Analysis of the effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

UK legislation aims to conserve and enhance biological diversity within the UK and so accurate measurements of forest biodiversity are important to assess efficacy of management activities in this context. Forest structural diversity metrics can be used as indicators of biodiversity and airborne LiDAR data provide a means of producing these metrics. Forest structure metrics derived from LiDAR can be significantly affected by the canopy conditions the datasets are collected under. Existing studies have combined and compared leaf-on and leaf-off LiDAR datasets in existing analyses, however the majority of these utilise field sites where climate, species and terrain are very different to those found in the UK. Additionally, studies comparing leaf-on and leaf-off LiDAR over forested areas assess the changes in pulse penetration through the canopy and how this effects forest structure metrics and not the effect on modelled forest structure diversity. The novel aim of this research is to assess and compare the accuracy of forest structural diversity modelled from two LiDAR surveys collected under leaf-on and leaf-off conditions, and do so in a UK forest environment. A robust methodology for correcting the absolute and relative accuracy between datasets was adopted and comparative analysis of ground detection and return height metrics (maximum, mean and percentiles of return height) and return height diversity (L-CV, CV, kurtosis, standard deviation, skewness and variance) was undertaken. Regression models describing the field tree size diversity measurements were constructed using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Both surveys were consistently effected by growth and differing survey parameters making the isolation and assessment of the effects of seasonal change difficult. Despite this, models created using diversity variables from both LiDAR datasets were generally very similar. Both leaf-on and leaf-off LiDAR dataset models described 65% of the variance in tree height diversity (R² 0.65, RMSE 0.05, p <0.0001), however models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.68, 0.41 and 0.19 respectively and leaf-off models 0.71, 0.62 and 0.26 respectively). When diversity variables calculated from both LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity their efficacy was increased (R² of 0.77 for tree height diversity and 0.72 for DBH diversity). The results suggest strongly that tree height diversity models derived from airborne LiDAR collected (and where appropriate combined) under any seasonal conditions can be used to differentiate between single and multiple storey UK forest structure with confidence. However, leaf-off LiDAR acquisitions can generate models with the ability to better explain the diversity of crown shapes in a forest stand than leaf-on, with no improvement in model performance when the two are combined

Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p<0.05) were generated for all 23 forest metrics using both the DR and FW lidar datasets, with R2 values for the best fit models in the range R2=0.43-0.94 for the DR data and R2=0.28-0.97 for the FW data (with normalised RMSE values being 18%-66% and 16%-48% respectively). For all but two forest metrics the difference between the NRMSE of the best performing DR and FW models was ≤7%, and there was an even split (11:12) as to which lidar dataset (DR or FW) generated the best model per forest metric. Overall, the DR data performed better at modelling structure variables, whilst the FW data performed better at modelling composition and deadwood variables. Neither showed a clear advantage at modelling variables from a particular vegetation layer (canopy, shrub or ground). Height, intensity/amplitude, and ITC-derived crown variables were shown to be important inputs across the best performing models (DR or FW), but the additional echo-width variables available from FW point data were relatively unimportant. Of perhaps greater significance to the choice between lidar data type (i.e. DR or FW) in determining the predictive power of the best performing models was the selection of leaf-on and/or leaf-off data. Of the 23 best models, 10 contained both leaf-on and leaf-off lidar variables, whilst 11 contained only leaf-on and two only leaf-off data. We therefore conclude that although FW lidar has greater vertical profile information than DR lidar, the greater complimentary information about the entire forest canopy profile that is available from both leaf-on and leaf-off data is of more benefit to forest inventory, in general, than the selection between DR or FW lidar

