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. (Centre de Ciència i Tecnologia Forestal de Catalunya (CTFC))
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:	Agencia Estatal de Investigación CGL2017-85490-R
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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