Environmental factors impacting the performance of airborne lidar sensors in the surf zone

Strand, Michael P.; Witherspoon, Ned H.; Holloway, John H.; Tinsley, Kenneth R.; Petee, Danny A.; Taylor, James S.; Branham, Elizabeth A.; Thomas, Joseph

doi:10.1117/12.501569

Cited by 1 publication

(1 citation statement)

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“…This inherently makes comparison between scans difficult. There are also numerous environmental factors that can influence lidar return values such as temperature and humidity [79,80]. We performed our experiment in a controlled laboratory environment in which we could be reasonably certain that no external factors would impact the intensity return values between the scans on different days.…”

Section: Model Coefficientsmentioning

confidence: 99%

Quantifying Forest Litter Fuel Moisture Content with Terrestrial Laser Scanning

et al. 2023

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Electromagnetic radiation at 1550 nm is highly absorbed by water and offers a novel way to collect fuel moisture data, along with 3D structures of wildland fuels/vegetation, using lidar. Two terrestrial laser scanning (TLS) units (FARO s350 (phase shift, PS) and RIEGL vz-2000 (time of flight, TOF)) were assessed in a series of laboratory experiments to determine if lidar can be used to estimate the moisture content of dead forest litter. Samples consisted of two control materials, the angle and position of which could be manipulated (pine boards and cheesecloth), and four single-species forest litter types (Douglas-fir needles, ponderosa pine needles, longleaf pine needles, and southern red oak leaves). Sixteen sample trays of each material were soaked overnight, then allowed to air dry with scanning taking place at 1 h, 2 h, 4 h, 8 h, 12 h, and then in 12 h increments until the samples reached equilibrium moisture content with the ambient relative humidity. The samples were then oven-dried for a final scanning and weighing. The spectral reflectance values of each material were also recorded over the same drying intervals using a field spectrometer. There was a strong correlation between the intensity and standard deviation of intensity per sample tray and the moisture content of the dead leaf litter. A multiple linear regression model with a break at 100% gravimetric moisture content produced the best model with R2 values as high as 0.97. This strong relationship was observed with both the TOF and PS lidar units. At fuel moisture contents greater than 100% gravimetric water content, the correlation between the pulse intensity values recorded by both scanners and the fuel moisture content was the strongest. The relationship deteriorated with distance, with the TOF scanner maintaining a stronger relationship at distance than the PS scanner. Our results demonstrate that lidar can be used to detect and quantify fuel moisture across a range of forest litter types. Based on our findings, lidar may be used to quantify fuel moisture levels in near real-time and could be used to create spatial maps of wildland fuel moisture content.

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Section: Model Coefficientsmentioning

confidence: 99%