Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area

Abstract

The Ice, Cloud, and land Elevation Satellite (ICESat)-2 is the next generation of National Aeronautics and Space Administration (NASA)'s ICESat mission launched in September 2018. The new photon-counting LiDAR onboard ICESat-2 introduces new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we propose an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data sets from two types of photon-counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed that our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best results compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for ICESat-2 vegetation study.