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Insect diversity estimation in polarimetric lidar

Bernenko, Dolores LU ; Li, Meng LU orcid ; Månefjord, Hampus LU orcid ; Jansson, Samuel LU ; Runemark, Anna LU ; Kirkeby, Carsten and Brydegaard, Mikkel LU (2024) In PLoS ONE 19(11).
Abstract
Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the number of... (More)
Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the 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. Our analysis shows 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 the physical properties of the clustered observations, 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. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
lidar, insect, polarimetric, diversity, hca
in
PLoS ONE
volume
19
issue
11
article number
e0312770
pages
26 pages
publisher
Public Library of Science (PLoS)
external identifiers
  • pmid:39485810
  • scopus:85207879197
ISSN
1932-6203
DOI
10.1371/journal.pone.0312770
language
English
LU publication?
yes
id
a738aa96-ff4e-40a5-ba87-f0c882784b8c
date added to LUP
2024-11-04 09:20:24
date last changed
2025-01-13 13:07:08
@article{a738aa96-ff4e-40a5-ba87-f0c882784b8c,
  abstract     = {{Identifying flying insects is a significant challenge for biologists. Entomological lidar offers a unique solution, enabling rapid identification and classification in field settings. No other method can match its speed and efficiency in identifying insects in flight. This non-intrusive tool is invaluable for assessing insect biodiversity, informing conservation planning, and evaluating efforts to address declining insect populations. Although the species richness of co-existing insects can reach tens of thousands, current photonic sensors and lidars can differentiate roughly one hundred signal types. While the retrieved number of clusters correlate with Malaise trap diversity estimates, this taxonomic specificity, the 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. Our analysis shows 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 the physical properties of the clustered observations, 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.}},
  author       = {{Bernenko, Dolores and Li, Meng and Månefjord, Hampus and Jansson, Samuel and Runemark, Anna and Kirkeby, Carsten and Brydegaard, Mikkel}},
  issn         = {{1932-6203}},
  keywords     = {{lidar; insect; polarimetric; diversity; hca}},
  language     = {{eng}},
  month        = {{11}},
  number       = {{11}},
  publisher    = {{Public Library of Science (PLoS)}},
  series       = {{PLoS ONE}},
  title        = {{Insect diversity estimation in polarimetric lidar}},
  url          = {{http://dx.doi.org/10.1371/journal.pone.0312770}},
  doi          = {{10.1371/journal.pone.0312770}},
  volume       = {{19}},
  year         = {{2024}},
}