全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于遗传神经网络的克钦湖叶绿素反演研究

DOI: 10.11867/j.issn.1001-8166.2012.02.0202, PP. 202-208

Keywords: 人工神经网络,遗传算法,克里格内插法,高光谱,叶绿素a

Full-Text   Cite this paper   Add to My Lib

Abstract:

]叶绿素a浓度能够在一定程度上反映内陆湖泊水质情况。为实现对克钦湖水体叶绿素a浓度的监测,于2010年8月15日对克钦湖进行了现场光谱测量和同步采样。通过分析叶绿素a浓度和光谱数据之间的关系,建立基于反射比、人工神经网络和遗传神经网络的叶绿素a浓度估测模型。结果表明利用R700nm/R670nm反射比建立的模型估测精度为R2=0.67;人工神经网络模型的估测精度较高,R2=0.882;将遗传算法引入神经网络之后,模型的估测精度进一步提高,R2达到0.956,将模型预测的结果与克里格内插法相结合对研究区的叶绿素a空间分布情况进行定量估测,发现北湖的叶绿素a浓度明显高于南湖,有由北向南逐渐递减的趋势,这为今后利用高光谱数据对克钦湖叶绿素a浓度大面积遥感反演提供了研究基础。

References

[1]  Li Suju, Wu Qing, Wang Xuejun, et al. Correlations between reflectance spectra and contents of chlorophyll-a in Chaohu Lake[J]. Journal of Lake Sciences, 2002, 14(3): 228-234.[李素菊,吴情,王学军,等.巢湖浮游植物叶绿素含量与反射光谱特征的关系[J]. 湖泊科学,2002,14(3): 228-234.]
[2]  Song Ping, Liu Yuanbo, Liu Chunyan. Advances in satellite retrieval of terrestrial surface water parameters[J].Advances in Earth Science,2011,7(26): 731-740.[宋平,刘元波,刘春燕.陆地水体参数的卫星遥感反演研究进展[J]. 地球科学进展,2011,7(26):731-740.]
[3]  Shu Xiaozhou, Yin Qiu, Kuang Dingbo. Relationship between algal chlorophyll concentration and spectral reflectance of inland water[J].Journal of Remote Sensing,2000, 4(1): 41-45.[疏小舟,尹球,匡定波. 内陆水体藻类叶绿素浓度与反射光谱特征的关系[J]. 遥感学报,2000,4(1): 41-45.]
[4]  Hoogenboom H J,Dekker A G,Althuis I J A. Simulation of AVIRIS sensitivity for detecting Chlorophyll over coastal and inland waters[J]. Remote Sensing of Environment,1998,65: 333-340.
[5]  Frater R N. Hyperspectral remote sensing of turbidity and chlorophyll-a among nebraska sand hills lakes[J].International Journal of Remote Sensing, 1998, 19(8): 1 579-1 589.
[6]  Lu Zhijuan, Zhu Ling, Pei Hongping, et al. The model of chlorophyll-a concentration forecast in the West Lake based on wavelet analysis and BP neural networks[J]. Acta Ecological Sinica, 2008, 28(10): 4 965-4 973.[卢志娟,朱玲,裴洪平,等.基于小波分析与BP神经网络的西湖叶绿素a浓度预测模型[J]. 生态学报, 2008,28(10): 4 965-4 973.]
[7]  Kong Weijuan, Ma Ronghua, Duan Hongtao. The neural network model for estimation of chlorophyll-a with water temperature in Lake Taihu[J]. Journal of Lake Sciences, 2009, 21(2): 193-198.[孔维娟,马荣华,段洪涛. 结合温度因子估算太湖叶绿素a含量的神经网络模型[J]. 湖泊科学,2009,21(2): 193-198.]
[8]  Gao Duo, Fang Shenghui, Zhang Xuehu, et al. Water quality parameter identification of Wuhan Donghu Lake based on genetic algorithm[J]. Journal of Geomatics, 2006, 31(6): 42-44. [高铎,方圣辉,张雪虎,等. 基于遗传算法的东湖水质参数反演方法探讨[J]. 测绘信息与工程, 2006, 31(6): 42-44.]
[9]  Yi Weihong, Yang Liu, Zhang Zhengxiang. Method of wetland classification based on Landsat7 ETM+ image[J].Wetland Science, 2004, 2(3):208-212.[衣伟宏,杨柳,张正祥.基于ETM+影像的扎龙湿地遥感分类研究[J]. 湿地科学,2004,2(3): 208-212.]
