Diameter At Breast Height Estimation Research Articles

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.

EVALUATION OF IPAD PRO 2020 LIDAR FOR ESTIMATING TREE DIAMETERS IN URBAN FOREST

Abstract. Remote Sensing (RS) techniques are increasingly used in urban tree inventory measurements for their improved accuracy and promptness over the conventional methods. The focus of this study is to evaluate the application of iPad Pro 2020 and its LiDAR sensor for urban trees reconstruction and Diameter at Breast Height (DBH) measurements. Altogether, 101 trees were scanned. We have used individual- and multiple-tree scan modes with different settings (Resolution: 10 mm, 15 mm, 20 mm; Confidence: High, Low). With these methods and settings, we have established 12 combinations. The 3DScannerAPP was used to scan and generate point clouds and to estimate DBH circle-fitting algorithm was used within the DendroCloud software. Among 12 methods, the only method with 10 mm resolution, high confidence, and multiple-tree mode has not achieved a 100% detection rate (97%). For multiple-tree mode, the highest estimation accuracy was 7.52% of relative RMSE, and for single-tree mode, it was 7.27%. Low confidence setting had significantly higher accuracy of DBH estimation than high confidence. Furthermore, single-tree mode had a significantly higher accuracy of DBH estimation than multiple-tree mode. The most efficient combination for DBH estimation of urban trees using 3DScannerAPP within iPad Pro 2020, when time and accuracy is considered, was multiple-tree mode with 15 mm resolution and low confidence.

Comparison of spruce and beech tree attributes from field data, airborne and terrestrial laser scanning using manual and automatic methods

This study’s aim was to make a comparative analysis of airborne (ALS) and terrestrial (TLS) laser scanning and field data collection methods in estimating the tree attributes height (H), diameter at breast height (DBH), crown base (CB), and crown diameter (CD). Manual and automatic methods of extracting tree attributes from ALS and TLS data were evaluated for spruce and beech at a study site within the Czech part of the Silesian Beskids. Strong correlation (r) was observed among scanning H and field-measured H for spruce (r = 0.91 for ALS automatic vs. field, r = 0.94 for ALS manual vs. field, r = 0.91 for TLS manual vs. field), and with standard error of the estimates (SEE) from 1.4 m to 2.2 m. DBH estimates from TLS manual method showed r = 0.96 for spruce and beech with average SEE of 2.9 cm. DBH estimates for beech from TLS automatic method were poorer (r = 0.73, SEE = 9.1 cm) than for spruce (r = 0.96, SEE = 2.5 cm). TLS estimates of CB were better both for manual and automatic methods (r = 0.85, SEE = 1.4 m for manual; r = 0.73, SEE = 2.4 for automatic) than ALS estimates (r = 0.67, SEE = 4.0 m for manual; r = 0.61, SEE = 4.2 m for automatic). CD estimates from ALS showed better agreement with field measurements (r = 0.71, SEE = 1.2 m) than did CD estimates from TLS (r = 0.52, SEE = 1.4 m). TLS technology was evaluated as being most time demanding for both manual and automatic modes in comparison with ALS and field technologies. Field technology for in situ measurement was shown to be time effective compared with ALS and/or TLS technologies that used manual methods to extract tree attributes. ALS technology with automatic methods of tree attributes extraction was found to be most efficient, showing that automation is a key process and has strong potential for applications to inventory trees within forests over large areas in Central Europe.

Assessment of the Influence of Survey Design and Processing Choices on the Accuracy of Tree Diameter at Breast Height (DBH) Measurements Using UAV-Based Photogrammetry

