Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests
One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous <i>Abies pinsapo</i>–<i>Pinus</i>–<i>Quercus</i>–<i>Juniperus</i> with distinct stru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/8/2023 |
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| author | Roberto Crespo Calvo Mª Ángeles Varo Martínez Francisco Ruiz Gómez Antonio Jesús Ariza Salamanca Rafael M. Navarro-Cerrillo |
| author_facet | Roberto Crespo Calvo Mª Ángeles Varo Martínez Francisco Ruiz Gómez Antonio Jesús Ariza Salamanca Rafael M. Navarro-Cerrillo |
| author_sort | Roberto Crespo Calvo |
| collection | DOAJ |
| description | One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous <i>Abies pinsapo</i>–<i>Pinus</i>–<i>Quercus</i>–<i>Juniperus</i> with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for <i>Abies pinsapo</i> (R<sup>2</sup> = 0.69), <i>Juniperus</i> spp. (R<sup>2</sup> = 0.70), <i>Pinus halepensis</i> (R<sup>2</sup> = 0.59), <i>Pinus</i> spp. mixed (R<sup>2</sup> = 0.80), and <i>Pinus</i> spp.–<i>Juniperus</i> spp. (R<sup>2</sup> = 0.59) forests. The highest values of biomass were obtained for <i>Pinus halepensis</i> forests (190.43 Mg ha<sup>−1</sup>). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to <i>Pinus</i> spp.–evergreen (2.56), <i>Quercus suber</i> (2.54), and <i>Pinus</i> mixed (2.49) forests, with the minimum being obtained for <i>Juniperus</i> (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to <i>Pinus halepensis</i> and <i>Pinus pinaster</i> forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation. |
| first_indexed | 2024-03-11T04:35:08Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T04:35:08Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-95f56dcf4b0847699427fdd206e9bb612023-11-17T21:10:57ZengMDPI AGRemote Sensing2072-42922023-04-01158202310.3390/rs15082023Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean ForestsRoberto Crespo Calvo0Mª Ángeles Varo Martínez1Francisco Ruiz Gómez2Antonio Jesús Ariza Salamanca3Rafael M. Navarro-Cerrillo4Agencia de Medio Ambiente y Agua de Andalucía, C/Johan G. Gutenberg, 1, 41092 Sevilla, SpainDepartment of Forestry Engineering, Silviculture Laboratory, Dendrochronology and Climate Change, DendrodatLab—ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, 14071 Córdoba, SpainDepartment of Forestry Engineering, Silviculture Laboratory, Dendrochronology and Climate Change, DendrodatLab—ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, 14071 Córdoba, SpainDepartment of Forestry Engineering, Silviculture Laboratory, Dendrochronology and Climate Change, DendrodatLab—ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, 14071 Córdoba, SpainDepartment of Forestry Engineering, Silviculture Laboratory, Dendrochronology and Climate Change, DendrodatLab—ERSAF, University of Cordoba, Campus de Rabanales, Crta. IV, km. 396, 14071 Córdoba, SpainOne of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous <i>Abies pinsapo</i>–<i>Pinus</i>–<i>Quercus</i>–<i>Juniperus</i> with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for <i>Abies pinsapo</i> (R<sup>2</sup> = 0.69), <i>Juniperus</i> spp. (R<sup>2</sup> = 0.70), <i>Pinus halepensis</i> (R<sup>2</sup> = 0.59), <i>Pinus</i> spp. mixed (R<sup>2</sup> = 0.80), and <i>Pinus</i> spp.–<i>Juniperus</i> spp. (R<sup>2</sup> = 0.59) forests. The highest values of biomass were obtained for <i>Pinus halepensis</i> forests (190.43 Mg ha<sup>−1</sup>). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to <i>Pinus</i> spp.–evergreen (2.56), <i>Quercus suber</i> (2.54), and <i>Pinus</i> mixed (2.49) forests, with the minimum being obtained for <i>Juniperus</i> (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to <i>Pinus halepensis</i> and <i>Pinus pinaster</i> forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation.https://www.mdpi.com/2072-4292/15/8/2023forest fuelsensor integrationbiomassstructural complexityfuel moisture |
| spellingShingle | Roberto Crespo Calvo Mª Ángeles Varo Martínez Francisco Ruiz Gómez Antonio Jesús Ariza Salamanca Rafael M. Navarro-Cerrillo Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests Remote Sensing forest fuel sensor integration biomass structural complexity fuel moisture |
| title | Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests |
| title_full | Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests |
| title_fullStr | Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests |
| title_full_unstemmed | Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests |
| title_short | Improvements of Fire Fuels Attributes Maps by Integrating Field Inventories, Low Density ALS, and Satellite Data in Complex Mediterranean Forests |
| title_sort | improvements of fire fuels attributes maps by integrating field inventories low density als and satellite data in complex mediterranean forests |
| topic | forest fuel sensor integration biomass structural complexity fuel moisture |
| url | https://www.mdpi.com/2072-4292/15/8/2023 |
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