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Sammanfattning

Spaceborne light detection and ranging (LiDAR) provides a promising method for large-scale characterizing leaf area index (LAI). However, the quality of point cloud data from spaceborne LiDAR, especially Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), is susceptible to atmosphere and background noise, introducing considerable uncertainty in LAI retrieval. Thus, efficiently screening out the high-quality point cloud is a significant guarantee for high-quality LAI retrieval. In this study, we proposed a quality control (QC) method that employed the number of 10-m windows without ground points in the ICESat-2 100-m segment as the QC flag. This method divided segments into 11 QC flags from 0 to 10 and was applied to LAI retrieval across Chinese forests from 2019 to 2020. The field measurements at locations identical to ICESat-2 ground tracks were used to validate the ICESat-2 LAI at different QC flags. The results showed that the proposed method effectively improved point cloud quality recognition and LAI accuracy, with ICESat-2 LAI (QC

Nyckelord

Forests; Photonics; Point cloud compression; Laser radar; Accuracy; Quality control; Land surface; Spatial resolution; Laser beams; MODIS; Clumping index (CI); forest leaf area index (LAI); Ice; Cloud; and land elevation Satellite-2 (ICESat-2); large scale; quality control (QC)

Publicerad i

IEEE Transactions on Geoscience and Remote Sensing
2025, volym: 63, artikelnummer: 4415311
Utgivare: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

SLU författare

UKÄ forskningsämne

Skogsvetenskap
Jordobservationsteknik

Publikationens identifierare

  • DOI: https://doi.org/10.1109/TGRS.2025.3592523

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/143582