UAV-derived Estimates of Vertical and Horizontal Structure across Forest Density Gradients

2020 ◽  
Author(s):  
Temuulen Sankey ◽  
Adam Belmonte
Keyword(s):  
Density Gradient ◽  
Forest Structure ◽  
Survival Rates ◽  
Tree Height ◽  
Single Species ◽  
High Density ◽  
Individual Tree ◽  
Horizontal Structure ◽  
Forest Density ◽  
Uav Image

<p>Restoring forest ecosystems and predicting forest response to projected climate change have become an increasingly high priority for land managers. Restoration and management goals require accurate, quantitative estimates of vertical and horizontal forest structure, which has relied upon either field-based measurements, manned airborne, or satellite remote sensing datasets. We use unmanned aerial vehicle (UAV) image-derived structure from motion (SfM) models and high resolution multispectral and thermal orthoimagery to: 1) quantify vertical and horizontal forest structure at both fine- (< 4 ha) and mid-scales (4-400 ha) across a forest density gradient, and 2) quantify horizontal structure, health, and survival rates in a genetics experimental garden also with a density gradient. In both cases, we find that UAV multispectral and thermal image-derived SfM model estimates of individual tree height and canopy diameter are most accurate in low-density conditions, with accuracies degrading significantly in high-density conditions. In addition, UAV thermal images demonstrate significant differences in tree health and survival rates among various populations and genotypes within a single species.  Mid-scale estimates of canopy cover and forest density follow a similar pattern across the density gradient, demonstrating the effectiveness of UAV image-derived estimates in low to medium-density conditions as well as the challenges associated with high-density conditions.</p>

scholarly journals A Novel Tree Height Extraction Approach for Individual Trees by Combining TLS and UAV Image-Based Point Cloud Integration

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 537 ◽  
Author(s):  
Jiarong Tian ◽  
Tingting Dai ◽  
Haidong Li ◽  
Chengrui Liao ◽  
Wenxiu Teng ◽  
...  
Keyword(s):  
Point Cloud ◽  
Coniferous Forest ◽  
Tree Height ◽  
High Density ◽  
Point Cloud Data ◽  
Individual Tree ◽  
Cloud Data ◽  
Tree Farms ◽  
Uav Image ◽  
Structure Evaluation

Research Highlights: This study carried out a feasibility analysis on the tree height extraction of a planted coniferous forest with high canopy density by combining terrestrial laser scanner (TLS) and unmanned aerial vehicle (UAV) image–based point cloud data at small and midsize tree farms. Background and Objectives: Tree height is an important factor for forest resource surveys. This information plays an important role in forest structure evaluation and forest stock estimation. The objectives of this study were to solve the problem of underestimating tree height and to guarantee the precision of tree height extraction in medium and high-density planted coniferous forests. Materials and Methods: This study developed a novel individual tree localization (ITL)-based tree height extraction method to obtain preliminary results in a planted coniferous forest plots with 107 trees (Metasequoia). Then, the final accurate results were achieved based on the canopy height model (CHM) and CHM seed points (CSP). Results: The registration accuracy of the TLS and UAV image-based point cloud data reached 6 cm. The authors optimized the precision of tree height extraction using the ITL-based method by improving CHM resolution from 0.2 m to 0.1 m. Due to the overlapping of forest canopies, the CSP method failed to delineate all individual tree crowns in medium to high-density forest stands with the matching rates of about 75%. However, the accuracy of CSP-based tree height extraction showed obvious advantages compared with the ITL-based method. Conclusion: The proposed method provided a solid foundation for dynamically monitoring forest resources in a high-accuracy and low-cost way, especially in planted tree farms.

scholarly journals Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

2020 ◽  
Vol 12 (5) ◽  
pp. 863 ◽  
Author(s):  
Ana Paula Dalla Corte ◽  
Franciel Eduardo Rex ◽  
Danilo Roberti Alves de Almeida ◽  
Carlos Roberto Sanquetta ◽  
Carlos A. Silva ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Point Cloud ◽  
Tree Height ◽  
High Density ◽  
Forest Plantations ◽  
Density Point ◽  
Individual Tree ◽  
Total Height ◽  
Area Of Interest ◽  
Mean Square Errors

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.

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

2020 ◽  
Vol 12 (19) ◽  
pp. 3260
Author(s):  
Martin Krůček ◽  
Kamil Král ◽  
KC Cushman ◽  
Azim Missarov ◽  
James R. Kellner
Keyword(s):  
Random Error ◽  
Laser Scanning ◽  
Tree Height ◽  
High Density ◽  
Mountain Forest ◽  
Large Area ◽  
Estimation Errors ◽  
Individual Tree ◽  
Individual Trees ◽  
The Impact

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 >15 cm DBH, and 87% of stems >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.

scholarly journals Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

2020 ◽  
Vol 13 (1) ◽  
pp. 77
Author(s):  
Tianyu Hu ◽  
Xiliang Sun ◽  
Yanjun Su ◽  
Hongcan Guan ◽  
Qianhui Sun ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Canopy Cover ◽  
Tree Height ◽  
Flying Height ◽  
Tree Level ◽  
Forest Site ◽  
Lidar System ◽  
Individual Tree ◽  
Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

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.

