Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

Huo, Langning; Lindberg, Eva; Holmgren, Johan

doi:10.1016/j.rse.2021.112857

Cited by 34 publications

(22 citation statements)

References 42 publications

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“…As shown in the ground observations used in this study (Supplementary Table 5) , there were only 41.6% ( ± 19.7%) of dead trees belonging to medium or tall classes (≥15 m in height estimated from DBH using species-specific allometry equations 57 ). Higher resolution drone or/and aerial imagery 32 and active remote sensing technologies such as Light Detection and Ranging (LiDAR) could compensate for the mapping of small understory dead trees 58 , 59 . Third, the geometric distortion in NAIP aerial images can lead to the omission of dead trees with small crowns shaded by surrounding canopies (Supplementary Fig.…”

Section: Discussionmentioning

confidence: 99%

Scattered tree death contributes to substantial forest loss in California

Cheng,

Oehmcke,

Brandt

et al. 2024

Nat Commun

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In recent years, large-scale tree mortality events linked to global change have occurred around the world. Current forest monitoring methods are crucial for identifying mortality hotspots, but systematic assessments of isolated or scattered dead trees over large areas are needed to reduce uncertainty on the actual extent of tree mortality. Here, we mapped individual dead trees in California using sub-meter resolution aerial photographs from 2020 and deep learning-based dead tree detection. We identified 91.4 million dead trees over 27.8 million hectares of vegetated areas (16.7-24.7% underestimation bias when compared to field data). Among these, a total of 19.5 million dead trees appeared isolated, and 60% of all dead trees occurred in small groups ( ≤ 3 dead trees within a 30 × 30 m grid), which is largely undetected by other state-level monitoring methods. The widespread mortality of individual trees impacts the carbon budget and sequestration capacity of California forests and can be considered a threat to forest health and a fuel source for future wildfires.

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Section: Discussionmentioning

confidence: 99%

Scattered tree death contributes to substantial forest loss in California

Cheng,

Oehmcke,

Brandt

et al. 2024

Nat Commun

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“…The accuracy of the Hovermap™ hardware itself is only 3 cm [50], far larger than many of the tree diameters sampled in this study. While recent work with MLS has achieved more accurate tree detection and even DBH measurements at 4-6 cm [39,74,75], these require ideal environmental conditions and complex preprocessing to reduce noise in the point clouds.…”

Section: Tree Dbhmentioning

confidence: 99%

Use of Mobile Laser Scanning (MLS) to Monitor Vegetation Recovery on Linear Disturbances

Jones

Dongen

Aubry³

et al. 2022

Forests

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Seismic lines are narrow, linear corridors cleared through forests for oil and gas exploration. Their inconsistent recovery has led to Alberta’s forests being highly fragmented, resulting in the need for seismic line restoration programs and subsequent monitoring. Light detection and ranging (LiDAR) is becoming an increasingly popular technology for the fast and accurate measurement of forests. Mobile LiDAR scanners (MLS) are emerging as an alternative to traditional aerial LiDAR due to their increased point cloud density. To determine whether MLS could be effective for collecting vegetation data on seismic lines, we sampled 17 seismic lines using the Emesent Hovermap™ in leaf-on and leaf-off conditions. Processing the LiDAR data was conducted with GreenValley International’s LiDAR 360 software, and data derived from the point clouds were compared to physically measured field data. Overall, the tree detection algorithm was unsuccessful at accurately segmenting the point clouds. Complex vegetation environments on seismic lines, including small conifers with obscured stems or extremely dense and tall shrubs with overlapping canopies, posed a challenge for the software’s capacity to differentiate trees As a result, tree densities and diameters were overestimated, while tree heights were underestimated. Exploration of alternative algorithms and software is needed if measuring vegetation data on seismic lines with MLS is to be implemented.

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“…Moreover, most of the applications are framed in forest environments, where tall and dense trees are present, and the main focus is crown identification and classification [14,15,17,[21][22][23][24]. On the other hand, recent studies have been conducted over areas with lower vegetation, using photogrammetric or LiDAR data sources [16,[25][26][27][28][29][30]. In particular, tree height extraction exploiting UAV-acquired imagery has been addressed by several researchers, with different specific characteristics of the observed plants, providing promising results [14,21,23,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…Originally, the algorithms for vegetation filtering were designed for LiDAR datasets, although they can be employed also for photogrammetric applications [24]. Indeed, different software implements automatic filters for the vegetation that yet lead to better performances in forests rather than in areas with low trees [14,[25][26][27]. However, fruit farmland or vineyards are characterized by the presence of sparse trees and relatively low heights, ranging approximately from 1 m to 4 m [38].…”

Section: Introductionmentioning

confidence: 99%

UAV Photogrammetric Surveys for Tree Height Estimation

Vacca,

Vecchi

2024

Drones

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In the context of precision agriculture (PA), geomatic surveys exploiting UAV (unmanned aerial vehicle) platforms allow the dimensional characterization of trees. This paper focuses on the use of low-cost UAV photogrammetry to estimate tree height, as part of a project for the phytoremediation of contaminated soils. Two study areas with different characteristics in terms of mean tree height (5 m; 0.7 m) are chosen to test the procedure even in a challenging context. Three campaigns are performed in an olive grove (Area 1) at different flying altitudes (30 m, 40 m, and 50 m), and one UAV flight is available for Area 2 (42 m of altitude), where three species are present: oleander, lentisk, and poplar. The workflow involves the elaboration of the UAV point clouds through the SfM (structure from motion) approach, digital surface models (DSMs), vegetation filtering, and a GIS-based analysis to obtain canopy height models (CHMs) for height extraction based on a local maxima approach. UAV-derived heights are compared with in-field measurements, and promising results are obtained for Area 1, confirming the applicability of the procedure for tree height extraction, while the application in Area 2 (shorter tree seedlings) is more problematic.

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Towards low vegetation identification: A new method for tree crown segmentation from LiDAR data based on a symmetrical structure detection algorithm (SSD)

Cited by 34 publications

References 42 publications

Scattered tree death contributes to substantial forest loss in California

Scattered tree death contributes to substantial forest loss in California

Use of Mobile Laser Scanning (MLS) to Monitor Vegetation Recovery on Linear Disturbances

UAV Photogrammetric Surveys for Tree Height Estimation

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