[An Identification Study on Field-Derived Spectra of Grassland Combustibles and Soil Based on Fractal Theory]

Grassland fire disaster is an important influence factor to grassland ecological system in China. Therefore, it is crucial to study on the monitoring, prediction and management of grassland fire. Remote Sensing (RS) provides detailed data and saves a lot of manpower, material resources and financial resources on the research of grassland fire. However, it is difficult to identify the grassland fuel and soil with Remote Sensing. In this paper, we introduced fractal into the spectral analyses of the field-derived spectra (FDS) of grassland fuel and soil to solve the problem above. The study area laid on the Westward of Changling, Jinlin province, China. Study subjects included soil and dominant species: Leymus chinensis, Reed, Chloris virgate, Kalimeris integrifolia and Artemisia mongolica. FDS of study subjects were measured with ASD FS3 and continuums of FDS were calculated by Matlab 2010. Meanwhile, Box-counting values of FDS and continuums were calculated by Matlab 2010. According to the spectral and continuum analysis, it is difficult to identify soil, Leymus chinensis, Reed, Chloris virgate, and Artemisia mongolica because of the similar spectral curves. However, the Artemisia mongolica can be identified for the strong reflection. For typical fractal characteristics of FDS and continuum, clustering analyses of study subjects were done according to box-counting values of FDS and continuum. The results of clustering analyses show that Box-counting values of FDS and continuum are important indexes to identify the study subjects. This study provides a new thought to identity the grassland combustibles and soil with Remote Sensing.