牧草品质的高光谱遥感监测模型研究

doi:10.3964/j.issn.1000-0593(2015)10-2851-05

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Abstract Crude protein (CP), crude fat (CFA) and crude fiber (CFI) are key indicators for evaluation of the quality and feeding value of pasture. Hence, identification of these biological contents is an essential practice for animal husbandry. As current approaches to pasture quality estimation are time-consuming and costly, and even generate hazardous waste, a real-time and non-destructive method is therefore developed in this study using pasture canopy hyperspectral data. A field campaign was carried out in August 2013 around Qinghai Lake in order to obtain field spectral properties of 19 types of natural pasture using the ASD Field Spec 3, a field spectrometer that works in the optical region (350～2 500 nm) of the electromagnetic spectrum. In additional to the spectral data, pasture samples were also collected from the field and examined in laboratory to measure the relative concentration of CP (%), CFA (%) and CFI (%). After spectral denoising and smoothing, the relationship of pasture quality parameters with the reflectance spectrum, the first derivatives of reflectance (FDR), band ratio and the wavelet coefficients (WCs) was analyzed respectively. The concentration of CP, CFA and CFI of pasture was found closely correlated with FDR with wavebands centered at 424, 1 668, and 918 nm as well as with the low-scale (scale=2, 4) Morlet, Coiflets and Gassian WCs. Accordingly, the linear, exponential, and polynomial equations between each pasture variable and FDR or WCs were developed. Validation of the developed equations indicated that the polynomial model with an independent variable of Coiflets WCs (scale=4, wavelength=1 209 nm), the polynomial model with an independent variable of FDR, and the exponential model with an independent variable of FDR were the optimal model for prediction of concentration of CP, CFA and CFI of pasture, respectively. The R² of the pasture quality estimation models was between 0.646 and 0.762 at the 0.01 significance level. Results suggest that the first derivatives or the wavelet coefficients of hyperspectral reflectance in visible and near-infrared regions can be used for pasture quality estimation, and that it will provide a basis for real-time prediction of pasture quality using remote sensing techniques.

Key words：Pasture quality parameters;Hyperspectral;Remote sensing inversion;Wavelet analysis

Received: 2014-07-13 Accepted: 2014-11-09

ZTFLH:

TP274

Corresponding Authors: GONG Cai-lan E-mail: mwwsitp@gmail.com

Cite this article:

MA Wei-wei,GONG Cai-lan,HU Yong, et al. Hyperspectral Remote Sensing Estimation Models for Pasture Quality [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(10): 2851-2855.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2015)10-2851-05 OR https://www.gpxygpfx.com/EN/Y2015/V35/I10/2851

[1] ZHENG Kai，GU Hong-ru，SHEN Yi-xin，et al(郑凯，顾洪如，沈益新，等). Pratacultural Science(草业科学)，2006(5): 57.
[2] Norris K H，Barnes R F，Moore J E，et al. Journal of Animal Science，1976，43(4): 889.
[3] Valdes E V. Journal of Animal Science，1985，65(3): 753.
[4] ZHAO Huan-huan，HU Yue-gao，ZHAO Qi-bo，et al(赵环环，胡跃高，赵其波，等). Chinese Journal of Animal Nutrition (动物营养学报)，2001，(4): 40.
[5] Starks P J，Zhao D，Phillips W A，et al. Crop Science，2006，46(2): 927.
[6] Starks P J，Zhao D，Phillips W A，et al. Grass and Forage Science，2006，61(2): 101.
[7] HUANG Wen-jiang，WANG Ji-hua，LIU Liang-yun，et al (黄文江，王纪华，刘良云，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2004，(4): 203.
[8] XU Xin-gang，ZHAO Chun-jiang，WANG Ji-hua，et al(徐新刚，赵春江，王纪华，等). Journal of Infrared and Millmeter Waves(红外与毫米波学报)，2013，(4): 351.
[9] WANG Xiu-zhen，HUANG Jing-feng，LI Yun-mei，et al(王秀珍，黄敬峰，李云梅，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2003，(2): 144.
[10] LIANG Hui-ping，LIU Xiang-nan(梁惠平，刘湘南). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报)，2010，(1): 250.
[11] China Meteorological Administration(中国气象局). The Standard of Ecology Monitoring in the Grassland(草地生态监测标准)，2008.
[12] HUANG Jing-feng，WANG Fu-min，WANG Xiu-zhen(黄敬峰，王福民，王秀珍). Hyperspectral Experiment for Paddy Rice Remote Sensing(水稻高光谱遥感实验研究). Hangzhou: Zhejiang University Press(杭州: 浙江大学出版社)，2010.
[13] SONG Kai-shan，ZHANG Bai，WANG Zong-ming，et al(宋开山，张柏，王宗明，等). Journal of Plant Ecology(植物生态学报)，2008，(1): 152.
[14] GUO Yang-yang，ZHANG Lian-peng，WANG De-gao，et al(郭洋洋，张连蓬，王德高，等). Bulletin of Surveying and Mapping(测绘通报)，2010，(8): 31.
[15] Zhang J，Yuan L，Pu R，et al. Computers and Electronics in Agriculture，2014，100(0): 79.
[16] Blackburn G A. International Journal of Remote Sensing，1998，19(4): 657.
[17] DUAN Ya-li，SU Rong-guo，SHI Xiao-yong，et al(段亚丽，苏荣国，石晓勇，等). Chinese Journal of Lasers(中国激光)，2012，(07): 220.