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

Abstract

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.

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

“…The simulation of discrete-return low density ALS data provides essential information to support forest management at regional scales due to the widespread use of ALS data in operational forestry [25]. Field surveys have been traditionally carried out to derive ground truth data for training modes but these are challenging and expensive [13].…”

“…In DART, to constructing the scenes, different elements are provided such as plots with different characteristics (soil, soil+vegetation and vegetation) and trees. For more information on the scene components see Lamelas et al [24] and Roberts et al [25]. These elements are created using voxels that can be filled using turbid medium or facets.…”

“…An alternative approach involves model training through radiative transfer simulations of the terrain characteristics. These simulations can provide a cost-effective alternative to field surveys while improving the control in the experiments [25]. Radiative Transfer Models (RTM) approaches are subject to an appropriate model parameterization that also requires in situ data, however they offer a better universality than empirical approaches [26].…”

“…However, few investigations have examined small-footprint discrete-return measurements due to the computational demands of simulating multi-pulse ALS acquisitions over complex forested landscapes. From the best of our knowledge, the only study available was conducted by Roberts et al [25]. These authors examined the accuracy of the DART model to replicate small-footprint ALS measurements collected over Irish conifer plantations and how survey characteristics influenced the precision of discrete-return metrics.…”

Characterizing the Spatial Variations of Forest Sunlit and Shaded Components Using Discrete Aerial Lidar

“…(2) Ray-tracing algorithm: The ray-tracing based methods have been successfully developed during the past decades, such as deterministic ray-tracing [20], Monte Carlo ray-tracing model [21], and discrete anisotropic radiative transfer model (DART) [22]. Compared to the GO models, DART can simulate complex forest scenes through the importation of 3D trees generated in external modeling software or using turbid layers with defined crown structure parameters [23]. However, it is difficult to reconstruct the same virtual forest scene with the real forest plot used in DART software, which will introduce apparent errors to the estimation and simulation of the four forest components and BRDF [24,25].…”