Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles
One of the primary purposes of forest fire research is to predict crisis situations and, also, to optimize rescue operations during forest fires. The research results presented in this paper provide a model of Cross-Country Mobility (CCM) of fire brigades in forest areas before or during a fire. In...
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| Format: | Article |
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MDPI AG
2022-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/8/3939 |
| _version_ | 1797437022886952960 |
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| author | Marian Rybansky |
| author_facet | Marian Rybansky |
| author_sort | Marian Rybansky |
| collection | DOAJ |
| description | One of the primary purposes of forest fire research is to predict crisis situations and, also, to optimize rescue operations during forest fires. The research results presented in this paper provide a model of Cross-Country Mobility (CCM) of fire brigades in forest areas before or during a fire. In order to develop a methodology of rescue vehicle mobility in a wooded area, the structure of a forest must first be determined. We used a Digital Surface Model (DSM) and Digital Elevation Model (DEM) to determine the Canopy Height Model (CHM). DSM and DEM data were scanned by LiDAR. CHM data and field measurements were used for determining the approximate forest structure (tree height, stem diameters, and stem spacing between trees). Due to updating the CHM and determining the above-mentioned forest structure parameters, tree growth equations and vegetation growth curves were used. The approximate forest structure with calculated tree density (stem spacing) was used for modeling vehicle maneuvers between the trees. Stem diameter data were used in cases where it was easier for the vehicle to override the trees rather than maneuver between them. Although the results of this research are dependent on the density and quality of the input LiDAR data, the designed methodology can be used for modeling the optimal paths of rescue vehicles across a wooded area during forest fires. |
| first_indexed | 2024-03-09T11:11:58Z |
| format | Article |
| id | doaj.art-63cda37f10cd4eda9c41dc25c04f6ff6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T11:11:58Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-63cda37f10cd4eda9c41dc25c04f6ff62023-12-01T00:42:00ZengMDPI AGApplied Sciences2076-34172022-04-01128393910.3390/app12083939Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue VehiclesMarian Rybansky0Faculty of Military Technology, University of Defence, Kounicova 65, 662 10 Brno, Czech RepublicOne of the primary purposes of forest fire research is to predict crisis situations and, also, to optimize rescue operations during forest fires. The research results presented in this paper provide a model of Cross-Country Mobility (CCM) of fire brigades in forest areas before or during a fire. In order to develop a methodology of rescue vehicle mobility in a wooded area, the structure of a forest must first be determined. We used a Digital Surface Model (DSM) and Digital Elevation Model (DEM) to determine the Canopy Height Model (CHM). DSM and DEM data were scanned by LiDAR. CHM data and field measurements were used for determining the approximate forest structure (tree height, stem diameters, and stem spacing between trees). Due to updating the CHM and determining the above-mentioned forest structure parameters, tree growth equations and vegetation growth curves were used. The approximate forest structure with calculated tree density (stem spacing) was used for modeling vehicle maneuvers between the trees. Stem diameter data were used in cases where it was easier for the vehicle to override the trees rather than maneuver between them. Although the results of this research are dependent on the density and quality of the input LiDAR data, the designed methodology can be used for modeling the optimal paths of rescue vehicles across a wooded area during forest fires.https://www.mdpi.com/2076-3417/12/8/3939forest firerescue vehiclevegetation structureoptimal pathfindingcanopy height model (CHM) |
| spellingShingle | Marian Rybansky Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles Applied Sciences forest fire rescue vehicle vegetation structure optimal pathfinding canopy height model (CHM) |
| title | Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles |
| title_full | Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles |
| title_fullStr | Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles |
| title_full_unstemmed | Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles |
| title_short | Determination of Forest Structure from Remote Sensing Data for Modeling the Navigation of Rescue Vehicles |
| title_sort | determination of forest structure from remote sensing data for modeling the navigation of rescue vehicles |
| topic | forest fire rescue vehicle vegetation structure optimal pathfinding canopy height model (CHM) |
| url | https://www.mdpi.com/2076-3417/12/8/3939 |
| work_keys_str_mv | AT marianrybansky determinationofforeststructurefromremotesensingdataformodelingthenavigationofrescuevehicles |