Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation.
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| Title: | Assessing the Potential of High-Resolution Multispectral and Structural Imagery for Plant Species Mapping in Mine Rehabilitation. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18173029 |
