Insect Diversity Estimation in Polarimetric Lidar

TL;DR

This study explores the use of polarimetric lidar to estimate insect diversity, demonstrating that while it can differentiate signal types, the specificity is limited and only provides a partial view of species richness.

Contribution

The paper introduces a large dataset of insect observations and evaluates the effectiveness of polarimetric lidar and clustering algorithms in insect species differentiation.

Findings

Polarimetric lidar provides only minor improvements in insect classification.

Unpolarized light can partially predict polarimetric properties.

Physical properties help estimate the lower bound of species diversity.

Abstract

Identification of insects in flight is a particular challenge for ecologists in several settings with no other method able to count and classify insects at the pace of entomological lidar. Thus, it can play a unique role as a non-intrusive diagnostic tool to assess insect biodiversity, inform planning, and evaluate mitigation efforts aimed at tackling declines in insect abundance and diversity. While species richness of co-existing insects could reach tens of thousands, to date, photonic sensors and lidars can differentiate roughly one hundred signal types. This taxonomic specificity or number of discernible signal types is currently limited by instrumentation and algorithm sophistication. In this study we report 32,533 observations of wild flying insects along a 500-meter transect. We report the benefits of lidar polarization bands for differentiating species and compare the performance of two unsupervised clustering algorithms, namely Hierarchical Cluster Analysis and Gaussian Mixture Model. We demonstrate that polarimetric properties could be partially predicted even with unpolarized light, thus polarimetric lidar bands provide only a minor improvement in specificity. Finally, we use physical properties of the clustered observation, such as wing beat frequency, daily activity patterns, and spatial distribution, to establish a lower bound for the number of species represented by the differentiated signal types.