Another dimension from LiDAR – Obtaining foliage density from full waveform data

LiDAR tells the user where surfaces are, not what they are. In this study we investigate the potential for waveform LiDAR to provide more information on the nature of the returns over forestry. Waveform LiDAR was acquired for ten Pinus radiata plots in a New Zealand plantation, along with comprehensive leaf area sampling in 2m vertical bands. The decay rate of each waveform peak was shown to be a useful tool for estimating foliage density, and has potential for identifying regions containing ground and understorey. Leaf Area Density (LAD) is an expression of foliage density per unit height, and a relationship between waveform decay rate and LAD was developed with an R2 of 56%. Incorporating the proportion of discrete LiDAR that fell in that band (which itself has an R2 of 50%) improves this model to explain 69% of the variation in LAD. This is a good result, especially given the costs and difficulties in measuring leaf area directly. As foliage density varies dramatically on a fine scale it was not possible to differentiate the nature of every single LiDAR return – but by averaging over a small area local variation in LAD could be easily mapped. Ground returns could be distinguished as having short decays, and broad leafed understorey typically had values between those of the canopy and ground, although surface roughness and slope make it impossible to robustly identify single returns. This study produced a useful model for estimating LAD in Pinus radiata which could easily be extended to other coniferous species

Estimation of Aboveground Biomass in Alpine Forests: A Semi-Empirical Approach Considering Canopy Transparency Derived from Airborne LiDAR Data

In this study, a semi-empirical model that was originally developed for stem volume estimation is used for aboveground biomass (AGB) estimation of a spruce dominated alpine forest. The reference AGB of the available sample plots is calculated from forest inventory data by means of biomass expansion factors. Furthermore, the semi-empirical model is extended by three different canopy transparency parameters derived from airborne LiDAR data. These parameters have not been considered for stem volume estimation until now and are introduced in order to investigate the behavior of the model concerning AGB estimation. The developed additional input parameters are based on the assumption that transparency of vegetation can bemeasured by determining the penetration of the laser beams through the canopy. These parameters are calculated for every single point within the 3D point cloud in order to consider the varying properties of the vegetation in an appropriate way. Exploratory Data Analysis (EDA) is performed to evaluate the influence of the additional LiDAR derived canopy transparency parameters for AGB estimation. The study is carried out in a 560 km2 alpine area in Austria, where reference forest inventory data and LiDAR data are available. The investigations show that the introduction of the canopy transparency parameters does not change the results significantly according to R2 (R2 = 0.70 to R2 = 0.71) in comparison to the results derived from, the semi-empirical model, which was originally developed for stem volume estimation

Estimation of Effective Plant Area Index for South Korean Forests Using LiDAR System

Light Detection and Ranging (LiDAR) systems can be used to estimate both vertical and horizontal forest structure. Woody components, the leaves of trees and the understory can be described with high precision, using geo-registered 3D-points. Based on this concept, the Effective Plant Area Indices (PAIe) for areas of Korean Pine (Pinus koraiensis), Japanese Larch (Larix leptolepis) and Oak (Quercus spp.) were estimated by calculating the ratio of intercepted and incident LIDAR laser rays for the canopies of the three forest types. Initially, the canopy gap fraction (GLiDAR) was generated by extracting the LiDAR data reflected from the canopy surface, or inner canopy area, using k-means statistics. The LiDAR-derived PAIe was then estimated by using GLIDAR with the Beer-Lambert law. A comparison of the LiDAR-derived and field-derived PAIe revealed the coefficients of determination for Korean Pine, Japanese Larch and Oak to be 0.82, 0.64 and 0.59, respectively. These differences between field-based and LIDAR-based PAIe for the different forest types were attributed to the amount of leaves and branches in the forest stands. The absence of leaves, in the case of both Larch and Oak, meant that the LiDAR pulses were only reflected from branches. The probability that the LiDAR pulses are reflected from bare branches is low as compared to the reflection from branches with a high leaf density. This is because the size of the branch is smaller than the resolution across and along the 1 meter LIDAR laser track. Therefore, a better predictive accuracy would be expected for the model if the study would be repeated in late spring when the shoots and leaves of the deciduous trees begin to appear

