Unsupervised segmentation methods are important to extract boundary features from large forest vegetation databases. Finding optimized segmentation algorithms for images with natural vegetation is crucial because of the computational load and the required reproducibility of results. In this paper, we present an approach how to automatically select optimized parameter values for JSEG segmentation. The parameter evaluation is based on a spatial comparison between segmented regions and manually acquired ground truth. City block distance will be used as error metric to define discrepancies between available ground truth and segmentation. Varying the parameter range of values systematically allows to compute corresponding error areas. The smallest error area represents the optimized parameter value.Dependent on the lightness distribution of the selected images and the chosen color quantization, the spatial comparison with the ground truth is limited to local optimization.