Ma, Su, & Guo, 2016


Comparison of Canopy Cover Estimation from Airborne LiDAR

Ma Q., Su Y., Guo Q. (2016)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.  


Canopy cover is an important forest structureparameter for many applications in ecology, hydrology, and forest management. Light detection and ranging (LiDAR) is a promising tool for estimating canopy cover because it can penetrate forest canopy.  Various algorithms have been developed to calculate canopy coverfrom LiDAR data. However, little attention was paid to evaluating how different factors, such as estimation algorithm, LiDARpoint density and scan angle, influence canopy cover estimates;and how LiDAR-derived canopy cover differs from estimates usingtraditional methods, such as field measurements, aerial and satel-lite imagery. In this study, we systematically compared canopycover estimations from LiDAR data, quick field measurements,aerial imagery, and satellite imagery using different algorithms.The results show that LiDAR-derived canopy cover estimates aremarginally influenced by the estimation algorithms. LiDAR data with a point density of 1 point/m2can generate comparable canopy cover estimates to data with a higher density. The uncertainty of canopy cover estimates from LiDAR data increased drastically as scan angles exceed 12°. Plot-level canopy cover estimates derived from quick field measurements do not have strong correlation with LiDAR-derived estimations. Both the aerial imagery-derived andsatellite imagery-derived canopy cover estimates are comparable to LiDAR-derived canopy cover estimates at the forest stand scale, buttend to be overestimated in sparse forests and be underestimated in dense forests, particularly for the aerial imagery-derived estimates. The results from this study can provide practical guidance for the selection of data sources, sampling schemes, and estimation methods in regional canopy cover mapping.].


Ma Q., Su Y., Guo Q. (2016): Comparison of Canopy Cover Estimation from Airborne LiDAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. . DOI: 10.1109/JSTARS.2017.2711482

This Paper/Book acknowledges NSF CZO grant support.