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REMOTE SENSING FOR LAND & RESOURCES    2017, Vol. 29 Issue (2) : 187-192     DOI: 10.6046/gtzyyg.2017.02.27
Contents |
Extraction of floating-leaved vegetation information based on HyMap data
TAO Ting1, RUAN Renzong1, SUI Xiuzhen2, WANG Yuqiang3, LIN Peng1
1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China;
2. Yiwu City, Zhejiang Province Survey and Design Institute, Yiwu 322000, China;
3. Shandong Province Disaster Reduction Center, Ji’nan 250000, China;
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Abstract  In this paper, Sacramento, California - San Joaquin River Delta was taken as the study area, and HyMap hyperspectral data with 3 m spatial resolution acquired in June 2007 combined with ground truth data were used for pattern recognition of floating-leaved vegetation in the study area. The study was based on the spectral differences of wetland vegetations, and the “trilateral” parameters of vegetation were analyzed. Then the authors selected suitable vegetation indices combined with “trilateral” parameter features and built a decision tree model to extract the floating-leaved vegetation of the study area in comparison with the maximum likelihood classification results. The results show that the use of decision tree classification model can achieve overall accuracy of 82.68%, and that, compared with the maximum likelihood method, the total accuracy was improved by 6%, which can well identify the floating-leaved vegetation in the wetland vegetation of the study area.
Keywords threshold      mask image      interference information      overlap      ERDAS     
Issue Date: 03 May 2017
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HAN Lirong. Extraction of floating-leaved vegetation information based on HyMap data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 187-192.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2017.02.27     OR     https://www.gtzyyg.com/EN/Y2017/V29/I2/187
[1] Marion L,Paillisson J M.A mass balance assessment of the contribution of floating-leaved macrophytes in nutrient stocks in an eutrophic macrophyte-dominated Lake[J].Aquatic Botany,2003,75(3):249-260.
[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] Andrew M E,Ustin S L.The role of environmental context in mapping invasive plants with hyperspectral image data[J].Remote Sensing of Environment,2008,112(12):4301-4317.
[4] 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.
[5] 客 涵.南四湖沉水、浮叶植物群落结构与水环境因子相关研究[D].济南:山东大学,2014.
Ke H.Research on Relationship Among Submerged,Floating Plant Community Structure and Water Environmental Factors[D].Ji’nan:Shandong University,2014.
[6] Matthews M W,Bernard S,Robertson L.An algorithm for detecting trophic status(chlorophyll-a),cyanobacterial-dominance,surface scums and floating vegetation in inland and coastal waters[J].Remote Sensing of Environment,2012,124:637-652.
[7] Rauch A,Fesl C,Schagerl M.Influence of environmental variables on algal associations from a floating vegetation mat(Schwingmoor Lake Lunzer Obersee,Austria)[J].Aquatic Botany,2006,84(2):129-136.
[8] Andrew M E,Ustin S L.Habitat suitability modelling of an invasive plant with advanced remote sensing data[J].Diversity and Distributions,2009,15(4):627-640.
[9] 柴 颖,阮仁宗,傅巧妮.高光谱数据湿地植被类型信息提取[J].南京林业大学学报:自然科学版,2015,39(1):181-184.
Chai Y,Run R Z,Fu Q N.Extraction of wetland vegetation information using hyperspectral image data[J].Journal of Nanjing Forestry University:Natural Sciences Edition,2015,39(1):181-184.
[10] Gitelson A A,Kaufman Y J,Stark R,et al.Novel algorithms for remote estimation of vegetation fraction[J].Remote Sensing of Environment,2002,80(1):76-87.
[11] Underwood E C,Ustin S L,Ramirez C M.A comparison of spatial and spectral image resolution for mapping invasive plants in coastal California[J].Environmental Management,2007,39(1):63-83.
[12] Sims D A,Gamon J A.Estimation of vegetation water content and photosynthetic tissue area from spectral reflectance:A comparison of indices based on liquid water and chlorophyll absorption features[J].Remote Sensing of Environment,2003,84(4):526-537.
[13] 潘 琛,杜培军,张海荣.决策树分类法及其在遥感图像处理中的应用[J].测绘科学,2008,33(1):208-211.
Pan C,Du P J,Zhang H R.Decision tree classification and its application in processing of remote sensing images[J].Science of Surveying and Mapping,2008,33(1):208-211.
[14] 赵 萍.基于知识的江南典型区土地利用/覆被分类研究[D].南京:南京大学,2003.
Zhao P.Study on Knowledge-Based Jiangnan Typical Area Land Use/Cover Classification[D].Nanjing:Nanjing University,2003.
[15] 韦 玮.基于多角度高光谱CHRIS数据的湿地信息提取技术研究[D].北京:中国林业科学研究院,2011.
Wei W.Study on Wetland Information Extraction Using Multi-Angle Hyperspectral CHRIS Image Data[D].Beijing:Chinese Academy of Forestry,2011.
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