View in EDS HTML Full Text PDF Full Text

Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation.

Saved in:
Bibliographic Details
Title: Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation.
Authors: McKenna, Phillip B.1 (AUTHOR) p.mckenna@uq.edu.au, Hernandez-Santin, Lorna1,2 (AUTHOR), Spedding, Trevor1,2 (AUTHOR), Cross, Ken2 (AUTHOR), Erskine, Peter D.1 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 17, p3029. 24p.
Subjects: Multispectral imaging, Vegetation mapping, Biodiversity monitoring, Classification, Remote sensing, Abandoned mined lands reclamation, High resolution imaging
Geographic Terms: Australia, Central Queensland
Abstract: Highlights: What are the main findings? The inclusion of structural data significantly improved classification performance for 29 plant species, increasing overall accuracy by up to 10%. Predictive accuracy for eleven Eucalyptus species ranged from 79–100%, while ten Acacia species achieved similar performance (74–100%). What are the implications of the main findings? The fusion of multispectral with LiDAR-derived CHMs enhances remote monitoring capabilities and supports the development of revised closure criteria with greater emphasis on remote sensing metrics. Criteria based on time-series monitoring provide opportunities to assess ecological trajectories, including the development of unique digital IDs for canopy and midstorey species to track growth, health, and ecological function over time. Biodiversity monitoring is essential for evaluating mine rehabilitation success. Traditionally, assessments have relied on ground-based plot measurements, but advances in remote sensing offer opportunities to complement or replace plot-based surveys with spatially continuous monitoring approaches. We evaluated drone-derived multispectral imagery, the Soil Adjusted Vegetation Index (SAVI), and canopy height models (CHM) for mapping plant species used in mine rehabilitation in central Queensland, Australia. Ten classification models tested four combinations of spectral and structural data. Incorporating CHM improved overall accuracy by up to 10%, with notable gains for vegetation classes containing Eucalyptus and Acacia species. Species-level accuracy ranged from 79% to 100% for Eucalyptus and 74% to 100% for Acacia species. Misclassification was greatest among closely related red gums (Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh.) and Corymbia citriodora (Hook.) K.D.Hill & L.A.S.Johnson. These results demonstrate that structural information substantially improves species discrimination and has the potential to enhance biodiversity monitoring across plot (500 m2), block (1–100 ha), and landscape (100–1000 ha) scales. [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: 197043879
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22McKenna%2C+Phillip+B%2E%22">McKenna, Phillip B.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> p.mckenna@uq.edu.au</i><br /><searchLink fieldCode="AR" term="%22Hernandez-Santin%2C+Lorna%22">Hernandez-Santin, Lorna</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Spedding%2C+Trevor%22">Spedding, Trevor</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Cross%2C+Ken%22">Cross, Ken</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Erskine%2C+Peter+D%2E%22">Erskine, Peter D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 17, p3029. 24p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Multispectral+imaging%22">Multispectral imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Vegetation+mapping%22">Vegetation mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Biodiversity+monitoring%22">Biodiversity monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Remote+sensing%22">Remote sensing</searchLink><br /><searchLink fieldCode="DE" term="%22Abandoned+mined+lands+reclamation%22">Abandoned mined lands reclamation</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Australia%22">Australia</searchLink><br /><searchLink fieldCode="DE" term="%22Central+Queensland%22">Central Queensland</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The inclusion of structural data significantly improved classification performance for 29 plant species, increasing overall accuracy by up to 10%. Predictive accuracy for eleven Eucalyptus species ranged from 79–100%, while ten Acacia species achieved similar performance (74–100%). What are the implications of the main findings? The fusion of multispectral with LiDAR-derived CHMs enhances remote monitoring capabilities and supports the development of revised closure criteria with greater emphasis on remote sensing metrics. Criteria based on time-series monitoring provide opportunities to assess ecological trajectories, including the development of unique digital IDs for canopy and midstorey species to track growth, health, and ecological function over time. Biodiversity monitoring is essential for evaluating mine rehabilitation success. Traditionally, assessments have relied on ground-based plot measurements, but advances in remote sensing offer opportunities to complement or replace plot-based surveys with spatially continuous monitoring approaches. We evaluated drone-derived multispectral imagery, the Soil Adjusted Vegetation Index (SAVI), and canopy height models (CHM) for mapping plant species used in mine rehabilitation in central Queensland, Australia. Ten classification models tested four combinations of spectral and structural data. Incorporating CHM improved overall accuracy by up to 10%, with notable gains for vegetation classes containing Eucalyptus and Acacia species. Species-level accuracy ranged from 79% to 100% for Eucalyptus and 74% to 100% for Acacia species. Misclassification was greatest among closely related red gums (Eucalyptus tereticornis Sm. and Eucalyptus camaldulensis Dehnh.) and Corymbia citriodora (Hook.) K.D.Hill & L.A.S.Johnson. These results demonstrate that structural information substantially improves species discrimination and has the potential to enhance biodiversity monitoring across plot (500 m2), block (1–100 ha), and landscape (100–1000 ha) scales. [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=197043879
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18173029
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 24
        StartPage: 3029
    Subjects:
      – SubjectFull: Multispectral imaging
        Type: general
      – SubjectFull: Vegetation mapping
        Type: general
      – SubjectFull: Biodiversity monitoring
        Type: general
      – SubjectFull: Classification
        Type: general
      – SubjectFull: Remote sensing
        Type: general
      – SubjectFull: Abandoned mined lands reclamation
        Type: general
      – SubjectFull: High resolution imaging
        Type: general
      – SubjectFull: Australia
        Type: general
      – SubjectFull: Central Queensland
        Type: general
    Titles:
      – TitleFull: Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: McKenna, Phillip B.
      – PersonEntity:
          Name:
            NameFull: Hernandez-Santin, Lorna
      – PersonEntity:
          Name:
            NameFull: Spedding, Trevor
      – PersonEntity:
          Name:
            NameFull: Cross, Ken
      – PersonEntity:
          Name:
            NameFull: Erskine, Peter D.
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 17
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1