Please wait a minute...
REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (3) : 86-90     DOI: 10.6046/gtzyyg.2016.03.14
Species identification of wetland vegetation based on spectral characteristics
CHAI Ying1, RUAN Renzong1, CHAI Guowu2, FU Qiaoni1
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China;
2. Hydrology and Water Resources Rureau of Henan Province, Nanyang 474500, China
Download: PDF(1930 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    

Certain spectral characteristics have a direct impact on accuracy and efficiency of identifying the wetland vegetation. In this paper, the authors mapped wetland vegetation with 3 m spatial resolution for HyMap image data from Sherman Island of California's Sacramento-San Joaquin delta. The first-derivative spectral features and spectral absorption features of different species were analyzed by the method of stepwise discriminate analysis, and the spectral characteristic parameters with better classification accuracy were screened to identify species of wetland vegetation in C4.5 decision tree classifier. The results showed that the absorption features of four plants have larger differences than first-derivative spectral features. The results also showed that C4.5 decision tree classifier in combination with the first-derivative spectral characteristics and spectral absorption characteristics could be effective in distinguishing wetland vegetation and allowing for species-level detection.

Keywords mangrove forests      NDMI      MNDPI      OLI      decision tree     
:  TP751.1  
Issue Date: 01 July 2016
E-mail this article
E-mail Alert
Articles by authors
ZHANG Xuehong
Cite this article:   
ZHANG Xuehong. Species identification of wetland vegetation based on spectral characteristics[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 86-90.
URL:     OR

[1] 柳萍萍,林辉,孙华,等.高光谱数据的降维处理方法研究[J].中南林业科技大学学报,2011,31(11):34-38. Liu P P,Lin H,Sun H,et al.Dimensionality reduction method of hyperion EO-1 data[J].Journal of Central South University of Forest,2011,31(11):34-38.
[2] Hestir E L,Khanna S,Andrew M E,et al.Identification of invasive vegetation using hyperspectral remote sensing in the California Delta ecosystem[J].Remote Sensing of Environment,2008,112(11):4034-4047.
[3] 邱琳,林辉,臧卓,等.基于均值置信区间带的湿地植被高光谱特征波段选择[J].中南林业科技大学学报,2013,33(1):41-45. Qiu L,Lin H,Zang Z,et al.Hyper-spectral characteristic band selection for wetland vegetation based on mean confidence interval[J].Journal of Central South University of Forestry and Technology,2013,33(1):41-45.
[4] 刘雪华,孙岩,吴燕.光谱信息降维及判别模型建立用于识别湿地植物物种[J].光谱学与光谱分析,2012,32(2):459-464. Liu X H,Sun Y,Wu Y.Reduction of hyperspectral dimensions and construction of discriminating models for identifying wetland plant species[J].Spectroscopy and Spectral Analysis,2012,32(2):459-464.
[5] Lawrence R L,Wood S D,Sheley R L.Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications(randomForest)[J].Remote Sensing of Environment,2006,100(3):356-362.
[6] Jassby A D,Cloern J E.Organic matter sources and rehabilitation of the Sacramento-San Joaquin Delta(California,USA)[J].Aquatic Conservation:Marine and Freshwater Ecosystems,2000,10(5):323-352.
[7] Becker B L,Lusch D P,Qi J G.Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis[J].Remote Sensing of Environment,2005,97(2):238-248.
[8] 浦瑞良,宫鹏.高光谱遥感及其应用[M].北京:高等教育出版社,2000:51-61. Pu R L,Gong P.Hyperspectral Remote Sensing and Its Applications[M].Beijing:Higher Education Press,2000:51-61.
[9] Fung T,Ma H F Y,Siu W L.Band selection using hyperspectral data of subtropical tree species[J].Geocarto International,2003,18(4):3-11.
[10] 时王侠.基于粗糙集理论和C4.5算法相结合的遥感影像分类研究[D].福州:福建师范大学,2008. Shi W X.The Classification of Remote Sensing Image Based on Rough Sets and C4.5 Algorithm[D].Fuzhou:Fujian Normal University,2008.
[11] 徐元进,胡光道,张振飞.包络线消除法及其在野外光谱分类中的应用[J].地理与地理信息科学,2005,21(6):11-14. Xu Y J,Hu G D,Zhang Z F.Continuum removal and its application to the spectrum classification of field object[J].Geography and Geo-Information Science,2005,21(6):11-14.
[12] McFeeters S K.The use of the normalized difference water index(NDWI) in the delineation of open water features[J].International Journal of Remote Sensing,1996,17(7):1425-1432.
[13] 刘刚.MATLAB数字图像处理[M].北京:机械工业出版社,2010:35-39. Liu G.MATLAB Digital Image Processing[M].Beijing:China Machine Press,2010:35-39.

