scholarly journals Influence of Agisoft Metashape Parameters on UAS Structure from Motion Individual Tree Detection from Canopy Height Models

Forests ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 250
Author(s):  
Wade T. Tinkham ◽  
Neal C. Swayze
Keyword(s):  
Structure From Motion ◽  
Ponderosa Pine ◽  
Point Cloud ◽  
Image Resolution ◽  
Canopy Height ◽  
Computer Hardware ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Ponderosa Pine Forests ◽  
Tree Detection

Applications of unmanned aerial systems for forest monitoring are increasing and drive a need to understand how image processing workflows impact end-user products’ accuracy from tree detection methods. Increasing image overlap and making acquisitions at lower altitudes improve how structure from motion point clouds represents forest canopies. However, only limited testing has evaluated how image resolution and point cloud filtering impact the detection of individual tree locations and heights. We evaluate how Agisoft Metashape’s build dense cloud Quality (image resolution) and depth map filter settings influence tree detection from canopy height models in ponderosa pine forests. Finer resolution imagery with minimal filtering provided the best visual representation of vegetation detail for trees of all sizes. These same settings maximized tree detection F-score at >0.72 for overstory (>7 m tall) and >0.60 for understory trees. Additionally, overstory tree height bias and precision improve as image resolution becomes finer. Overstory and understory tree detection in open-canopy conifer systems might be optimized using the finest resolution imagery that computer hardware enables, while applying minimal point cloud filtering. The extended processing time and data storage demands of high-resolution imagery must be balanced against small reductions in tree detection performance when down-scaling image resolution to allow the processing of greater data extents.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

2021 ◽  
Author(s):  
Matthew B. Creasy ◽  
Wade Travis Tinkham ◽  
Chad M. Hoffman ◽  
Jody C. Vogeler
Keyword(s):  
Forest Structure ◽  
Ponderosa Pine ◽  
Laser Scanning ◽  
Point Clouds ◽  
System Structure ◽  
Detection Methods ◽  
Unmanned Aerial System ◽  
Individual Tree ◽  
Detection Rates ◽  
Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

Influence of ULS data acquisition characteristics on the achievable stem reconstruction accuracies

2020 ◽  
Author(s):  
Moritz Bruggisser ◽  
Johannes Otepka ◽  
Norbert Pfeifer ◽  
Markus Hollaus
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Diameter ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Detection Rates ◽  
Point Count ◽  
Tree Stem

<p>Unmanned aerial vehicles-borne laser scanning (ULS) allows time-efficient acquisition of high-resolution point clouds on regional extents at moderate costs. The quality of ULS-point clouds facilitates the 3D modelling of individual tree stems, what opens new possibilities in the context of forest monitoring and management. In our study, we developed and tested an algorithm which allows for i) the autonomous detection of potential stem locations within the point clouds, ii) the estimation of the diameter at breast height (DBH) and iii) the reconstruction of the tree stem. In our experiments on point clouds from both, a RIEGL miniVUX-1DL and a VUX-1UAV, respectively, we could detect 91.0 % and 77.6 % of the stems within our study area automatically. The DBH could be modelled with biases of 3.1 cm and 1.1 cm, respectively, from the two point cloud sets with respective detection rates of 80.6 % and 61.2 % of the trees present in the field inventory. The lowest 12 m of the tree stem could be reconstructed with absolute stem diameter differences below 5 cm and 2 cm, respectively, compared to stem diameters from a point cloud from terrestrial laser scanning. The accuracy of larger tree stems thereby was higher in general than the accuracy for smaller trees. Furthermore, we recognized a small influence only of the completeness with which a stem is covered with points, as long as half of the stem circumference was captured. Likewise, the absolute point count did not impact the accuracy, but, in contrast, was critical to the completeness with which a scene could be reconstructed. The precision of the laser scanner, on the other hand, was a key factor for the accuracy of the stem diameter estimation. <br>The findings of this study are highly relevant for the flight planning and the sensor selection of future ULS acquisition missions in the context of forest inventories.</p>

