Funct. Mater. 2017; 24 (3): 442-450.

doi:https://doi.org/10.15407/fm24.03.442

The novel method for LAI inversion using Lidar and hyperspectral data

Zuowei Huang1, Feng Liu2,Guangwei Hu1

1 School of Architecture and urban planning, Hunan University of Technology , Zhuzhou 412008,China
2 School of Geosciences and Information-Physics,Central South University,Changsha 410083,China

Abstract: 

For inversion of Leaf area index (LAI) in large scale, it is of great significance to integrate space-borne Lidar and optical remote sensing data effectively. In order to improve the estimation precision of leaf area index, an analyzing method based on Lidar and hyperspectral data was proposed. Through the processing of Lidar (Light Identification Detection and Ranging) and hyperspectral data, the LAI estimation model was established based on statistic analysis method in the study area. The results showed that the Lidar and hyperspectral data joint inversion model which considers the optical remote sensing of biophysical parameters can provide good estimates of LAI inversion, shows high accuracy (R2=0.8948, RMSE=0.2120),which reveals the great potential to enhance the accuracy of LAI estimation by using Lidar and hyperspectral data.

Keywords: 
LAI, Lidar, Hyperspectral data, Spectral unmixing, data inversion

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References: 

1. Jianxi Huang, Yuan Zeng, Andres Kuusk et al., Int. J. Rem. Sens., 32(22), 7591, 2011.

2. Zhiqiang Xiao, Shunlin Liang, Jindi Wang, et al., Rem. Sens. Environ., 115, 97, 2011

3. Sprintsin M, Cohen S, Maseyk K, et al., Agric For Meteorol, 151(5), 565, 2011.

4. Majasalmi T, Rautiainen M, Stenberg P, et al. Agric For Meteorol, 154/155(3), 38, 2012.

5. Kauwe M G, Disney M I, Quaife T, et al., Rem. Sens. Environ., 115(2), 767, 2011.

6. Gonzalez-Sanpedro M C, Toan T L, Moreno J, et. al., Rem. Sens Environ, 112(3): 810, 2008.

7. Zhang L P and Huang X., J. Rem. Sens., 13(4), 559, 2011.

8. Zhong J Q and Wang R S. J. Rem. Sens., 27(10): 2055, 2006.

9. Bruzzone L, Chi M, Marconcini M. A,, IEEE Trans. Geoscie. Rem. Sens., 44(11), 3363, 2006.

10. Chen Chong, Zhu Yanjun, Li Xianfeng, et al. Acta Agricul. Univ. Jiangxiensis, 33(3), 508, 2011.

11. Brantley S T, Zinnert J C, Young D R., Rem.Sens Environ, 115(2), 514, 2011.

12. Cheng Q, Huang J F, Wang R C and Tang Y L, Trans. Chinese Soc. Agricult. Eng., 19(5),104, 2013.

13. Clevers J G P W., Rem. Sens Environ, 29(1), 25, 2012.

14. Morsdorf F, Frey O, Meier E, et al., Int. J. .Rem. Sens., 29(5), 1387, 2008.

15. Pang Y, Zhao F, Li Z Y, Zhou S F, Deng G, Liu Q W and Chen E X., J. Rem. Sens., 12(1), 152, 2008.

16. Zeng Q H, Mao J H, Li X H and Liu X F. 2008. Geomat.Inform. Scienc. Wuhan Univ., 33(1), 25, 2008.

17. Zhang Y, Meng Q Y, Wu J L, Zhao F., Spectr. Spectr.Anal., 31 ( 10 ), 2789, 2011.

18. Zeng Q H, Mao J H, Li X H, Liu X F., Geom..Inform. Scien. Wuhan Univ., 33(1), 25, 2008..

19. Madugundu R, Nizalapur V, Jha C S., Int J Appl Earth Obs Geoinf, 10(2), 211, 2008.

20. Potithep S, Nagai S, Nasahara K N, et al., , Agric For Meteorol, 169(2), 148, 2013.

21. Zarate-Valdez J L, Whiting M L, Lampinen B D, et al., Comput Electron Agr, 85(1), 24, 2012.

22. Luo Shezhou, Cheng Feng, Wang Fang, et al. Rem. Sens. Techn. Appl., 27(5), 740, 2012.

23. Heiskanen J, Rautiainen M, Korhonen L, et al. Int J Appl Earth Obs Geoinf, 13(4), 595, 2011.

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