Modelling of Forest Structure with Remote Sensing Data: Potential and Limits of Hyperspectral Information - Softcover
Synopsis
Forest structure, e.g. the composition and distribution of tree species, is a key element for characterizing ecological functions and ecological state of forest ecosystems. The aim of this master thesis was to model 15 forest structure measures of a mixed temperate forest in the Bavarian Forest National Park (Germany) with hyperspectral remote sensing data (HyMap). The findings indicated that hyperspectral data has high potential to identify forest structure even in heterogeneous mixed forests like the Bavarian Forest National Park. Forest structure measures were derived from vegetation surveys on 102 ground plots and served as dependent variables in a decision tree based random forest model. The independent variables were obtained from hyperspectral data in 7 m resolution, which was transformed by a minimum noise fraction rotation (MNF). With these two datasets random forest model performance on each forest structure measure was compared to model performance derived from literature. Furthermore, descriptive statistics, correlation analysis and ordination methods were used to discuss the results.
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About the Author
Hannes Müller, M.Sc.: Studied environmental science (geoecology) at the University of Bayreuth, since 2011 PhD student at the Humboldt Universität zu Berlin.
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Modelling of Forest Structure with Remote Sensing Data
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Forest structure, e.g. the composition and distribution of tree species, is a key element for characterizing ecological functions and ecological state of forest ecosystems. The aim of this master thesis was to model 15 forest structure measures of a mixed temperate forest in the Bavarian Forest National Park (Germany) with hyperspectral remote sensing data (HyMap). The findings indicated that hyperspectral data has high potential to identify forest structure even in heterogeneous mixed forests like the Bavarian Forest National Park. Forest structure measures were derived from vegetation surveys on 102 ground plots and served as dependent variables in a decision tree based random forest model. The independent variables were obtained from hyperspectral data in 7 m resolution, which was transformed by a minimum noise fraction rotation (MNF). With these two datasets random forest model performance on each forest structure measure was compared to model performance derived from literature. Furthermore, descriptive statistics, correlation analysis and ordination methods were used to discuss the results. 92 pp. Englisch. Seller Inventory # 9783639473414
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mueller HannesHannes Mueller, M.Sc.: Studied environmental science (geoecology) at the University of Bayreuth, since 2011 PhD student at the Humboldt Universitaet zu Berlin.Forest structure, e.g. the composition and distribution of . Seller Inventory # 4991222
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Modelling of Forest Structure with Remote Sensing Data
Published by
AV Akademikerverlag Nov 2013, 2013
ISBN 10: 3639473418
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Forest structure, e.g. the composition and distribution of tree species, is a key element for characterizing ecological functions and ecological state of forest ecosystems. The aim of this master thesis was to model 15 forest structure measures of a mixed temperate forest in the Bavarian Forest National Park (Germany) with hyperspectral remote sensing data (HyMap). The findings indicated that hyperspectral data has high potential to identify forest structure even in heterogeneous mixed forests like the Bavarian Forest National Park. Forest structure measures were derived from vegetation surveys on 102 ground plots and served as dependent variables in a decision tree based random forest model. The independent variables were obtained from hyperspectral data in 7 m resolution, which was transformed by a minimum noise fraction rotation (MNF). With these two datasets random forest model performance on each forest structure measure was compared to model performance derived from literature. Furthermore, descriptive statistics, correlation analysis and ordination methods were used to discuss the results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch. Seller Inventory # 9783639473414
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Modelling of Forest Structure with Remote Sensing Data : Potential and Limits of Hyperspectral Information
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Forest structure, e.g. the composition and distribution of tree species, is a key element for characterizing ecological functions and ecological state of forest ecosystems. The aim of this master thesis was to model 15 forest structure measures of a mixed temperate forest in the Bavarian Forest National Park (Germany) with hyperspectral remote sensing data (HyMap). The findings indicated that hyperspectral data has high potential to identify forest structure even in heterogeneous mixed forests like the Bavarian Forest National Park. Forest structure measures were derived from vegetation surveys on 102 ground plots and served as dependent variables in a decision tree based random forest model. The independent variables were obtained from hyperspectral data in 7 m resolution, which was transformed by a minimum noise fraction rotation (MNF). With these two datasets random forest model performance on each forest structure measure was compared to model performance derived from literature. Furthermore, descriptive statistics, correlation analysis and ordination methods were used to discuss the results. Seller Inventory # 9783639473414
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Modelling of Forest Structure with Remote Sensing Data | Potential and Limits of Hyperspectral Information
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Taschenbuch. Condition: Neu. Modelling of Forest Structure with Remote Sensing Data | Potential and Limits of Hyperspectral Information | Hannes Müller | Taschenbuch | 92 S. | Englisch | 2013 | AV Akademikerverlag | EAN 9783639473414 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 105554864
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