An Identification Study on Field-Derived Spectra of Grassland Combustibles and Soil Based on Fractal Theory
LUO Xiao-long1, 2, TONG Zhi-jun3*, ZHAO Yun-sheng1, ZHANG Ji-quan3
1. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China 2. Institute of Grassland Science, Northeast Normal University, Changchun 130024, China 3. School of Environment, Northeast Normal University, Changchun 130024, China
Abstract：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.
骆晓龙1, 2，佟志军3*，赵云升1，张继权3 . 基于分形理论的不同草原可燃物及裸土野外光谱识别研究 [J]. 光谱学与光谱分析, 2016, 36(08): 2553-2557.
LUO Xiao-long1, 2, TONG Zhi-jun3*, ZHAO Yun-sheng1, ZHANG Ji-quan3 . An Identification Study on Field-Derived Spectra of Grassland Combustibles and Soil Based on Fractal Theory. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(08): 2553-2557.
[1] BAO Yu-long, ZHANG Ji-quan, ZHAO Yun-sheng, et al(包玉龙, 张继权, 赵云升, 等). Infrared(红外), 2011, 32(7): 38. [2] Cruz M G, McCaw W L, Anderson W R, et al. Environmental Modelling & Software, 2013, 40: 21. [3] Duwala(都瓦拉). A Study of Grassland Fire Monitoring and Early Warning and Assessment Inner Mongolia(内蒙古草原火灾监测预警及评价研究). Chinse Academy of Agricultural Sciences(中国农业科学院), 2012. 182. [4] Tong Z, Zhang J, Luo X , et al. Study on Grassland Fires Disaster Emergency Spatial Decision Supprot System Based on Case-Base. Istanbul, 2013. 397. [5] Liu X, Zhang J, Tong Z. Natural Hazards, 2015, 75(3): 2331. [6] Arthur A D, Catling P C, Reid A. Austral Ecology, 2012, 37(8): 958. [7] Dubinin M, Potapov P, Lushchekina A, et al. Remote Sensing of Environment, 2010, 114(8): 1638. [8] Lentile L B, Holden Z A, Smith A M, et al. International Journal of Wildland Fire, 2006, 15(3): 319. [9] Roy D P, Jin Y, Lewis P E, et al. Remote Sensing of Environment, 2005, 97(2): 137. [10] Rodriguez Gonzalez J, Del Barrio G, Duguy B. Ecological Modelling, 2008, 211(1-2): 121. [11] Gelet M, Suryabhagavan K V, Balakrishnan M. International Journal of Ecology and Environmental Sciences, 2010, 36(2-3): 117. [12] WANG Xiao-jun, ZHANG Qiang, GU Xuan-qing(王小军, 张 强, 古璇清). Acta Geographica Sinica(地理学报), 2012，(9): 1201. [13] Vaát R, Kodeová R, Borvka L, et al. Geoderma, 2014, 232-234: 208. [14] Isaacson P J, Pieters C M, Besse S, et al. Journal of Geophysical Research, 2011, 116.
