Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem
Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This st...
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| Autor*in: |
Mitsopoulos, Ioannis [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality - Ren, Chunhui ELSEVIER, 2022, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:235 ; year:2019 ; day:1 ; month:04 ; pages:266-275 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.jenvman.2019.01.056 |
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| 520 | |a Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. | ||
| 520 | |a Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. | ||
| 650 | 7 | |a Mediterranean |2 Elsevier | |
| 650 | 7 | |a Random forest algorithm |2 Elsevier | |
| 650 | 7 | |a Pine forests |2 Elsevier | |
| 650 | 7 | |a Fire severity |2 Elsevier | |
| 650 | 7 | |a Remote sensing |2 Elsevier | |
| 650 | 7 | |a Fire management |2 Elsevier | |
| 700 | 1 | |a Chrysafi, Irene |4 oth | |
| 700 | 1 | |a Bountis, Diamantis |4 oth | |
| 700 | 1 | |a Mallinis, Giorgos |4 oth | |
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10.1016/j.jenvman.2019.01.056 doi GBV00000000000512.pica (DE-627)ELV045695725 (ELSEVIER)S0301-4797(19)30057-X DE-627 ger DE-627 rakwb eng 300 VZ 70.00 bkl 71.00 bkl Mitsopoulos, Ioannis verfasserin aut Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem 2019transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Mediterranean Elsevier Random forest algorithm Elsevier Pine forests Elsevier Fire severity Elsevier Remote sensing Elsevier Fire management Elsevier Chrysafi, Irene oth Bountis, Diamantis oth Mallinis, Giorgos oth Enthalten in Elsevier Ren, Chunhui ELSEVIER Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality 2022 Amsterdam [u.a.] (DE-627)ELV008002754 volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 https://doi.org/10.1016/j.jenvman.2019.01.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 70.00 Sozialwissenschaften allgemein: Allgemeines VZ 71.00 Soziologie: Allgemeines VZ AR 235 2019 1 0401 266-275 10 |
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10.1016/j.jenvman.2019.01.056 doi GBV00000000000512.pica (DE-627)ELV045695725 (ELSEVIER)S0301-4797(19)30057-X DE-627 ger DE-627 rakwb eng 300 VZ 70.00 bkl 71.00 bkl Mitsopoulos, Ioannis verfasserin aut Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem 2019transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Mediterranean Elsevier Random forest algorithm Elsevier Pine forests Elsevier Fire severity Elsevier Remote sensing Elsevier Fire management Elsevier Chrysafi, Irene oth Bountis, Diamantis oth Mallinis, Giorgos oth Enthalten in Elsevier Ren, Chunhui ELSEVIER Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality 2022 Amsterdam [u.a.] (DE-627)ELV008002754 volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 https://doi.org/10.1016/j.jenvman.2019.01.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 70.00 Sozialwissenschaften allgemein: Allgemeines VZ 71.00 Soziologie: Allgemeines VZ AR 235 2019 1 0401 266-275 10 |
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10.1016/j.jenvman.2019.01.056 doi GBV00000000000512.pica (DE-627)ELV045695725 (ELSEVIER)S0301-4797(19)30057-X DE-627 ger DE-627 rakwb eng 300 VZ 70.00 bkl 71.00 bkl Mitsopoulos, Ioannis verfasserin aut Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem 2019transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Mediterranean Elsevier Random forest algorithm Elsevier Pine forests Elsevier Fire severity Elsevier Remote sensing Elsevier Fire management Elsevier Chrysafi, Irene oth Bountis, Diamantis oth Mallinis, Giorgos oth Enthalten in Elsevier Ren, Chunhui ELSEVIER Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality 2022 Amsterdam [u.a.] (DE-627)ELV008002754 volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 https://doi.org/10.1016/j.jenvman.2019.01.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 70.00 Sozialwissenschaften allgemein: Allgemeines VZ 71.00 Soziologie: Allgemeines VZ AR 235 2019 1 0401 266-275 10 |
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10.1016/j.jenvman.2019.01.056 doi GBV00000000000512.pica (DE-627)ELV045695725 (ELSEVIER)S0301-4797(19)30057-X DE-627 ger DE-627 rakwb eng 300 VZ 70.00 bkl 71.00 bkl Mitsopoulos, Ioannis verfasserin aut Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem 2019transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Mediterranean Elsevier Random forest algorithm Elsevier Pine forests Elsevier Fire severity Elsevier Remote sensing Elsevier Fire management Elsevier Chrysafi, Irene oth Bountis, Diamantis oth Mallinis, Giorgos oth Enthalten in Elsevier Ren, Chunhui ELSEVIER Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality 2022 Amsterdam [u.a.] (DE-627)ELV008002754 volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 https://doi.org/10.1016/j.jenvman.2019.01.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 70.00 Sozialwissenschaften allgemein: Allgemeines VZ 71.00 Soziologie: Allgemeines VZ AR 235 2019 1 0401 266-275 10 |
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10.1016/j.jenvman.2019.01.056 doi GBV00000000000512.pica (DE-627)ELV045695725 (ELSEVIER)S0301-4797(19)30057-X DE-627 ger DE-627 rakwb eng 300 VZ 70.00 bkl 71.00 bkl Mitsopoulos, Ioannis verfasserin aut Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem 2019transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. Mediterranean Elsevier Random forest algorithm Elsevier Pine forests Elsevier Fire severity Elsevier Remote sensing Elsevier Fire management Elsevier Chrysafi, Irene oth Bountis, Diamantis oth Mallinis, Giorgos oth Enthalten in Elsevier Ren, Chunhui ELSEVIER Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality 2022 Amsterdam [u.a.] (DE-627)ELV008002754 volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 https://doi.org/10.1016/j.jenvman.2019.01.056 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U 70.00 Sozialwissenschaften allgemein: Allgemeines VZ 71.00 Soziologie: Allgemeines VZ AR 235 2019 1 0401 266-275 10 |
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Enthalten in Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality Amsterdam [u.a.] volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 |
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Enthalten in Cohort, signaling, and early-career dynamics: The hidden significance of class in black-white earnings inequality Amsterdam [u.a.] volume:235 year:2019 day:1 month:04 pages:266-275 extent:10 |
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Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. 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Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem |
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Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. |
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Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. |
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Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level. |
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