| 光谱学与光谱分析 |
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| Modeling Continuous Scaling of NDVI Based on Fractal Theory |
| LUAN Hai-jun1, 2, TIAN Qing-jiu1, 2*, YU Tao3, HU Xin-li3, HUANG Yan1, 2, DU Ling-tong1, 2, ZHAO Li-min3, WEI Xi4, HAN Jie3, ZHANG Zhou-wei5, LI Shao-peng6 |
| 1. International Institute for Earth System Science, Nanjing University, Nanjing 210093, China 2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100101, China4. School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China5. School of Earth and Space Sciences, Peking University, Beijing 100871, China6. College of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi 830052, China |
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Abstract Scale effect was one of the very important scientific problems of remote sensing. The scale effect of quantitative remote sensing can be used to study retrievals’ relationship between different-resolution images, and its research became an effective way to confront the challenges, such as validation of quantitative remote sensing products et al. Traditional up-scaling methods cannot describe scale changing features of retrievals on entire series of scales; meanwhile, they are faced with serious parameters correction issues because of imaging parameters’ variation of different sensors, such as geometrical correction, spectral correction, etc. Utilizing single sensor image, fractal methodology was utilized to solve these problems. Taking NDVI(computed by land surface radiance) as example and based on Enhanced Thematic Mapper Plus (ETM+) image, a scheme was proposed to model continuous scaling of retrievals. Then the experimental results indicated that: (a) For NDVI, scale effect existed, and it could be described by fractal model of continuous scaling; (2) The fractal method was suitable for validation of NDVI. All of these proved that fractal was an effective methodology of studying scaling of quantitative remote sensing.
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Received: 2012-11-26
Accepted: 2013-02-25
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Corresponding Authors:
TIAN Qing-jiu
E-mail: tianqj@nju.edu.cn
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