This work provides a systematic evaluation of how survey design and computer processing choices (such as the software used or the workflow/parameters chosen) influence unmanned aerial vehicle (UAV)-based photogrammetry retrieval of tree diameter at breast height (DBH), an important 3D structural parameter in forest inventory and biomass estimation. The study areas were an agricultural field located in the province of Málaga, Spain, where a small group of olive trees was chosen for the UAV surveys, and an open woodland area in the outskirts of Sofia, the capital of Bulgaria, where a 10 ha area grove, composed mainly of birch trees, was overflown. A DJI Phantom 4 Pro quadcopter UAV was used for the image acquisition. We applied structure from motion (SfM) to generate 3D point clouds of individual trees, using Agisoft and Pix4D software packages. The estimation of DBH in the point clouds was made using a RANSAC-based circle fitting tool from the TreeLS R package. All trees modeled had their DBH tape-measured on the ground for accuracy assessment. In the first study site, we executed many diversely designed flights, to identify which parameters (flying altitude, camera tilt, and processing method) gave us the most accurate DBH estimations; then, the resulting best settings configuration was used to assess the replicability of the method in the forested area in Bulgaria. The best configuration tested (flight altitudes of about 25 m above tree canopies, camera tilt 60°, forward and side overlaps of 90%, Agisoft ultrahigh processing) resulted in root mean square errors (RMSEs; %) of below 5% of the tree diameters in the first site and below 12.5% in the forested area. We demonstrate that, when carefully designed methodologies are used, SfM can measure the DBH of single trees with very good accuracy, and to our knowledge, the results presented here are the best achieved so far using (above-canopy) UAV-based photogrammetry.

Estimating Forest Structure from UAV-Mounted LiDAR Point Cloud Using Machine Learning

Monitoring the structure of forest stands is of high importance for forest managers to help them in maintaining ecosystem services. For that purpose, Unmanned Aerial Vehicles (UAVs) open new prospects, especially in combination with Light Detection and Ranging (LiDAR) technology. Indeed, the shorter distance from the Earth’s surface significantly increases the point density beneath the canopy, thus offering new possibilities for the extraction of the underlying semantics. For example, tree stems can now be captured with sufficient detail, which is a gateway to accurately locating trees and directly retrieving metrics—e.g., the Diameter at Breast Height (DBH). Current practices usually require numerous site-specific parameters, which may preclude their use when applied beyond their initial application context. To overcome this shortcoming, the machine learning Hierarchical Density-Based Spatial Clustering of Application of Noise (HDBSCAN) clustering algorithm was further improved and implemented to segment tree stems. Afterwards, Principal Component Analysis (PCA) was applied to extract tree stem orientation for subsequent DBH estimation. This workflow was then validated using LiDAR point clouds collected in a temperate deciduous closed-canopy forest stand during the leaf-on and leaf-off seasons, along with multiple scanning angle ranges. The results show that the proposed methodology can correctly detect up to 82% of tree stems (with a precision of 98%) during the leaf-off season and have a Maximum Scanning Angle Range (MSAR) of 75 degrees, without having to set up any site-specific parameters for the segmentation procedure. In the future, our method could then minimize the omission and commission errors when initially detecting trees, along with assisting further tree metrics retrieval. Finally, this research shows that, under the study conditions, the point density within an approximately 1.3-meter height above the ground remains low within closed-canopy forest stands even during the leaf-off season, thus restricting the accurate estimation of the DBH. As a result, autonomous UAVs that can both fly above and under the canopy provide a clear opportunity to achieve this purpose.

Assessment of Above-Ground Carbon Storage by Urban Trees Using LiDAR Data: The Case of a University Campus

The biomass represented by urban trees is important for urban decision-makers, green space planners, and managers seeking to optimize urban ecosystem services. Carbon storage by urban trees is one of these services. Suitable methods for assessing carbon storage by urban trees are being explored. The latest technologies in remote sensing and data analyses can reduce data collection costs while improving accuracy. This paper introduces an assessment approach that combines ground measurements with unmanned aerial vehicle-based light detection and ranging (LiDAR) data to estimate carbon storage by urban trees. Methods underpinning the approach were tested for the case of the Vancouver campus of the University of British Columbia (UBC), Canada. The study objectives were (1) to test five automated individual tree detection (AITD) algorithms and select one on the basis of the highest segmentation accuracy, (2) to develop a model to estimate the diameter at breast height (DBH), and (3) to estimate and map carbon storage over the UBC campus using LiDAR heights, estimated DBHs, and an existing tree-level above-ground carbon estimation model. Of the segmentation algorithms tested, the Dalponte AITD had the highest F score of 0.83. Of the five CW thresholds (th) tested in the DBH estimation model, we chose one resulting in the lowest Akaike’s information criterion, the highest log-likelihood, and the lowest root-mean-squared error (19.55 cm). Above-ground carbon was estimated for each tree in the study area and subsequently summarized, resulting in an estimated 5.27 kg C·m−2 over the main campus of UBC, Vancouver. The approach could be used in other urban jurisdictions to obtain essential information on urban carbon storage in support of urban landscape governance, planning, and management.

