Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia)
International audience This article describes forest fires in Yakutia as one of the main types of natural hazards, as well as the factors of their occurrence and proposes methods of geoinformation modeling of forest fire risk in the Republic of Sakha (Yakutia). Forests of Yakutia occupy a significan...
Published in: | Успехи современного естествознания (Advances in Current Natural Sciences) |
---|---|
Main Authors: | , , |
Other Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | Russian |
Published: |
HAL CCSD
2019
|
Subjects: | |
Online Access: | https://hal.archives-ouvertes.fr/hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358/document https://hal.archives-ouvertes.fr/hal-02408358/file/37237.pdf https://doi.org/10.17513/use.37237 |
id |
ftccsdartic:oai:HAL:hal-02408358v1 |
---|---|
record_format |
openpolar |
institution |
Open Polar |
collection |
Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
op_collection_id |
ftccsdartic |
language |
Russian |
topic |
Spatial Modelling Machine learning NDVI Artificial intelligence Arctic Russia Yakutia GIS Fire forest forecast Forest fires Forest fire risk Remote Sensing MODIS [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environmental and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation |
spellingShingle |
Spatial Modelling Machine learning NDVI Artificial intelligence Arctic Russia Yakutia GIS Fire forest forecast Forest fires Forest fire risk Remote Sensing MODIS [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environmental and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Janiec, Pior Gadal, Sébastien Ivanova, Svetlana Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
topic_facet |
Spatial Modelling Machine learning NDVI Artificial intelligence Arctic Russia Yakutia GIS Fire forest forecast Forest fires Forest fire risk Remote Sensing MODIS [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environmental and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation |
description |
International audience This article describes forest fires in Yakutia as one of the main types of natural hazards, as well as the factors of their occurrence and proposes methods of geoinformation modeling of forest fire risk in the Republic of Sakha (Yakutia). Forests of Yakutia occupy a significant area. Forest fires annually cover an area of hundreds of thousands of hectares. The article analyzes the impact of various factors on the risk of forest fires using the linear correlation coefficient and the coefficient of determination. GIS modeling was used to predict the risk of forest fires. It was carried out in stages: the collection of different types of data and their integration into the GIS database, checking the impact of individual factors on the fire occurrence, assessing the risk of forest fires using artificial intelligence and machine learning. The study used global sources of fire information-the fire information management system (FMS). The data available in the service are collected from satellite systems and MODIS Collection 6 Active Fire Product, as well as VIIRS 375 m, 750 m. The article presents the results of assessing the impact of individual factors on the occurrence of forest fires. The analysis showed that the greatest influence was exerted by such factors as solar radiation, maximum temperature (July), NDVI index, altitude, slope, human factor (distance to roads, distance from settlements and rivers). The following artificial intelligence and machine learning methods were used to assess fire risk: random forest and maximum entropy methods. With the help of GIS modeling, geographical maps of the risk of forest fires in Yakutia were created. The maps highlight areas of very low, low, medium, high, very high and extremely high probability of forest fires. В данной статье рассматриваются лесные пожары в Якутии как один из основных видов стихийных угроз природного характера, а также факторы их возникновения и предлагаются методы геоинформационно-го моделирования риска лесных пожаров на ... |
author2 |
Aix Marseille Université (AMU) Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS) COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) North-Eastern Federal University |
format |
Article in Journal/Newspaper |
author |
Janiec, Pior Gadal, Sébastien Ivanova, Svetlana |
author_facet |
Janiec, Pior Gadal, Sébastien Ivanova, Svetlana |
author_sort |
Janiec, Pior |
title |
Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
title_short |
Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
title_full |
Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
title_fullStr |
Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
title_full_unstemmed |
Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) |
title_sort |
geoinformation modelling of forest fire risk in the sakha republic (yakutia) |
publisher |
HAL CCSD |
publishDate |
2019 |
url |
https://hal.archives-ouvertes.fr/hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358/document https://hal.archives-ouvertes.fr/hal-02408358/file/37237.pdf https://doi.org/10.17513/use.37237 |
