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Quantifying understory vegetation density using small-footprint airborne lidar
Abstract
The ability to quantify understory vegetation structure in forested environments on a broad scale has the potential to greatly improve our understanding of wildlife habitats, nutrient cycling, wildland fire behavior, and wildland firefighter safety. Lidar data can be used to model understory vegetation density, but the accuracy of these models is impacted by factors such as the specific lidar metrics used as independent variables, overstory conditions such as density and height, and lidar pulse density. Few previous studies have examined how these factors affect estimation of understory density. In this study we compare two widely-used lidar-derived metrics, overall relative point density (ORD) and normalized relative point density (NRD) in an understory vertical stratum, for their respective abilities to accurately model understory vegetation density. We also use a bootstrapping analysis to examine how lidar pulse density, overstory vegetation density, and canopy height can affect the ability to characterize understory conditions. In doing so, we present a novel application of an automated field photo-based understory cover estimation technique as reference data for comparison to lidar. Our results highlight that NRD is a far superior metric for characterizing understory density than ORD (R2NRD = 0.44 vs. R2ORD = 0.14). In addition, we found that pulse density had the strongest positive effect on predictive power, suggesting that as pulse density increases, the ability to ac…
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Airborne lidar can characterize the structure of vegetation within the aboveground height ranges that are most relevant to human movement.
“and even in the presence of tall vegetation, airborne lidar can characterize the structure of vegetation within the aboveground height ranges that are most relevant to human movement26–29.”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [26]
The normalized relative density (NRD) of lidar point returns is a proxy for vegetation density.
“The normalized relative density (NRD) of lidar point returns was used as a proxy for vegetation density26.”
From A singular, broadly-applicable model for estimating on- and off-path walking travel rates using airborne lidar data (2024) · cites this paper as [26]
Understory vegetation density is thought to be a much better predictor of travel conductance.
“which is thought to be a much better predictor of travel conductance [8,34].”
From Escape Route Index: A Spatially-Explicit Measure of Wildland Firefighter Egress Capacity (2019) · cites this paper as [34]
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Lone, K., Loe, L. E., Gobakken, T., Linnell, J. D. C., Odden, J., Remmen, J., & Mysterud, A. (2014) · Oikos · doi:10.1111/j.1600-0706.2013.00938.x · Cited by 227
Campbell, M. J., Dennison, P. E., & Butler, B. W. (2017) · International Journal of Wildland Fire · doi:10.1071/wf17031 · Cited by 42
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Cited by
Sorted by citations
Escape Route Index: A Spatially-Explicit Measure of Wildland Firefighter Egress Capacity
Campbell, M. J., Page, W. G., Dennison, P. E., & Butler, B. W. (2019) · Fire · doi:10.3390/fire2030040 · Cited by 37
Self-citationCampbell, M. J., Cutler, S. L., & Dennison, P. E. (2024) · Scientific Reports · doi:10.1038/s41598-024-71359-6 · Cited by 7
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Metadata
- Authors
- Campbell, M. J., Dennison, P. E., Hudak, A. T., Parham, L. M., & Butler, B. W.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 215
- Pages
- 330-342
- ISSN
- 0034-4257
- Cited by
- 126
- References
- 2
- Language
- en
- Licence
- Funders
- US Forest Service (14JV11221637123, 15CR11221637105)
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