Terrestrial laser scanning (TLS) provides a unique opportunity to study forest canopy structure and its spatial patterns such as foliage quantity and dispersal. Using TLS point clouds for estimating ...leaf area density with voxel-based methods is biased by the physical dimensions of laser beams, which violates the common assumption of beams being infinitely thin. Real laser beams have a footprint size larger than several millimeters. This leads to difficulties in estimating leaf area density from light detection and ranging (LiDAR) in vegetation, where the target objects can be of similar or even smaller size than the beam footprint. To compensate for this bias, we propose a method to estimate the per-pulse cover fraction, defined as the fraction of laser beamsâ footprint area that is covered by vegetation targets, using the LiDAR return intensity and an experimental calibration measurement. We applied this method to a Leica P40 single-return instrument, and report our experimental results. We found that conifer foliage had a lower average per-pulse cover fraction than broadleaved foliage, indicating an increased number of partial hits in conifer foliage. We further discuss limitations of our method that stem from unknown target properties that influence the LiDAR return intensity and highlight potential ways to overcome the limitations and manage the remaining uncertainty. Our methodâs output, the per-beam cover fraction, may be useful in a weight function for methods that estimate leaf area density from LiDAR point clouds.
•A method for quantifying clumping at crown level using silhouette to total area ratio•Application to empirical data from young Norway spruces shows notable crown clumping•Discussion on issues ...related to leaf area estimation from TLS data in conifer trees
The clumping of coniferous needles into shoots is widely acknowledged as a structural feature that cannot be ignored in radiation regime models of coniferous forests. However, higher level clumping, i.e. the aggregation of leaves and shoots in tree crowns and forest stands, is still rarely accounted for in the models. Clumping reduces the light interception of and increases the light penetration depth in a plant stand. To improve forest radiation regime models with respect to this forest structural parameter, we propose a method that can quantify clumping at different hierarchical levels by estimating the silhouette to total area ratio from point clouds acquired by laser scanners. Our method is based on estimating attenuation coefficients in a voxel grid, and subsequently computing the total leaf area and spherically averaged silhouette area of a tree crown or forest stand. We tested our method with empirical data in young Norway spruce trees, where we compared leaf area and silhouette area to destructive and photogrammetric reference measurements. The accuracy of leaf area estimates depended strongly on the voxel size, with voxel sizes below 10 cm side length exhibiting up to 100% higher estimates than the reference leaf area, and large voxels with 90 cm side length being closest to the reference measurements due to crown clumping. The silhouette area estimates varied less with voxel size and were slightly higher than the reference estimates. We analyzed possible error sources and point out ways to improve the measurements of leaf and silhouette area for conifer trees using laser scanning data.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Clumping is critical for quantifying the radiation regime of forest canopies, but challenging to measure. We developed a method to measure clumping index (CI) of forest stands using voxel-based ...estimates of leaf area density from terrestrial lidar data. Our method uses the principle of the silhouette to total area ratio (STAR), a commonly used shoot clumping correction approach. We adapted the concept to forest stands, and derived that STAR at canopy scale (STARf) is no longer simply a clumping index, but a summary variable for forest structure in general. CI can be calculated from STARf when leaf area index is known.
We measured CI and STARf of 38 forest stands in Finland, Estonia, and Czechia to study the natural range of these variables, their relationships to other forest variables, and to Landsat 8 OLI surface reflectance. CI did not include clumping below voxel scale (20 cm), and ranged from 0.6 to 0.9, with the lowest values (i.e., the most clumped canopies) in conifer forests and temperate oak forests, and the highest CI values (i.e., the most random canopies) in boreal broadleaved forests. CI was closely correlated with surface reflectance in conifer forests, which may be explained by contradicting influence of clumping that decreases canopy reflectance, but increases visibility of the forest floor.
From the viewpoint of forest reflectance modeling, STAR is a useful variable due to its close relationship with the photon recollision probability, i.e., the probability that a photon will interact with a canopy element after being scattered from another canopy element. The photon recollision probability is used to model the influence of forest structure on reflectance. Our method provides a physically-based means of measuring STARf, and thus the photon recollision probability, hence contributing to the development of new methods for interpreting forest canopy structure from optical remote sensing data.
•We quantified clumping in 38 forest stands comprising ∼2100 trees in Europe.•Clumping obtained from TLS data using the silhouette to total area ratio of stands.•Conifer and temperate floodplain forests had the highest degrees of clumping.•Clumping correlated with L8 OLI surface reflectance, especially in conifer forests.•Clumping at stand level is best measured by TLS, indirect estimates are insufficient.
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Photon recollision probability p is a spectrally invariant structural parameter and a powerful tool to link canopy optical properties at any wavelengths to model reflectance, transmittance, or ...absorption of vegetation canopies. The concepts of the p-theory have been reported and examined at the shoot and canopy scales, but not yet for the crown level. Currently, the p-value is estimated indirectly, such as converted from the spherically averaged silhouette to total area ratio (STAR¯) or canopy transmittance measurements. In this work, we first validate the theoretical considerations of the p concept at the crown level (e.g., its relationship with STAR¯), and then provide the first method to directly estimate photon recollision probability using Terrestrial Laser Scanning (TLS) data. The proposed geometric method is data-driven and avoids explicit reconstructions of tree structures. The p-value estimated here is the average recollision probability over spatial locations. We showed that the average recollision probability can be interpreted as the local spherical openness on phytoelement (leaf or needle) surfaces, which enabled a simple visibility calculation by avoiding explicit ray tracing. The developed method was tested on synthetic crowns of needle-leaved tree species, for which the reference p-values were known. Results confirmed the validity of the p-STAR¯ relationship at the crown level, and showed that p-values can be accurately estimated from TLS point clouds with a relative root measure square error of less than 10%. This study displays the distinct advantage of TLS in delineating detailed tree crown structures and highlights its potential in studies of forest reflectance modeling.
