A clustering approach for detection of ground in micropulse photon-counting LiDAR altimeter data
Observations from satellite lidar instruments have provided evidence in the remarkable changes in polar ice sheets on a global scale. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is scheduled for launch by NASA in 2017 and will monitor the elevation changes of polar ice sheets and vegetation canopy. To validate ICESat-2's approach of photon-counting laser altimetry, measurements obtained from the Multiple Altimeter Beam Experimental Lidar (MABEL) instrument are critical. In support of the ICESat-2 mission, this paper derives an algorithm for the detection of ground and vegetation canopy in photon-counting laser altimeter data. This approach uses a density-based clustering model and modifies the shape of search area. Based on results from MABEL observations, the proposed approach is seen to be robust in detecting ground and vegetation canopy as well as background noise reduction. In addition, this approach can be quickly implemented and adaptive to photon-counting lidar data sets with different point cloud densities.