Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms
The frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2022.2128440 |
| _version_ | 1811242847530123264 |
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| author | Yong Piao Dongkun Lee Sangjin Park Ho Gul Kim Yihua Jin |
| author_facet | Yong Piao Dongkun Lee Sangjin Park Ho Gul Kim Yihua Jin |
| author_sort | Yong Piao |
| collection | DOAJ |
| description | The frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring and prevention of forest fires. This study proposed a method to establish a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multi-layer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas. First, extreme drought years were selected using the standardized precipitation index (SPI). An inventory drought map was constructed using the enhanced vegetation index (EVI) based on satellite image data. Then, 70% of the inventory maps based on forest fires and droughts were used to construct hazard susceptibility maps and 30% of these were used to validate three machine learning models: Classification and Regression Trees (CART), Random Forest (RF) and boosted Regression Trees (BRT). Eleven conditioning factors related to climate, topography, hydrology, and human activities were considered for the analysis. The results of the three models were then validated using the area under the receiver operating characteristic (ROC) curve (AUC), and the best performing model was selected (BRT; forest fire: 85%, drought: 80%). Finally, the susceptibility maps of forest fires and droughts were combined to construct the MHPM for the Gangwon Province, South Korea. The results show that the MHPM of forest fires and droughts constructed in this study is valid and reliable. This multi-hazard map can provide key information for planners and decision-makers to develop forest fire prevention and management plans and to more effectively prevent and reduce the frequency of forest fires. |
| first_indexed | 2024-04-12T13:57:09Z |
| format | Article |
| id | doaj.art-963ed3f3cb3c4f7ebf2d988bfeb72065 |
| institution | Directory Open Access Journal |
| issn | 1947-5705 1947-5713 |
| language | English |
| last_indexed | 2024-04-12T13:57:09Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj.art-963ed3f3cb3c4f7ebf2d988bfeb720652022-12-22T03:30:20ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132022-12-011312649267310.1080/19475705.2022.2128440Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithmsYong Piao0Dongkun Lee1Sangjin Park2Ho Gul Kim3Yihua Jin4Interdisciplinary Program in Landscape Architecture and Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, KoreaResearch Institute of Agriculture Life Science, Seoul National University, Seoul, KoreaInterdisciplinary Program in Landscape Architecture and Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, KoreaDepartment of Human Environment Design, Cheongju University, Cheongju, KoreaDepartment of Landscape Architecture, Agricultural College, Yanbian University, Yanji, ChinaThe frequency of forest fires in Gangwon-do has increased in recent years due to advanced climate change and dry weather. The Gangwon-do area, the largest forest area in South Korea, has rich forest resources and ecological diversity, therefore there is an urgent need for more effective monitoring and prevention of forest fires. This study proposed a method to establish a multi-hazard probability map (MHPM) for two related hazards (forest fires and droughts) based on a multi-layer hazards approach and machine learning algorithms for monitoring forest fire susceptibility areas. First, extreme drought years were selected using the standardized precipitation index (SPI). An inventory drought map was constructed using the enhanced vegetation index (EVI) based on satellite image data. Then, 70% of the inventory maps based on forest fires and droughts were used to construct hazard susceptibility maps and 30% of these were used to validate three machine learning models: Classification and Regression Trees (CART), Random Forest (RF) and boosted Regression Trees (BRT). Eleven conditioning factors related to climate, topography, hydrology, and human activities were considered for the analysis. The results of the three models were then validated using the area under the receiver operating characteristic (ROC) curve (AUC), and the best performing model was selected (BRT; forest fire: 85%, drought: 80%). Finally, the susceptibility maps of forest fires and droughts were combined to construct the MHPM for the Gangwon Province, South Korea. The results show that the MHPM of forest fires and droughts constructed in this study is valid and reliable. This multi-hazard map can provide key information for planners and decision-makers to develop forest fire prevention and management plans and to more effectively prevent and reduce the frequency of forest fires.https://www.tandfonline.com/doi/10.1080/19475705.2022.2128440Multi-hazardnatural hazardforest firedroughtremote sensing |
| spellingShingle | Yong Piao Dongkun Lee Sangjin Park Ho Gul Kim Yihua Jin Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms Geomatics, Natural Hazards & Risk Multi-hazard natural hazard forest fire drought remote sensing |
| title | Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms |
| title_full | Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms |
| title_fullStr | Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms |
| title_full_unstemmed | Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms |
| title_short | Multi-hazard mapping of droughts and forest fires using a multi-layer hazards approach with machine learning algorithms |
| title_sort | multi hazard mapping of droughts and forest fires using a multi layer hazards approach with machine learning algorithms |
| topic | Multi-hazard natural hazard forest fire drought remote sensing |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2022.2128440 |
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