- Datasets
Datasets
Create datasets to upload and publish data. Further organize your data using folders and assign metadata at both the file and dataset level.
Fuel type & fuel models maps for Romanian DA (Mehedinti and Gorj) counties
The product provides the fuel type and parameters following a standard classification of fuels agreed for the project. The final output – fuel classes and static fuel map with the spatial resolution of 1 ha (10 x 10 m) for the Romanian demonstration area (i.e. Mehedinți and Gorj counties). Developer: INCDS (“Marin Drăcea” National Institute for Research and Development in Forestry).
To create the fuel types map, multiple datasets were integrated. First, forested areas were extracted from the most recent digital forest maps, developed at the Forest District level. These maps, with a nominal scale of 1:5000, include all forestry parcels (i.e., forest management units) within the study area. Forest fuel types (e.g., broadleaved, needleleaved) were classified based on the species composition of each parcel. Next, to distinguish between open and closed forests, the Copernicus Tree Cover Density dataset was used (https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018). This pan-European map provides tree cover density values ranging from 0% to 100% at spatial resolutions of 10 m and 100 m, using data from the reference year 2018.The forest management database also supported the mapping of forest roads, buildings, and certain agricultural and pasture lands managed by forestry services. For mapping the remaining fuel classes, the Land Cover Map of Europe (Malinowski et al., 2020) was employed. This map, based on Sentinel-2 imagery with a 10 m spatial resolution, enabled the automatic classification of land cover classes. These classes were then matched to the predominantly non-forested fuel types found within the study area.
Finally, the fuel models map was generated using the FirEUrisk fuel types crosswalk to the Fire Behavior Fuel Models (FBFM) system, as proposed by Aragoneses et al. (2023).
Malinowski, R., Lewiński, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupiński, M., Nowakowski, A., Wojtkowski, C., Krupiński, M., Krätzschmar, E., Schauer, P. (2020), Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
doi:10.3390/rs12213523
Aragoneses, E., García, M., Salis, M., Ribeiro, L. M., & Chuvieco, E. (2023). Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system. Earth System Science Data, 15(3), 1287–1315. https://doi.org/10.5194/essd-15-1287-2023
- 0 8 0 37 0
- 0 1 0 26 0
- 0 1 0 32 0
PS2-Exposure: Exposure (E)
This cartographic product estimates Exposure based on a simple approach that considers the resources potentially exposed to wildfire effects. Even though different methods have been proposed to estimate this variable (see deliverable D.1.4 and (Chuvieco et al. 2023), within this version of the integrated risk component only three categories of exposure were considered, given them a certain weight in relation to the importance of being potentially affected by fire:
• Unburnable (code 0), including water, bare soil and urban dense.
• Burnable (code 0.8), including all vegetation categories that are susceptible of being burned.
• Burnable and with Wildland Urban Interface (WUI (Code 1). Includes burnable categories within a radius of 1 km of an urbanized area.
Fire risk component: Exposure
Contact details of the developer: Avi Bar Massada <avi-b@sci.haifa.ac.il>
For mapping the WUI at ET scale, first the extension of the urbanized area was mapped, based on the 2019 World Settlement Footprint dataset (WSF). This dataset was generated by automated interpretation of 10-m resolution Sentinel-1 and Sentinel-2 satellite imagery, and each pixel is either built-up or not. Next, vegetation data was gathered from the 2020 European Space Agency (ESA) global land cover product (Zanaga et al. 2020). This dataset is a classification of the same 10-m Sentinel-1 and Sentinel-2 satellite imagery, in conjunction with auxiliary data, into 11 land cover classes. This dataset was reclassified into two classes: woody vegetation classes (‘forests’ and ‘shrublands’, “1”) and others (“0”). Hence, the research focused on fire hazard only from woody vegetation, regardless of species identity and fuel characteristics.
The most common approach for WUI mapping distinguishes between WUI intermix and interface classes (Radeloff et al. 2005). An intermix WUI is a unit area that: 1: comprises enough buildings exposed to wildfire risk; and 2: contains or is directly surrounded by sufficient amounts of flammable vegetation. An interface WUI is a unit area that contains buildings that are not directly surrounded by flammable vegetation yet is within a relatively short distance from a contiguous patch of flammable vegetation which can produce firebrands. To map the WUI, the parameters of the existing point-based WUI mapping approach were modified (Bar-Massada et al. 2013), which is based on the original US WUI mapping approach.
The new approach adapts the 10 m-cell grid of the underlying datasets and includes the following steps:
1. Including all cells within 100 m of a built-up cell as candidate WUI locations, regardless of the housing density within (and around) them in order to identify all built-up areas that are potentially at risk.
2. Calculating the percent cover of woody vegetation within a 500 m radius around candidate WUI location cells; if woody cover was greater than 50%, these cells were mapped as ‘intermix WUI’.
