基于Landsat-8陆地卫星数据的火点检测方法

A method for fire detection using Landsat 8 data

传统的火点检测算法通常利用高温地物在中红外波段或热红外波段的高发射率特性来提取火点,然而受制于影像空间分辨率的限制如MODIS、AVHRR等,使得很多小规模火情现象被漏检.研究发现短波红外数据也同样能被用于高温地物的识别和检测,并且相较于热红外波段数据对低温和高温地物的区分度更大,在精确识别和定位高温目标方面更加准确.文章利用空间分辨率为30米的Landsat-8 OLI传感器数据,根据高温火点在近红外及短波红外波段的波谱特性,利用改进的归一化燃烧指数(NBRS)结果自适应地确定阈值来提取疑似火点,然后再利用高温火点在短波红外的峰值关系进行误检点剔除,从而得到最终的火点产品.提出的算法能检测到所占像元面积10%左右的火点,并能够有效地排除云层及建筑物的干扰,在保证较低漏检率的同时还能达到90%左右的准确率,相比于传统算法的火点提取精度有很大的提高.

Traditional fire detection methods use the high temperature emission characteristics in mid or thermal infrared bands of the M ODIS or AVHRR data to extract burning area. It is very hard for these methods to identify small fire regions such as sub-pixel due to the limitation of spatial resolution. Recently researchers have found that shortwave infrared（ SWIR） data can also be used to identify and detect high temperature targets. Compared with the thermal infrared data,SWIR has a big discrimination against different features with different temperature. Thus it can identify accurately the location of high temperature targets. In this paper,we acquired fire point products by using Landsat-8 OLI data which has spatial resolution up to 30 m. The main procedure includes two steps. The improved Normalized Burning Ratio Short-wave（ NBRS） is calculated at first to adaptively acquire suspected fire points based on the spectral characteristics of fire points in the near infrared and shortwave infrared. Then most false positive points are excluded based on the relationship between peak value in shortwave infrared band of fire points. This algorithm is capable of detecting the burning area around 10% in one pixel. With the premise of avoiding the interference of cloud and constructions,it can also keep a nearly 90% accuracy and lowmissing rate around 10%.