globalchange  > 气候变化事实与影响
DOI: 10.1016/j.jag.2016.07.018
Scopus记录号: 2-s2.0-84997604985
论文题名:
The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area
作者: Richter R; , Reu B; , Wirth C; , Doktor D; , Vohland M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 52
起始页码: 464
结束页码: 474
语种: 英语
英文关键词: CARS ; Hyperspectral data ; PLS-DA ; RF ; Sample selection ; Spectral variable selection ; SVM ; Tree species classification
Scopus关键词: accuracy assessment ; airborne sensing ; algorithm ; broad-leaved forest ; floodplain forest ; image classification ; least squares method ; species richness ; spectral analysis ; stand dynamics ; tree ; vegetation classification ; Central Europe
英文摘要: The success of remote sensing approaches to assess tree species diversity in a heterogeneously mixed forest stand depends on the availability of both appropriate data and suitable classification algorithms. To separate the high number of in total ten broadleaf tree species in a small structured floodplain forest, the Leipzig Riverside Forest, we introduce a majority based classification approach for Discriminant Analysis based on Partial Least Squares (PLS-DA), which was tested against Random Forest (RF) and Support Vector Machines (SVM). The classifier performance was tested on different sets of airborne hyperspectral image data (AISA DUAL) that were acquired on single dates in August and September and also stacked to a composite product. Shadowed gaps and shadowed crown parts were eliminated via spectral mixture analysis (SMA) prior to the pixel-based classification. Training and validation sets were defined spectrally with the conditioned Latin hypercube method as a stratified random sampling procedure. In the validation, PLS-DA consistently outperformed the RF and SVM approaches on all datasets. The additional use of spectral variable selection (CARS, “competitive adaptive reweighted sampling”) combined with PLS-DA further improved classification accuracies. Up to 78.4% overall accuracy was achieved for the stacked dataset. The image recorded in August provided slightly higher accuracies than the September image, regardless of the applied classifier. © 2016 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80036
Appears in Collections:气候变化事实与影响

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作者单位: Sytematic Botany and Functional Biodiversity, Institute for Biology, Leipzig University, Johannisallee 21, Leipzig, Germany; Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research, Permoserstraße 15, Leipzig, Germany; Geoinformatics and Remote Sensing, Institute for Geography, Leipzig University, Johannisallee 19a, Leipzig, Germany; German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig, Germany; Escuela de Biología, Universidad Industrial de Santander, Cra. 27 Calle 9, Bucaramanga, Colombia

Recommended Citation:
Richter R,, Reu B,, Wirth C,et al. The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,52
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