基于高光谱数据季相特征的山地草甸植被分类识别

doi:10.3964/j.issn.1000-0593(2022)06-1939-09

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Abstract The mountain meadow on the north slope of the Tianshan Mountains has the highest grassland productivity, and the grassland degradation is serious. The classification and recognition of grassland vegetation play an important role in monitoring the background status of the grassland ecosystem. It is also the key to carrying out ecological reconstruction, which can quickly, accurately, and effectively evaluate grassland the dynamics and degree of grassland degradation. In this paper, we explored the classification method in grassland vegetation of the typical mountain meadow vegetation in the middle section of the north slope of Tianshan Mountain in Xinjiang. Firstly, a hyperspectral imaging spectrometer obtained original reflectance spectra of typical vegetation in four key growth periods (SOC710VP). Then, Savitzky-Golay filtering and the minimum noise fraction transformation (MNF) were used to smooth and reduce the dimensions of the spectrum data. Thirdly, classification models were established by the support vector machine (SVM), the backpropagation artificial neural network (BP-ANN) and the spectral angle mapper (SAM). Finally, a comparative analysis of the classification results from three models was made. The results showed that the dimension reduction and noise removal of grassland vegetation hyperspectral data could be effectively carried out by using the S-G filter and MNF transform preprocessing method. This processing reduced the redundancy of data and shortened the classification time while obtaining a smoother classification image. The parameters such as “green peak”, “red valley”, and “red edge” of mountain meadow vegetation varied greatly in different seasons. The spectral curve characteristics in the vigorous vegetation growth period (from April to May) were easier to distinguish than those in the withering date. Thus, the classification accuracy was higher in this period. The overall classification accuracy of the SVM model exceeded 90%, and the Kappa coefficient exceeded 0.9 in the green-up date (April) and tillering stage (May). Based on the SVM model, the classification accuracy of the polynomial kernel function was higher in the vigorous growth period (from April to May), and the radial basis function (RBF) showed better performance in the mature period (from June to September). BP-ANN had higher classification accuracy in the tillering stage, the overall classification accuracy was 91.07%, and the kappa coefficient was 0.89. However, the classification effect was general in other periods. Moreover, the classification time was still longer than that of SVM although after the reduction of MNF transformation dimensionality. SAM had the fastest classification speed, but the classification accuracy was low in each growth stage. The highest value was 77.80% of the overall classification accuracy in tillering stage, and the kappa coefficient was 0.73. Therefore, the SVM classification model using the polynomial kernel function was suitable for classifying and recognising mountain meadow vegetation, which had complete classification category results, higher accuracy and relatively few misclassification. It was a better classification method than BP-ANN and SAM.

Key words：Hyperspectral data; Multi-seasonal; Mountain meadow; Classification and recognition

Received: 2021-05-12 Accepted: 2021-08-16

ZTFLH:

TP79

Corresponding Authors: LIU Yan E-mail: liuyan@idm.cn

Cite this article:

ZHENG Yi,WANG Yao,LIU Yan. Study on Classification and Recognition of Mountain Meadow Vegetation Based on Seasonal Characteristics of Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1939-1947.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2022)06-1939-09 OR https://www.gpxygpfx.com/EN/Y2022/V42/I06/1939

[1] LIU Yan，NIE Lei，YANG Yun（刘艳，聂磊，杨耘）. Ecology and Environment Science（生态环境学报），2018，27（5）：802.
[2] TONG Qing-xi，ZHANG Bing，ZHANG Li-fu（童庆禧，张兵，张立福）. Journal of Remote Sensing（遥感学报），2016，20（5）：689.
[3] YANG Kai-ge，FENG Xue-zhi，XIAO Peng-feng（杨凯歌，冯学智，肖鹏峰）. National Remote Sensing Bulletin（遥感学报），2016，20（3）：409.
[4] CHAI Ying，RUAN Ren-zong，CHAI Guo-wu，et al（柴颖，阮仁宗，柴国武，等）. Remote Sensing for Land & Resource（国土资源遥感），2016，28（3）：86.
[5] WＵ Fu-yu，WANG Xue，DING Jian-wei，et al（武复宇，王雪，丁建伟, 等）. National Remote Sensing Bulletin（遥感学报）, 2020, 24(4): 439.
[6] MOU Duo-duo，LIU Lei（牟多铎，刘磊）. Remote Sensing Technology and Application（遥感技术与应用），2019，34（1）：115.
[7] LIN Chuan，GONG Zhao-ning，ZHAO Wen-ji，et al（林川，宫兆宁，赵文吉，等）. Acta Ecologica Sinica（生态学报），2013，33（4）：1172.
[8] XIAO Bo，MAO Wen-hua，LIANG Xiao-hong，et al（肖波，毛文华，梁小红，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析），2012，32（6）：1620.
[9] LIU Dai-zhi，HUANG Shi-qi，WANG Yi-ting，et al（刘代志，黄世奇，王艺婷，等）. Hyperspectral Remote Sensing Image Processing and Application（高光谱遥感图像处理与应用）. Beijing：Science Press（北京：科学出版社），2016. 2.
[10] YU Xu-chu，FENG Wu-fa，YANG Guo-peng，et al（余旭初，冯伍法，杨国鹏，等）. Analysis and Application of Hyperspectral Image（高光谱影像分析与应用）. Beijing: Science Press（北京: 科学出版社），2013. 171.
[11] DAI Xiao-ai，JIA Hu-jun，ZHANG Xiao-xue，et al（戴晓爱，贾虎军，张晓雪，等）. Remote Sensing for Land & Resources（国土资源遥感），2016，28（3）：174.
[12] XU Yong-ming（徐永明）. ENVI Software Comprehensive Practice Course（ENVI遥感软件综合实习教程）. Beijing: Science Press（北京: 科学出版社），2019. 130.
[13] SHI Gui-hua，WANG Ying-shun，HOU Qiong，et al（师桂花，王英舜，侯琼，等）. Chinese Journal of Agrometeorology(中国农业气象)，2013，34（1）：114.
[14] YANG Ke-ming，LIU Fei，SUN Yang-yang，et al（杨可明，刘飞，孙阳阳，等）. Journal of Image and Graphics（中国图象图形学报），2015，20（6）：0836.