Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images
An efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and dev...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/14/2/327 |
| _version_ | 1797620977468702720 |
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| author | Xia Zhou Ji Yang Kunlong Niu Bishan Zou Minjian Lu Chongyang Wang Jiayi Wei Wei Liu Chuanxun Yang Haoling Huang |
| author_facet | Xia Zhou Ji Yang Kunlong Niu Bishan Zou Minjian Lu Chongyang Wang Jiayi Wei Wei Liu Chuanxun Yang Haoling Huang |
| author_sort | Xia Zhou |
| collection | DOAJ |
| description | An efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and developed an optimized forest fire risk model (fire-potential-index slope, FPIS). Combined with Landsat 8 satellite images, the study retrieved and analyzed the variations of forest fire risk in Zhaoqing City, Guangdong province, for four consecutive periods in the dry season, 2019. It was found that the high forest fire risk area was mainly distributed in the valley plains of Huaiji district, Fengkai district and Guangning district, the depressions of the Sihui district, and mountain-edge areas of Dinghu district and Gaoyao district, and accounted for 8.9% on 20 October but expanded to 19.89% on 7 December 2019. However, the further trend analysis indicated that the forest fire risk with significant increasing trend only accounted for 6.42% in Zhaoqing. Compared to the single high forest fire risk results, the changing trend results effectively narrowed the key areas for forest fire prevention (2.48%–12.47%) given the actual forest fires in the city. For the four forest fire events (Lingshan mountain, Hukeng industrial area, Xiangang county and Huangniuling ridge forest fires), it was found that the forest fire risk with significant increasing trend in these regions accounted for 26.63%, 35.84%, 54.6% and 73.47%, respectively, which further proved that the forest fire risk changing trend had a better indicated significance for real forest fire events than the high forest fire risk results itself (1.89%–71.69%). This study suggested that the forest fire risk increasing trend could be well used to reduce the probability of misjudgment and improve the accuracy of the early-warning areas when predicting forest fires. |
| first_indexed | 2024-03-11T08:49:10Z |
| format | Article |
| id | doaj.art-b4a7b460a1d346e3baeaba5f0fd8916f |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-11T08:49:10Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-b4a7b460a1d346e3baeaba5f0fd8916f2023-11-16T20:34:26ZengMDPI AGForests1999-49072023-02-0114232710.3390/f14020327Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series ImagesXia Zhou0Ji Yang1Kunlong Niu2Bishan Zou3Minjian Lu4Chongyang Wang5Jiayi Wei6Wei Liu7Chuanxun Yang8Haoling Huang9Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Xijiang Forest Farm, Zhaoqing 526020, ChinaGuangdong Xijiang Forest Farm, Zhaoqing 526020, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaGuangdong Climate Center, Guangzhou 510080, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaKey Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangdong Engineering Technology Center for Remote Sensing Big Data Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, ChinaAn efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and developed an optimized forest fire risk model (fire-potential-index slope, FPIS). Combined with Landsat 8 satellite images, the study retrieved and analyzed the variations of forest fire risk in Zhaoqing City, Guangdong province, for four consecutive periods in the dry season, 2019. It was found that the high forest fire risk area was mainly distributed in the valley plains of Huaiji district, Fengkai district and Guangning district, the depressions of the Sihui district, and mountain-edge areas of Dinghu district and Gaoyao district, and accounted for 8.9% on 20 October but expanded to 19.89% on 7 December 2019. However, the further trend analysis indicated that the forest fire risk with significant increasing trend only accounted for 6.42% in Zhaoqing. Compared to the single high forest fire risk results, the changing trend results effectively narrowed the key areas for forest fire prevention (2.48%–12.47%) given the actual forest fires in the city. For the four forest fire events (Lingshan mountain, Hukeng industrial area, Xiangang county and Huangniuling ridge forest fires), it was found that the forest fire risk with significant increasing trend in these regions accounted for 26.63%, 35.84%, 54.6% and 73.47%, respectively, which further proved that the forest fire risk changing trend had a better indicated significance for real forest fire events than the high forest fire risk results itself (1.89%–71.69%). This study suggested that the forest fire risk increasing trend could be well used to reduce the probability of misjudgment and improve the accuracy of the early-warning areas when predicting forest fires.https://www.mdpi.com/1999-4907/14/2/327forest fire riskchanging trendindicating significancesoptimized fire-potential-index |
| spellingShingle | Xia Zhou Ji Yang Kunlong Niu Bishan Zou Minjian Lu Chongyang Wang Jiayi Wei Wei Liu Chuanxun Yang Haoling Huang Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images Forests forest fire risk changing trend indicating significances optimized fire-potential-index |
| title | Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images |
| title_full | Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images |
| title_fullStr | Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images |
| title_full_unstemmed | Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images |
| title_short | Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images |
| title_sort | assessment of the forest fire risk and its indicating significances in zhaoqing city based on landsat time series images |
| topic | forest fire risk changing trend indicating significances optimized fire-potential-index |
| url | https://www.mdpi.com/1999-4907/14/2/327 |
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