[10]  Tang Junwu, Tian Guoliang, Wang Xiaoyong, et al. The methods of water spectra measurement and analysisⅠ: Above-water method[J]. Journal of Remote Sensing, 2004, 8(1): 37-44.[唐军武,田国良,汪小勇,等.水体光谱测量与分析Ⅰ:水面以上测量法[J]. 遥感学报,2004,8(1): 37-44.]
[11]  Jiao Hongbo, Zha Yong, Li Yunmei, et al. Modelling chlorophyll-a concentration in Taihu Lake from hyperspectral reflectance data[J]. Journal of Remote Sensing, 2006, 10(2): 242-248.[焦红波,查勇,李云梅,等. 基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型[J]. 遥感学报,2006,10(2):242-248.]
[12]  Oron G,Gitelson A. Real-time quality monitoring by remote sensing of contaminated water-bodies: Waste stabilization pond effluent[J]. Water Research,1996,30(12): 3 106-3 114.
[13]  Gitelson A. The peak near 700nm on radiance spectra of algae and water relationships of its magnitude and position with chlorophyll[J]. Internation Journal of Remote Sensing,1993,13(17): 3 367-3 373.
[14]  Liu Ying, Wang Ke, Zhou Bin, et al. Preliminary study on hyperspectral remote sensing of Qiandao Lake chlorophyll-a concentration[J].Journal of Zhejiang University (Agriculture & Life Science),2003, 29(6): 621-626.[刘英,王珂,周斌,等. 千岛湖水体叶绿素浓度高光谱遥感检测初报[J]. 浙江大学学报:农业与生命科学版,2003,29(6): 621-626.]
[15]  Koponen S,Pulliainen J,Kallio K,et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data[J].Remote Sensing of Environment,2002,79:51-59.
[16]  Li Weichao, Song Dameng, Chen Bin. Artificial neural network based on genetic algorithm[J]. Computer Engineering and Design, 2006, 27(2): 316-318.[李伟超,宋大盟,陈斌. 基于遗传算法的人工神经网络[J]. 计算机工程与设计,2006,27(2): 316-318.]
[17]  Zhou Shiguan, Li Zhongxia. Genetic algorithm for optimization of neural network structure and weight distribution[J]. Measurement and Control Technique,2004, 23(4): 48-49. [周世官,李钟侠.神经网络结构及其权值优化的遗传算法[J]. 测控技术,2004,23(4):48-49.]
[18]  Yu Jianli, Kroumov V, Sun Zengqi, et al. Fast algorithm for path planning based on neural network[J]. Robot,2001, 23(3): 150-158.[禹建丽,Kroumov V,孙增圻,等.一种快速神经网络路径规划算法[J]. 机器人,2001,23(3): 150-158.]
[19]  Jin Jianbin, Wang Yuanqin, Chen Yuan. Application of artificial neural network based on genetic algorithm to cooperative transport of multi-robots system[J]. Computer and Modernization,2010, 1(9): 88-91.[靳建彬,王元钦,陈源.基于遗传算法的BP神经网络优化策略研究[J]. 计算机与现代化,2010,1(9): 88-91.]
[20]  Wang Xuejun. The combination of spatial analysis technique and GIS[J].Geographical Research, 1997, 16(3): 70-74.[王学军. 空间分析技术与地理信息系统的结合[J]. 地理研究,1997,16(3): 70-74.]

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133