Airborne laser scanner (LiDAR) proxies for understory light conditions

Alexander, Cici; Moeslund, Jesper Erenskjold; Bøcher, Peder Klith; Arge, Lars; Svenning, Jens‐Christian

doi:10.1016/j.rse.2013.02.028

Cited by 46 publications

(44 citation statements)

References 23 publications

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“…Habitat structure provided by vegetation and topography affects the available niche space and provides a variety of essential animal habitat elements, such as nesting sites, shelter and cover (Dennis, 2004;MacArthur & MacArthur, 1961). Moreover, canopy structure drives microclimatic conditions and light availability on the forest floor, thus affecting habitat suitability for plants and the small-scale distribution of temperature extremes (Alexander, Moeslund, Bøcher, Arge, & Svenning, 2013;Suggitt et al, 2011). Habitat structure should therefore be related to beta diversity of both plants and animals.…”

Section: Introductionmentioning

confidence: 99%

Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure

Zellweger

Roth

Bugmann

et al. 2017

Global Ecol Biogeogr

102

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Aim The aim was to investigate the relationship between climate, topography and soil pH, as well as vegetation structure and the beta diversity of plants, butterflies and birds; and to investigate the correlations of (woody) plant beta diversity with animal beta diversity. Location Switzerland (central Europe). Methods We used pairwise Sørensen dissimilarity as measure of total beta diversity and partitioned it into its turnover and nestedness components. Variation partitioning was used to assess the independent and cumulative effects of environmental predictors, with vegetation structure being derived from airborne light detection and ranging (LiDAR) data. We also checked for independent effects of plant and woody plant beta diversity on butterfly and bird beta diversity, respectively, and for independent effects of spatial distance on beta diversity. Results Climate emerged as the strongest statistical predictor of beta diversity across taxonomic groups, with large independent effects on species turnover. Climate effects were most pronounced for plants, followed by butterflies and birds. We also found large independent effects of vegetation structure on total beta diversity and its turnover component across taxonomic groups, particularly for birds. Plant and woody plant beta diversity substantially improved the predictions of butterfly and bird beta diversity, respectively. Spatial distance had hardly any independent effect on beta diversity. Main conclusions Climate is a stronger filter for plant communities than for butterfly and bird communities, which are more affected by vegetation structure, probably owing to associated resources and niches. Vegetation structure is a crucial predictor of beta diversity, and therefore contiguous and detailed 3‐D habitat structure data are highly relevant to further our understanding of niche‐based community assembly. Plant and animal beta diversity appear to be non‐independent, suggesting that differences in the response times of interacting taxa should be accounted for in environmental change impact assessments on biodiversity.

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Section: Introductionmentioning

confidence: 99%

Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure

Zellweger

Roth

Bugmann

et al. 2017

Global Ecol Biogeogr

102

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“…inside_points intercept theo_points intercept − be f ore_points intercept (8) (iii) For the real and synthetic point clouds, the trajectory information was used in order to reconstruct the exact emission point per return. This allowed computing inside_points LiDAR , theo_points LiDAR and before_points LiDAR as described above.…”

Section: Ac =mentioning

confidence: 99%

“…One important implication of leaf and plant surface distributions is the attenuation of sunlight (e.g., [7][8][9][10][11]). If LAI distributions are available in sufficient detail for a 3D radiative transfer modelling (RTM) scene, ray tracing based techniques will be usable in order to simulate direct and diffuse radiation for individual positions in the canopy.…”

Section: Introductionmentioning

confidence: 99%

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Calibration and Validation of a Detailed Architectural Canopy Model Reconstruction for the Simulation of Synthetic Hemispherical Images and Airborne LiDAR Data

Bremer

Wichmann²,

Rutzinger

2017

Remote Sensing

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Abstract:Canopy density measures such as the Leaf Area Index (LAI) have become standardized mapping products derived from airborne and terrestrial Light Detection And Ranging (aLiDAR and tLiDAR, respectively) data. A specific application of LiDAR point clouds is their integration into radiative transfer models (RTM) of varying complexity. Using, e.g., ray tracing, this allows flexible simulations of sub-canopy light condition and the simulation of various sensors such as virtual hemispherical images or waveform LiDAR on a virtual forest plot. However, the direct use of LiDAR data in RTMs shows some limitations in the handling of noise, the derivation of surface areas per LiDAR point and the discrimination of solid and porous canopy elements. In order to address these issues, a strategy upgrading tLiDAR and Digital Hemispherical Photographs (DHP) into plausible 3D architectural canopy models is suggested. The presented reconstruction workflow creates an almost unbiased virtual 3D representation of branch and leaf surface distributions, minimizing systematic errors due to the object-sensor relationship. The models are calibrated and validated using DHPs. Using the 3D models for simulations, their capabilities for the description of leaf density distributions and the simulation of aLiDAR and DHP signatures are shown. At an experimental test site, the suitability of the models, in order to systematically simulate and evaluate aLiDAR based LAI predictions under various scan settings is proven. This strategy makes it possible to show the importance of laser point sampling density, but also the diversity of scan angles and their quantitative effect onto error margins.

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“…Due to the angular viewpoint, canopy closure is expected to represent the light conditions more accurately than canopy cover. Indeed, Alexander, Moeslund, Bøcher, Arge, and Svenning () used averaged Ellenberg indicator values for light to show that canopy closure is an ecologically more meaningful proxy for understory light conditions than canopy cover. Moreover, canopy cover and canopy closure can be used to estimate functional variables such as Leaf Area Index (LAI), which is often applied to describe light absorption by trees (Binkley, Campoe, Gspaltl, & Forrester, ; Schleppi & Paquette, ).…”

Section: Introductionmentioning

confidence: 99%

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Zellweger

Baltensweiler

Schleppi

et al. 2019

Ecology and Evolution

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Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three‐dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (Llight) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macroclimate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, βSIM) depend on light approximation methodology. Diffuse light explained Llight best, followed by direct light, CCl and CCo (R2 = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for βSIM compared with CCl and CCo (R2 = .30, .27 and .24, respectively). The independent effect of macroclimate on βSIM dropped from an R2 of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below‐canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.

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Airborne laser scanner (LiDAR) proxies for understory light conditions

Cited by 46 publications

References 23 publications

Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure

Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure

Calibration and Validation of a Detailed Architectural Canopy Model Reconstruction for the Simulation of Synthetic Hemispherical Images and Airborne LiDAR Data

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

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