Potential of Digital Sensors for Land Cover and Tree Species Classifications - A Case Study in the Framework of the DGPF-Project

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(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 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.