Assessing the utility of LiDAR to differentiate among vegetation structural classes

Jones, Trevor; Coops, Nicholas C.; Sharma, Tara

doi:10.1080/01431161.2011.559289

Cited by 14 publications

(9 citation statements)

References 14 publications

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“…Kurtosis of height distribution, a LiDAR‐derived variable describing vertical stratification of canopy vegetation, also indicates differences among forest age and structure classes. Jones et al () found that variables describing kurtosis and standard deviation of height were significant in differentiating between young and mature forests in heavily managed coastal forests in British Columbia. Lower values of kurtosis for height distribution were indicative of structurally complex, multilayered canopies in natural stands, whereas elevated kurtosis values indicated structurally simple canopies in riparian hardwood forest types (Antonarakis et al ).…”

Section: Discussionmentioning

confidence: 99%

Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR‐derived canopy data

Hagar

Eskelson²,

Haggerty

et al. 2014

Wildl. Soc. Bull.

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LiDAR (Light Detection And Ranging) is an emerging remote-sensing tool that can provide fine-scale data describing vertical complexity of vegetation relevant to species that are responsive to forest structure. We used LiDAR data to estimate occupancy probability for the federally threatened marbled murrelet (Brachyramphus marmoratus) in the Oregon Coast Range of the United States. Our goal was to address the need identified in the Recovery Plan for a more accurate estimate of the availability of nesting habitat by developing occupancy maps based on refined measures of nest-strand structure. We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District, and canopy metrics calculated from discrete return airborne LiDAR data, to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast, and 5 LiDAR-derived variables describing canopy structure. With an area under the curve value (AUC) of 0.74, this model had acceptable discrimination and fair agreement (Cohen's k ¼ 0.24), especially considering that all sites in our sample were regarded by managers as potential habitat. The LiDAR model provided better discrimination between occupied and unoccupied sites than did a model using variables derived from Gradient Nearest Neighbor maps that were previously reported as important predictors of murrelet occupancy (AUC ¼ 0.64, k ¼ 0.12). We also evaluated LiDAR metrics at 11 known murrelet nest sites. Two LiDAR-derived variables accurately discriminated nest sites from random sites (average AUC ¼ 0.91). LiDAR provided a means of quantifying 3-dimensional canopy structure with variables that are ecologically relevant to murrelet nesting habitat, and have not been as accurately quantified by other mensuration methods. Published 2014. This article is a U.S. Government work and is in the public domain in the USA.KEY WORDS Brachyramphus marmoratus, forest structure, habitat model, LiDAR, marbled murrelet, remote sensing.

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Section: Discussionmentioning

confidence: 99%

Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR‐derived canopy data

Hagar

Eskelson²,

Haggerty

et al. 2014

Wildl. Soc. Bull.

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“…LiDAR data are also used successfully to accurately describe a variety of vegetation metrics such as height, crown cover, volume, and diameter (Leiterer et al 2012;Wulder et al 2012). The data are capable of providing detailed information to describe three-dimensional texture, foliage-clustering characteristics, and gap distribution in an individual tree crown (Jones et al 2012;Li et al 2013). Additionally, there has been marked success in classifying forest structural classes (Jones et al 2012;Reese et al 2014;Valbuena et al 2016), differentiating between coniferous and deciduous trees (Leiterer et al 2012;Tiede et al 2012;Alberti et al 2013), and estimating the position of alpine treelines (Coops et al 2013).…”

Section: Lidar and Its Application For Classifying Abiotic And Biotic...mentioning

confidence: 99%

“…Airborne laser scanning estimates of individual height have been shown to be more consistent than manual, field-based measurements; however, ALS estimates of plot mean tree height may be lower than field-measured height, and bias increases with stand height but is not evident in the ALS data for maximum tree heights (Naesset & Økland 2002). Canopy height descriptors, height percentiles, and canopy volume profiles are some of the most widely used metrics for determining structural or seral stages (Jones et al 2012). Canopy structure is necessary for differentiating coniferous and deciduous trees (Alberti et al 2013;Kumar et al 2015), detecting residual trees (García-Feced et al 2011), and quantifying canopy height ranges (Latifi et al 2015;Lopatin et al 2015).…”

Section: Jemmentioning

confidence: 99%

“…Installation of plots and their subsequent measurement can be time-consuming and expensive, and often can be neglected in difficult-to-access areas. Temporal monitoring is additionally challenging and costly (Lucas et al 2008;Jones et al 2012) because plots must be relocated and remeasured. Moreover, manual interpretation of aerial photography introduces biases and requires specialized interpreters (Morgan et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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LiDAR as an Advanced Remote Sensing Technology to Augment Ecosystem Classification and Mapping

Campbell

Coops²,

Saunders³

2017

JEM

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Observing landscape patterns at various temporal and spatial scales is central to classifying and mapping ecosystems. Traditionally, ecosystem mapping is undertaken through a combination of fieldwork and aerial photography interpretation. These methods, however, are time-consuming, prone to subjectivity, and difficult to update. Light Detection and Ranging (LiDAR) is an advanced remote sensing technology that has rapidly increased in application in the past decade and has the potential to significantly increase and refine information content of ecosystem mapping, especially in the vertical dimension. LiDAR technology is therefore well-suited to providing detailed information on topography and vegetation structure and has considerable potential to be used for ecosystem classification and mapping. In this article, the potential to use LiDAR data to advance ecosystem mapping is examined. The current state of the science for using LiDAR data to classify and map key ecosystem attributes within an existing ecosystem mapping scheme is discussed by focusing on British Columbia Terrestrial Ecosystem Mapping and its associated Predictive Ecosystem Mapping. The article concludes by summarizing which components of ecosystem mapping and classification are best suited to the application of LiDAR data, followed by a discussion of the feasibility and future directions for mapping ecosystems with LiDAR technology.

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“…Advances in Earth observation systems and analysis techniques have greatly improved the ability to characterize canopy structure over large areas in not only the horizontal but also the vertical dimension [22][23][24]. In particular, light detection and ranging (LiDAR) systems, especially airborne laser scanning (ALS), are suitable to provide very detailed vertical and horizontal information on canopy structure based on the physical measurement principle of active sensing and full-waveform digitization [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

Leiterer

Torabzadeh

Furrer

et al. 2015

Forests

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Canopy layers form essential structural components, affecting stand productivity and wildlife habitats. Airborne laser scanning (ALS) provides horizontal and vertical information on canopy structure simultaneously. Existing approaches to assess canopy layering often require prior information about stand characteristics or rely on pre-defined height thresholds. We developed a multi-scale method using ALS data with point densities >10 pts/m 2 to determine the number and vertical extent of canopy layers (canopylayer, canopylength), seasonal variations in the topmost canopy layer (canopytype), as well as small-scale heterogeneities in the canopy (canopyheterogeneity). We first tested and developed the method on a small forest patch (800 ha) and afterwards tested transferability and robustness of the method on a larger patch (180,000 ha). We validated the approach using an extensive set of ground data, achieving overall accuracies >77% for canopytype and canopyheterogeneity, and >62% for canopylayer and canopylength. We conclude that our method provides a robust characterization of canopy layering supporting automated canopy structure monitoring. OPEN ACCESSForests 2015, 6 4147

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Assessing the utility of LiDAR to differentiate among vegetation structural classes

Cited by 14 publications

References 14 publications

Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR‐derived canopy data

Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR‐derived canopy data

LiDAR as an Advanced Remote Sensing Technology to Augment Ecosystem Classification and Mapping

Towards Automated Characterization of Canopy Layering in Mixed Temperate Forests Using Airborne Laser Scanning

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