dd8e4fef35f1695f8664eada5a27e84a FIR_a2023v6p419.pdf f9f5d0734df8848541b7986367cac41f6f466099 FIR_a2023v6p419.pdf f6d08400557cc26c82fa0d5cac0ee2e29665e7588dd2191a5989f15d0c65d41c FIR_a2023v6p419.pdf Title: Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring Subject: 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. Keywords: fire management systems; prescribed burning; pyric herbivory; fuel load reductions; drone-derived surface models; RGB drone imagery; photogrammetric processing; 3D vegetation modeling; optimization; quality assessments Author: Christian Mestre-Runge, Marvin Ludwig, Maria Teresa SebastiĆ , Josefina Plaixats and Agustin Lobo Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Nov 2 12:48:30 2023 CET ModDate: Thu Nov 2 12:51:25 2023 CET Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 30 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 8862534 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- ATVZIO+URWPalladioL-Bold Type 1 Custom yes yes no 10 0 JYMHGJ+URWPalladioL-Roma Type 1 Custom yes yes no 15 0 UWWXWS+URWPalladioL-Ital Type 1 Custom yes yes no 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Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 864 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Study Area Destination: subsection.2.1 Children: Item: Title: UAV Flight Destination: subsubsection.2.1.1 Item: Title: Reference Objects Destination: subsubsection.2.1.2 Item: Title: GNSS Measurements: Ground Control Points and GNSS Validation Points Destination: subsubsection.2.1.3 Item: Title: SfM-MVS Method Outline Destination: subsection.2.2 Children: Item: Title: Optimized Sparse Cloud in SfM Destination: subsubsection.2.2.1 Item: Title: Dense Point Clouds in MVS Destination: subsubsection.2.2.2 Item: Title: Digital Surface and Terrain Models Destination: subsection.2.3 Item: Title: Canopy Height Model: Volume and Height Destination: subsection.2.4 Item: Title: Quality Assessment Destination: subsection.2.5 Children: Item: Title: Optimized Sparse Cloud in SfM Destination: subsubsection.2.5.1 Item: Title: Dense Surface Point Cloud in MVS Destination: subsubsection.2.5.2 Item: Title: Dense Terrain Point Clouds Destination: subsubsection.2.5.3 Item: Title: Digital Elevation Models: DSM and DTM Destination: subsubsection.2.5.4 Item: Title: Canopy Height Models: Volume and Height Destination: subsubsection.2.5.5 Item: Title: Results Destination: section.3 Children: Item: Title: Optimized Spare Cloud in SfM Destination: subsection.3.1 Item: Title: Dense Surface Point Clouds in MVS Destination: subsection.3.2 Item: Title: Dense Terrain Point Clouds Destination: subsection.3.3 Item: Title: Digital Surface and Terrain Models Destination: subsection.3.4 Item: Title: Canopy Height Models: Volume and Height Destination: subsection.3.5 Item: Title: Discussion Destination: section.4 Children: Item: Title: Optimized Sparse Cloud in SfM Destination: subsection.4.1 Item: Title: Dense Surface Clouds and DSM Generation Destination: subsection.4.2 Item: Title: Vegetation Point Filtering and DTM Generation Destination: subsection.4.3 Item: Title: Height and Volume Estimation from CHM Destination: subsection.4.4 Item: Title: Conclusions Destination: section.5 Item: Title: Appendix A Destination: section.A. Item: Title: Appendix B Destination: section.B. Item: Title: References Destination: section.C. Info: Title: Optimizing Drone-Based Surface Models for Prescribed Fire Monitoring Author: Christian Mestre-Runge, Marvin Ludwig, Maria Teresa SebastiĆ , Josefina Plaixats and Agustin Lobo Subject: 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. Keywords: fire management systems; prescribed burning; pyric herbivory; fuel load reductions; drone-derived surface models; RGB drone imagery; photogrammetric processing; 3D vegetation modeling; optimization; quality assessments Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Thu Nov 02 12:48:30 CET 2023 ModDate: Thu Nov 02 12:51:25 CET 2023 ID: 0x4e6e3c1fd93f6d3802daa72114593491, 0x4e6e3c1fd93f6d3802daa72114593491 Filters: FilterPipeline: FlateDecode Fonts: Type1: Font: BaseFont: UWWXWS+URWPalladioL-Ital FontSubset: true FirstChar: 45 LastChar: 122 FontDescriptor: FontName: UWWXWS+URWPalladioL-Ital Flags: Symbolic FontBBox: -170, -305, 1010, 941 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: OWNPHU+VnURWPalladioL FontSubset: true FirstChar: 128 LastChar: 254 FontDescriptor: FontName: OWNPHU+VnURWPalladioL Flags: Symbolic FontBBox: -166, -283, 1021, 943 FontFile: true EncodingDictionary: Differences: true Font: BaseFont: VIWIOF+CMSY10 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