期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 764, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.scitotenv.2020.142844
关键词
Fire; NDVI; Machine learning; Risk map; Time series; Mediterranean
资金
- Ministry of Science and Technology of Israel through the Eshkol Scholarship [3-14504]
Mapping fire risk is crucial for fire management and firefighting efforts. This study investigates the impact of long-term vegetation metrics on fire risk mapping, finding that they can significantly improve model performance and provide important dynamic information.
Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose Lime-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models. (C) 2020 Published by Elsevier B.V.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据