YIC-IR  > 海岸带信息集成与综合管理实验室
Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery
Ai, JQ; Gao, W; Gao, ZQ; Shi, RH; Zhang, C; Liu, CS; Shi, RH (reprint author), E China Normal Univ, Coll Geog Sci, Shanghai 200241, Peoples R China.
2016-04-07
发表期刊JOURNAL OF APPLIED REMOTE SENSING
ISSN1931-3195
卷号10期号:2页码:026001
产权排序[Ai, Jinquan; Gao, Wei; Shi, Runhe; Zhang, Chao; Liu, Chaoshun] E China Normal Univ, Coll Geog Sci, Shanghai 200241, Peoples R China; [Ai, Jinquan; Gao, Wei; Shi, Runhe; Zhang, Chao; Liu, Chaoshun] E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China; [Gao, Wei] Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA; [Gao, Zhiqiang] Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
摘要Accurate mapping of invasive species in a cost-effective way is the first step toward understanding and predicting the impact of their invasions. However, it is challenging in coastal wetlands due to confounding effects of biodiversity and tidal effects on spectral reflectance. The aim of this work is to describe a method to improve the accuracy of mapping an invasive plant (Spartina alterniflora), which is based on integration of pan-sharpening and classifier ensemble techniques. A framework was designed to achieve this goal. Five candidate image fusion algorithms, including principal component analysis fusion algorithm, modified intensity-huesaturation fusion algorithm, wavelet-transform fusion algorithm, Ehlers fusion algorithm, and Gram-Schmidt fusion algorithm, were applied to pan-sharpening Landsat 8 operational land imager (OLI) imagery. We assessed the five fusion algorithms with respect to spectral and spatial fidelity using visual inspection and quantitative quality indicators. The optimal fused image was selected for subsequent analysis. Then, three classifiers, namely, maximum likelihood, artificial neural network, and support vector machine, were employed to preclassify the fused and raw OLI 30-m band images. Final object-based S. alterniflora maps were generated through classifier ensemble analysis of outcomes from the three classifiers. The results showed that the introduced method obtained high classification accuracy, with an overall accuracy of 90.96% and balanced misclassification errors between S. alterniflora and its coexistent species. We recommend future research to adopt the proposed method for monitoring long-term or multiseasonal changes in land coverage of invasive wetland plants. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
关键词Invasive Wetland Plant Estuarine Data Fusion Image Classification Landsat S. Alterniflora
作者部门海岸带信息集成与综合管理实验室
学科领域Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
DOI10.1117/1.JRS.10.026001
项目资助者Ministry of Science and Technology(2014FY210600) ; Chinese Academy of Sciences(XDA11020702 ; National Nature Science Foundation of China(31500392 ; State Key Laboratory of Estuarine and Coastal Research(SKLEC-KF201411) ; Shanghai Municipal Commission of Science and Technology(15DZ1207805) ; Health and Family Planning(15GWZK0201) ; SKLEC ; KZZD-EW-14) ; 41571083)
收录类别SCI
关键词[WOS]SPATIAL-DISTRIBUTION ; CHONGMING DONGTAN ; COVER CHANGE ; SALT-MARSH ; VEGETATION ; SUPPORT ; CHINA ; ALGORITHMS ; ACCURACY ; MACHINE
文章类型Article
语种英语
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000375658700001
引用统计
被引频次:5[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.yic.ac.cn/handle/133337/17062
专题海岸带信息集成与综合管理实验室
通讯作者Shi, RH (reprint author), E China Normal Univ, Coll Geog Sci, Shanghai 200241, Peoples R China.
作者单位1.E China Normal Univ, Coll Geog Sci
2.E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci
3.Colorado State Univ, Nat Resource Ecol Lab
4.Chinese Acad Sci, Yantai Inst Coastal Zone Res
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Ai, JQ,Gao, W,Gao, ZQ,et al. Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery[J]. JOURNAL OF APPLIED REMOTE SENSING,2016,10(2):026001.
APA Ai, JQ.,Gao, W.,Gao, ZQ.,Shi, RH.,Zhang, C.,...&Shi, RH .(2016).Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery.JOURNAL OF APPLIED REMOTE SENSING,10(2),026001.
MLA Ai, JQ,et al."Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery".JOURNAL OF APPLIED REMOTE SENSING 10.2(2016):026001.
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文件名: Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery.pdf
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文件名: Errata -Integrating pan-sharpening and classifier ensemble techniques to map an invasive plant (Spartina alterniflora) in an estuarine wetland using Landsat 8 imagery(vol 10, 026001, 2016).pdf
格式: Adobe PDF
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