3. Identifying patches of woody vegetation that were larger than 5 km2.
4. Identifying all candidate WUI locations that were within 600 m to large vegetation patches; these cells were mapped as ‘interface WUI’. The choice of 600 m reflects findings about the approximate median value of maximum travel distances of flying embers. This threshold differs from the previously used distance of 2,400 m (Carlson et al. 2022; Radeloff et al. 2005), which reflects maximum travel distances. The result of this process is a WUI map at either PS scale (if built-up information was based on individual buildings; five WUI maps overall) or ET scale (when the input was the WSF dataset; one WUI map for the entire ET). Based on these maps, we quantified the overall extent and the spatial distribution of interface, intermix, and total WUI at the country level for each country in our study area. We also calculated the variation in WUI cover within countries, by overlaying a map of statistical regions at NUTS-3 level obtained from Eurostat, the EU’s statistical agency. The WUI maps generated here are available for download in the project’s data repository.
References
Bar-Massada, A., Stewart, S.I., Hammer, R.B., Mockrin, M.H., & Radeloff, V.C. (2013). Using structure locations as a basis for mapping the wildland urban interface. Journal of Environmental Management, 128, 540-547.
Carlson, A.R., Helmers, D.P., Hawbaker, T.J., Mockrin, M.H., & Radeloff, V.C. (2022). The wildland–urban interface in the United States based on 125 million building locations. Ecological Applications, 32.
Chuvieco, E., Yebra, M., Martino, S., Thonicke, K., Gómez-Giménez, M., San-Miguel, J., Oom, D., Ramona Velea, Florent Mouillot, Juan R. Molina, Ana I. Miranda, Diogo Lopes, Michele Salis, Marin Bugaric, Mikhail Sofiev, Evgeny Kadantsev, Ioannis Gitas, Dimitris Stavrakoudis, George Eftychidis, Bar-Massada, A., Alex Neidermeier, Valerio Pampanoni, Pettinari, M.L., Arrogante, F., Ochoa, C., Moreira, B., & Viegas, D. (2023). Towards an integrated approach to wildfire risk assessment: when, where, what and how may the landscapes burn. Fire, 6, 215, doi210.3390/fire6050215.
Radeloff, V.C., Hammer, R.B., Stewart, S.I., Fried, J.S., Holcomb, S.S., & McKeefry, J.F. (2005). The wildland-urban interface in the United States. Ecological Applications, 15, 799-805.
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., & Arino, O. (2020). ESA WorldCover 10 m 2020 v100.
- 0 1 0 98 0
- 0 1 0 29 0
- 0 1 0 29 0
PS3-Houses value (HV)
Houses Value
- 0 1 0 97 0
PS3-Ecological values Eva)
Ecological Value
- 0 1 0 99 0
PS3-Recovery time (RT)
Recovery Time
- 0 1 0 101 0
PS3-Coping capacity (CC)
Coping Capacity
- 0 1 0 100 0
- 0 1 0 110 0
To create the fuel types map, multiple datasets were integrated. First, forested areas were extracted from the most recent digital forest maps, developed at the Forest District level. These maps, with a nominal scale of 1:5000, include all forestry parcels (i.e., forest management units) within the study area. Forest fuel types (e.g., broadleaved, needleleaved) were classified based on the species composition of each parcel. Next, to distinguish between open and closed forests, the Copernicus Tree Cover Density dataset was used (https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018). This pan-European map provides tree cover density values ranging from 0% to 100% at spatial resolutions of 10 m and 100 m, using data from the reference year 2018.The forest management database also supported the mapping of forest roads, buildings, and certain agricultural and pasture lands managed by forestry services. For mapping the remaining fuel classes, the Land Cover Map of Europe (Malinowski et al., 2020) was employed. This map, based on Sentinel-2 imagery with a 10 m spatial resolution, enabled the automatic classification of land cover classes. These classes were then matched to the predominantly non-forested fuel types found within the study area.
Finally, the fuel models map was generated using the FirEUrisk fuel types crosswalk to the Fire Behavior Fuel Models (FBFM) system, as proposed by Aragoneses et al. (2023).
Malinowski, R., Lewiński, S., Rybicki, M., Gromny, E., Jenerowicz, M., Krupiński, M., Nowakowski, A., Wojtkowski, C., Krupiński, M., Krätzschmar, E., Schauer, P. (2020), Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
doi:10.3390/rs12213523
Aragoneses, E., García, M., Salis, M., Ribeiro, L. M., & Chuvieco, E. (2023). Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system. Earth System Science Data, 15(3), 1287–1315. https://doi.org/10.5194/essd-15-1287-2023
• Unburnable (code 0), including water, bare soil and urban dense.
• Burnable (code 0.8), including all vegetation categories that are susceptible of being burned.
