Discrimination of biological scatterers in polarimetric weather radar data: Opportunities and challenges
Remote Sensing
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Abstract
For radar aeroecology studies, the identification of the type of scatterer is critically important. Here, we used a random forest (RF) algorithm to develop a variety of scatterer classification models based on the backscatter values in radar resolution volumes of six radar variables (reflectivity, radial velocity, spectrum width, differential reflectivity, correlation coefficient, and differential phase) from seven types of biological scatterers and one type of meteorological scatterer (rain). Models that discriminated among fewer classes and/or aggregated similar types into more inclusive classes classified with greater accuracy and higher probability. Bioscatterers that shared similarities in phenotype tended to misclassify against one another more frequently than against more dissimilar types, with the greatest degree of misclassification occurring among vertebrates. Polarimetric variables proved critical to classification performance and individual polarimetric variables played central roles in the discrimination of specific scatterers. Not surprisingly, purposely overfit RF models (in one case study) were our highest performing. Such models have a role to play in situations where the inclusion of natural history can play an outsized role in model performance. In the future, bioscatter classification will become more nuanced, pushing machine-learning model development to increasingly rely on independent validation of scatterer types and more precise knowledge of the physical and behavioral properties of the scatterer.
Suggested Citation
Gauthreaux, S., Diehl, R.H., 2020, Discrimination of biological scatterers in polarimetric weather radar data: Opportunities and challenges: Remote Sensing, v. 12, no. 3, 545, 31 p., https://doi.org/10.3390/rs12030545.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Discrimination of biological scatterers in polarimetric weather radar data: Opportunities and challenges |
| Series title | Remote Sensing |
| DOI | 10.3390/rs12030545 |
| Volume | 12 |
| Issue | 3 |
| Publication Date | February 06, 2020 |
| Year Published | 2020 |
| Language | English |
| Publisher | MDPI |
| Contributing office(s) | Northern Rocky Mountain Science Center |
| Description | 545, 31 p. |