[1] AN Jianjian, MENG Qingyan, HU Die, HU Xinli, YANG Jian, YANG Tianliang. The detection and determination of the working state of cooling tower in the thermal power plant based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2021, 33(2): 93-99.
[2] WANG Jiaxin, SA Chula, MAO Kebiao, MENG Fanhao, LUO Min, WANG Mulan. Temporal and spatial variation of soil moisture in the Mongolian Plateau and its response to climate change[J]. Remote Sensing for Land & Resources, 2021, 33(1): 231-239.
[3] LIU Hui, QI Zengxiang, HUANG Fuqiang. Spatio-temporal difference and correlation of urbanization with avian habitats in Dongting Lake area[J]. Remote Sensing for Land & Resources, 2020, 32(3): 191-199.
[4] Gang DENG, Zhiguang TANG, Chaokui LI, Hao CHEN, Huanhua PENG, Xiaoru WANG. Extraction and analysis of spatiotemporal variation of rice planting area in Hunan Province based on MODIS time-series data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 177-185.
[5] Jisheng XIA, Mengying MA, Zhongren FU. Extraction of mechanical damage surface using GF-2 remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(2): 26-32.
[6] Qi CAO, Manjiang SHI, Liang ZHOU, Ting WANG, Lijun PENG, Shilei ZHENG. Study of the response characteristics of thermal environment with spatial and temporal changes of bare land in the mountain city[J]. Remote Sensing for Land & Resources, 2019, 31(4): 190-198.
[7] Ying LIU, Hui YUE, Enke HOU. Drought monitoring based on MODIS in Shaanxi[J]. Remote Sensing for Land & Resources, 2019, 31(2): 172-179.
[8] Linlin LIANG, Liming JIANG, Zhiwei ZHOU, Yuxing CHEN, Yafei SUN. Object-oriented classification of unmanned aerial vehicle image for thermal erosion gully boundary extraction[J]. Remote Sensing for Land & Resources, 2019, 31(2): 180-186.
[9] Chao MA, Fei YANG, Xuecheng WANG. Extracting tea plantations in southern hilly and mountainous region based on mesoscale spectrum and temporal phenological features[J]. Remote Sensing for Land & Resources, 2019, 31(1): 141-148.
[10] Yueru WANG, Pengpeng HAN, Shujing GUAN, Yu HAN, Lin YI, Tinggang ZHOU, Jinsong CHEN. Information extraction of Dracaena sanderiana planting area based on Landsat8 OLI data[J]. Remote Sensing for Land & Resources, 2019, 31(1): 133-140.
[11] Yun GE, Shunliang JIANG, Famao YE, Changlong JIANG, Ying CHEN, Yiling TANG. Aggregating CNN features for remote sensing image retrieval[J]. Remote Sensing for Land & Resources, 2019, 31(1): 49-57.
[12] Xianyu GUO, Kun LI, Zhiyong WANG, Hongyu LI, Zhi YANG. Fine classification of rice with multi-temporal compact polarimetric SAR based on SVM+SFS strategy[J]. Remote Sensing for Land & Resources, 2018, 30(4): 20-27.
[13] Ziyi WANG, Tingbin ZHANG, Guihua YI, Kanghui ZHONG, Xiaojuan BIE, Jibin WANG, Jiaojiao SUN. Extraction of hydrothermal alteration mineral groups of porphyry copper deposits using Landsat8 OLI data[J]. Remote Sensing for Land & Resources, 2018, 30(3): 89-95.
[14] Xiao SANG, Qiaozhen GUO, Yingyang PAN, Ying FU. Research on land use dynamic change and prediction in Lucheng City of Shanxi Province based on TM and OLI[J]. Remote Sensing for Land & Resources, 2018, 30(2): 125-131.
[15] Lu LIU. Urban sprawl metrics based on night-time light data for metropolitan areas[J]. Remote Sensing for Land & Resources, 2018, 30(2): 208-213.
Full text



Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech