Small-footprint Airborne Laser Scanner Research Articles

Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping

Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.

Sensitivity Analysis of the DART Model for Forest Mensuration with Airborne Laser Scanning

Airborne Laser Scanning (ALS) measurements are increasingly vital in forest management and national forest inventories. Despite the growing reliance on ALS data, comparatively little research has examined the sensitivity of ALS measurements to varying survey conditions over commercially important forests. This study investigated: (i) how accurately the Discrete Anisotropic Radiative Transfer (DART) model was able to replicate small-footprint ALS measurements collected over Irish conifer plantations, and (ii) how survey characteristics influenced the precision of discrete-return metrics. A variance-based global sensitivity analysis demonstrated that discrete-return height distributions were accurately and consistently simulated across 100 forest inventory plots with few perturbations induced by varying acquisition parameters or ground topography. In contrast, discrete return density, canopy cover and the proportion of multiple returns were sensitive to fluctuations in sensor altitude, scanning angle, pulse repetition frequency and pulse duration. Our findings corroborate previous studies indicating that discrete-return heights are robust to varying acquisition parameters and may be reliable predictors for the indirect retrieval of forest inventory measurements. However, canopy cover and density metrics are only comparable for ALS data collected under similar acquisition conditions, precluding their universal use across different ALS surveys. Our study demonstrates that DART is a robust model for simulating discrete-return measurements over structurally complex forests; however, the replication of foliage morphology, density and orientation are important considerations for radiative transfer simulations using synthetic trees with explicitly defined crown architectures.

Tree species classification in a temperate mixed forest using a combination of imaging spectroscopy and airborne laser scanning

Knowledge of the spatial distribution of tree species is important for efficiently managing and monitoring forested ecosystems, especially in mixed forests of the temperate zone. In this study, we fused imaging spectroscopy (IS) data with leaf-on and off small-footprint airborne laser scanning (ALS) data, for tree species identification in a dense temperate forest in Switzerland. In addition to the spectral reflectance of the sunlit part of the tree crowns, structural features computed based on the height, intensity and point distribution of ALS data in both the vertical and horizontal dimensions are used as features. Features were extracted using a pixel-based (1 m × 1 m) and an individual tree crown approach. In addition, applying a floating forward feature selection approach revealed that the ALS-derived features provided relevant structural information for species identification, while IS-derived features added complementary biochemical information. Comparing the accuracies of three different combinations of ALS and IS data, shows the highest classification accuracy (kappa = 90.3%) was obtained by fusing a selected set of features at individual tree crowns (ITC), while the best kappa accuracies resulting from IS or ALS data alone were 74.7% and 75.1%, respectively. Inclusion of the ITC information improved the classification results for all datasets, however, this improvement is significantly higher for ALS derived datasets (+31%). Our results show that accurate ITC information drastically improves classification accuracy of tree species in dense forests and that multi-seasonal ALS structural attributes play a major part in species discrimination.

Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm

Accurate three-dimensional information on canopy structure contributes to better understanding of radiation fluxes within the canopy and the physiological processes associated with them. Small-footprint airborne laser scanning (ALS) data proved valuable for characterising the three-dimensional structure of forest canopies and the retrieval of biophysical parameters such as plant and leaf area index (PAI and LAI), fractional cover or canopy layering. Nevertheless, few studies analysed combined occluded and observed canopy elements in dense vegetation as a result of airborne laser scanning geometries. The occluded space contains a substantial amount of vegetation elements (i.e. leaf, needle and wood material), which are missing in the analysis of the three-dimensional canopy structure. Consequently, this will lead to erroneous retrieval of biophysical parameters. In this study, we introduce a voxel traversal algorithm to characterise ALS observation patterns inside a voxel grid. We analyse the dependence of occluded and unobserved canopy volume on pulse density, flight strip overlap and season of overflight in a temperate mixed forest. ALS measurements under leaf-on and leaf-off conditions were used. For cross-comparison purposes, terrestrial laser scanning (TLS) measurements on a 50×50m2 subplot under leaf-on conditions were used. TLS acquisitions were able to depict the three-dimensional structure of the forest plot in high detail, ranging up to the top-most canopy layer.Our results at 1m voxel size show that even with the highest average pulse density of 11pulses/m2, at least 25% of the forest canopy volume remains occluded in the ALS acquisition under leaf-on conditions. Comparison with TLS acquisitions further showed that roughly 28% of the vegetation elements detected by the TLS acquisitions were not detected by the ALS system due to occlusion effects. By combining leaf-on and leaf-off acquisitions, we were able to recover roughly 7% of the occluded vegetation elements from the leaf-on acquisition. We find that larger flight strip overlap can significantly increase the amount of observed canopy volume due to the added observation angles and increased pulse density.

