| 光谱学与光谱分析 |
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| Using Fractal Dimensions of Hyperspectral Curves to Analyze the Healthy Status of Vegetation |
| DU Hua-qiang1, JIN Wei1, GE Hong-li1, FAN Wen-yi2, XU Xiao-jun1 |
1. School of Environmental Sciences and Technology, Zhejiang Forestry College, Lin’an 311300, China
2. College of Forestry, Northeast Forestry University, Harbin 150040, China |
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Abstract The reflectance spectral curves of leaves can reflect many information of vegetation growth, and its variation maybe means that the healthy status of vegetation will change. Many spectral feature parameters such as red edge position, height of green peak, depth of red band absorption, the area of red edge and some vegetation index have been used to describe this change. However, the change of vegetation healthy status is not some feature parameters, but a comprehensive variation of the whole curve. So, a comprehensive index maybe has more value to describe the change of hyperspectral curve of vegetation and indicates its healthy status. Fractal is an appropriate mathematical tool, and fractal dimension can be used to explain the comprehensive variation of a curve. Therefore, in the present study, fractal theory was used to analyze the healthy status of different vegetation. Firstly, analytical spectral devices (ASD) were used to measure the hyperspectral curves of different vegetations with different healthy status. Secondly, spectral curves were analyzed, and some parameters which can really reflect different healthy status were obtained. Finally, the fractal dimension of reflectance spectral curves inside a spectral band zone between 450 and 780nm was computed by variation method, and the relationship between fractal dimensions and spectral feature parameters was established The research results showed that (1) the hyperspectral curves of vegetation have fractal feature, and their fractal dimensions gradually decrease with the health deterioration of leaves, (2) fractal dimension has positive correlation with the height of green peak, the depth of red band absorption and the area of red edge, (3) multivariate analysis showed that fractal dimensions have a significant linear relationship with the three spectral feature parameters just mentioned above. So, the fractal dimension of hyperspectral curve can serve as a new comprehensive parameter to analyze quantitatively the healthy status of vegetations.
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Received: 2008-10-08
Accepted: 2009-01-12
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Corresponding Authors:
DU Hua-qiang
E-mail: ddhill@126.com
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[1] Horler D N H, Barber J, Barringer A R. International Journal of Remote Sensing, 1980，1(2): 121.
[2] Dawson T P，Curran P J. International Journal of Remote Sensing, 1998, 19(11): 2133.
[3] Moses A C, Andrew K S. Remote Sensing of Environment, 2006, 101(2): 181.
[4] Helmi Z M S, Mohamad A M S, Azadeh G. American Journal of Applied Sciences, 2006, 3(6): 1864.
[5] Driss H, John R M, Elizabeth P, et al. Remote Sensing of Environment, 2004, 90: 337.
[6] WU Tong, NI Shao-xiang, LI Yun-mei, et al(吴 彤, 倪绍祥, 李云梅, 等). Journal of Remote Sensing(遥感学报), 2007, 11(1): 103.
[7] JIANG Jin-bao, CHEN Yun-hao, HUANG Wen-jiang(蒋金豹，陈云浩，黄文江). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2007, 27(12): 2475.
[8] LI Xiang-yang, LIU Guo-shun, SHI Zhou, et al(李向阳, 刘国顺, 史 舟, 等). Journal of Remote Sensing(遥感学报), 2007, 11(2): 269.
[9] Lam N S N, De Cola L. Introduction to Fractals in Geography, Fractal Measurement. In Dit, Dominick Mosco & Ann Sullivan (Ed.), Fractal in Geography. New Jersey: Englewood Cliffs, 1993. 3, 23.
[10] WU Ji-you，YANG Xu-dong，ZHANG Fu-jun, et al(吴继友, 杨旭东, 张福军, 等). Journal of Remote Sensing(遥感学报), 1997, 5(2): 124.
[11] FAN Wen-yi, DU Hua-qiang, LIU Zhe(范文义, 杜华强, 刘 哲). Journal of Northeast Forestry University(东北林业大学学报), 2004, 32(2): 45.
[12] GE Shi-rong, SUO Shuang-fu(葛世荣,索双富). Tribology(摩擦学报), 1997, 17(4): 354.
[13] Zhu X H, Yang X C, Xie W J, et al. China Ocean Engineering, 2000, 14(4): 533.
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