View in EDS HTML Full Text PDF Full Text

Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains.

Saved in:
Bibliographic Details
Title: Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains.
Authors: Zhou, Wenqiang1,2 (AUTHOR), Deng, Shiwen2,3 (AUTHOR) dengswen@gmail.com, Zang, Shuying1,2,3 (AUTHOR), Guo, Dianfan1,2 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1627. 28p.
Subjects: Data augmentation, Remote sensing, Feature selection, Supervised learning, Remote-sensing images, Forest measurement
Abstract: Highlights: What are the main findings? Significantly enhanced tree above-ground biomass (AGB) estimation precision can be achieved using an innovative semi-supervised ensemble learning framework. Active sample augmentation is achieved under sparse sampling conditions through a refined Query-by-Committee strategy. What are the implications of the main findings? Precision and stability of AGB remote sensing estimation were improved in scenarios with limited samples. This study provides a novel paradigm for addressing the challenge of sparse ground-truth samples in remote sensing retrieval. Accurate estimation of tree Above-Ground Biomass (AGB) is critical for the timely monitoring of forest dynamics. However, the scarcity of high-quality in situ measured data introduces considerable uncertainty into the precise spatial mapping of AGB. In this study, a novel method for AGB mapping is proposed to enhance the accuracy of AGB estimation based on the Semi-Supervised Ensemble Learning (SSEL) strategy. By expanding the sample set via an iterative self-training approach based on an Inverted Query-by-Committee (I-QBC) strategy, the model significantly enhances the accuracy of AGB estimation. Using Sentinel-2 data, the experimental results show that: (1) The I-QBC-driven SSEL model demonstrated significantly higher estimation accuracy for AGB compared to conventional tree-based ensemble models. Optimal stability ( R 2 = 0.80) and peak accuracy ( R 2 = 0.88) were achieved at sample increments of 20 and 30, respectively. (2) Among various feature types, Recursive Feature Elimination with Cross-Validation (RFECV) identified GNDVI, PSSRa, slope and texture correlation as the most critical predictors for AGB estimation in the study area. (3) The total AGB stock in the study area is estimated to range from 1.46 × 107 Mg to 1.71 × 107 Mg. The SSEL model provides a valuable reference for AGB estimation under sparse ground-truth sample conditions, while offering a novel approach for large-scale AGB 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.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194141152
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Wenqiang%22">Zhou, Wenqiang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deng%2C+Shiwen%22">Deng, Shiwen</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> dengswen@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Zang%2C+Shuying%22">Zang, Shuying</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Dianfan%22">Guo, Dianfan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1627. 28p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Supervised+learning%22">Supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Remote-sensing+images%22">Remote-sensing images</searchLink><br /><searchLink fieldCode="DE" term="%22Forest+measurement%22">Forest measurement</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? Significantly enhanced tree above-ground biomass (AGB) estimation precision can be achieved using an innovative semi-supervised ensemble learning framework. Active sample augmentation is achieved under sparse sampling conditions through a refined Query-by-Committee strategy. What are the implications of the main findings? Precision and stability of AGB remote sensing estimation were improved in scenarios with limited samples. This study provides a novel paradigm for addressing the challenge of sparse ground-truth samples in remote sensing retrieval. Accurate estimation of tree Above-Ground Biomass (AGB) is critical for the timely monitoring of forest dynamics. However, the scarcity of high-quality in situ measured data introduces considerable uncertainty into the precise spatial mapping of AGB. In this study, a novel method for AGB mapping is proposed to enhance the accuracy of AGB estimation based on the Semi-Supervised Ensemble Learning (SSEL) strategy. By expanding the sample set via an iterative self-training approach based on an Inverted Query-by-Committee (I-QBC) strategy, the model significantly enhances the accuracy of AGB estimation. Using Sentinel-2 data, the experimental results show that: (1) The I-QBC-driven SSEL model demonstrated significantly higher estimation accuracy for AGB compared to conventional tree-based ensemble models. Optimal stability ( R 2 = 0.80) and peak accuracy ( R 2 = 0.88) were achieved at sample increments of 20 and 30, respectively. (2) Among various feature types, Recursive Feature Elimination with Cross-Validation (RFECV) identified GNDVI, PSSRa, slope and texture correlation as the most critical predictors for AGB estimation in the study area. (3) The total AGB stock in the study area is estimated to range from 1.46 × 107 Mg to 1.71 × 107 Mg. The SSEL model provides a valuable reference for AGB estimation under sparse ground-truth sample conditions, while offering a novel approach for large-scale AGB 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194141152
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18101627
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 28
        StartPage: 1627
    Subjects:
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Feature selection
        Type: general
      – SubjectFull: Supervised learning
        Type: general
      – SubjectFull: Remote-sensing images
        Type: general
      – SubjectFull: Forest measurement
        Type: general
    Titles:
      – TitleFull: Sample Augmentation for Tree Above-Ground Biomass Estimation Under Limited Field Data: A Case Study in the Greater Khingan Mountains.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Zhou, Wenqiang
      – PersonEntity:
          Name:
            NameFull: Deng, Shiwen
      – PersonEntity:
          Name:
            NameFull: Zang, Shuying
      – PersonEntity:
          Name:
            NameFull: Guo, Dianfan
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1