Examining the effects of sampling point densities on laser canopy height and density metrics

Abstract

Forest resource managers rely on the information extracted from forest resource inventories to manage forests sustainably and efficiently, thereby supporting more precise decision-making. Light detection and ranging (LiDAR) is a relatively new technology that has proven to enhance forest resource inventories. However, the relationship between LiDAR sampling point density (which is directly related to acquisition and processing costs) and accuracy and precision of forest variable estimation has not yet been established across a range of forest ecosystems. In this study, 2 airborne LiDAR surveys using the same sensor, but configured with disparate parameters, were carried out over the York Regional Forest near Toronto, Canada producing 2 data sets characterized by different sampling point densities. The effects of 2 sampling point densities on 23 laser canopy height and density metrics typically used in forest studies at the plot level were examined with comparisons grouped by first and last return data. The minimum (h min ) and maximum (h max ) laser canopy heights were statistically different for first and last returns. The proportion of laser returns (i.e., canopy density) in the upper (d 1 ) and lower (d 10 ) range of laser canopy heights was statistically different for the first returns, whereas only a single canopy density metric was different for the last returns (d 9 ). These results suggest that changes in sampling point density (due to changes in scan angle and altitude) only affect laser canopy height and density metrics that are characterized by the small percentage of returns from the very top (h max ; d 1 ) and base of the canopy (h min ; d 10 ) (i.e., those metrics that characterize the tail ends of the distributions of laser canopy heights). Consequently, higher sampling point densities may add little value to current LiDAR forest research or operations at the stand level, as metrics derived from the canopy profile can be implemented for biophysical variable estimation. Implications for forest management are in terms of identifying which aspects of LiDAR project design: a) impact the quality and cost-effectiveness of derived FRI information; b) should be specified within a LiDAR request for proposals; or c) scrutinized within LiDAR project tender documentation.Key words: LiDAR, airborne laser scanning, FRI, canopy profile, biophysical variables, sampling density RÉSUMÉLes gestionnaires des ressources forestières doivent référer aux informations obtenues des inventaires forestiers pour pouvoir aménager les forêts de façon durable et efficace ce qui de fait nécessite une prise de décision plus précise. La détection de la lumière et le calcul des distances (LiDAR) constituent une technologie relativement nouvelle qui s'avère capable d'améliorer les inventaires des ressources forestières. Cependant, la relation entre la densité des points d' échantillonnage du LiDAR (qui a un impact direct sur les coûts d'acquisition et de traitement des données) et l' exactitude et la précision de l' estimatio...

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

“…However, research into the impact that increasing ALS point density has on the accuracy of forest inventory attribute estimation would suggest that the increased point density afforded by image-based point clouds may not be of any particular advantage for the area-based approach (e.g., [30,31,48]). The higher point density associated with image-based point clouds may however be advantageous for characterizing discontinuities, such as the boundaries between features (e.g., between a forest stand and a harvested area) [7], but does not contribute to greater vertical accuracies.…”

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

“…Consequently, acquiring repeat LiDAR datasets over the study area could be achieved with relative ease during optimal conditions or when new systems were being tested. The study site has been the focus of several LiDAR forest studies (Hopkinson et al 2004;Chasmer et al 2006;Hopkinson et al 2008;Lim et al 2008) and in addition to the York Region Forest Resource Inventory (FRI), several field plots have been established within the North Tract to provide a means of plot-level model calibration.…”

“…Holmgren et al 2003;Hopkinson 2007;Lim et al 2008;Naesset 2009;Montaghi 2013). Historically, a focus on these configuration elements has been justified because they directly influence the sampling geometry and return signal detection.…”

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

“…The minimum distance between first and last returns also appears to increase with increasing flying altitude, potentially altering the statistical distribution of LiDAR returns within a forest canopy [31]. However, Lim et al [34] examined the statistical nature of 23 LiDAR-derived height and density metrics for two LiDAR sampling densities (data acquired on separate acquisitions at different altitudes). Only a very small number of metrics corresponding to the tails of the distribution of the laser canopy heights differed between the two surveys, indicating that plot-level data characterized by higher laser sampling densities do not necessarily result in richer data for biophysical variable estimation.…”