LUO Ling, MAO Dehua, ZHANG Bai, WANG Zongming, YANG Guang. Remote Sensing Estimation for Light Use Efficiency of Phragmites australis Based on Landsat OLI over Typical Wetlands[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 524-533. DOI: 10.13203/j.whugis20180294
Citation: LUO Ling, MAO Dehua, ZHANG Bai, WANG Zongming, YANG Guang. Remote Sensing Estimation for Light Use Efficiency of Phragmites australis Based on Landsat OLI over Typical Wetlands[J]. Geomatics and Information Science of Wuhan University, 2020, 45(4): 524-533. DOI: 10.13203/j.whugis20180294

Remote Sensing Estimation for Light Use Efficiency of Phragmites australis Based on Landsat OLI over Typical Wetlands

Funds: 

The National Natural Science Foundation of China 41771383

The National Natural Science Foundation of China 41671219

More Information
  • Author Bio:

    LUO Ling, PhD, specializes in the research on remote sensing of resource and environment.luoling@iga.ac.cn

  • Corresponding author:

    MAO Dehua, PhD, associate professor.maodehua@iga.ac.cn

  • Received Date: February 28, 2019
  • Published Date: April 04, 2020
  • As a key parameter in monitoring vegetation productivity by remote sensing driven model, rapid and accurate acquisition of vegetation light use efficiency (LUE) in large area has been a key problem. Selecting typical Phragmites australis wetland in Northeast China as study area, multitemporal Landsat OLI (operational land imager) image and the object-oriented classification method were used to extract Phragmites australis wetland. Based on the principle of vegetation physiology and ecology, the relationship among LUE, vegetation indexes and chlorophyll content was analyzed, the feasibility of accurate estimation of LUE for wetland vegetation by spectral vegetation index was discussed. Results show that areas of Phragmites australis wetland in Qixinghe Wetland, Chagan Lake Wetland and Shuangtai Estuary Wetland were 122.19, 75.29 and 439.61 km2, respectively, and overall classification accuracy was more than 82%. With the exception of EVI (enhanced vegetation index), other six vegetation indices showed the same spatial pattern characteristics with those of three wetlands. Totally, values of vegetation indices for different land covers were: cultivated land > Phragmites australis > other wetland vegetation > water body. There exists close relationship among LUE, chlorophyll and vegetation index. NDVI (normalized difference vegetation index) was most sensitive to LUE (P < 0.01;R2=0.62), which was the best one to characterize LUE of Phragmites australis in this study. This study verified the theoretical hypothesis that LUE could be inversed efficiently by remote sensing vegetation index taking chlorophyll as the intermediate variable, which can provide references for the study of vegetation productivity and carbon cycle on regional scale.
  • [1]
    Rocha A V, Goulden M L. Why is Marsh Productivity So High? New Insights from Eddy Covariance and Biomass Measurements in a Typha Marsh[J]. Agricultural and Forest Meteorology, 2009, 149(1):159-168 doi: 10.1016/j.agrformet.2008.07.010
    [2]
    梅雪英, 张修峰.长江口典型湿地植被储碳、固碳功能研究——以崇明东滩芦苇带为例[J].中国生态农业学报, 2008, 16(2):269-272 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stnyyj200802001

    Mei Xueying, Zhang Xiufeng. Carbon Storage and Fixation by a Typical Wetland Vegetation in Changjiang River Estuary-A Case Study of Phragmites australis in East Beach of Chongming Island[J]. Chinese Journal of Eco-Agriculture, 2008, 16(2):269-272 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=stnyyj200802001
    [3]
    吕国红, 周广胜, 周莉, 等.盘锦湿地芦苇群落土壤碱解氮及溶解性有机碳季节动态[J].气象与环境学报, 2006, 22(4):59-63 doi: 10.3969/j.issn.1673-503X.2006.04.011

