Vegetation optical depth as a key predictor for fire risk escalation
Excluding direct consideration of vegetation dynamics reduces the accuracy in fire risk estimation. Satellite retrievals of vegetation dynamics can enhance the fire risk prediction when used as indicators of fuel water status and fuel load. However, the fire risk-vegetation relationship carries comp...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Ecological Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000597 |
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| author | Dinuka Kankanige Yi Y. Liu Ashish Sharma |
| author_facet | Dinuka Kankanige Yi Y. Liu Ashish Sharma |
| author_sort | Dinuka Kankanige |
| collection | DOAJ |
| description | Excluding direct consideration of vegetation dynamics reduces the accuracy in fire risk estimation. Satellite retrievals of vegetation dynamics can enhance the fire risk prediction when used as indicators of fuel water status and fuel load. However, the fire risk-vegetation relationship carries complexities as different mechanisms dominate during fire risk escalation and decline, with vegetation responding differently to each process. This study investigates whether vegetation parameters can be utilized in fire risk prediction in the absence of fire weather information, and how they can be utilized to effectively reflect on the fire risk increment from a minimum point, which is the concern in bushfire occurrence. Using the McArthur Forest Fire Danger Index (FFDI) as a measure of fire danger, a clear association with the satellite-observed vegetation optical depth (VOD) was noted for segments illustrating risk increment. An application over Australia showed clear improvements when incorporating VOD into a predictive model as compared to the use of fire risk persistence alone. On average, the VOD-induced predictive model exhibited better performance than the persistence model when evaluated over a 12-month lead span. The former model showed higher Nash-Sutcliffe efficiency (NSE) in 55.6% of pixels that indicated VOD causes FFDI. The latter performed better only in 18.2% of those pixels. Across the entire spatial domain, from the first to the ninth lead month, the VOD-induced model showed higher mean NSE (0.65 ± 0.23 to 0.52 ± 0.34) and lower or nearly equal mean root mean square error (RMSE) (4.6 ± 3.7 to 7.9 ± 5.4) than the persistence model. Our study provides insights on fire risk escalation in fire-prone regions in the absence of fire weather data. With further improvements, the proposed method can serve as a foundation for developing a novel forecast index solely based on time series data of fire risk and vegetation dynamics. |
| format | Article |
| id | doaj-art-a7b60e9b9e89492bb0752bc5c21db7a0 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-a7b60e9b9e89492bb0752bc5c21db7a02025-01-31T05:10:57ZengElsevierEcological Informatics1574-95412025-05-0186103050Vegetation optical depth as a key predictor for fire risk escalationDinuka Kankanige0Yi Y. Liu1Ashish Sharma2Corresponding author.; School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.School of Civil and Environmental Engineering, University of New South Wales, Sydney, NSW 2052, Australia.Excluding direct consideration of vegetation dynamics reduces the accuracy in fire risk estimation. Satellite retrievals of vegetation dynamics can enhance the fire risk prediction when used as indicators of fuel water status and fuel load. However, the fire risk-vegetation relationship carries complexities as different mechanisms dominate during fire risk escalation and decline, with vegetation responding differently to each process. This study investigates whether vegetation parameters can be utilized in fire risk prediction in the absence of fire weather information, and how they can be utilized to effectively reflect on the fire risk increment from a minimum point, which is the concern in bushfire occurrence. Using the McArthur Forest Fire Danger Index (FFDI) as a measure of fire danger, a clear association with the satellite-observed vegetation optical depth (VOD) was noted for segments illustrating risk increment. An application over Australia showed clear improvements when incorporating VOD into a predictive model as compared to the use of fire risk persistence alone. On average, the VOD-induced predictive model exhibited better performance than the persistence model when evaluated over a 12-month lead span. The former model showed higher Nash-Sutcliffe efficiency (NSE) in 55.6% of pixels that indicated VOD causes FFDI. The latter performed better only in 18.2% of those pixels. Across the entire spatial domain, from the first to the ninth lead month, the VOD-induced model showed higher mean NSE (0.65 ± 0.23 to 0.52 ± 0.34) and lower or nearly equal mean root mean square error (RMSE) (4.6 ± 3.7 to 7.9 ± 5.4) than the persistence model. Our study provides insights on fire risk escalation in fire-prone regions in the absence of fire weather data. With further improvements, the proposed method can serve as a foundation for developing a novel forecast index solely based on time series data of fire risk and vegetation dynamics.http://www.sciencedirect.com/science/article/pii/S1574954125000597Fire danger indexVegetation dynamicsFire risk incrementTime series forecast |
| spellingShingle | Dinuka Kankanige Yi Y. Liu Ashish Sharma Vegetation optical depth as a key predictor for fire risk escalation Ecological Informatics Fire danger index Vegetation dynamics Fire risk increment Time series forecast |
| title | Vegetation optical depth as a key predictor for fire risk escalation |
| title_full | Vegetation optical depth as a key predictor for fire risk escalation |
| title_fullStr | Vegetation optical depth as a key predictor for fire risk escalation |
| title_full_unstemmed | Vegetation optical depth as a key predictor for fire risk escalation |
| title_short | Vegetation optical depth as a key predictor for fire risk escalation |
| title_sort | vegetation optical depth as a key predictor for fire risk escalation |
| topic | Fire danger index Vegetation dynamics Fire risk increment Time series forecast |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000597 |
| work_keys_str_mv | AT dinukakankanige vegetationopticaldepthasakeypredictorforfireriskescalation AT yiyliu vegetationopticaldepthasakeypredictorforfireriskescalation AT ashishsharma vegetationopticaldepthasakeypredictorforfireriskescalation |