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.

Individual Tree Diameter Estimation in Small-Scale Forest Inventory Using UAV Laser Scanning

Unmanned aerial vehicle laser scanning (UAVLS) systems present a relatively new means of remote sensing and are increasingly applied in the field of forest ecology and management. However, one of the most essential parameters in forest inventory, tree diameter at breast height (DBH), cannot be directly extracted from aerial point cloud data due to the limitations of scanning angle and canopy obstruction. Therefore, in this study DBH-UAVLS point cloud estimation models were established using a generalized nonlinear mixed-effects (NLME) model. The experiments were conducted using Larix olgensis as the subject species, and a total of 8364 correctly delineated trees from UAVLS data within 118 plots across 11 sites were used for DBH modeling. Both tree- and plot-level metrics were obtained using light detection and ranging (LiDAR) and were used as the models’ independent predictors. The results indicated that the addition of site-level random effects significantly improved the model fitting. Compared with nonparametric modeling approaches (random forest and k-nearest neighbors) and uni- or multivariable weighted nonlinear least square regression through leave-one-site-out cross-validation, the NLME model with local calibration achieved the lowest root mean square error (RMSE) values (1.94 cm) and the most stable prediction across different sites. Using the site in a random-effects model improved the transferability of LiDAR-based DBH estimation. The best linear unbiased predictor (BLUP), used to conduct local model calibration, led to an improvement in the models’ performance as the number of field measurements increased. The research provides a baseline for unmanned aerial vehicle (UAV) small-scale forest inventories and might be a reasonable alternative for operational forestry.

Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees

We applied a supervised individual-tree segmentation algorithm to ultra-high-density drone lidar in a temperate mountain forest in the southern Czech Republic. We compared the number of trees correctly segmented, stem diameter at breast height (DBH), and tree height from drone-lidar segmentations to field-inventory measurements and segmentations from terrestrial laser scanning (TLS) data acquired within two days of the drone-lidar acquisition. Our analysis detected 51% of the stems &gt;15 cm DBH, and 87% of stems &gt;50 cm DBH. Errors of omission were much more common for smaller trees than for larger ones, and were caused by removal of points prior to segmentation using a low-intensity and morphological filter. Analysis of segmented trees indicates a strong linear relationship between DBH from drone-lidar segmentations and TLS data. The slope of this relationship is 0.93, the intercept is 4.28 cm, and the r2 is 0.98. However, drone lidar and TLS segmentations overestimated DBH for the smallest trees and underestimated DBH for the largest trees in comparison to field data. We evaluate the impact of random error in point locations and variation in footprint size, and demonstrate that random error in point locations is likely to cause an overestimation bias for small-DBH trees. A Random Forest classifier correctly identified broadleaf and needleleaf trees using stem and crown geometric properties with overall accuracy of 85.9%. We used these classifications and DBH estimates from drone-lidar segmentations to apply allometric scaling equations to segmented individual trees. The stand-level aboveground biomass (AGB) estimate using these data is 76% of the value obtained using a traditional field inventory. We demonstrate that 71% of the omitted AGB is due to segmentation errors of omission, and the remaining 29% is due to DBH estimation errors. Our analysis indicates that high-density measurements from low-altitude drone flight can produce DBH estimates for individual trees that are comparable to TLS. These data can be collected rapidly throughout areas large enough to produce landscape-scale estimates. With additional refinement, these estimates could augment or replace manual field inventories, and could support the calibration and validation of current and forthcoming space missions.

An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height

ABSTRACT Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect – a lightweight depth sensor – for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual urban trees (n = 51) were recorded from one viewing angle at a distance of 1 m to 5 m away using the various Field of View (FOV) settings on the depth sensor, from which resultant point clouds provided DBH estimates using a circle-fitting approach. The optimal capture method was observed at a distance of 2 m using the binned Near Field of View (NFOV) setting. Root Mean Square Error (RMSE) of DBH using this method was 8.43 cm; however, after removing trees with irregular or non-circular stems, this improved to 3.53 cm. Variations in ambient light were observed to have little effect on DBH estimates. The results of this study suggest when in an outdoor environment, the Azure Kinect should be used at a distance no greater than 3 m away, using the binned NFOV sensor setting, for DBH estimates.

