Drone-Based Environmental Emergency Response in the Brazilian Amazon

This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited....

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Bibliographic Details
Published in:Drones (Basel) 2023-08, Vol.7 (9), p.554
Main Authors: Custodio, Janiele, Abeledo, Hernan
Format: Article
Language:English
Subjects:
Amazon rainforest
Amazon River region
Assistance in emergencies
Biodiversity
Case studies
Climate change
Deforestation
Drone aircraft
Drones
Emergency communications systems
Emergency response
Emission standards
environmental emergency response
Environmental policy
facility location–allocation problem
Forest & brush fires
Forest fires
Land degradation
Optimization
Paris Agreement
Rainforests
Remote sensing
Satellite imagery
Satellites
Sensors
Surveillance
Technology application
wildfires
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Description
Summary:This paper introduces a location–allocation model to support environmental emergency response strategic planning using a drone-based network. Drones are used to verify potential emergencies, gathering additional information to support emergency response missions when time and resources are limited. The resulting discrete facility location–allocation model with mobile servers assumes a centralized network operated out of sight by first responders and government agents. The optimization problem seeks to find the minimal cost configuration that meets operational constraints and performance objectives. To test the practical applicability of the proposed model, a real-life case study was implemented for the municipality of Ji-Paraná, in the Brazilian Amazon, using demand data from a mobile whistle-blower application and from satellite imagery projects that monitor deforestation and fire incidents in the region. Experiments are performed to understand the model’s sensitivity to various demand scenarios and capacity restrictions.
ISSN:2504-446X
2504-446X
DOI:10.3390/drones7090554