Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring
Mestre Runge, Cristian ![ORCID Identifier](/img/uab/orcid.ico)
(University of Marburg. Department of Biology)
Ludwig, Marwin (University of Münster. Institute of Landscape Ecology)
Sebastià, Ma.T. ![ORCID Identifier](/img/uab/orcid.ico)
(Laboratory ECOFUN. Forest Science and Technology Centre of Catalonia)
Plaixats i Boixadera, Josefina ![ORCID Identifier](/img/uab/orcid.ico)
(Universitat Autònoma de Barcelona. Departament de Ciència Animal i dels Aliments)
Lobo, Agustin ![ORCID Identifier](/img/uab/orcid.ico)
(Geoscience Barcelona)
Date: |
2023 |
Abstract: |
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. |
Grants: |
Agencia Estatal de Investigación CGL2017-85490-R
|
Note: |
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. |
Rights: |
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. ![Creative Commons](/img/licenses/by.ico) |
Language: |
Anglès |
Document: |
Article ; recerca ; Versió publicada |
Published in: |
Fire, Vol. 6 Núm. 11 (november 2023) , p. 419, ISSN 2571-6255 |
DOI: 10.3390/fire6110419
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Record created 2024-01-31, last modified 2024-05-17