Journal
REMOTE SENSING OF ENVIRONMENT
Volume 198, Issue -, Pages 1-16Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2017.05.032
Keywords
LiDAR; Canopy height; LiDAR metrics; Pulse density; Footprint size; Forest inventory; Stand structure
Funding
- AWARE (the Assessment of Wood Attributes from Remote Sensing) [NSERC CRDPJ462973-14]
- Canadian Wood Fiber Centre (CWFC)
- FP-Innovations
- Tembec
Ask authors/readers for more resources
Airborne laser scanning (LiDAR) is used in forest inventories to quantify stand structure with three dimensional point clouds. However, the structure of point clouds depends not only on stand structure, but also on the LiDAR instrument, its settings, and the pattern of flight. The resulting variation between and within datasets (particularly variation in pulse density and footprint size) can induce spurious variation in LiDAR metrics such as maximum height (h(max)) and mean height of the canopy surface model (C-mean). In this study, we first compare two LiDAR datasets acquired with different parameters, and observe that h(max) and C-mean are 56 cm and 1.0 m higher, respectively, when calculated using the high-density dataset with a small footprint. Then, we present a model that explains the observed bias using probability theory, and allows us to recompute the metrics as if the density of pulses were infinite and the size of the two footprints were equivalent. The model is our first step in developing methods for correcting various LiDAR metrics that are used for area-based prediction of stand structure. Such methods may be particularly useful for monitoring forest growth over time, given that acquisition parameters often change between inventories. (C) 2017 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available