• Burnable and with Wildland Urban Interface (WUI (Code 1). Includes burnable categories within a radius of 1 km of an urbanized area.
Fire risk component: Exposure
Contact details of the developer: Avi Bar Massada <avi-b@sci.haifa.ac.il>
For mapping the WUI at ET scale, first the extension of the urbanized area was mapped, based on the 2019 World Settlement Footprint dataset (WSF). This dataset was generated by automated interpretation of 10-m resolution Sentinel-1 and Sentinel-2 satellite imagery, and each pixel is either built-up or not. Next, vegetation data was gathered from the 2020 European Space Agency (ESA) global land cover product (Zanaga et al. 2020). This dataset is a classification of the same 10-m Sentinel-1 and Sentinel-2 satellite imagery, in conjunction with auxiliary data, into 11 land cover classes. This dataset was reclassified into two classes: woody vegetation classes (‘forests’ and ‘shrublands’, “1”) and others (“0”). Hence, the research focused on fire hazard only from woody vegetation, regardless of species identity and fuel characteristics.
The most common approach for WUI mapping distinguishes between WUI intermix and interface classes (Radeloff et al. 2005). An intermix WUI is a unit area that: 1: comprises enough buildings exposed to wildfire risk; and 2: contains or is directly surrounded by sufficient amounts of flammable vegetation. An interface WUI is a unit area that contains buildings that are not directly surrounded by flammable vegetation yet is within a relatively short distance from a contiguous patch of flammable vegetation which can produce firebrands. To map the WUI, the parameters of the existing point-based WUI mapping approach were modified (Bar-Massada et al. 2013), which is based on the original US WUI mapping approach.
The new approach adapts the 10 m-cell grid of the underlying datasets and includes the following steps:
1. Including all cells within 100 m of a built-up cell as candidate WUI locations, regardless of the housing density within (and around) them in order to identify all built-up areas that are potentially at risk.
2. Calculating the percent cover of woody vegetation within a 500 m radius around candidate WUI location cells; if woody cover was greater than 50%, these cells were mapped as ‘intermix WUI’.
3. Identifying patches of woody vegetation that were larger than 5 km2.
4. Identifying all candidate WUI locations that were within 600 m to large vegetation patches; these cells were mapped as ‘interface WUI’. The choice of 600 m reflects findings about the approximate median value of maximum travel distances of flying embers. This threshold differs from the previously used distance of 2,400 m (Carlson et al. 2022; Radeloff et al. 2005), which reflects maximum travel distances. The result of this process is a WUI map at either PS scale (if built-up information was based on individual buildings; five WUI maps overall) or ET scale (when the input was the WSF dataset; one WUI map for the entire ET). Based on these maps, we quantified the overall extent and the spatial distribution of interface, intermix, and total WUI at the country level for each country in our study area. We also calculated the variation in WUI cover within countries, by overlaying a map of statistical regions at NUTS-3 level obtained from Eurostat, the EU’s statistical agency. The WUI maps generated here are available for download in the project’s data repository.
References
Bar-Massada, A., Stewart, S.I., Hammer, R.B., Mockrin, M.H., & Radeloff, V.C. (2013). Using structure locations as a basis for mapping the wildland urban interface. Journal of Environmental Management, 128, 540-547.
Carlson, A.R., Helmers, D.P., Hawbaker, T.J., Mockrin, M.H., & Radeloff, V.C. (2022). The wildland–urban interface in the United States based on 125 million building locations. Ecological Applications, 32.
Chuvieco, E., Yebra, M., Martino, S., Thonicke, K., Gómez-Giménez, M., San-Miguel, J., Oom, D., Ramona Velea, Florent Mouillot, Juan R. Molina, Ana I. Miranda, Diogo Lopes, Michele Salis, Marin Bugaric, Mikhail Sofiev, Evgeny Kadantsev, Ioannis Gitas, Dimitris Stavrakoudis, George Eftychidis, Bar-Massada, A., Alex Neidermeier, Valerio Pampanoni, Pettinari, M.L., Arrogante, F., Ochoa, C., Moreira, B., & Viegas, D. (2023). Towards an integrated approach to wildfire risk assessment: when, where, what and how may the landscapes burn. Fire, 6, 215, doi210.3390/fire6050215.
Radeloff, V.C., Hammer, R.B., Stewart, S.I., Fried, J.S., Holcomb, S.S., & McKeefry, J.F. (2005). The wildland-urban interface in the United States. Ecological Applications, 15, 799-805.
Zanaga, D., Van De Kerchove, R., Daems, D., De Keersmaecker, W., Brockmann, C., Kirches, G., Wevers, J., Cartus, O., Santoro, M., Fritz, S., & Arino, O. (2020). ESA WorldCover 10 m 2020 v100.