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Potential of Digital Sensors for Land Cover and Tree Species Classifications – A Case Study in the Framework of the DGPF-Project Potenzial digitaler Sensoren zur Klassifizierung der Landbedeckung und Baumarten – eine Fallstudie im Rahmen des DGPF-Projektes

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The study is intended as a contribution to assessing the value of digital image data for semi-automatic analysis of classified land cover and tree species and was carried out in the framework of the DGPF-project. Sensor specific strengths of ADS40-2, 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 the 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 partly covered deciduous tree species.

Anhand der Bilddaten aus den Kamerasystemen ADS40-2nd, Quattro DigiCAM, DMC, JAS-150, Ultracam-X, und RMK-Top15 wurden zwei Klassifikationsverfahren (Maximum Likelihood und logistische Regression) getestet. Dabei wurden sensor-spezifische Eigenschaften erläutert, sowie die Stärken und Schwächen 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 atmosphärischen Verhältnissen vorherrschten. Quantitative Analysen zeigen, dass sich mit jedem Kamerasysteme sehr ähnlich gute Resultate erzielen liessen. Das erste Verfahren zeigt für 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 für die unterschiedlichen Kameras. Hierzu soll in weiteren Analysen das Klassifikationsverfahren an die jeweiligen Kameras angepasst werden. Für das zweite Verfahren liegt der Kappa Koeffizient für 8 Baumarten zwischen 0,7 und 0,9. Bei diesem Verfahren soll in zukünftigen Analysen die Genauigkeit der Erkennung von nicht dominanten, kleinen und teilweise verdeckten Baumarten erhöht werden.


Document Type: Research Article


Publication date: May 1, 2010

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  • Photogrammetrie - Fernerkundung - Geoinformation (PFG) is an international scholarly journal covering the progress and application of photogrammetric methods, remote sensing technology and the intricately connected field of geoinformation processing.

    Papers published in PFG highlight new developments and applications of these technologies in practice. The journal hence addresses both researchers and student of these disciplines at academic institutions and universities and the downstream users in both the private sector and public administration.

    PFG places special editorial emphasis on the communication of new methodologies in data acquisition, new approaches to optimized processing and interpretation of all types of data which were acquired by photogrammetric methods, remote sensing, image processing and the computer-aided interpretation of such data in general.

    PFG is the official journal of the German Society of Photogrammetry and Remote Sensing.
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