Characterizing forest ecological structure using pulse types and heights of airborne laser scanning

Miura, N.; Jones, Simon

doi:10.1016/j.rse.2009.12.017

Cited by 67 publications

(36 citation statements)

References 34 publications

(31 reference statements)

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“…Primarily these relative density layers are sub-components of broader vegetation analysis, with summary statistics typically generated to enable integration with additional factors [8,9]. However, this study used these relative density layers as the primary input for exploratory classifications, enabling characterisation of the overall vertical structure.…”

Section: Point Density Ratiosmentioning

confidence: 99%

“…Due to a limited capacity to currently synthesis the large and complex volumes of data required to measure and map vertical structure [6], current structural vegetation classifications have not been able to fully characterise vertical vegetation structure at continental scales (10 8 km 2 ) [5]. Limited studies have utilised high spatial resolution LiDAR data to characterise vertical vegetation structure [8][9][10]. The majority have researched structurally simple vegetation communities at course vertical and horizontal spatial resolutions [11].…”

Section: Introductionmentioning

confidence: 99%

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Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Tasker

Phinn

2018

The 2nd International Electronic Conference on Remote Sensing

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A quantitative understanding of vegetation structure is vital to inform long-term protection and management of Australia's vegetation communities. Although airborne light detection and ranging (LiDAR) systems are increasingly utilised to provide three-dimensional measures of vegetation structure at high spatial resolutions (1 -10 m 2 ), only limited studies characterise vertical vegetation structure using these datasets. This study assesses the capacity of high spatial resolution LiDAR data to accurately characterise the structural forms of Australian vegetation communities. Four study sites, each covering approximately 25 km 2 , were selected to provide examples across a range of vegetation structural forms, from shrubland to tall closed forest. A novel vertical segmentation methodology was developed to process airborne LiDAR data from each study site at 1 or 2 m vertical and horizontal spatial resolutions. Ratios were applied to standardise point density values, prior to exploratory analysis utilising multi-dimensional clustering algorithms to classify distinct vertical structure patterns. Comparisons were subsequently performed between the exploratory analysis results and established structural classifications for Australian vegetation communities. The use of the vertical segmentation technique was found to improve the identification of sub-canopy features in multi-story vegetation communities, particularly shrubs and herbaceous ground covers 0.5 -4 m tall. Exploratory analysis results saw increased noise in structurally complex and dense vegetation communities due to reduced subcanopy returns. Further development and application of vertical segmentation methods in multistory vegetation communities should be evaluated due to the potential for targeted management and monitoring of vegetation communities and wildlife populations.

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Section: Point Density Ratiosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Tasker

Phinn

2018

The 2nd International Electronic Conference on Remote Sensing

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“…2018, 10, 338 3 of 22 structure through height, height variation, and density of the vegetation. ALS data have been used to map and monitor old deciduous trees within stands [26], stands with mixed species and multiple canopy layers [29][30][31][32], site type [33], as well as amount of dead wood [34], and canopy gaps [35], all being related to diversity of forest ecosystem. In addition, sparse ALS data was used by [36] to locate potential wildlife habitats, but concluded that information on shrub and herb layers, important habitat characteristics for game birds studied, was challenging.…”

Section: Introductionmentioning

confidence: 99%

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

et al. 2018

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Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.

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“…ALS data may be used to separate single-story and multi-story stand structures through the shape of the distribution of ALS point data (Maltamo et al 2005) or by examination of the height variability of local maxima in a canopy surface model defined from the points (Zimble et al 2003). Correlation has been reported between the vegetation cover in different height intervals and the number of points in the intervals which makes it possible to use the distribution of points to characterize forest ecological structure (Miura and Jones 2010). A quantitative measure of the vertical vegetation structure can be derived by dividing the canopy into key layers and fitting a Weibull distribution, for example, to each layer (Coops et al 2007;Jaskierniak et al 2011).…”

Section: Introductionmentioning

confidence: 99%

Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

Lindberg

Olofsson

Holmgren

et al. 2012

Remote Sensing of Environment

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Access to the published version may require journal subscription. Published with permission from: Elsevier.Standard set statement from the publisher: NOTICE: this is the author's version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in REMOTE SENSING OF ENVIRONMENT, 118, MARCH, (2012) AbstractThis study presents and compares new methods to describe the 3D canopy structure with Airborne Laser Scanning (ALS) waveform data as well as ALS point data. The ALS waveform data were analyzed in three different ways; by summing the intensity of the waveforms in height intervals (a); by first normalizing the waveforms with an algorithm based on Beer-Lambert law to compensate for the shielding effect of higher vegetation layers on reflection from lower layers and then summing the intensity (b); and by deriving points from the waveforms (c). As a comparison, conventional, discrete return ALS point data from the laser scanning system were also analyzed (d). The study area was located in hemi-boreal, spruce dominated forest in the southwest of Sweden (Lat. 58° N, Long. 13° E). The vegetation volume profile was defined as the volume of all tree crowns and shrubs in 1 dm height intervals in a field plot and the total vegetation volume as the sum of the vegetation volume profile in the field plot. The total vegetation volume was estimated for 68 field plots with 12 m radius from the proportion between the amount of ALS reflections from the vegetation and the total amount of ALS reflections based on Beer-Lambert law. ALS profiles were derived from the distribution of the ALS data above the ground in 1 dm height intervals. The ALS profiles were rescaled using the estimated total vegetation volume to derive the amount of vegetation at different heights above the ground. The root mean square error (RMSE) for cross validated regression estimates of the total vegetation volume was 31.9% for ALS waveform data (a), 27.6% for normalized waveform data (b), 29.1% for point data derived from the ALS waveforms (c), and 36.5% for ALS point data from the laser scanning system (d). The correspondence between the estimated vegetation volume profiles was also best for the normalized waveform data and the point data derived from the ALS waveforms and worst for ALS point data from the laser scanning system as demonstrated by the Reynolds error index. The results suggest that ALS waveform data describe the volumetric aspects of vertical vegetation structure somewhat more accurately than ALS point data from the laser scanning system and that compensation for the shielding effect of higher vegetation layers is useful. The new methods for estimation of vegetation volume profiles from ALS data could be used i...

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Characterizing forest ecological structure using pulse types and heights of airborne laser scanning

Cited by 67 publications

References 34 publications

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Estimation of 3D vegetation structure from waveform and discrete return airborne laser scanning data

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