Speaker
Description
Monitoring native plants is essential for environmental conservation, yet many traditional methods lack sufficient accuracy for interspecific discrimination. This work investigates plant optical properties across the short-, mid-, and long-wave infrared (0.88–15 µm) and proposes a high-precision, non-destructive remote monitoring approach based on hyperspectral infrared radiometry. In the LWIR, where thermal emission dominates under atmospheric conditions, variations in chemical composition—such as cellulose, xylan, and terpenes—directly affect spectral emissivity, producing species-specific emission signatures. Although LWIR radiometry remains underexplored due to high instrumentation costs and background noise complexity, it enables chemically driven discrimination that is less influenced by pigmentation and morphology than SWIR-based methods. High-resolution spectral emissivity data will be organized into a reference database and analyzed using a Python routine to enable robust multispectral classification. The proposed approach offers improved predictive accuracy and a viable alternative for remote monitoring of native species.