    Lü Guohong, Zhou Guangsheng, Zhou Li, et al. Seasonal Dynamics of Dissolved Organic Carbon and Available N in Panjin Reed Wetland[J]. Journal of Meteorology and Environment, 2006, 22(4):59-63 doi: 10.3969/j.issn.1673-503X.2006.04.011
    [4]
    Gamon J A, Field C B, Bilger W, et al. Remote Sensing of the Xanthophyll Cycle and Chlorophyll Fluorescence in Sunflower Leaves and Canopies[J]. Oecologia, 1990, 85:1-7 doi: 10.1007/BF00317336
    [5]
    王莉雯, 卫亚星.植被光能利用率高光谱遥感反演研究进展[J].测绘与空间地理信息, 2015, 38(6):15-22, 38 doi: 10.3969/j.issn.1672-5867.2015.06.006

    Wang Liwen, Wei Yaxing. A Review on Inversion of Vegetation Light Use Efficiency by Hyper Spectral Remote Sensing[J]. Geomatics & Spatial Information Technology, 2015, 38(6):15-22, 38 doi: 10.3969/j.issn.1672-5867.2015.06.006
    [6]
    Ruimy A, Saugier B, Dedieu G. Methodology for the Estimation of Terrestrial Net Primary Production from Remotely Sensed Data[J]. Journal of Geophysical Research:Atmospheres, 1994, 99(3):5263-5283 doi: 10.1029-93JD03221/
    [7]
    Goetz S J, Prince S D. Remote Sensing of Net Primary Production in Boreal Forest Stands[J]. Agricultural and Forest Meteorology, 1996, 78(3):149-179 doi: 10.1139-cjfr-28-3-375/
    [8]
    吕宪国.湿地科学研究进展及研究方向[J].中国科学院院刊, 2002, 17(3):170-172 doi: 10.3969/j.issn.1000-3045.2002.03.004

    Lü Xianguo. A Review and Prospect for Wetland Science[J]. Bulletin of Chinese Academy of Sciences, 2002, 17(3):170-172 doi: 10.3969/j.issn.1000-3045.2002.03.004
    [9]
    杨永兴.国际湿地科学研究的主要特点、进展与展望[J].地理科学进展, 2002(2):111-120 doi: 10.3969/j.issn.1007-6301.2002.02.003

    Yang Yongxing. Main Characteristics, Progress and Prospect of International Wetland Science Research[J]. Progress in Geography, 2002(2):111-120 doi: 10.3969/j.issn.1007-6301.2002.02.003
    [10]
    Dronova I, Gong P, Wang L. Object-Based Analysis and Change Detection of Major Wetland Cover Types and Their Classification Uncertainty During the Low Water Period at Poyang Lake, China[J]. Remote Sensing of Environment, 2015, 115(12):3220-3236 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c9813a15c725f4b1ee1e3f6b71ed3027
    [11]
    Hu Y H, Lee H B, Scarpace F. Optimal Linear Spectral Unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37:639-644 doi: 10.1109/36.739139
    [12]
    Liu H Q, Huete A R. A Feedback Based Modification of the NDVI to Minimize Canopy Background and Atmospheric Noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33:457-465 doi: 10.1109/TGRS.1995.8746027
    [13]
    Huete A R. A Soil Adjusted Vegetation Index(SAVI)[J]. Remote Sensing of Environment, 1988, 25(3):295-309 doi: 10.1016/0034-4257(88)90106-X
    [14]
    李延峰, 毛德华, 王宗明, 等.双台河口国家级自然保护区芦苇叶面积指数遥感反演与空间格局分析[J].湿地科学, 2014, 12(2):163-169 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=shidkx201402006

    Li Yanfeng, Mao Dehua, Wang Zongming, et al. Remote Sensing Retrieval and Spatial Pattern Analysis of Leaf Area Index of Phragmites australis in Shuangtai Estuary National Nature Reserve[J]. Wetland Science, 2014, 12(2):163-169 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=shidkx201402006
    [15]
    梁建平, 马大喜, 毛德华, 等.双台河口国际重要湿地芦苇湿地生物量遥感估算[J].国土资源遥感, 2016, 28(3):60-66 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gtzyyg201603010