geographic |
Arctic Sakha |
geographic_facet |
Arctic Sakha |
genre |
Arctic Republic of Sakha Sakha Republic Yakutia Саха Якути* Якутия |
genre_facet |
Arctic Republic of Sakha Sakha Republic Yakutia Саха Якути* Якутия |
op_source |
ISSN: 1681-7494 Успехи современного естествознания (Advances in Current Natural Sciences) https://hal.archives-ouvertes.fr/hal-02408358 Успехи современного естествознания (Advances in Current Natural Sciences), 2019, pp.37-42. ⟨10.17513/use.37237⟩ http://www.natural-sciences.ru/en/article/view?id=37237 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.17513/use.37237 hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358/document https://hal.archives-ouvertes.fr/hal-02408358/file/37237.pdf doi:10.17513/use.37237 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.17513/use.37237 |
container_title |
Успехи современного естествознания (Advances in Current Natural Sciences) |
container_issue |
№11 2019 |
container_start_page |
37 |
op_container_end_page |
42 |
_version_ |
1766349324372934656 |
spelling |
ftccsdartic:oai:HAL:hal-02408358v1 2023-05-15T15:19:08+02:00 Geoinformation Modelling of Forest Fire Risk in the Sakha Republic (Yakutia) Геоинформационное моделирование риска лесных пожаров в Республике Саха (Якутия) Janiec, Pior Gadal, Sébastien Ivanova, Svetlana Aix Marseille Université (AMU) Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS) COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) North-Eastern Federal University 2019-12-04 https://hal.archives-ouvertes.fr/hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358/document https://hal.archives-ouvertes.fr/hal-02408358/file/37237.pdf https://doi.org/10.17513/use.37237 ru rus HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.17513/use.37237 hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358 https://hal.archives-ouvertes.fr/hal-02408358/document https://hal.archives-ouvertes.fr/hal-02408358/file/37237.pdf doi:10.17513/use.37237 info:eu-repo/semantics/OpenAccess ISSN: 1681-7494 Успехи современного естествознания (Advances in Current Natural Sciences) https://hal.archives-ouvertes.fr/hal-02408358 Успехи современного естествознания (Advances in Current Natural Sciences), 2019, pp.37-42. ⟨10.17513/use.37237⟩ http://www.natural-sciences.ru/en/article/view?id=37237 Spatial Modelling Machine learning NDVI Artificial intelligence Arctic Russia Yakutia GIS Fire forest forecast Forest fires Forest fire risk Remote Sensing MODIS [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SHS.STAT]Humanities and Social Sciences/Methods and statistics [SDE.ES]Environmental Sciences/Environmental and Society [SHS.GEO]Humanities and Social Sciences/Geography [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation info:eu-repo/semantics/article Journal articles 2019 ftccsdartic https://doi.org/10.17513/use.37237 2021-12-12T02:02:43Z International audience This article describes forest fires in Yakutia as one of the main types of natural hazards, as well as the factors of their occurrence and proposes methods of geoinformation modeling of forest fire risk in the Republic of Sakha (Yakutia). Forests of Yakutia occupy a significant area. Forest fires annually cover an area of hundreds of thousands of hectares. The article analyzes the impact of various factors on the risk of forest fires using the linear correlation coefficient and the coefficient of determination. GIS modeling was used to predict the risk of forest fires. It was carried out in stages: the collection of different types of data and their integration into the GIS database, checking the impact of individual factors on the fire occurrence, assessing the risk of forest fires using artificial intelligence and machine learning. The study used global sources of fire information-the fire information management system (FMS). The data available in the service are collected from satellite systems and MODIS Collection 6 Active Fire Product, as well as VIIRS 375 m, 750 m. The article presents the results of assessing the impact of individual factors on the occurrence of forest fires. The analysis showed that the greatest influence was exerted by such factors as solar radiation, maximum temperature (July), NDVI index, altitude, slope, human factor (distance to roads, distance from settlements and rivers). The following artificial intelligence and machine learning methods were used to assess fire risk: random forest and maximum entropy methods. With the help of GIS modeling, geographical maps of the risk of forest fires in Yakutia were created. The maps highlight areas of very low, low, medium, high, very high and extremely high probability of forest fires. В данной статье рассматриваются лесные пожары в Якутии как один из основных видов стихийных угроз природного характера, а также факторы их возникновения и предлагаются методы геоинформационно-го моделирования риска лесных пожаров на ... Article in Journal/Newspaper Arctic Republic of Sakha Sakha Republic Yakutia Саха Якути* Якутия Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Arctic Sakha Успехи современного естествознания (Advances in Current Natural Sciences) №11 2019 37 42 |