A Novel Noise Filtering Evaluation Criterion of ICESat-2 Signal Photon Data in Forest Environments

Abstract

As a continuation of the Ice, Cloud, and land Elevation Satellite (ICESat), the ICESat-2 contains a micro-pulse photon-counting Advanced Topographic Laser Altimeter System (ATLAS), which is expected to provide comprehensive earth observation data. However, the abundant noise photons present in the ICESat-2 data pose a tremendous challenge to photon data noise filtering algorithms. There have been many studies on photon cloud noise filtering algorithms, including the official noise filtering algorithm of NASA named the differential, regressive, and Gaussian adaptive nearest neighbor (DRAGANN) algorithm. However, to data there has been no in-depth study on an evaluation criterion of the ICESat-2 reference signal photon data. To address this limitation, in this study a novel evaluation criterion of the ICESat-2 reference signal photon data is proposed through the combination of Goddard&#x2019;s light detection and ranging (LiDAR) and hyperspectral and thermal imager (G-LiHT) data for evaluating the noise filtering performance of the DRAGANN algorithm on ATL08 data. The proposed evaluation criterion uses the G-LiHT digital terrain model (DTM) data as a signal photon lower boundary and the DTM &#x002B; canopy height model (CHM) data as a signal photon upper boundary and thus can describe the signal photon range more accurately and systematically than the manually labeled reference data. In the study area, the DRAGANN algorithm can achieve mean recall, precision, accuracy, and <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-value of 0.97, 0.66, 0.73, and 0.77, respectively. The results show that DRAGANN algorithm can filter noise photons from the ATLAS data effectively at different laser intensities and observation times. Also, the results demonstrate that the observation time has a greater influence than the laser beam intensity on the noise filtering accuracy of the DRAGANN algorithm.