A Fine-Scale and Highly Reliable Ground and Canopy Top Extraction Method Using ICESat-2 Data

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 17; pp. 5266 - 5279
• Main Authors: Chang, Jingxin, Jiang, Yonghua, Lin, Zhiyong, Tan, Meilin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregation; Algorithms; Altimeters; and land elevation satellite-2 (ICESat-2); Canopies; Canopy; Carbon; Carbon capture and storage; Carbon sequestration; Carbon sinks; cloud; Detection; Distance; Ground photon; ice; Laser altimeters; Laser radar; Noise; Noise measurement; Noise reduction; Noise threshold; Orbits; Outliers (landforms); Outliers (statistics); photon classification; photon noise removal algorithm; Photonics; Photons; Plant cover; remote sensing; Removal; Statistical analysis; top-of-canopy (TOC) photon; Total organic carbon
• DOI: 10.1109/JSTARS.2024.3359654
• ISSN: 1939-1404

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with an advanced topographic laser altimeter system (ATLAS) that obtains small spots and high-density photon data. ATLAS has the potential for high-precision detection of global surfaces and helps calculate global carbon sinks. One important carbon sink includes forests and making it critical to precisely measure their carbon storage and obtain both ground and top-of-canopy (TOC) levels of vegetation. Here, we explored a precise and highly reliable ground and TOC detection method based on ICESat-2 data to accurately remove noise and classify photons. This method can be adapted to different scenes and topographies. The first step included the removal of pronounced dispersion noise and pseudosignal photon aggregation noise. We introduced two algorithms for coarse noise removal-an adaptive threshold ellipsoid filter algorithm and an outlier photon cluster removal algorithm based on the photon group distance. Then, the local outlier factor algorithm was modified to remove the noise of near-signal photons to obtain the result of fine denoising. Finally, the modified local direction center algorithm was combined with the uniaxial inverse distance-weighted statistics to distinguish the ground from the TOC. Across varied scenes and terrains, the proposed denoising method demonstrated an overall accuracy exceeding 0.94. Additionally, the root-mean-square error of the ground and TOC obtained via the classification method was under 1.4 m and 3.2 m, respectively. These findings highlight that this method has a high level of robustness in effectively detecting both the ground and TOC.