Quantifying understory vegetation density using small-footprint airborne lidar

REMOTE SENSING OF ENVIRONMENT

卷:215

Article

Campbell, Michael J.¹  Dennison, Philip E.²  Hudak, Andrew T.³  Parham, Lucy M.²  Butler, Bret W.⁴ 

[1] Ft Lewis Coll, Dept Geosci, 1000 Rim Dr, Durango, CO 81301 USA

[2] Univ Utah, Dept Geog, 332 South 1400 East, Salt Lake City, UT 84112 USA

[3] US Forest Serv, USDA, Rocky Mt Res Stn, Forest Sci Lab, 1221 South Main St, Moscow, ID 59808 USA

[4] US Forest Serv, USDA, Rocky Mt Res Stn, Missoula Fire Lab, 5775 Highway 10 West, Missoula, MT 59808 USA

关键词: Lidar;    Discrete return;    Small footprint;    Understory;    Overstory;    vegetation density;   

DOI  :  10.1016/j.rse.2018.06.023

来源: Elsevier

【 摘 要 】

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 (R-NRD(2) = 0.44 vs. R-OR(D)2 = 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 accurately characterize understory density using lidar increases. Overstory density and canopy height had nearly identical negative effects on predictive power, suggesting that shorter, sparser canopies improve lidar's ability to analyze the understory. Our study highlights important considerations and limitations for future studies attempting to use lidar to quantify understory vegetation structure.

【 授权许可】

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