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Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring.
- Source :
-
Fire (2571-6255) . Nov2023, Vol. 6 Issue 11, p419. 30p. - Publication Year :
- 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 cm3) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 25716255
- Volume :
- 6
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Fire (2571-6255)
- Publication Type :
- Academic Journal
- Accession number :
- 173825114
- Full Text :
- https://doi.org/10.3390/fire6110419