    Liang Jianping, Ma Daxi, Mao Dehua, et al. Remote Sensing Based Estimation of Phragmites australis Aboveground Biomass in Shuangtai Estuary National Nature Reserve[J]. Remote Sensing for Land and Resources, 2016, 28(3):60-66 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gtzyyg201603010
    [16]
    Tian Y L, Luo L, Mao D H, et al. Using Landsat Images to Quantify Different Human Threats to the Shuangtai Estuary Site, China[J]. Ocean & Coastal Management, 2017, 135:56-64 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=9e4769bc36c26459c3f42dc7b6f96e50
    [17]
    李凤秀, 张柏, 刘殿伟, 等.湿地小叶章叶绿素含量的高光谱遥感估算模型[J].生态学杂志, 2008, 27(7):1077-1083 http://d.old.wanfangdata.com.cn/Periodical/stxzz200807004

    Li Fengxiu, Zhang Bai, Liu Dianwei, et al. Hyperspectral Remote Sensing Estimation Models for Chlorophylla Concentration of Calamagrostis Angustifolia[J]. Chinese Journal of Ecology, 2008, 27(7):1077-1083 http://d.old.wanfangdata.com.cn/Periodical/stxzz200807004
    [18]
    岑奕, 张良培, 村松加奈子.纪伊半岛地区植被净初级生产力的遥感应用研究[J].武汉大学学报·信息科学版, 2008, 33(12):1221-1224 http://ch.whu.edu.cn/CN/Y2008/V33/I12/1221

    Cen Yi, Zhang Liangpei, Kanako M. Net Primary Production Estimation in Kii Peninsula Using Terra/MODIS Data[J]. Geomatics and Information Science of Wuhan University, 2008, 33(12):1221-1224 http://ch.whu.edu.cn/CN/Y2008/V33/I12/1221
    [19]
    Turner D P, Ritts W D, Cohen W B, et al. Scaling Gross Primary Production (GPP) over Boreal and Deciduous Forest Landscapes in Support of MODIS GPP Product Validation[J]. Remote Sensing of Environment, 2003, 88(3):256-270 doi: 10.1016/j.rse.2003.06.005
    [20]
    Zhao M, Running S W, Nemani R R. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS)Terrestrial Primary Production to the Accuracy of Meteorological Reanalyses[J]. Journal of Geophysical Research, 2006, 111(G1):G01002 doi: 10.1029/2004JG000004
    [21]
    Wu C Y, Niu Z. Modelling Light Use Efficiency Using Vegetation Index and Land Surface Temperature from MODIS in Harvard Forest[J]. International Journal of Remote Sensing, 2012, 33(7):2261-2276 doi: 10.1080/01431161.2011.608090
    [22]
    Inoue Y, Peñuelas J, Miyata A, et al. Normalized Difference Spectral Indices for Estimating Photosynthetic Efficiency and Capacity at a Canopy Scale Derived from Hyperspectral and CO2 Flux Measurements in Rice[J]. Remote Sensing of Environment, 2008, 112(1):156-172 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=6030fbe87d3e54df71cbe6b216782881
    [23]
    Nakaji T, Ide R, Takagi K, et al. Utility of Spectral Vegetation Indices for Estimation of Light Conversion Efficiency in Coniferous Forests in Japan[J]. Agricultural and Forest Meteorology, 2008, 148(5):776-787 doi: 10.1016/j.agrformet.2007.11.006
    [24]
    Wu C Y, Niu Z, Gao S. The Potential of the Satellite Derived Green Chlorophyll Index for Estimating Midday Light Use Efficiency in Maize, Coniferous Forest and Grassland[J]. Ecological Indicators, 2012, 14(1):66-73 doi: 10.1016/j.ecolind.2011.08.018
    [25]
    Wu C Y, Chen J M, Desai A R, et al. Remote Sensing of Canopy Light Use Efficiency in Temperature and Boreal Forest of North America Using MODIS Imagery[J]. Remote Sensing of Environment, 2012, 118:60-72 doi: 10.1016/j.rse.2011.11.012
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