摘要
用归一化差分植被指数(NDVI)和空气相对湿度构造森林火险综合指数。将支持向量机(SVM)方法用于森林火险预报预测试验,利用气象资料和卫星遥感资料,建立甘肃省林区森林火险分类推理模型和回归推理模型,并进行相应的预报试验。结果显示:分类推理模型具有良好的预报能力,预报效果明显优于传统的逐步回归方法;回归推理模型预报效果与逐步回归方法相差无几。
The forest fire danger composite index（FI） is constructed by normalized difference vegetation index （NDVI） and the relative humidity. Support vector machine （SVM） methods used for forest fire forecasting prediction test. Using meteorological data and secondary planet remote sensing data, the classification illation model and regression illation model of forest fire were based. The forecasting test was progressed too. The results show that classification illation model has favorable prediction ability, its prediction effect is superior to successive regression technique. But there is little difference in prediction effect between the regression illation model and successive regression technique.
出处
《林业科学》
EI
CAS
CSCD
北大核心
2007年第10期77-82,共6页
Scientia Silvae Sinicae
基金
甘肃省自然科学基金项目(ZS031-A25-025-G)
关键词
森林火险综合指数
支持向量机
森林火险预测
分类模型
回归估计
forest fire integrated index（FI）
support vector machine （SVM）
prediction of forest fire disaster
classification model
regression estimation
