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Tree cover is quantified using a variety of structural metrics that relate to canopy density, which are often modelled from remotely sensed data. Comparing different metrics, and maps of such metrics, is difficult due to a poor understanding as to how they relate to each other. Two commonly used metrics in Australia are crown projective cover (CPC) and foliage projective cover (FPC). CPC and FPC are the proportion of ground area covered by the vertical projection of tree crowns, and the foliage of tree crowns, respectively. They are dimensionless proportions that vary between zero and one. The relationship between CPC and FPC is a function of the plant area index (PAI), the foliage clumping factor at a zenith angle of zero, the foliage projection function at a zenith angle of zero, tree stand density, mean crown radii, and the proportion of wood to all canopy elements ( α ). The non-linear relationship was investigated using a dataset of 745 field sites across Australia, for which 1003 coincident CPC and FPC measurements had been made. As measurements of LAI and the other variables were not available, the parameter k was introduced to simplify the equations, which then had only two unknowns: α and k . Best-fit values of α and k were determined using non-linear weighted least-squares regression across all the field sites. Using these values to predict FPC from CPC, and vice versa, achieved low root mean square errors (0.05-0.07) across the field data. The models allow different mapping products to be compared, and also have the potential to facilitate the derivation of FPC from airborne lidar data when field measurements of FPC are not available for calibration. This was demonstrated using a lidar dataset and 12 coincident field sites, across which FPC was derived from a lidar fractional cover metric with an RMSE of 0.08. Further research is required to investigate the stability of this method across different areas and lidar systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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.

Abstract: Canopy cover and canopy closure are two closely related measures of vegetation structure. They are used for estimating understory light conditions and their influence on a broad range of biological components in forest ecosystems, from the demography and population dynamics of individual species to community structure. Angular canopy closure is more closely related to the direct and indirect light experienced by a plant or an animal than vertical canopy cover, but more challenging to estimate. We used airborne laser scanner (ALS) data to estimate canopy cover for 210 5-m radius vegetation plots in semi-open habitats and forests in protected nature areas in Denmark. The method was based on the area of Thiessen (Voronoi) polygons generated from the ALS points. We also estimated angular canopy closure by transforming ALS points from Cartesian to spherical coordinates, and calculating the percentage of azimuth and zenith angle intervals which contained points. We compared these estimates with field-based estimates using densiometer for 60 vegetation plots in forest. Finally, we compared ALS-based estimates of canopy cover and canopy closure to field-based estimates of understory light, based on the average Ellenberg indicator values for light for the plant species present in a given plot. The correlations of Ellenberg values with ALS-based canopy closure were higher (r2: 0.47) than those with ALS-based canopy cover (r2: 0.26) and densiometer readings (r2: 0.41) for the forest sites. ALS-based canopy closure is thus a reasonable indicator of understory light availability and has the advantage over field-based methods that it can be rapidly estimated for extensive areas. [Copyright &y& Elsevier]

Abstract: The effect of ongoing climate change on sub-arctic and alpine forests has led to increased interest in monitoring potential changes in the forest–tundra ecotone. In addition to climate change, insect damage, browsing pressure by herbivores such as moose and reindeer, as well as anthropogenic impacts will contribute to changes in the forest–tundra ecotone. These changes are difficult to monitor with manual methods because of the complex mosaic pattern of the ecotone. In this study, the possibility to predict maximum tree height, above ground tree biomass and canopy cover with airborne laser scanning (ALS) was therefore tested at a forest–tundra ecotone site near Abisko in northern Sweden (Lat. N 68°20′, Long. E 19°01′, 420-700ma.s.l.). The forest in the area is dominated by mountain birch (Betula pubescens ssp. czerepanovii), which has highly irregular stem and canopy forms. Predictions from two different laser data acquisitions were compared. The first laser data set had 6.1pointsm−2 and was obtained in 2008 with a TopEye MKII scanner carried by a helicopter flown at 500m a.g.l. The second laser data set had 1.4pointsm−2 and was obtained in 2010 with an Optech ALTM Gemini scanner carried by a fixed-wing aircraft flown at 1740m a.g.l. Linear regression models were developed for the predictions using data from 73 sample plots with ten meter radius surveyed in 2009 and 2010. The relative RMSEs obtained for the TopEye and Optech data after leave-one-out cross-validation were, respectively, 8.8% and 9.5% for maximum tree height; 18.7% and 21.2% for above ground tree biomass; and, 16.8% and 18.7% for vertical canopy cover on plot level. The results were clearly improved by introducing a new procedure to compensate for unevenly distributed laser points. In conclusion, ALS has strong potential as a data source to map mountain birch biomass in the forest–tundra ecotone, even when using sparse point density ALS data. [Copyright &y& Elsevier]