Mapping leaf metal content over industrial brownfields using airborne hyperspectral imaging and optimized vegetation...

Area of Science:

• Environmental Science
• Remote Sensing
• Plant Physiology

Background:

• Assessing ecological risks of contaminated sites requires monitoring plant metal uptake.
• Traditional methods for metal uptake analysis are destructive.
• Visible Near-Infrared (VNIR) reflectance spectroscopy offers a remote, non-destructive alternative.

Purpose of the Study:

• To develop and validate a methodology for mapping multiple metal (Cr, Cu, Ni, Zn) contents in Rubus fruticosus L. leaves using airborne VNIR imaging.
• To correlate leaf metal concentrations with spectral reflectance data under field conditions.
• To assess the accuracy and applicability of remote sensing for plant metal uptake monitoring.

Main Methods:

• Collected airborne VNIR reflectance data from Rubus fruticosus L. in industrial brownfields.
• Optimized normalized vegetation indices (NVIs) to correlate with leaf metal (Cr, Cu, Ni, Zn) content.
• Validated correlations using field measurements and applied NVIs to airborne imagery for spatial mapping.

Main Results:

• High correlations (r ≥ 0.87 for Zn, r ≤ -0.76 for Cr, Cu, Ni) were found between pigment-related NVIs and leaf metal content.
• Accurate prediction of Cu and Zn content was achieved (r ≥ 0.84, RPD ≥ 2.06).
• Spatial mapping of metal content at high resolution across the study site was successfully demonstrated.

Conclusions:

• VNIR spectroscopy, combined with optimized NVIs, is a powerful tool for non-destructively assessing plant metal uptake.
• This approach enables efficient risk assessment and monitoring of phytoextraction in trace metal-polluted areas.
• Remote sensing offers a scalable solution for environmental monitoring of contaminated sites.