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, China 3. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing 100101, China 4. School of Automation, University of Electronic Science and Technology of China, Chengdu 611731, China 5. School of Earth and Space Sciences, Peking University, Beijing 100871, China 6. 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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[1] Liang Shunlin. Remote Sens. Rev., 2000, 19: 225. [2] Jin Zhenyu, Tian Qingjiu, Chen Jingming, et al. J. Environ. Manage., 2007, 85: 628. [3] Li Xiaowen, Wang Jindi, Alan S. Sci. China Ser. E, 1999, 42: 652. [4] Wen Jianguang, Liu Qiang, Liu Qinhuo, et al. Int. J. Remote Sens., 2009, 30: 5397. [5] Xu Xiru, Fan Wenjie, Tao Xin. Sci. China Ser. D, 2009, 52: 393. [6] Zhang X, Yan G, Li Q, et al. Int. J. Remote Sens., 2006, 27: 5359. [7] Liang L, Schwartz M D, Fei S L. Remote Sens. Environ., 2011, 115: 143. [8] Hilker T, Hall F G, Coops N C, et al. Remote Sens. Environ., 2010, 114: 2863. [9] Nagler P L, Brown T, Hultine K R, et al. Remote Sens. Environ., 2012, 118: 227. [10] Chasmer L, Barr A, Hopkinson C, et al. Remote Sens. Environ., 2009, 113: 82. [11] CHEN Yu, CHEN Ling(陈 颙, 陈 凌). Fractal Geometry(分形几何学). Beijing: Earthquake Press(北京: 地震出版社), 2005. [12] XU Xi-ru(徐希孺). Physical Principles of Remote-Sensing(遥感物理). Beijing: Peking University Press(北京: 北京大学出版社), 2005. 13. [13] Zhang Renhua, Tian Jing, Li Zhaoliang, et al. Sci. China Ser. Earth Sci., 2010, 53: 741. [14] ZHAO Ying-shi(赵英时). Remote Sensing Application: Analytic Principles and Methods(遥感应用分析原理与方法). Beijing: Science Press(北京: 科学出版社), 2003. 374. [15] Hu Z L, Islam S. IEEE Transanctions on Geiscience and Remote Sensing, 1997, 35(3): 747. [16] LIANG Shun-lin(梁顺林). Quantitative Remote Sensing of Land Surfaces(定量遥感). Translated by FAN Wen-jie(范闻捷, 译). Beijing: Science Press(北京: 科学出版社), 2009. 180. [17] John V M, Carol J B, Alan H S. Remote Sensing Reviews, 2000, 19: 9. [18] Mandelbrot B B. Science, 1967, 155: 636. [19] Liang S L, Fang H L, Chen M Z, et al. Remote Sens. Environ., 2002, 83: 149. |
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