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Validation and Refinement of GEDI/ICESat-2 Forest Height Retrievals Assisted by a Priori Continuous CHM Products.

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Title: Validation and Refinement of GEDI/ICESat-2 Forest Height Retrievals Assisted by a Priori Continuous CHM Products.
Authors: Zhang, Tao1 (AUTHOR), Zhu, Jianjun1,2 (AUTHOR), Fu, Haiqiang1 (AUTHOR), Fang, Yumin1,2 (AUTHOR), Fan, Zenghui1 (AUTHOR), Shang, Kaichao1 (AUTHOR), Pan, Yi2 (AUTHOR), Fan, Chong1 (AUTHOR) fanchong@csu.edu.cn
Source: Remote Sensing. Jun2026, Vol. 18 Issue 12, p1995. 24p.
Subjects: LIDAR, Tree height, Outlier detection, Forest mapping, Standard deviations
Abstract: Highlights: What are the main findings? A physically constrained outlier filtering strategy assisted by an a priori continuous CHM is proposed to adaptively remove gross errors in spaceborne LiDAR observations. By applying a 1 σ adaptive truncation, the overall RMSE of GEDI and ICESat-2 canopy height retrievals is stably reduced to approximately 3 m, achieving accuracy improvements of 12.6% to 36.0%. What are the implications of the main findings? The strategy successfully recovers the application value of daytime and weak/coverage beam observations, increasing the spatial density of high-quality forest height reference points by 1.5 to 4.4 times. It overcomes the limitation of sparse control points caused by conventional strict filtering, providing a highly transferable and low-cost approach for large-scale forest structure mapping. Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1 σ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Validation and Refinement of GEDI/ICESat-2 Forest Height Retrievals Assisted by a Priori Continuous CHM Products.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Tao%22">Zhang, Tao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jianjun%22">Zhu, Jianjun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fu%2C+Haiqiang%22">Fu, Haiqiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fang%2C+Yumin%22">Fang, Yumin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Zenghui%22">Fan, Zenghui</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shang%2C+Kaichao%22">Shang, Kaichao</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Yi%22">Pan, Yi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fan%2C+Chong%22">Fan, Chong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fanchong@csu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jun2026, Vol. 18 Issue 12, p1995. 24p.
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  Data: <searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Tree+height%22">Tree height</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+mapping%22">Forest mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A physically constrained outlier filtering strategy assisted by an a priori continuous CHM is proposed to adaptively remove gross errors in spaceborne LiDAR observations. By applying a 1 σ adaptive truncation, the overall RMSE of GEDI and ICESat-2 canopy height retrievals is stably reduced to approximately 3 m, achieving accuracy improvements of 12.6% to 36.0%. What are the implications of the main findings? The strategy successfully recovers the application value of daytime and weak/coverage beam observations, increasing the spatial density of high-quality forest height reference points by 1.5 to 4.4 times. It overcomes the limitation of sparse control points caused by conventional strict filtering, providing a highly transferable and low-cost approach for large-scale forest structure mapping. Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1 σ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18121995
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 1995
    Subjects:
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Tree height
        Type: general
      – SubjectFull: Outlier detection
        Type: general
      – SubjectFull: Forest mapping
        Type: general
      – SubjectFull: Standard deviations
        Type: general
    Titles:
      – TitleFull: Validation and Refinement of GEDI/ICESat-2 Forest Height Retrievals Assisted by a Priori Continuous CHM Products.
        Type: main
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          Name:
            NameFull: Zhang, Tao
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            NameFull: Zhu, Jianjun
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            NameFull: Fu, Haiqiang
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            NameFull: Fang, Yumin
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            NameFull: Fan, Zenghui
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            NameFull: Shang, Kaichao
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            NameFull: Pan, Yi
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            NameFull: Fan, Chong
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            – D: 15
              M: 06
              Text: Jun2026
              Type: published
              Y: 2026
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            – TitleFull: Remote Sensing
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