EVALUATING UAS & ALS LIDAR’S ABILITY TO DETECT UNDERSTORY SHRUBS IN RHODE ISLAND FORESTS

Author

Noah Henkenius, University of Rhode IslandFollow

Date of Award

2025

Degree Type

Thesis

Degree Name

Master of Science in Biological and Environmental Sciences (MSBES)

Department

Biological Sciences

First Advisor

Jason Parent

Abstract

Understory shrubs provide important habitat for wildlife and assist with nutrient cycling in forests. The use of remote sensing to map shrubs in forest understories is challenging due to the obscuring effect of the forest overstory. We tested the effectiveness of leaf-off aerial LiDAR and uncrewed aerial system (UAS) (point densities of 8+ pts/m² and 250+ pts/m², respectively) to map shrub cover in a mixed deciduous forest in Rhode Island (USA). We used a terrestrial laser scanner (TLS) and field vegetation surveys to provide validation data. We found that UAS and aerial LiDAR had average omission errors of 42% and 64%, respectively. Omission errors increased in areas with taller and more closed tree canopies. The presence of coniferous trees substantially increased errors for the aerial LiDAR, but tree type did not have a significant effect for the UAS data. LiDAR was much more effective at detecting vegetation with a vertical density > 0.5 m, which had omission errors of 12% and 42% for UAS and aerial LiDAR, respectively.

Recommended Citation

Henkenius, Noah, "EVALUATING UAS & ALS LIDAR’S ABILITY TO DETECT UNDERSTORY SHRUBS IN RHODE ISLAND FORESTS" (2025). Open Access Master's Theses. Paper 2653.
https://digitalcommons.uri.edu/theses/2653