Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring
Mestre Runge, Cristian (University of Marburg. Department of Biology)
Ludwig, Marwin (University of Münster. Institute of Landscape Ecology)
Sebastià, Ma.T. (Laboratory ECOFUN. Forest Science and Technology Centre of Catalonia)
Plaixats i Boixadera, Josefina (Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Lobo, Agustin (Geoscience Barcelona)

Data:	2023
Resum:	Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909. 20 cm) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.
Ajuts:	Fundación Española para la ciencia y la Tecnología CGL2017-85490-R Fundación Española para la ciencia y la Tecnología 2019 FI_B 01167
Nota:	This research was funded by the OPEN2PRESERVE (SOE2/P5E0804), from the EU SUDOE; and IMAGINE (CGL2017-85490-R), from the Spanish Science Foundation, and supported by a FI Fellowship to C.M.R. (2019 FI_B 01167) by the Catalan Government.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Fire, Vol. 6 Núm. 11 (november 2023) , p. 419, ISSN 2571-6255

DOI: 10.3390/fire6110419

30 p, 8.5 MB

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