encing process of the sparse cloud [50]. This approach focuses on minimizing the error of georeferencing check points within the sparse cloud by identifying the optimal filter pa- rameters. Consequently, only tie points with low reprojection errors are used. This appli- cation is available as a Python module for MetashapeTools (https://github.com/en-vima/MetashapeTools/, accessed on 30 August 2023). An orthomosaic is a detailed and geometrically accurate image of an area, composed of multiple photos that have been orthorectified. Within this framework, once the Figure 4. (a) Illustrates the optimized workflow for the Metashape Structure from Motion (SfM) (Ludwig et al, 2020 [50]). (b) Repres
Open resource ↗en-vima/MetashapeTools · pdf-raw-page:7 lines:1-31Unverified paper record
Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring
Fire · 2 Nov 2023 · 10.3390/fire6110419
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.
Plant phenotyping relevance
ドローン画像のSfM-MVS処理を改良・精度検証し、植生の高さ・体積・バイオマス監視に用いる手法が研究の中心である。
abstractwe 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.
Code and data availability
The paper's SfM sparse-cloud optimization analysis was implemented as a Python module in the authors' public MetashapeTools repository (co-author Marvin Ludwig), explicitly linked in the text. The bl_gimbal repository is only a gimbal hardware controller, not phenotyping analysis, and the Data Availability Statement is
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