•Direct estimation of photon recollision probability p from TLS for the first time.•Crown level p calculated for synthetic needle crowns and linked to crown structure.•p can be accurately estimated from TLS point clouds (RMSE% below 10%).•Single-scan TLS can produce accuracy comparable to multi-scan data.
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•Bayesian inversion of PARAS model produced promising LAI retrieval accuracy.•Non-uniform priors improve inversion by counteracting optical saturation effects.•Landsat 8 OLI data showed better LAI ...retrieval accuracy than Sentinel-2 MSI.•We highlight benefits of uncertainty quantification in reflectance model inversion.
The inversion of reflectance models is a generalizable tool to obtain estimates on forest biophysical parameters, such as leaf area index, with theoretically little information need from a study area, instead relying on the knowledge about physical processes in the forest radiation regime. The use of prior information can greatly improve the reflectance model inversion, however, the literature does not yet provide much information on the selection of priors and their influence on the inversion results. In this study, we used a Bayesian approach to invert the PARAS forest reflectance model and retrieve leaf area index from Sentinel-2 MSI and Landsat 8 OLI multispectral satellite images. The PARAS model is based on the theory of spectral invariants, which describes the influence of wavelength-independent parameters on forest radiative transfer. The Bayesian inversion approach is highly flexible, provides uncertainty quantification, and enables the explicit incorporation of prior knowledge into the inversion process. We found that the choice of prior information is crucial in inverting a forest reflectance model to predict leaf area index. Regularizing and informative priors for leaf area index strongly improved the predictions, relative to an uninformative prior, in that they counteracted the saturation effect of the optical signal occuring at high values for leaf area index. The predictions of leaf area index were more accurate for Landsat 8 than for Sentinel-2, due to potential inconsistencies in the visible bands of Sentinel-2 in our data, and the higher spectral resolution.
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We report a new version and an empirical evaluation of a forest reflectance model based on photon recollision probability (p). For the first time, a p-based approach to modeling forest reflectance ...was tested in a wide range of differently structured forests from different biomes. To parameterize the model, we measured forest canopy structure and spectral characteristics for 50 forest plots in four study sites spanning from boreal to temperate biomes in Europe (48°–62°N). We compared modeled forest reflectance spectra against airborne hyperspectral data at wavelengths of 450–2200 nm. Large overestimation occurred, especially in the near-infrared region, when the model was parameterized considering only leaves or needles as plant elements and assuming a Lambertian canopy. The model root mean square error (RMSE) was on average 80%, 80%, 54% for coniferous, broadleaved, and mixed forests, respectively. We suggest a new parameterization that takes into account the nadir to hemispherical reflectance ratio of the canopy and contribution of woody elements to the forest reflectance. We evaluated the new parameterization based on inversion of the model, which resulted in average RMSE of 20%, 15%, and 11% for coniferous, broadleaved, and mixed forests. The model requires only few structural parameters and the spectra of foliage, woody elements, and forest floor as input. It can be used in interpretation of multi- and hyperspectral remote sensing data, as well as in land surface and climate modeling. In general, our results also indicate that even though the foliage spectra are not dramatically different between coniferous and broadleaved forests, they can still explain a large part of reflectance differences between these forest types in the near-infrared, where sensitivity of the reflectance of dense forests to changes in the scattering properties of the foliage is high.
•First extensive empirical evaluation of a forest reflectance model using p-theory.•New parameterization taking into account woody elements and directional scattering.•Uncertainties of the modeled forest reflectance reduced in the near-infrared region.•Forest spectra and their relationships with plant area index simulated correctly.•Based on field and airborne data in 50 forest plots from boreal to temperate biomes.
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Forest floor vegetation can account for a notable fraction of forest productivity and species diversity, and the composition of forest floor vegetation is an important indicator of site type. The ...signal from the forest floor influences the interpretation of optical remote sensing (RS) data. Retrieval of forest floor reflectance properties has commonly been investigated with multiangular RS data, which often have a coarse spatial resolution. We developed a method that utilizes a forest reflectance model based on photon recollision probability to retrieve forest floor reflectance from near-nadir data. The method was tested in boreal, hemiboreal, and temperate forests in Europe, with hemispherical photos and airborne LiDAR as alternative data sources to provide forest canopy structural information. These two data sources showed comparable performance, thus demonstrating the value of using airborne LiDAR as the structural reflectance model input to derive wall-to-wall maps of forest floor reflectance. We derived such maps from multispectral Sentinel-2 MSI and hyperspectral PRISMA satellite images for a boreal forest site. The validation against in situ measurements showed fairly good performance of the retrievals in sparse forests (that had effective plant area index less than 2). In dense forests, the retrievals were less accurate, due to the small contribution of forest floor to the RS signal. We also demonstrated the use of the method in monitoring the recovery of forest floor vegetation after a thinning disturbance. The reflectance model that we used is computationally efficient, making it well applicable also to data from new and forthcoming hyperspectral satellite missions.
•Retrieval of forest floor reflectance from near-nadir remote sensing data.•Using a forest reflectance model based on photon recollision probability.•Good performance in sparse forests, but less accurate in dense forests.•Airborne LiDAR as good as in situ hemispherical photos in providing model input.•Informative maps of forest floor reflectance from PRISMA and Sentinel-2 MSI data.
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