Competition theory — science and application in mixed forest stands: review of experimental and modelling methods and suggestions for future research

2013 ◽  
Vol 21 (2) ◽  
pp. 71-84 ◽  
Author(s):  
Guy R. Larocque ◽  
Nancy Luckai ◽  
Shailendra N. Adhikary ◽  
Arthur Groot ◽  
F. Wayne Bell ◽  
...  
Keyword(s):  
Mixed Forest ◽  
Single Species ◽  
Experimental Designs ◽  
Competitive Interactions ◽  
Future Research ◽  
Relative Importance ◽  
Forest Stands ◽  
Management Scenarios ◽  
Mixed Stands ◽  
Individual Tree

Competition in forest stands has long been of interest to researchers. However, much of the knowledge originates from empirical studies that examined the effects of competition. For instance, many studies were focused on the effects of the presence of herbaceous species on the development of tree seedlings or the decrease in individual tree growth with increases in stand density. Several models that incorporate competitive effects have been developed to predict tree and stand growth, but with simplified representations of competitive interactions. While these studies provided guidance useful for forest management, they contributed only partially to furthering our understanding of competitive mechanisms. Also, most competition studies were conducted in single-species stands. As competitive interactions occurring in mixed stands are characterized by a higher degree of complexity than those in single-species stands, a better understanding of these mechanisms can contribute to developing optimal management scenarios. The dynamics of forest stands with at least two species may be affected not only by competition, but also by facilitation or complementarity mechanisms. Thus, knowledge of the mechanisms may provide insight into the relative importance of intra- versus inter-specific competition and whether competition is symmetric or asymmetric. Special attention to the implementation of field experimental designs is warranted for mixed stands. While traditional spacing trials are appropriate for single-species stands, the examination of competitive interactions in mixed stands requires more complex experimental designs to examine the relative importance of species combinations. Forest productivity models allow resource managers to test different management scenarios, but again most of these models were developed for single-species stands. As competitive interactions are more complex in mixed stands, models developed to predict their dynamics will need to include more mechanistic representations of competition.

scholarly journals Stand structure links up canopy processes and forest management

2009 ◽  
Vol 51 (1) ◽  
pp. 40-48
Author(s):  
Toomas Frey
Keyword(s):  
Forest Management ◽  
Stand Structure ◽  
Net Primary Production ◽  
Stand Density ◽  
Tree Height ◽  
Belowground Biomass ◽  
Unit Mass ◽  
Total Biomass ◽  
Individual Tree ◽  
Mean Values

Stand structure links up canopy processes and forest management Above- and belowground biomass and net primary production (Pn) of a maturing Norway spruce (Picea abies (L.) Karst.) forest (80 years old) established on brown soil in central Estonia were 227, 50 and 19.3 Mg ha correspondingly. Stand structure is determined mostly by mean height and stand density, used widely in forestry, but both are difficult to measure with high precision in respect of canopy processes in individual trees. However, trunk form quotient (q2) and proportion of living crown in relation to tree height are useful parameters allowing describe stand structure tree by tree. Based on 7 model trees, leaf unit mass assimilation activity and total biomass respiration per unit mass were determined graphically as mean values for the whole tree growth during 80 years of age. There are still several possible approaches not used carefully enough to integrate experimental work at instrumented towers with actual forestry measurement. Dependence of physiological characteristics on individual tree parameters is the missing link between canopy processes and forest management.

Genetic resistance to western gall rust in jack pine and its relationship with tree height growth

2015 ◽  
Vol 45 (8) ◽  
pp. 970-977 ◽  
Author(s):  
Y.H. Weng ◽  
P. Lu ◽  
Q.F. Meng ◽  
M. Krasowski
Keyword(s):  
Growth Traits ◽  
Tree Height ◽  
Jack Pine ◽  
Height Growth ◽  
Progeny Testing ◽  
Gene Effects ◽  
Individual Tree ◽  
Improvement Programs ◽  
Heritability Estimates ◽  
Tree Height Growth

Developing resistance to western gall rust (WGR) is important for maintaining healthy and productive jack pine plantations. In this study, we estimated genetic parameters of resistance to WGR and its relationship with tree height growth, based on data collected from three second-generation full-sib progeny testing series of jack pine planted in New Brunswick, Canada. Results indicated that (i) resistance to WGR in jack pine was controlled by both additive and dominance gene effects, with the latter playing a greater role; (ii) narrow-sense heritability estimates for resistance to WGR were low (mean = 0.05; series range = 0.00∼0.09), and broad-sense heritability estimates were moderate on an individual-tree basis (mean = 0.53) and considerably higher on the full-sib family mean basis (mean = 0.87); (iii) additive genetic correlation between tree height growth and WGR incidence was low (≤0.06) in two series and only slightly higher and favorable (–0.19) in one series, suggesting that selection on growth traits would not negatively affect WGR resistance; and (iv) mid-parental additive genetic and dominance effects on WGR were empirically correlated (>0.65), indicating that incorporating breeding for WGR resistance into current jack pine tree improvement programs with a seed orchard approach could partly capture the benefit from dominance effects. Although genetic gains in WGR resistance could be realized through various breeding and deployment schemes, it appeared that rapid improvement could be achieved through backward selection on full-sib family means.

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