Ponderosa pine mortality following fire in northern Arizona

2003 ◽  
Vol 12 (1) ◽  
pp. 7 ◽  
Author(s):  
Charles W. McHugh ◽  
Thomas E. Kolb
Keyword(s):  
Pinus Ponderosa ◽  
Prescribed Fire ◽  
Ponderosa Pine ◽  
Tree Mortality ◽  
Variable Model ◽  
Individual Tree ◽  
Logistic Regression Models ◽  
Ponderosa Pine Forests ◽  
Northern Arizona ◽  
Crown Volume

Sampling of 1367 trees was conducted in the Side wildfire (4 May 1996), Bridger-Knoll wildfire (20 June 1996) and Dauber prescribed fire (9 September 1995) in northern Arizona ponderosa pine forests (Pinus ponderosa). Tree mortality was assessed for 3 years after each fire. Three-year post-fire mortality was 32.4% in the Side wildfire, 18.0% in the Dauber prescribed fire, and 13.9% in the Bridger-Knoll wildfire. In the Dauber and Side fires, 95% and 94% of 3-year post-fire mortality occurred by year 2, versus 76% in the Bridger-Knoll wildfire. Compared with trees that lived for 3 years after fire, dead trees in all fires had more crown scorch, crown consumption, bole scorch, ground char, and bark beetle attacks. Logistic regression models were used to provide insight on factors associated with tree mortality after fire. A model using total crown damage by fire (scorch + consumption) and bole char severity as independent variables was the best two-variable model for predicting individual tree mortality for all fires. The amount of total crown damage associated with the onset of tree mortality decreased as bole char severity increased. Models using diameter at breast height (dbh) and crown volume damage suggested that tree mortality decreased as dbh increased in the Dauber prescribed fire where trees were smallest, and tree mortality increased as dbh increased in the Side and Bridger-Knoll wildfires where trees were largest. Moreover, a U-shaped dbh–mortality distribution for all fires suggested higher mortality for the smallest and largest trees compared with intermediate-size trees. We concluded that tree mortality is strongly influenced by interaction between crown damage and bole char severity, and differences in resistance to fire among different-sized trees can vary among sites.

Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation

2016 ◽  
Vol 79 (2) ◽  
pp. 126-136 ◽  
Author(s):  
Amrit Kathuria ◽  
Russell Turner ◽  
Christine Stone ◽  
Joaqin Duque-Lazo ◽  
Ron West
Keyword(s):  
Pinus Radiata ◽  
Point Cloud ◽  
Lidar Data ◽  
Individual Tree ◽  
Detection Model ◽  
Tree Detection

scholarly journals INDIVIDUAL TREE DETECTION FROM UAV LIDAR DATA IN A MIXED SPECIES WOODLAND

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 657-663 ◽  
Author(s):  
A. Zaforemska ◽  
W. Xiao ◽  
R. Gaulton
Keyword(s):  
Point Cloud ◽  
Voronoi Tessellation ◽  
Mean Shift ◽  
Region Growing ◽  
Single Species ◽  
Mixed Species ◽  
Individual Tree ◽  
Local Maxima ◽  
Tree Detection ◽  
Segmentation Algorithms

<p><strong>Abstract.</strong> The study evaluates five existing segmentation algorithms to determine the method most suitable for individual tree detection across a species-diverse forest: raster-based region growing, local maxima centroidal Voronoi tessellation, point-cloud level region growing, marker controlled watershed and continuously adaptive mean shift. Each of the methods has been tested twice over one mixed and five single species plots: with their parameters set as constant and with the parameters calibrated for every plot. Overall, continuous adaptive mean shift performs best across all the plots with average F-score of 0.9 with fine-tuned parameters and 0.802 with parameters held at constant. Raster-based algorithms tend to achieve higher scores in coniferous plots, due to the clearly discernible tops, which significantly aid the detection of local maxima. Their performance is also highly dependent on the moving size window used to detect the local maxima, which ideally should be readjusted for every plot. Crown overlap, suppressed and leaning trees are the most likely sources of error for all the algorithms tested.</p>

scholarly journals Performance and Sensitivity of Individual Tree Segmentation Methods for UAV-LiDAR in Multiple Forest Types

2022 ◽  
Vol 14 (2) ◽  
pp. 298
Author(s):  
Kaisen Ma ◽  
Zhenxiong Chen ◽  
Liyong Fu ◽  
Wanli Tian ◽  
Fugen Jiang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Tree Height ◽  
Canopy Height ◽  
Forest Resource ◽  
Forest Types ◽  
Individual Tree ◽  
Area Index ◽  
Efficient Operation ◽  
Segmentation Methods ◽  
Layer Stacking

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.

