PDF
Full
Outline
To investigate the spread characteristics of forest fires under complex topography and multi-factor coupling conditions, this study develops an optimized forest fire spread model that integrates terrain-slope correction, wind-field effects, and vegetation indices. First, Gaussian filtering was applied to correct the digital elevation model (DEM) to reduce noise, and terrain slope and aspect were derived from the refined DEM. Subsequently, the enhanced vegetation index (EVI) was introduced to improve the forest fire spread prediction model, enhancing prediction accuracy in areas with dense vegetation cover. By combining the model with CA, the predicted fire spread can be visualized. Finally, the predicted fire variable values were compared with the observed data from Muli Tibetan Autonomous County to verify the scientific validity and effectiveness of the model. The results indicate that the model is highly sensitive to vegetation changes in low EVI value ranges, with an effect size of 0.870, suggesting that the introduction of EVI improves fire prediction accuracy in areas with high vegetation cover. The improved fire spread model achieved an area prediction error rate and perimeter error rate of 29.40% and 5.79%, respectively, which are lower than the pre-improvement values of 44.27% and 16.99%. The Kappa coefficient of the improved model is 0.8238, which is closer to 1 compared to the pre-improvement model.
PDF
78
32
| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
关闭全屏
No. 86 Xueyuan South Road, Haidian District, Beijing
010-62199257
qkjq@cast.org.cn
Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House