Estimating Tree Growth Using Crown Metrics Derived from LiDAR Data

In order to determine current tree condition and predict future growth using LiDAR data, tree height, diameter at breast height, diameter 4 m above the ground, tree volume, tree volume growth, diameter at breast height growth and diameter growth 4 m above ground for individual trees were estimated from various crown height metrics and measurements obtained using a small footprint airborne laser scanner flown over a planted forest in Japan. Ground-truth values for tree height, diameter at breast height, diameter 4 m above ground, tree volume, and volume and diameter growth were collected. The actual values were compared with the laser-derived crown height metrics, including: percentiles, maximum, mean, coefficient of variation and crown density, all for the first and last crown height laser pulses. The regressions explained 75–79 % of the variability in ground-truth tree height, diameter at breast height, diameter 4 m above ground and tree volume. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 1.30 m (6.7 %), 5.2 cm (22.2 %), 3.8 cm (18.7 %) and 0.22 m3 (43.3 % of ground-truth mean), respectively. The regressions also explained 69–77 % of the variability in ground-truth averages. Cross-validation of the regressions revealed standard deviations of the differences between predicted and ground-truth values of 0.15 cm yr−1 (43.7 %), 0.1 cm yr−1 (31.0 %) and 0.008 m3 yr−1 (58.5 % of ground-truth mean), respectively. The study confirms that it may be possible to predict individual tree growth based on LiDAR data.

Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data

Successful implementation of projects under the REDD+ mechanism, securing payment for storing forest carbon as an ecosystem service, requires quantification of biomass. Airborne laser scanning (ALS) is a relevant technology to enhance estimates of biomass in tropical forests. We present the analysis and results of modeling aboveground biomass (AGB) in a Tanzanian rainforest utilizing data from a small-footprint ALS system and 153 field plots with an area of 0.06–0.12 ha located on a systematic grid. The study area is dominated by steep terrain, a heterogeneous forest structure and large variation in AGB densities with values ranging from 43 to 1147 Mg·ha−1, which goes beyond the range that has been reported in existing literature on biomass modeling with ALS data in the tropics. Root mean square errors from a 10-fold cross-validation of estimated values were about 33% of a mean value of 462 Mg·ha−1. Texture variables derived from a canopy surface model did not result in improved models. Analyses showed that (1) variables derived from echoes in the lower parts of the canopy and (2) canopy density variables explained more of the AGB density than variables representing the height of the canopy.

Detection of fallen trees in forested areas using small footprint airborne laser scanning data

Deadwood was identified as an important indicator for habitat condition and biodiversity in forests. The assessment of downed trees is therefore part of sustainable forest management and ecological monitoring. However, manual quantification of deadwood in forests is challenging, time consuming, and considered cost-inefficient. Full-waveform airborne laser scanning (FWF-ALS) can be used to support the assessment process. The amplitude and width of the backscattered pulses contain information on the properties of the surface. We used these observations for the identification of downed trees in a Natura2000 forest site. A high density FWF-ALS data set was acquired under leaf-off conditions. Echo width and type (i.e., first, intermediate, and last) information as well as normalized echo heights were used to filter the point cloud and derive a digital height model (DHM). This DHM depicts downed stems as line-like features. Image processing was applied to derive and refine regions representing fallen trees. Terrestrial reference data consisting of locations and dimensions of downed trees, as well as state of decay were used for evaluation. Direct identification of downed trees in FWF-ALS point clouds was possible (completeness 75%, correctness 90%), but it was influenced by factors such as dimension, state of decay, vegetation density, and penetration of the laser.

Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data

Different approaches for estimation of change in biomass between two points in time by means of airborne laser scanner data were tested. Both field and laser data were collected at two occasions on 52 sample plots in a mountain forest in southeastern Norway. In the first approach, biomass change was estimated as the difference between predicted biomass for the two measurement occasions. Joint models for the biomass at both occasions were fitted using different height and density variables from laser data as explanatory variables. The second approach modelled the observed change directly using the change in different variables extracted from the laser data as explanatory variables. In the third approach we modelled the relative change in biomass. The explanatory variables were also expressed as relative change between measurement occasions. In all approaches we allowed spline terms to be entered. We also investigated the aptness of models for which the residual variance was modeled by allowing it to be proportional to the area of the plot on which biomass was assessed. All alternative models were initially assessed by AIC. All models were also evaluated by estimating biomass change on the model development data. This evaluation indicated that the two direct approaches (approach 2 and 3) were better than relying on modeling biomass at both occasions and taking change as the difference between biomass estimates. Approach 2 seemed to be slightly better than approach 3 based on assessments of bias in the evaluation.

Estimating Vertical Distribution of Vegetation Cover in Temperate Heterogeneous Forests Using Airborne Laser Scanning Data

Vertical structure is important for understanding forest environment, yet difficult to characterize, especially in temperate heterogeneous forests where the structure is complex. This study used data from a small-footprint airborne laser scanning (ALS) to estimate vegetation coverage in four stratum ranges in a warm temperate forest in Japan: >12 m, 8 - 12 m, 4 - 8 m, and 0 - 4 m in height. Field data were collected in 17 broad-leaved and 12 coniferous sample plots, consisting of the proportion of vegetation cover in each stratum range. The field and ALS measurements were conducted in summer, during leaf-on conditions. Using echo attributes (first, last, intermediate, and only), we calculated the vegetation coverage index (VCI) at 1-m height intervals. The cumulative sum of the VCI (CUMVCI) was then computed and compared with field observations. Linear regression analysis showed that the ALS data gave reasonable estimates of vegetation coverage in the upper two or three stratum ranges in broad-leaved stands, and in the upper two stratum ranges in coniferous stands. The model gave reproducible estimates until approximately 95% of the total returns had been applied. We conclude that ALS data can provide useful information on natural habitats in the management of warm temperate forest.

Prediction of Timber Quality Parameters of Forest Stands by Means of Small Footprint Airborne Laser Scanner Data

The aim of this study was to explore the capability of airborne laser scanner (ALS) data to explain the variation in fieldmeasured variables representing timber quality within square 0.25 ha grid cells in a mature conifer forest in the southeast of Norway. These variables were the mean ratio between stem diameter at six m above ground and the diameter at breast height (RD6), the volume of saw logs (VSL), the proportion of saw logs relative to the total volume (PSL), the ratio between tree height and diameter at breast height (HD), mean basal area diameter (Dg), and crown height (CH). Each of these variables was modeled using a mixed modeling approach. Model fit was expressed by the Pseudo-R2, and were 0.85, 0.50, 0.78, 0.57, 0.74, and 0.58 for the respective quality variables. Furthermore, much of the residual error could be attributed to the different forest stands from which the grid cells originated even though we used field-observed tree species proportions as auxiliary information. It was concluded that more auxiliary information is needed to estimate models that are general across stands, but that the relationships between ALS-data and the quality variables considered here seem strong enough to be utilized for example to prioritize between stands in relation to harvest when specific quality distributions are sought.

Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment

This is a review of the latest developments in different fields of remote sensing for forest biomass mapping. The main fields of research within the last decade have focused on the use of small footprint airborne laser scanning systems, polarimetric synthetic radar interferometry and hyperspectral data. Parallel developments in the field of digital airborne camera systems, digital photogrammetry and very high resolution multispectral data have taken place and have also proven themselves suitable for forest mapping issues. Forest mapping is a wide field and a variety of forest parameters can be mapped or modelled based on remote sensing information alone or combined with field data. The most common information required about a forest is related to its wood production and environmental aspects. In this paper, we will focus on the potential of advanced remote sensing techniques to assess forest biomass. This information is especially required by the REDD (reducing of emission from avoided deforestation and degradation) process. For this reason, new types of remote sensing data such as fullwave laser scanning data, polarimetric radar interferometry (polarimetric systhetic aperture interferometry, PolInSAR) and hyperspectral data are the focus of the research. In recent times, a few state-of-the-art articles in the field of airborne laser scanning for forest applications have been published. The current paper will provide a state-of-the-art review of remote sensing with a particular focus on biomass estimation, including new findings with fullwave airborne laser scanning, hyperspectral and polarimetric synthetic aperture radar interferometry. A synthesis of the actual findings and an outline of future developments will be presented.

Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.1 points·m–2 collected over 132 sample plots and 61 stands. Field measurements of each plot were carried out within two concentric circles (200 m2 and 300 or 400 m2). The plot positions were altered randomly with Monte Carlo simulations. For various metrics derived from the canopy height distributions, the mean and the standard deviation (SD) of the differences between incorrect plot positions and ground-truth positions were compared. In general, SD was smaller for large field plots than for small plots, and the variation in SD among the Monte Carlo repetitions was smaller for large sample plots. The combined effects of field plot size and sample plot position error on the accuracy of mean tree height (hL), stand basal area (G), and stand volume (V) predicted at stand level using a two-stage procedure combining field training data and laser data were assessed. Standard deviation of the differences between predicted and observed hL was quite stable and of similar size for position errors up to 5 m. However, for G and V the influence of plot position error was more pronounced.

Synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data

We proposed a method to separate ground points and vegetation points from discrete return, small footprint airborne laser scanner data, called skewness change algorithm. The method, which makes use of intensity of laser scanner data, is especially applicable in steep, and forested areas. It does not take slope of forested area into account, while other algorithms consider the change of slope in steep forested area. The ground points and vegetation points can be used to estimate digital terrain model (DTM) and fractional vegetation cover, respectively. A few vegetation points which were classified into the ground points were removed as noise before the generation of DTM. This method was tested in a test area of 10000 square meters. A LiteMapper-5600 laser system was used and a flight was carried out over a ground of 700–800 m. In this tested area, a total number of 1546 field measurement ground points were measured with a total station TOPCON GTS-602 and TOPCON GTS-7002 for validation of DTM and the mean error value is −18.5 cm and the RMSE (root mean square error) is ±20.9 cm. A data trap sizes of 4 m in diameter from airborne laser scanner data was selected to compute vegetation fraction cover. Validation of fractional vegetation cover was carried out using 15 hemispherical photographs, which are georeferenced to centimeter accuracy by differential GPS. The gap fraction was computed over a range of zenith angles 10° using the gap light analyzer (GLA) from each hemispherical photograph. The R2 for the regression of fractional vegetation cover from these ALS data and the respective field measurements is 0.7554. So this study presents a method for synchronous estimation of DTM and fractional vegetation cover in forested area from airborne LIDAR height and intensity data.

Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data

Canopy height distributions were created from small-footprint airborne laser scanner (ALS) data collected over 40 field sample plots with size 1000 m 2 located in mature conifer forest. ALS data were collected with two different instruments, i.e., the ALTM 1233 and ALTM 3100 laser scanners (Optech Inc.). The ALTM 1233 data were acquired at a flying altitude of 1200 m and a pulse repetition frequency (PRF) of 33 kHz. Three different acquisitions were carried out with ALTM 3100, i.e., (1) a flying altitude of 1100 m and a PRF of 50 kHz, (2) a flying altitude of 1100 m and a PRF of 100 kHz, and (3) a flying altitude of 2000 m and a PRF of 50 kHz. Height percentiles, mean and maximum height values, coefficients of variation of the heights, and canopy density at different height intervals above the ground were derived from the four different ALS datasets and for single + first and last echoes of the ALS data separately. The ALS-derived height- and density variables were assessed in pair-wise comparisons to evaluate the effects of (a) instrument, (b) flying altitude, and (c) PRF. A systematic shift in height values of up to 0.3 m between sensors when the first echoes were compared was demonstrated. Also the density-related variables differed significantly between the two instruments. Comparisons of flying altitudes and PRFs revealed upwards shifted canopy height distributions for the highest flying altitude (2000 m) and the lowest PRF (50 kHz). The distribution of echoes on different echo categories, i.e., single and multiple (first and last) echoes, differed significantly between acquisitions. The proportion of multiple echoes decreased with increasing flying altitude and PRF. Different echo categories have different properties since it is likely that single echoes tend to occur in the densest parts of the tree crowns, i.e., near the apex where the concentration of biological matter is highest and distance to the ground is largest. To assess the influence of instrument, flying altitude, and PRF on biophysical properties derived from ALS data, regression analysis was carried out to relate ALS-derived metrics to mean tree height ( h L) and timber volume ( V). Cross validation revealed only minor differences in precision for the different ALS acquisitions, but systematic differences between acquisitions of up to 2.5% for h L and 10.7% for V were found when comparing data from different acquisitions.

Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data

Canopy height distributions were created from small-footprint airborne laser scanner data with an average sampling density of 1.13 points·m–2 collected over 132 sample plots and 61 forest stands. Field measurements of each plot were carried out within two concentric circles corresponding to fixed areas of 200 m2 and 300 or 400 m2. The laser point clouds were thinned to approximately 0.25, 0.13, and 0.06 point·m–2. For all comparisons, the maximum values of the first as well as last return canopy height distributions differed significantly between the full density and the thinned data. The combined effects of number of field plots, field plot sizes, and point densities on the accuracy of mean tree height, stand basal area, and stand volume predicted at stand level using a two-stage procedure combining field training data and laser data, were assessed using Monte Carlo simulation randomly selecting 75% and 50% of the field plots. The average standard deviation showed only a minor increase by decreasing point density and increased when the number of sample plots was reduced. The effects of field plot size varied with canopy structure and stem density.

Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography

We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the “only-and-ground” pulse to “only” pulse (OGF) was the best estimator of both LAI (adjusted R2 = 0.797) and canopy openness (adjusted R2 = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R2 = 0.791) and canopy openness (adjusted R2 = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R2 until a fairly low density of about 0.226 points/m2, while OGF performed marginally when the point density was reduced to about 1 point/m2, the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broad-leaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.

Ray tracing for modeling of small footprint airborne laser scanning returns

Airborne Laser Scanning (ALS) has been established as a valuable tool for the estimation of biophysical canopy variables, such as tree height and vegetation density. However, up to now most approaches are built upon empirical stand based methods for linking ALS data with the relevant canopy properties estimated by field work. These empirical methods mostly comprise regression models, where effects of site conditions and sensor configurations are contained in the models. Thus, these models are only valid for a specific study, which renders inter-comparison of different approaches difficult. Physically based approaches exist e.g. for the estimation of tree height and tree location, however systematic underestimation depending upon sampling and vegetation type remains an issue. Using a radiative transfer model that builds on the foundation of the Open-Source ray tracer povray we are simulating return signals for two ALS system settings (footprint size and laser wavelength). The tree crowns are represented by fractal models (L-systems), which explicitly resolve the position and orientation of single leafs. The model is validated using ALS data from an experiment with geometric reference targets. We were able to reproduce the effects of target size and target reflectance that were found in the real data with our modeling approach. A sensitivity study was carried out in order to determine the effect of properties such as beam divergence (0.5, 1, and 2 mrad), canopy reflectance (laser wavelength, 1064 and 1560 nm) on the ALS return statistics. Using the two laser wavelengths above, we were able to show that the laser wavelength will not significantly influence discrete return statistics in our model. It was found that first echo return statistics only differ significantly if the footprint size was altered by a factor of 4. Last return distributions were significantly different for all three modelled footprint sizes, and we were able to reproduce the effect of an increased number of ground returns for large footprint sizes. These forward simulations are a first step in the direction of physically based derivation of biophysical ALS data products and could improve the accuracy of the derived parameters by establishing correction terms.

