Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention

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dc.article.number1601
dc.catalogadorjca
dc.contributor.authorAndrada, María Eduarda
dc.contributor.authorRussell, David
dc.contributor.authorArévalo Ramírez, Tito
dc.contributor.authorKuang, Winnie
dc.contributor.authorKantor, George
dc.contributor.authorYandun, Francisco
dc.date.accessioned2023-10-04T18:40:31Z
dc.date.available2023-10-04T18:40:31Z
dc.date.issued2023
dc.description.abstractThis paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system’s capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management.
dc.description.funderProject of the Central Portugal Region
dc.description.funderCMU
dc.description.funderPortuguese Foundation for Science and Technology
dc.fechaingreso.objetodigital2023-10-02
dc.fuente.origenSCOPUS
dc.identifier.doi10.3390/f14081601
dc.identifier.issn1999-4907
dc.identifier.scopusidSCOPUS_ID:85168805094
dc.identifier.urihttps://doi.org/10.3390/f14081601
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75006
dc.identifier.wosidWOS:001056509500001
dc.information.autorucEscuela de Ingeniería; Arévalo Ramírez, Tito; 0000-0003-2542-6545; 1300544
dc.language.isoen
dc.pagina.final20
dc.pagina.inicio1
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.revistaForests
dc.rightsacceso abierto
dc.subjectAutonomous drones
dc.subjectFlammable material detection
dc.subjectForest inventory
dc.subjectForest monitoring
dc.subjectLiDAR
dc.subjectRemote sensing
dc.subjectSemantic mapping
dc.subjectSimultaneous localization and mapping (SLAM)
dc.subjectVegetation classification
dc.subject.ods15 Life on land
dc.subject.ods09 Industry, innovation and infrastructure
dc.subject.odspa15 Vida de ecosistemas terrestres
dc.subject.odspa09 Industria, innovación e infraestructura
dc.titleMapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention
dc.typeartículo
dc.volumen14
sipa.codpersvinculados1300544
sipa.indexSCOPUS
sipa.trazabilidadSCOPUS;2023-09-03
sipa.trazabilidadORCID;2023-10-02
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