Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs18173029

Authors: Phillip B. McKenna, Lorna Hernandez-Santin, Trevor Spedding, Ken Cross, Peter D. Erskine

Institution: The University of Queensland

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $3,258