Ray tracing for modeling of small footprint airborne laser scanning returns

Airborne Laser Scanning (ALS) has been established as a valuable tool for the estimation of biophysical canopy variables, such as tree height and vegetation density. However, up to now most approaches are built upon empirical stand based methods for linking ALS data with the relevant canopy properties estimated by field work. These empirical methods mostly comprise regression models, where effects of site conditions and sensor configurations are contained in the models. Thus, these models are only valid for a specific study, which renders inter-comparison of different approaches difficult. Physically based approaches exist e.g. for the estimation of tree height and tree location, however systematic underestimation depending upon sampling and vegetation type remains an issue. Using a radiative transfer model that builds on the foundation of the Open-Source ray tracer povray we are simulating return signals for two ALS system settings (footprint size and laser wavelength). The tree crowns are represented by fractal models (L-systems), which explicitly resolve the position and orientation of single leafs. The model is validated using ALS data from an experiment with geometric reference targets. We were able to reproduce the effects of target size and target reflectance that were found in the real data with our modeling approach. A sensitivity study was carried out in order to determine the effect of properties such as beam divergence (0.5, 1, and 2 mrad), canopy reflectance (laser wavelength, 1064 and 1560 nm) on the ALS return statistics. Using the two laser wavelengths above, we were able to show that the laser wavelength will not significantly influence discrete return statistics in our model. It was found that first echo return statistics only differ significantly if the footprint size was altered by a factor of 4. Last return distributions were significantly different for all three modelled footprint sizes, and we were able to reproduce the effect of an increased number of ground returns for large footprint sizes. These forward simulations are a first step in the direction of physically based derivation of biophysical ALS data products and could improve the accuracy of the derived parameters by establishing correction terms.

Assessment of defoliation during a pine sawfly outbreak: Calibration of airborne laser scanning data with hemispherical photography

During an outbreak of the pine sawfly in eastern Norway it was demonstrated that small-footprint airborne laser scanning could detect defoliation, measured as changes in leaf area index (LAI). Gap fraction derived from laser scanning data was strongly and positively correlated to ground-based optical measurements of LAI. In this paper, analyses of the ground truth data acquired from hemispherical photography analysed with the Hemiview image analysis programme are performed. Pre-treatment of the hemispherical images to increase brightness and contrast in dark areas did not influence obtained LAI values. However, the setting of threshold values must be done carefully in defoliated conifer forests to ascertain that small branches and shoots are classified as foliage and not as sky. The optimal radius of the window used for selecting laser data for calibration towards Hemiview data was 15 m, corresponding to the average height of the stands. Acquired Hemiview LAI values correlated strongly ( R 2 = 0.93) with the log-transformed inverse of the gap fraction measured with airborne laser scanning. In areas severely defoliated by the pine sawfly, Hemiview LAI values were significantly lower than in less affected areas.

Estimation of LAI and fractional cover from small footprint airborne laser scanning data based on gap fraction

We evaluate the potential of deriving fractional cover (fCover) and leaf area index (LAI) from discrete return, small footprint airborne laser scanning (ALS) data. fCover was computed as the fraction of laser vegetation hits over the number of total laser echoes per unit area. Analogous to the concept of contact frequency, an effective LAI proxy was estimated by a fraction of first and last echo types inside the canopy. Validation was carried out using 83 hemispherical photographs georeferenced to centimeter accuracy by differential GPS, for which the respective gap fractions were computed over a range of zenith angles using the Gap Light Analyzer (GLA). LAI was computed by GLA from gap fraction estimations at zenith angles of 0–60°. For ALS data, different data trap sizes were used to compute fCover and LAI proxy, the range of radii was 2–25 m. For fCover, a data trap size of 2 m radius was used, whereas for LAI a radius of 15 m provided best results. fCover was estimated both from first and last echo data, with first echo data overestimating field fCover and last echo data underestimating field fCover. A multiple regression of fCover derived from both echo types with field fCover showed no increase of R 2 compared to the regression of first echo data, and thus, we only used first echo data for fCover estimation. R 2 for the fCover regression was 0.73, with an RMSE of 0.18. For the ALS LAI proxy, R 2 was lower, at 0.69, while the RMSE was 0.01. For LAI larger radii (∼ 15 m ) provided best results for our canopy types, which is due to the importance of a larger range of zenith angles (0–60°) in LAI estimation from hemispherical photographs. Based on the regression results, maps of fCover and LAI were computed for our study area and compared qualitatively to equivalent maps based on imaging spectrometry, revealing similar spatial patterns and ranges of values.