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• W2158918975 abstract "Summary: The study is intended as a contribution to assessing the value of digital image data for semi-automatic analysis for classifying land cover and tree species and was carried out in the framework of the DGPF-project. Sensor specific strengths of ADS40-2 nd , Quattro DigiCAM, DMC, JAS-150, Ultracam-X, and RMK-Top15 cameras and weakness for classification purposes are presented and shortly discussed. The first approach is based on a maximum likelihood method in combination with a decision tree and produces 13 land cover classes. The second approach is based on logistic regression models and produces eight tree species classes. The classified images were visually assessed and quantitatively analyzed. The accuracy assessment reveals that in both approaches similar classification results are obtained by all sensors with overall Kappa coefficients between 0.6 and 0.9. However, a real sensor comparison was not possible since the image data was acquired at different dates. Thus, some variations in classification results are due to phenological differences and different illumination and atmospheric conditions. It is planned for the future that the classifications of the first approach will be adjusted to the characteristics of each sensor. In the second approach, further work is needed to improve distinguishing non-dominant, small and covered deciduous tree species. Zusammenfassung: Potenzial digitaler Sensoren zur Klassifizierung der Landbedeckung und Baumarten - eine Fallstudie im Rahmen des DGPF-Projektes. Anhand der Bilddaten aus den Kamerasystemen ADS40-2 nd , Quattro DigiCAM, DMC, JAS-150, Ultracam-X, und RMK-Top15 wurden zwei Klassifikationsverfahren (Maximum Likelihood und logistische Regression) getestet. Dabei wurden sensor-spezifische Eigenschaften erlautert, sowie die Starken und Schwachen der einzelnen Systeme aufgezeigt. Die Resultate wurden visuell und quantitativ bewertet. Direkte Sensorvergleiche erwiesen sich dabei als schwierig, da zum Aufnahmezeitpunkt der einzelnen Bilddaten sowohl eine unterschiedliche Vegetationsentwicklung wie auch Unterschiede in den Beleuchtungs- und atmospharischen Verhaltnissen vorherrschten. Quantitative Analysen zeigen, dass sich mit jedem Kamerasysteme sehr ahnlich gute Resultate erzielen liessen. Das erste Verfahren zeigt fur 13 Landnutzungsklassen Kappa Koeffizienten von gut 0.6 bei allen verwendeten Systemen. Allerdings unterscheidet sich die Genauigkeit der einzelnen spezifischen Klassen wie Mais oder Kartoffeln fur die unterschiedlichen Kameras. Hierzu soll in weiteren Analysen das Klassifikationsverfahren an die jeweiligen Kameras angepasst werden. Fur das zweite Verfahren liegt der Kappa Koeffizient fur 8 Baumarten zwischen 0.7 und 0.9. Bei diesem Verfahren soll in zukunftigen Analysen die Genauigkeit der Erkennung von nicht dominanten, kleinen und verdeckten Baumarten erhoht werden." @default.
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• W2158918975 date "2010-05-01" @default.
• W2158918975 modified "2023-09-26" @default.
• W2158918975 title "Potential of Digital Sensors for Land Cover and Tree Species Classifications A Case Study in the Framework of the DGPF-Project" @default.
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• W2158918975 doi "https://doi.org/10.1127/1432-8364/2010/0046" @default.
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