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Optimization of sensor deployment for acoustic detection and localization in terrestrial environments

Optimization of sensor deployment for acoustic detection and localization in terrestrial environments
Optimization of sensor deployment for acoustic detection and localization in terrestrial environments

The rapid evolution in miniaturization, power efficiency and affordability of acoustic sensors, combined with new innovations in smart capability, are vastly expanding opportunities in ground-level monitoring for wildlife conservation at a regional scale using massive sensor grids. Optimal placement of environmental sensors and probabilistic localization of sources have previously been considered only in theory, and not tested for terrestrial acoustic sensors. Conservation applications conventionally model detection as a function of distance. We developed probabilistic algorithms for near-optimal placement of sensors, and for localization of the sound source as a function of spatial variation in sound pressure. We employed a principled-GIS tool for mapping soundscapes to test the methods on a tropical-forest case study using gunshot sensors. On hilly terrain, near-optimal placement halved the required number of sensors compared to a square grid. A test deployment of acoustic devices matched the predicted success in detecting gunshots, and traced them to their local area. The methods are applicable to a broad range of target sounds. They require only an empirical estimate of sound-detection probability in response to noise level, and a soundscape simulated from a topographic habitat map. These methods allow conservation biologists to plan cost-effective deployments for measuring target sounds, and to evaluate the impacts of sub-optimal sensor placements imposed by access or cost constraints, or multipurpose uses.

Acoustic monitoring, acoustic sensors, AudioMoth, biodiversity monitoring, ecosystem management, optimisation, soundscape, submodularity
Piña-Covarrubias, Evelyn
11128d21-ddb4-4f07-b9e6-cd5abf2e83bc
Hill, Andrew P.
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Prince, Peter
78127c3d-514f-42fb-a70b-9360feab337a
Snaddon, Jake L.
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047
Piña-Covarrubias, Evelyn
11128d21-ddb4-4f07-b9e6-cd5abf2e83bc
Hill, Andrew P.
bfc05b70-7a90-40ab-8240-4d1f56aa3e4d
Prince, Peter
78127c3d-514f-42fb-a70b-9360feab337a
Snaddon, Jake L.
31a601f7-c9b0-45e2-b59b-fda9a0c5a54b
Rogers, Alex
60b99721-b556-4805-ab34-deb808a8666c
Doncaster, C. Patrick
0eff2f42-fa0a-4e35-b6ac-475ad3482047

Piña-Covarrubias, Evelyn, Hill, Andrew P., Prince, Peter, Snaddon, Jake L., Rogers, Alex and Doncaster, C. Patrick (2018) Optimization of sensor deployment for acoustic detection and localization in terrestrial environments. Remote Sensing in Ecology and Conservation. (doi:10.1002/rse2.97).

Record type: Article

Abstract

The rapid evolution in miniaturization, power efficiency and affordability of acoustic sensors, combined with new innovations in smart capability, are vastly expanding opportunities in ground-level monitoring for wildlife conservation at a regional scale using massive sensor grids. Optimal placement of environmental sensors and probabilistic localization of sources have previously been considered only in theory, and not tested for terrestrial acoustic sensors. Conservation applications conventionally model detection as a function of distance. We developed probabilistic algorithms for near-optimal placement of sensors, and for localization of the sound source as a function of spatial variation in sound pressure. We employed a principled-GIS tool for mapping soundscapes to test the methods on a tropical-forest case study using gunshot sensors. On hilly terrain, near-optimal placement halved the required number of sensors compared to a square grid. A test deployment of acoustic devices matched the predicted success in detecting gunshots, and traced them to their local area. The methods are applicable to a broad range of target sounds. They require only an empirical estimate of sound-detection probability in response to noise level, and a soundscape simulated from a topographic habitat map. These methods allow conservation biologists to plan cost-effective deployments for measuring target sounds, and to evaluate the impacts of sub-optimal sensor placements imposed by access or cost constraints, or multipurpose uses.

Text
Covarrubias et al 2018 Remote Sensing in Ecology and Conservation - Version of Record
Available under License Creative Commons Attribution.
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More information

Accepted/In Press date: 14 September 2018
e-pub ahead of print date: 18 October 2018
Keywords: Acoustic monitoring, acoustic sensors, AudioMoth, biodiversity monitoring, ecosystem management, optimisation, soundscape, submodularity

Identifiers

Local EPrints ID: 425995
URI: http://eprints.soton.ac.uk/id/eprint/425995
PURE UUID: b87d2cca-3872-43ff-819a-cb853278210f
ORCID for Evelyn Piña-Covarrubias: ORCID iD orcid.org/0000-0002-3564-7467
ORCID for Jake L. Snaddon: ORCID iD orcid.org/0000-0003-3549-5472
ORCID for C. Patrick Doncaster: ORCID iD orcid.org/0000-0001-9406-0693

Catalogue record

Date deposited: 09 Nov 2018 17:30
Last modified: 26 Nov 2021 03:01

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Contributors

Author: Evelyn Piña-Covarrubias ORCID iD
Author: Andrew P. Hill
Author: Peter Prince
Author: Jake L. Snaddon ORCID iD
Author: Alex Rogers

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