Hyperspectral Scanning for Adaptable Insect Light Scattering Model

Sammanfattning

Monitoring insect populations and diversity are important to assess ecosystem health. Traditional trapping and manual identification methods are slow and labor-intensive. Entomological lidar offers an automated alternative for monitoring insects in flight, but classifying species remains a challenge. Building empirical databases through catch-and-release methods is impractical due to the enormous number of insect species. As an alternative, this thesis focuses on laying the groundwork for a physics-based model by isolating the optical properties of the insect body. Hyperspectral reflectance data of female \textit{Aedes aegypti} mosquitoes were acquired in the shortwave infrared region (960–2500 nm) across two orthogonal rotation axes (yaw and roll) using de-polarized backscatter signal. A four-parameter diffuse reflectance model, which accounts for water and melanin absorption along with scattering parameters, was fitted to the recorded spectra, achieving more than 98% (R^2) agreement. A MATLAB framework was then developed using spherical harmonics to parameterize the angular dependence of these optical properties. This allows the framework to output the backscatter optical cross-section (OCS) as a function of pitch, yaw, and wavelength, providing foundational data for future insect lidar models.