Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation
Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bondur, V. G. [verfasserIn] Tsidilina, M. N. [verfasserIn] Cherepanova, E. V. [verfasserIn] |
|---|
| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Übergeordnetes Werk: |
Enthalten in: Izvestiya - Moscow : MAIK Nauka/Interperiodica Publ., 2006, 55(2019), 9 vom: Dez., Seite 1238-1253 |
|---|---|
| Übergeordnetes Werk: |
volume:55 ; year:2019 ; number:9 ; month:12 ; pages:1238-1253 |
| Links: |
|---|
| DOI / URN: |
10.1134/S000143381909010X |
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| Katalog-ID: |
SPR039051552 |
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| 245 | 1 | 0 | |a Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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| 520 | |a Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. | ||
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| 700 | 1 | |a Cherepanova, E. V. |e verfasserin |4 aut | |
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10.1134/S000143381909010X doi (DE-627)SPR039051552 (SPR)S000143381909010X-e DE-627 ger DE-627 rakwb eng 000 550 ASE 38.81 bkl 38.90 bkl Bondur, V. G. verfasserin aut Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. Tsidilina, M. N. verfasserin aut Cherepanova, E. V. verfasserin aut Enthalten in Izvestiya Moscow : MAIK Nauka/Interperiodica Publ., 2006 55(2019), 9 vom: Dez., Seite 1238-1253 (DE-627)34232098X (DE-600)2071674-6 1555-628X nnns volume:55 year:2019 number:9 month:12 pages:1238-1253 https://dx.doi.org/10.1134/S000143381909010X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.81 ASE 38.90 ASE AR 55 2019 9 12 1238-1253 |
| spelling |
10.1134/S000143381909010X doi (DE-627)SPR039051552 (SPR)S000143381909010X-e DE-627 ger DE-627 rakwb eng 000 550 ASE 38.81 bkl 38.90 bkl Bondur, V. G. verfasserin aut Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. Tsidilina, M. N. verfasserin aut Cherepanova, E. V. verfasserin aut Enthalten in Izvestiya Moscow : MAIK Nauka/Interperiodica Publ., 2006 55(2019), 9 vom: Dez., Seite 1238-1253 (DE-627)34232098X (DE-600)2071674-6 1555-628X nnns volume:55 year:2019 number:9 month:12 pages:1238-1253 https://dx.doi.org/10.1134/S000143381909010X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.81 ASE 38.90 ASE AR 55 2019 9 12 1238-1253 |
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10.1134/S000143381909010X doi (DE-627)SPR039051552 (SPR)S000143381909010X-e DE-627 ger DE-627 rakwb eng 000 550 ASE 38.81 bkl 38.90 bkl Bondur, V. G. verfasserin aut Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. Tsidilina, M. N. verfasserin aut Cherepanova, E. V. verfasserin aut Enthalten in Izvestiya Moscow : MAIK Nauka/Interperiodica Publ., 2006 55(2019), 9 vom: Dez., Seite 1238-1253 (DE-627)34232098X (DE-600)2071674-6 1555-628X nnns volume:55 year:2019 number:9 month:12 pages:1238-1253 https://dx.doi.org/10.1134/S000143381909010X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.81 ASE 38.90 ASE AR 55 2019 9 12 1238-1253 |
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10.1134/S000143381909010X doi (DE-627)SPR039051552 (SPR)S000143381909010X-e DE-627 ger DE-627 rakwb eng 000 550 ASE 38.81 bkl 38.90 bkl Bondur, V. G. verfasserin aut Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. Tsidilina, M. N. verfasserin aut Cherepanova, E. V. verfasserin aut Enthalten in Izvestiya Moscow : MAIK Nauka/Interperiodica Publ., 2006 55(2019), 9 vom: Dez., Seite 1238-1253 (DE-627)34232098X (DE-600)2071674-6 1555-628X nnns volume:55 year:2019 number:9 month:12 pages:1238-1253 https://dx.doi.org/10.1134/S000143381909010X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.81 ASE 38.90 ASE AR 55 2019 9 12 1238-1253 |
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10.1134/S000143381909010X doi (DE-627)SPR039051552 (SPR)S000143381909010X-e DE-627 ger DE-627 rakwb eng 000 550 ASE 38.81 bkl 38.90 bkl Bondur, V. G. verfasserin aut Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. Tsidilina, M. N. verfasserin aut Cherepanova, E. V. verfasserin aut Enthalten in Izvestiya Moscow : MAIK Nauka/Interperiodica Publ., 2006 55(2019), 9 vom: Dez., Seite 1238-1253 (DE-627)34232098X (DE-600)2071674-6 1555-628X nnns volume:55 year:2019 number:9 month:12 pages:1238-1253 https://dx.doi.org/10.1134/S000143381909010X lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 38.81 ASE 38.90 ASE AR 55 2019 9 12 1238-1253 |
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Bondur, V. G. |
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Bondur, V. G. ddc 000 bkl 38.81 bkl 38.90 Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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000 550 ASE 38.81 bkl 38.90 bkl Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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Bondur, V. G. |
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Bondur, V. G. Tsidilina, M. N. Cherepanova, E. V. |
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Bondur, V. G. |
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verfasserin |
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satellite monitoring of wildfire impacts on the conditions of various types of vegetation cover in the federal districts of the russian federation |
| title_auth |
Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
| abstract |
Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. |
| abstractGer |
Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. |
| abstract_unstemmed |
Abstract The results of an analysis of the long-term series of remote sensing data from Terra/Aqua satellites (Burned Area MCD64A1 monthly product, the 6th collection) and Landsat satellites are presented to estimate the dynamics of of the vegetation cover condition caused by wildfires throughout the Russian Federation and in its individual regions. An approach to the use of remote sensing and geoinformatics methods for monitoring the impact of wildfires on the state of various types of vegetation cover without involving ground truth data is proposed. Potential for using a normalized burn ratio index to estimate the degree of vegetation damage and recovery process using low-resolution satellite data is demonstrated. It was established that the total area of burned vegatation over the country ranged from 68.40 to 234.46 thousand $ km^{2} $ per year for the period from 2002 till 2017. Seasonal peaks of fire activity, patterns of the spatial distribution of fire sources, and the main types of burned vegetation were identified for each of the eight federal districts of Russia. The accuracy of burned areas detection was estimated as 75–81% depending on the information product used according to the low (500 m) and average (30 m) spatial resolution data. |
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9 |
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Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation |
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https://dx.doi.org/10.1134/S000143381909010X |
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Tsidilina, M. N. Cherepanova, E. V. |
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Tsidilina, M. N. Cherepanova, E. V. |
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| doi_str |
10.1134/S000143381909010X |
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2024-07-03T21:37:59.266Z |
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code="q">ASE</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">38.90</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">55</subfield><subfield code="j">2019</subfield><subfield code="e">9</subfield><subfield code="c">12</subfield><subfield code="h">1238-1253</subfield></datafield></record></collection>
|
| score |
7.400078 |