Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests

Jaskierniak, Dominik; Lucieer, Arko; Kuczera, George; Turner, Darren; Lane, Patrick; Benyon, Richard G.; Haydon, Shane R.

doi:10.1016/j.isprsjprs.2020.10.016

Cited by 57 publications

(38 citation statements)

References 35 publications

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“…The extraction of individual tree crowns from point clouds is a common problem with remotely sensed data [34,35]. In some cases (e.g., even-aged conifer forest), the development of automated crown extraction algorithms has proven very successful [35,36], but when trying to delineate crowns in uneven-aged mixed forest, results are still far from ideal [37,38], with a recent study suggesting a bottom-up approach may provide segmentation accuracies greater than 85% in mixed species eucalypt forests [39]. In the present case, crowns were manually extracted from the point clouds and linked to the field planting grid and associated pedigree information.…”

Section: Discussionmentioning

confidence: 99%

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

et al. 2021

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A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with individual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state.

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Section: Discussionmentioning

confidence: 99%

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

et al. 2021

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“…Following the accuracy assessment method in other studies [15], recall (r), precision (p), and F-score (F) as the overall accuracy are calculated using the following equations, respectively: r = TP/(TP + FN),…”

Section: Accuracy Assessmentmentioning

confidence: 99%

“…This technique allows high point density LiDAR data (>50 pulses per square meter (p/m 2 )) to be captured rapidly and on demand at low deployment costs [14]. The high-density data collected from UAV LiDAR enable accurate tree detection and new methods should be developed to deal with the increased level of forest structural detail captured by UAV LiDAR [15].…”

Section: Introductionmentioning

confidence: 99%

“…Subtropical mixed broadleaf forests, characterized by higher structural complexity, have been less investigated. In contrast to coniferous trees with significant treetops and symmetrical crown form, broadleaf trees usually have irregular crown shapes and tend to overlap each other [15,36]. Individual tree detection in subtropical mixed broadleaf forests is a great challenge and the existing methods developed for other forest types may be not suitable.…”

Section: Introductionmentioning

confidence: 99%

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A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data

Deng

Liang

2021

Remote Sensing

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Owing to the complex forest structure and large variation in crown size, individual tree detection in subtropical mixed broadleaf forests in urban scenes is a great challenge. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is a powerful tool for individual tree detection due to its ability to acquire high density point cloud that can reveal three-dimensional crown structure. Tree detection based on a local maximum (LM) filter, which is applied on a canopy height model (CHM) generated from LiDAR data, is a popular method due to its simplicity. However, it is difficult to determine the optimal LM filter window size and prior knowledge is usually needed to estimate the window size. In this paper, a novel tree detection approach based on crown morphology information is proposed. In the approach, LMs are firstly extracted using a LM filter whose window size is determined by the minimum crown size and then the crown morphology is identified based on local Gi* statistics to filter out LMs caused by surface irregularities contained in CHM. The LMs retained in the final results represent treetops. The approach was applied on two test sites characterized by different forest structures using UAV LiDAR data. The sensitivity of the approach to parameter setting was analyzed and rules for parameter setting were proposed. On the first test site characterized by irregular tree distribution and large variation in crown size, the detection rate and F-score derived by using the optimal combination of parameter values were 72.9% and 73.7%, respectively. On the second test site characterized by regular tree distribution and relatively small variation in crown size, the detection rate and F-score were 87.2% and 93.2%, respectively. In comparison with a variable-size window tree detection algorithm, both detection rates and F-score values of the proposed approach were higher.

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“…Despite the abovementioned limitations, an increasing number of studies have demonstrated that bottom-up ITD on high-density aerial LiDAR data are more suitable for processing complex hardwood forests than conventional raster-based ITD (see [22,49,116]). Improving ITD by integrating a stem detection step from high-density aerial LiDAR data is still an active research topic (e.g., [22,49,[114][115][116][117]).…”

Section: Transferability Of Itd Algorithms To Uls Datamentioning

confidence: 99%

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

et al. 2021

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UAV laser scanning (ULS) has the potential to support forest operations since it provides high-density data with flexible operational conditions. This study examined the use of ULS systems to estimate several tree attributes from an uneven-aged northern hardwood stand. We investigated: (1) the transferability of raster-based and bottom-up point cloud-based individual tree detection (ITD) algorithms to ULS data; and (2) automated approaches to the retrieval of tree-level (i.e., height, crown diameter (CD), DBH) and stand-level (i.e., tree count, basal area (BA), DBH-distribution) forest inventory attributes. These objectives were studied under leaf-on and leaf-off canopy conditions. Results achieved from ULS data were cross-compared with ALS and TLS to better understand the potential and challenges faced by different laser scanning systems and methodological approaches in hardwood forest environments. The best results that characterized individual trees from ULS data were achieved under leaf-off conditions using a point cloud-based bottom-up ITD. The latter outperformed the raster-based ITD, improving the accuracy of tree detection (from 50% to 71%), crown delineation (from R2 = 0.29 to R2 = 0.61), and prediction of tree DBH (from R2 = 0.36 to R2 = 0.67), when compared with values that were estimated from reference TLS data. Major improvements were observed for the detection of trees in the lower canopy layer (from 9% with raster-based ITD to 51% with point cloud-based ITD) and in the intermediate canopy layer (from 24% with raster-based ITD to 59% with point cloud-based ITD). Under leaf-on conditions, LiDAR data from aerial systems include substantial signal occlusion incurred by the upper canopy. Under these conditions, the raster-based ITD was unable to detect low-level canopy trees (from 5% to 15% of trees detected from lower and intermediate canopy layers, respectively), resulting in a tree detection rate of about 40% for both ULS and ALS data. The cylinder-fitting method used to estimate tree DBH under leaf-off conditions did not meet inventory standards when compared to TLS DBH, resulting in RMSE = 7.4 cm, Bias = 3.1 cm, and R2 = 0.75. Yet, it yielded more accurate estimates of the BA (+3.5%) and DBH-distribution of the stand than did allometric models −12.9%), when compared with in situ field measurements. Results suggest that the use of bottom-up ITD on high-density ULS data from leaf-off hardwood forest leads to promising results when estimating trees and stand attributes, which opens up new possibilities for supporting forest inventories and operations.

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Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests

Cited by 57 publications

References 35 publications

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings

A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data

Estimation of Northern Hardwood Forest Inventory Attributes Using UAV Laser Scanning (ULS): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-Level

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