Finland. We also compare trunk-based DBH estimates to corresponding estimates from individual tree crown segmentation (ITC) and fuse the results from the trunk- and crown-based estimates. The results showed that trunk segmentation performed slightly better than ITC in open canopy areas, whereas ITC performed better in closed canopy areas. The DBH prediction error was smaller for ITS (3.0 cm) than ITC (3.9 cm) when considering the trees that were recognized by both methods. We also conclude that a hybrid method, where both segmented tree trunks and tree crowns are fused, considerably increases the number of correctly segmented trees but does not decrease the prediction error associated with DGM compared to using either ITC or ITS individually.

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.

Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads

The collection of field-reference data is a key task in remote sensing-based forest inventories. However, traditional methods of collection demand extensive personnel resources. Thus, field-reference data collection would benefit from more automated methods. In this study, we proposed a method for individual tree detection (ITD) and stem attribute estimation based on a car-mounted mobile laser scanner (MLS) operating along forest roads. We assessed its performance in six ranges with increasing mean distance from the roadside. We used a Riegl VUX-1LR sensor operating with high repetition rate, thus providing detailed cross sections of the stems. The algorithm we propose was designed for this sensor configuration, identifying the cross sections (or arcs) in the point cloud and aggregating those into single trees. Furthermore, we estimated diameter at breast height (DBH), stem profiles, and stem volume for each detected tree. The accuracy of ITD, DBH, and stem volume estimates varied with the trees’ distance from the road. In general, the proximity to the sensor of branches 0–10 m from the road caused commission errors in ITD and over estimation of stem attributes in this zone. At 50–60 m from roadside, stems were often occluded by branches, causing omissions and underestimation of stem attributes in this area. ITD’s precision and sensitivity varied from 82.8% to 100% and 62.7% to 96.7%, respectively. The RMSE of DBH estimates ranged from 1.81 cm (6.38%) to 4.84 cm (16.9%). Stem volume estimates had RMSEs ranging from 0.0800 m3 (10.1%) to 0.190 m3 (25.7%), depending on the distance to the sensor. The average proportion of detected reference volume was highly affected by the performance of ITD in the different zones. This proportion was highest from 0 to 10 m (113%), a zone that concentrated most ITD commission errors, and lowest from 50 to 60 m (66.6%), mostly due to the omission errors in this area. In the other zones, the RMSE ranged from 87.5% to 98.5%. These accuracies are in line with those obtained by other state-of-the-art MLS and terrestrial laser scanner (TLS) methods. The car-mounted MLS system used has the potential to collect data efficiently in large-scale inventories, being able to scan approximately 80 ha of forests per day depending on the survey setup. This data collection method could be used to increase the amount of field-reference data available in remote sensing-based forest inventories, improve models for area-based estimations, and support precision forestry development.

Mapping tropical forest trees across large areas with lightweight cost-effective terrestrial laser scanning

Key messageWe used lightweight terrestrial laser scanning (TLS) to detect over 3000 stems per hectare across a 12-ha permanent forest plot in French Guiana, 81% of them < 10 cm in trunk diameter. This method retrieved 85% of the trees of a classic inventory. Finally, TLS revealed that stem positions of the classic inventory had geolocation errors of up to 6 m.ContextAccurate position mapping of tropical rainforest trees is crucial for baseline studies of tropical forest ecology but is labor-intensive. Terrestrial lidar scanning (TLS) is broadly used in temperate forest inventories, but its use in rainforests is restricted to the determination of individual tree volumes within small survey areas.AimsMapping tree stems across one large (12-ha) rainforest plot, including trees less than 10 cm DBH, and evaluating the precision of traditional mapping approaches.MethodsWe used lightweight TLS, co-registered the acquisitions, and developed a new efficient algorithm to process the TLS data.ResultsWe detected 36,422 stems of which 29,665 (81%) were < 10 cm in diameter at breast height (DBH). Of the trees ≥ 10 cm DBH previously censused in the plot, 85% were identified by TLS. Automatic DBH estimation from TLS data had an RMSE of 6 cm. RMSE was improved to 3 cm by a manual verification of the shape and quality of the stem points. The initial census map had substantial bias in tree geolocation with a maximum value around 6 m.ConclusionLightweight TLS technology is a promising tool for the estimation of stem tapering and volume. Here, we show that it also facilitates the establishment of large tropical forest inventories, by improving the positioning of trees, thus increasing the accuracy of forest inventories and their cost-effectiveness.