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Radar Measurements of Morphological Parameters and Species Identification Analysis of Migratory Insects.
- Source :
- Remote Sensing; Sep2019, Vol. 11 Issue 17, p1977-1977, 1p
- Publication Year :
- 2019
-
Abstract
- Migratory insect identification has been concerning entomology and pest managers for a long time. Their nocturnal behavior, as well as very small radar cross-section (RCS), makes individual detection challenging for any radar network. Typical entomological radars work at the X-band (9.4 GHz) with a vertical pencil beam. The measured RCS can be used to estimate insect mass and wingbeat frequency, and then migratory insects can be categorized into broad taxon classes using the estimated parameters. However, current entomological radars cannot achieve species identification with any higher precision or confidence. The limited frequency range of current insect radars have precluded the acquisition of more information useful for the identification of individual insects. In this paper, we report an improved measurement method of insect mass and body length using a radar with many more measurement frequencies than current entomological radars. The insect mass and body length can be extracted from the multi-frequency RCSs with uncertainties of 16.31% and 10.74%, respectively. The estimation of the thorax width and aspect ratio can also be achieved with uncertainties of 13.37% and 7.99%, respectively. Furthermore, by analyzing the statistical data of 5532 insects representing 23 species in East China, we found that the correct identification probabilities exceed 0.5 for all of the 23 species and are higher than 0.8 for 15 of the 23 species under the achievable measurement precision of the proposed technique. These findings provide promising improvements of individual parameter measurement for entomological radars and imply a possibility of species identification with higher precision. [ABSTRACT FROM AUTHOR]
- Subjects :
- PARAMETER identification
INSECTS
RADAR
ASPECT ratio (Images)
STATISTICS
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 11
- Issue :
- 17
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 139008462
- Full Text :
- https://doi.org/10.3390/rs11171977