Abstract
Abstract. Lidar scan angle can affect estimation of lidar-derived forest metrics used in area-based approaches (ABAs). As commonly used first-order metrics and various user-developed metrics are computed in the form of a grid or a raster, their response to various scan angles needs to be investigated similarly. The objective of this study was to highlight the impact of scan angles on 11 metrics (9 height-based and 2 other commonly used metrics) at the level of the grid-cell. The study area was divided into a grid of cell size 30 m. In every grid-cell, the flight lines that sampled at least 90% area of the grid-cell were identified. The flight lines and the corresponding point clouds were then classified based on their mean scan angle into four classes 0°–10°, 10°–20°, 20°–30° and 30°–40°. Metrics were computed for one flight line per class for each grid-cell. This resulted in a maximum of four values for a metric in every grid-cell. Comparing these values revealed the evolving nature of the metrics with the scan angle. For the comparison we used a paired t-test and simple linear regression. We observed that most of the metrics were systematically under-estimated with increasing scan angle. Gap-fraction, rumple index were affected more than standard deviation of height while the maximum height was relatively stable. Among the height percentiles, the higher percentiles were relatively more stable compared to the lower percentiles. Scan angles can indeed have an impact on the estimation of lidar derived metrics. Although, many of the metrics studied showed statistically significant differences in their computation for different scan angles, their impact on the accuracies of ABA models needs to be studied further by accounting for the differences as shown in this study.
Highlights
Lidar acquires an explicit three-dimensional representation of the forest structure
The fundamental unit of a predictive model is a small subset from the lidar point cloud, the area of which equals the area of a reference field plot
The steps of the process followed are: (i) for each grid-cell, we identified all the flight lines from which the lidar sensor sampled it either entirely or partially, and divided the point cloud in the grid-cell into subsets based on the flight lines
Summary
Lidar acquires an explicit three-dimensional representation of the forest structure. Such information is essential to model both ecological and resource management information, and there is a broad spectrum of methods, across various airborne LiDAR platforms, for improved characterisation of forest ecosystems and a better understanding of their functioning. In Area-based approaches (ABAs), a set of ALS variables (Xi) – derived from lidar data for a given area – is linked to a target variable (Y) measured at the same area on the ground (White et al, 2017). This is done for a handful of different plots to build a predictive model to predict the target variable for the entire forest. Some studies focussed on the effect of point density on the accuracy of stand attribute predictions (Bouvier et al, 2019; Næsset, 2009; Singh et al, 2016). In the range of explored pulse densities, i.e. from 0.06 to 12.7 pulses/m2, in all the studies considered together, only minor or even no impact on stand attribute predictions was found
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