Corrigendum to: Ponderosa pine mortality following fire in northern Arizona

2003 ◽  
Vol 12 (2) ◽  
pp. 245 ◽  
Author(s):  
Charles W. McHugh ◽  
Thomas E. Kolb
Keyword(s):  
Pinus Ponderosa ◽  
Prescribed Fire ◽  
Ponderosa Pine ◽  
Tree Mortality ◽  
Variable Model ◽  
Individual Tree ◽  
Logistic Regression Models ◽  
Ponderosa Pine Forests ◽  
Northern Arizona ◽  
Crown Volume

Sampling of 1367 trees was conducted in the Side wildfire (4 May 1996), Bridger-Knoll wildfire (20 June 1996) and Dauber prescribed fire (9 September 1995) in northern Arizona ponderosa pine forests (Pinus ponderosa). Tree mortality was assessed for 3 years after each fire. Three-year post-fire mortality was 32.4% in the Side wildfire, 18.0% in the Dauber prescribed fire, and 13.9% in the Bridger-Knoll wildfire. In the Dauber and Side fires, 95% and 94% of 3-year post-fire mortality occurred by year 2, versus 76% in the Bridger-Knoll wildfire. Compared with trees that lived for 3 years after fire, dead trees in all fires had more crown scorch, crown consumption, bole scorch, ground char, and bark beetle attacks. Logistic regression models were used to provide insight on factors associated with tree mortality after fire. A model using total crown damage by fire (scorch + consumption) and bole char severity as independent variables was the best two-variable model for predicting individual tree mortality for all fires. The amount of total crown damage associated with the onset of tree mortality decreased as bole char severity increased. Models using diameter at breast height (dbh) and crown volume damage suggested that tree mortality decreased as dbh increased in the Dauber prescribed fire where trees were smallest, and tree mortality increased as dbh increased in the Side and Bridger-Knoll wildfires where trees were largest. Moreover, a U-shaped dbh–mortality distribution for all fires suggested higher mortality for the smallest and largest trees compared with intermediate-size trees. We concluded that tree mortality is strongly influenced by interaction between crown damage and bole char severity, and differences in resistance to fire among different-sized trees can vary among sites.

scholarly journals INDIVIDUAL TREE DETECTION AND DETERMINATION OF TREE PARAMETERS USING UAV-BASED LIDAR DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 179-182
Author(s):  
F. Bayat ◽  
H. Arefi ◽  
F. Alidoost
Keyword(s):  
Point Cloud ◽  
Structural Parameters ◽  
Tree Height ◽  
Distance Measurements ◽  
Individual Tree ◽  
Tree Detection ◽  
Comprehensive Information ◽  
Canopy Area ◽  
Aerial Vehicle ◽  
2D And 3D

Abstract. Forest inventory provides comprehensive information about the geometric and biometric state of forests as well as vegetated areas. In this study, a point-based 3D method is presented for tree detection as well as measuring of structural properties of forests such as the tree height, tree position and canopy area using high resolution point cloud which is provided by an Unmanned Aerial Vehicle (UAV)-based LiDAR sensor. The proposed method is based on the density of point cloud and 2D and 3D distance measurements. It includes three main steps as pre-processing, tree detection, and extraction of tree structural attributes. After generating a canopy height model, an image is created based on the density of point cloud. Next, points are classified based on 2D and 3D distance measurements, sequentially, from the highest to the lowest. According to the results, the rate of tree detection is about 95% and the main structural parameters of a tree such as the position, height, area and length of the canopy are estimated with the accuracy of 1.97 m, 0.36 m, 12.78 m2 and 0.79 m, respectively.

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