Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/112158
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dc.contributor.authorAndrada, Maria Eduarda-
dc.contributor.authorRussell, David-
dc.contributor.authorArevalo-Ramirez, Tito-
dc.contributor.authorKuang, Winnie-
dc.contributor.authorKantor, George-
dc.contributor.authorYandun, Francisco-
dc.date.accessioned2024-01-23T10:21:08Z-
dc.date.available2024-01-23T10:21:08Z-
dc.date.issued2023-
dc.identifier.issn1999-4907pt
dc.identifier.urihttps://hdl.handle.net/10316/112158-
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.pt
dc.language.isoengpt
dc.publisherMDPIpt
dc.relationCMU Portugal Affiliated Ph.D. grant (ref. PRT/BD/ 152194/2021) from the Portuguese Foundation for Science and Technology (FCT)pt
dc.relationProject of the Central Portugal Region and cofunded by the program Portugal 2020, under the reference CENTRO-01-0247-FEDER-045931pt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectsemantic mappingpt
dc.subjectsimultaneous localization and mapping (SLAM)pt
dc.subjectLiDAR; forest inventorypt
dc.subjectautonomous dronespt
dc.subjectforest monitoringpt
dc.subjectremote sensingpt
dc.subjectflammable material detectionpt
dc.subjectvegetation classificationpt
dc.titleMapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Preventionpt
dc.typearticle-
degois.publication.firstPage1601pt
degois.publication.issue8pt
degois.publication.titleForestspt
dc.peerreviewedyespt
dc.identifier.doi10.3390/f14081601pt
degois.publication.volume14pt
dc.date.embargo2023-01-01*
uc.date.periodoEmbargo0pt
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextCom Texto completo-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.researchunitISR - Institute of Systems and Robotics-
crisitem.author.parentresearchunitUniversity of Coimbra-
Appears in Collections:I&D ISR - Artigos em Revistas Internacionais
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This item is licensed under a Creative Commons License Creative Commons