Synopsis
Delineation of forest types was made from 6 scenes of LANDSAT data and validated with National Forest Inventory (NFI) of Germany. Boundary of forest types was used from the official ATKIS vector data to cut out only forest cover. Algorithms for classification were selected to distinguish forest types with training data from NFI and used machine learning approach implemented in caret package of R statistical language. Both pixel based and object based image analysis (PBIA and OBIA) were applied. OBIA resulted the best approach. Mixed behavior was found in the accuracy of the classifications. In general the SVM was the best for 4 of the 6 scenes under evaluation. KNN and RF resulted the best for the rest of the scenes. General schema of the procedure is presented and tips for using every classification algorithm are disused.
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About the Author
Juan Ygnacio López Hernández born in Carora in 1967, Lara State, Venezuela. His degrees are: Forest Engenier (1993) University of Los Andes (ULA) Mérida, Venezuela, MSc ULA (1996) and PhD at Albert Ludwigs Freiburg University, Germany (2012). Today is head professor at ULA and chair of the Photogrammetry and Remote Sensing laboratory.
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Development of a Method for Forest Type Detection
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ISBN 10: 6202025409
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Delineation of forest types was made from 6 scenes of LANDSAT data and validated with National Forest Inventory (NFI) of Germany. Boundary of forest types was used from the official ATKIS vector data to cut out only forest cover. Algorithms for classification were selected to distinguish forest types with training data from NFI and used machine learning approach implemented in caret package of R statistical language. Both pixel based and object based image analysis (PBIA and OBIA) were applied. OBIA resulted the best approach. Mixed behavior was found in the accuracy of the classifications. In general the SVM was the best for 4 of the 6 scenes under evaluation. KNN and RF resulted the best for the rest of the scenes. General schema of the procedure is presented and tips for using every classification algorithm are disused. 192 pp. Englisch. Seller Inventory # 9786202025409
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Development of a Method for Forest Type Detection
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Development of a Method for Forest Type Detection
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Development of a Method for Forest Type Detection
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ISBN 10: 6202025409
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Delineation of forest types was made from 6 scenes of LANDSAT data and validated with National Forest Inventory (NFI) of Germany. Boundary of forest types was used from the official ATKIS vector data to cut out only forest cover. Algorithms for classification were selected to distinguish forest types with training data from NFI and used machine learning approach implemented in caret package of R statistical language. Both pixel based and object based image analysis (PBIA and OBIA) were applied. OBIA resulted the best approach. Mixed behavior was found in the accuracy of the classifications. In general the SVM was the best for 4 of the 6 scenes under evaluation. KNN and RF resulted the best for the rest of the scenes. General schema of the procedure is presented and tips for using every classification algorithm are disused.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 192 pp. Englisch. Seller Inventory # 9786202025409
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Development of a Method for Forest Type Detection
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Taschenbuch. Condition: Neu. Development of a Method for Forest Type Detection | Juan Ygnacio López Hernández | Taschenbuch | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202025409 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 113378485
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Development of a Method for Forest Type Detection
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ISBN 10: 6202025409
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Delineation of forest types was made from 6 scenes of LANDSAT data and validated with National Forest Inventory (NFI) of Germany. Boundary of forest types was used from the official ATKIS vector data to cut out only forest cover. Algorithms for classification were selected to distinguish forest types with training data from NFI and used machine learning approach implemented in caret package of R statistical language. Both pixel based and object based image analysis (PBIA and OBIA) were applied. OBIA resulted the best approach. Mixed behavior was found in the accuracy of the classifications. In general the SVM was the best for 4 of the 6 scenes under evaluation. KNN and RF resulted the best for the rest of the scenes. General schema of the procedure is presented and tips for using every classification algorithm are disused. Seller Inventory # 9786202025409