Metabolic scaling theory and remote sensing to model large-scale patterns of forest biophysical properties

Advanced understanding of the global carbon budget requires large-scale and long-term information on forest carbon pools and fluxes. In situ and remote sensing measurements have greatly enhanced monitoring of forest carbon dynamics, but incomplete data coverage in space and time results in significant uncertainties in carbon accounting. Although theoretical and mechanistic models have enabled continental-scale and global mapping, robust predictions of forest carbon dynamics are difficult without initialization, adjustment, and parameterization using observations. Therefore, this dissertation is focused on a synergistic combination of lidar measurements and modeling that incorporates biophysical principles underlying forest growth. First, spaceborne lidar data from the Geoscience Laser Altimeter System (GLAS) were analyzed for monitoring and modeling of forest heights over the U.S. Mainland. Results showed the best GLAS metric representing the within-footprint heights to be dependent on topography. Insufficient data sampling by the GLAS sensor was problematic for spatially-complete carbon quantification. A modeling approach, called Allometric Scaling and Resource Limitations (ASRL), successfully alleviated this problem. The metabolic scaling theory and water-energy balance equations embedded within the model also provided a generalized mechanistic understanding of valid relationships between forest structure and geo-predictors including topographic and climatic variables. Second, the ASRL model was refined and applied to predict large-scale patterns of forest structure. This research successfully expanded model applicability by including eco-regional and forest-type variations, and disturbance history. Baseline maps (circa 2005; 1-km2 grids) of forest heights and aboveground biomass were generated over the U.S. Mainland. The Pacific Northwest/California forests were simulated as the most favorable region for hosting large trees, consistent with observations. Through sensitivity and uncertainty analyses, this research found that the refined ASRL model showed promise for prognostic applications, in contrast to conventional black-box approaches. The model predicted temporal evolution of forest carbon stocks during the 21st century. The results demonstrate the effects of CO2 fertilization and climate feedbacks across water- and energy-limited environments. This dissertation documents the complex mechanisms determining forest structure, given availability of local resources. These mechanisms can be used to monitor and forecast forest carbon pools in combination with satellite observations to advance our understanding of the global carbon cycle

Estimation of forest variables using airborne laser scanning

Airborne laser scanning can provide three-dimensional measurements of the forest canopy with high efficiency and precision. There are presently a large number of airborne laser scanning instruments in operation. The aims of the studies reported in this thesis were, to develop and validate methods for estimation of forest variables using laser data, and to investigate the influence of laser system parameters on the estimates. All studies were carried out in hemi-boreal forest at a test area in southwestern Sweden (lat. 58°30’N, long. 13°40’ E). Forest variables were estimated using regression models. On plot level, the Root Mean Square Error (RMSE) for mean tree height estimations ranged between 6% and 11% of the average value for different datasets and methods. The RMSE for stem volume estimations ranged between 19% and 26% of the average value for different datasets and methods. On stand level (area 0.64 ha), the RMSE was 3% and 11% of the average value for mean tree height and stem volume estimations, respectively. A simulation model was used to investigate the effect of different scanning angles on laser measurement of tree height and canopy closure. The effect of different scanning angles was different within different simulated forest types, e.g., different tree species. High resolution laser data were used for detection of individual trees. In total, 71% of the field measurements were detected representing 91% of the total stem volume. Height and crown diameter of the detected trees could be estimated with a RMSE of 0.63 m and 0.61 m, respectively. The magnitude of the height estimation errors was similar to what is usually achieved using field inventory. Using different laser footprint diameters (0.26 to 3.68 m) gave similar estimation accuracies. The tree species Norway spruce (Picea abies L. Karst.) and Scots pine (Pinus sylvestris L.) were discriminated at individual tree level with an accuracy of 95%. The results in this thesis show that airborne laser scanners are useful as forest inventory tools. Forest variables can be estimated on tree level, plot level and stand level with similar accuracies as traditional field inventories