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

The Influence of Cross-Section Thickness on Diameter at Breast Height Estimation from Point Cloud

Circle-fitting methods are commonly used to estimate diameter at breast height (DBH) of trees from horizontal cross-section of point clouds. In this paper, we addressed the problem of cross-section thickness optimization regarding DBH estimation bias and accuracy. DBH of 121 European beeches (Fagus sylvatica L.) and 43 Sessile oaks (Quercus petraea (Matt.) Liebl.) was estimated from cross-sections with thicknesses ranging from 1 to 100 cm. The impact of cross-section thickness on the bias, standard error, and accuracy of DBH estimation was statistically significant. However, the biases, standard errors, and accuracies of DBH estimation were not significantly different among 1–10-cm cross-sections, except for oak DBH estimation accuracy from an 8-cm cross-section. DBH estimations from 10–100-cm cross-sections were considerably different. These results provide insight to the influence of cross-section thickness on DBH estimation by circle-fitting methods, which is beneficial for point cloud data acquisition planning and processing. The optimal setting of cross-section thickness facilitates point cloud processing and DBH estimation by circle-fitting algorithms.

Prediction of Individual Tree Diameter and Height to Crown Base Using Nonlinear Simultaneous Regression and Airborne LiDAR Data

The forest growth and yield models, which are used as important decision-support tools in forest management, are commonly based on the individual tree characteristics, such as diameter at breast height (DBH), crown ratio, and height to crown base (HCB). Taking direct measurements for DBH and HCB through the ground-based methods is cumbersome and costly. The indirect method of getting such information is possible from remote sensing databases, which can be used to build DBH and HCB prediction models. The DBH and HCB of the same trees are significantly correlated, and so their inherent correlations need to be appropriately accounted for in the DBH and HCB models. However, all the existing DBH and HCB models, including models based on light detection and ranging (LiDAR) have ignored such correlations and thus failed to account for the compatibility of DBH and HCB estimates, in addition to disregarding measurement errors. To address these problems, we developed a compatible simultaneous equation system of DBH and HCB error-in-variable (EIV) models using LiDAR-derived data and ground-measurements for 510 Picea crassifolia Kom trees in northwest China. Four versatile algorithms, such as nonlinear seemingly unrelated regression (NSUR), two-stage least square (2SLS) regression, three-stage least square (3SLS) regression, and full information maximum likelihood (FIML) were evaluated for their estimating efficiencies and precisions for a simultaneous equation system of DBH and HCB EIV models. In addition, two other model structures, namely, nonlinear least squares with HCB estimation not based on the DBH (NLS and NBD) and nonlinear least squares with HCB estimation based on the DBH (NLS and BD) were also developed, and their fitting precisions with a simultaneous equation system compared. The leave-one-out cross-validation method was applied to evaluate all estimating algorithms and their resulting models. We found that only the simultaneous equation system could illustrate the effect of errors associated with the regressors on the response variables (DBH and HCB) and guaranteed the compatibility between the DBH and HCB models at an individual level. In addition, such an established system also effectively accounted for the inherent correlations between DBH with HCB. However, both the NLS and BD model and the NLS and NBD model did not show these properties. The precision of a simultaneous equation system developed using NSUR appeared the best among all the evaluated algorithms. Our equation system does not require the stand-level information as input, but it does require the information of tree height, crown width, and crown projection area, all of which can be readily derived from LiDAR imagery using the delineation algorithms and ground-based DBH measurements. Our results indicate that NSUR is a more reliable and quicker algorithm for developing DBH and HCB models using large scale LiDAR-based datasets. The novelty of this study is that the compatibility problem of the DBH model and the HCB EIV model was properly addressed, and the potential algorithms were compared to choose the most suitable one (NSUR). The presented method and algorithm will be useful for establishing similar compatible equation systems of tree DBH and HCB EIV models for other tree species.