Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in Australian Savannas

Grigorijs Goldbergs, Stefan W. Maier, Shaun R. Levick, Andrew Edwards

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Abstract

The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds-obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera-can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70%) for dominant and co-dominant trees, but yielded low detection rates ( < 35%) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.

Original languageEnglish
Article number161
Pages (from-to)1-19
Number of pages19
JournalRemote Sensing
Volume10
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

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savanna
imagery
cost
canopy
aboveground biomass
segmentation
detection
watershed
overstory
spatial resolution
ecosystem

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title = "Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in Australian Savannas",
abstract = "The reliability of airborne light detection and ranging (LiDAR) for delineating individual trees and estimating aboveground biomass (AGB) has been proven in a diverse range of ecosystems, but can be difficult and costly to commission. Point clouds derived from structure from motion (SfM) matching techniques obtained from unmanned aerial systems (UAS) could be a feasible low-cost alternative to airborne LiDAR scanning for canopy parameter retrieval. This study assesses the extent to which SfM three-dimensional (3D) point clouds-obtained from a light-weight mini-UAS quadcopter with an inexpensive consumer action GoPro camera-can efficiently and effectively detect individual trees, measure tree heights, and provide AGB estimates in Australian tropical savannas. Two well-established canopy maxima and watershed segmentation tree detection algorithms were tested on canopy height models (CHM) derived from SfM imagery. The influence of CHM spatial resolution on tree detection accuracy was analysed, and the results were validated against existing high-resolution airborne LiDAR data. We found that the canopy maxima and watershed segmentation routines produced similar tree detection rates (~70{\%}) for dominant and co-dominant trees, but yielded low detection rates ( < 35{\%}) for suppressed and small trees due to poor representativeness in point clouds and overstory occlusion. Although airborne LiDAR provides higher tree detection rates and more accurate estimates of tree heights, we found SfM image matching to be an adequate low-cost alternative for the detection of dominant and co-dominant tree stands.",
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Efficiency of individual tree detection approaches based on light-weight and low-cost UAS imagery in Australian Savannas. / Goldbergs, Grigorijs; Maier, Stefan W.; Levick, Shaun R.; Edwards, Andrew.

In: Remote Sensing, Vol. 10, No. 2, 161, 01.02.2018, p. 1-19.

Research output: Contribution to journalArticleResearchpeer-review

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