A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data

Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combinin...

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Main Authors: Raphaël Nussbaumer, Baptiste Schmid, Silke Bauer, Felix Liechti
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Remote Sensing
Subjects:
Online Access:https://www.mdpi.com/2072-4292/13/10/1989
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author Raphaël Nussbaumer
Baptiste Schmid
Silke Bauer
Felix Liechti
author_facet Raphaël Nussbaumer
Baptiste Schmid
Silke Bauer
Felix Liechti
author_sort Raphaël Nussbaumer
collection DOAJ
description Recent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.
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spelling doaj.art-7259c4348c0648bb86aa4fd893b847452023-11-21T20:28:19ZengMDPI AGRemote Sensing2072-42922021-05-011310198910.3390/rs13101989A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar DataRaphaël Nussbaumer0Baptiste Schmid1Silke Bauer2Felix Liechti3Swiss Ornithological Institute, 6204 Sempach, SwitzerlandSwiss Ornithological Institute, 6204 Sempach, SwitzerlandSwiss Ornithological Institute, 6204 Sempach, SwitzerlandSwiss Ornithological Institute, 6204 Sempach, SwitzerlandRecent and archived data from weather radar networks are extensively used for the quantification of continent-wide bird migration patterns. While the process of discriminating birds from weather signals is well established, insect contamination is still a problem. We present a simple method combining two Doppler radar products within a Gaussian mixture model to estimate the proportions of birds and insects within a single measurement volume, as well as the density and speed of birds and insects. This method can be applied to any existing archives of vertical bird profiles, such as the European Network for the Radar surveillance of Animal Movement repository, with no need to recalculate the huge amount of original polar volume data, which often are not available.https://www.mdpi.com/2072-4292/13/10/1989migration ecologyclassificationornithologyGaussian mixture modelvertical profileecological modelling
spellingShingle Raphaël Nussbaumer
Baptiste Schmid
Silke Bauer
Felix Liechti
A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
Remote Sensing
migration ecology
classification
ornithology
Gaussian mixture model
vertical profile
ecological modelling
title A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
title_full A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
title_fullStr A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
title_full_unstemmed A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
title_short A Gaussian Mixture Model to Separate Birds and Insects in Single-Polarization Weather Radar Data
title_sort gaussian mixture model to separate birds and insects in single polarization weather radar data
topic migration ecology
classification
ornithology
Gaussian mixture model
vertical profile
ecological modelling
url https://www.mdpi.com/2072-4292/13/10/1989
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