Potential of digital sensors for land cover and tree species classifications - a case study in the framework of the DGPF-project

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.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[3]  P. Burrough.  Principles of Geographical Information Systems for Land Resources Assessment , 1986 .

[4]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[5]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[6]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[7]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[8]  H.-P. Roser,et al.  Use of a wide angle CCD line camera for BRDF measurements , 2000 .

[9]  N. Gotelli.  Predicting Species Occurrences: Issues of Accuracy and Scale , 2003 .

[10]  C. Özkan,et al.  Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities , 2004 .

[11]  Mathias Schardt,et al.  Single tree detection in very high resolution remote sensing data , 2007 .

[12]  D. Leckie,et al.  Issues in species classification of trees in old growth conifer stands , 2005 .

[13]  Eija Honkavaara,et al.  Radiometric evaluation of digital aerial cameras , 2006 .

[14]  Zuyuan Wang,et al.  Automatic Forest Boundary Delineation in Aerial Images , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[15]  Zuyuan Wang,et al.  COLOR AND LIDAR DATA FUSION: APPLICATION TO AUTOMATIC FOREST BOUNDARY DELINEATION IN AERIAL IMAGES , 2007 .

[16]  Jean-Stéphane Bailly,et al.  Boosting: a classification method for remote sensing , 2007 .

[17]  Eva Ivits,et al.  Prediction of lichen diversity in an UNESCO biosphere reserve – correlation of high resolution remote sensing data with field samples , 2007 .

[18]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[19]  Åsa Persson,et al.  Species identification of individual trees by combining high resolution LiDAR data with multi‐spectral images , 2008 .

[20]  COMPARATIVE PROPERTIES OF FOUR AIRBORNE SENSORS AND THEIR APPLICABILITY TO LAND SURFACE INTERPRETATION , 2008 .

[21]  Christian Ginzler,et al.  High‐resolution digital surface models (DSMs) for modelling fractional shrub/tree cover in a mire environment , 2008 .

[22]  Prüfung von Luftbilddaten zweier unterschiedlicher Aufnahmesensoren hinsichtlich eines optimalen Aufnahmesystems zur Erfüllung von Aufgaben von Vermessungs- und Umweltverwaltung für das Landesamt für Natur und Umwelt , 2008 .

[23]  B. Koch,et al.  Full automatic detection of tree species based on delineated single tree crowns - a data fusion approach for airborne laser scanning data and aerial photographs. , 2008 .

[24]  C. Ginzler,et al.  Potential and limits of extraction of forest attributes by fusion of medium point density LiDAR data with ADS40 and RC30 images. , 2008 .

[25]  Eija Honkavaara,et al.  Digital Airborne Photogrammetry - A New Tool for Quantitative Remote Sensing? - A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images , 2009, Remote. Sens..

[26]  Michael Cramer,et al.  The DGPF-Test on Digital Airborne Camera Evaluation - Over- view and Test Design , 2010 .

[27]  Maria von Schönermark,et al.  Status report on the evaluation of the radiometric properties of digital photogrammetric airborne cameras , 2010 .