Object based image analysis of high resolution data in the alpine forest area

A typical multi-resolution satellite sensor is working in several multi-spectral modes as well as a panchromatic mode with a much higher spatial resolution. The integration of panchromatic data into the standard multispectral analysis is not a straightforward procedure. The additional image information of the panchromatic band in combination with the multi-spectral bands however, allows to retrieve the maximum image information from a given dataset. Especially the second order statistics of the panchromatic band shows very typical behaviour in analysing different forest stand parameters. The human vision is quite capable of selecting image objects inside high resolution panchromatic data. Each ‘object of interest’ is than analysed further according to the attributes linked to the selected set of ‘objects of interest’. Using traditional image fusion techniques, such as IHS and Brovey transformation, the visual interpretation is much aided but the spectral classification is hampered, as the original pixel values are enhanced with panchromatic values. Object oriented classification techniques, well known from radar analysis and GIS based classification of raster images can deal with topology descriptions and spatial object statistics. Manual construction of spatial objects however is expensive and time consuming. A proper solution would be to use an advanced segmentation technique, such as used in the Delphi2 Creative Technologies GmbH, eCognition software to allow advanced (semi) automatic object building and object analysis. In object based image analysis, the image fusion is a trivial issue as the objects in the panchromatic band can be displayed with any given attribute, including the original pixel values of the multi-spectral bands. The object oriented classification method allows a proper segmentation of the panchromatic data into a set of spatial objects. This makes a pre-selection of the ‘objects of interest’ possible. Object based image analysis therefore offers the possibility to continue the spectral analysis with ‘fused’ images. As spatial objects are selected on the basis of the image resolution preferred by the operator, all other resolution image layers are attributes to the selected object layer. This allows any resolution to be integrated and any type of surface description, depending on aggregation preferences of the user. The whole construction of pixel-objects and the object based image analysis allows an image interpretation, which surpasses traditional spectral analysis. Object-topology and object-texture allows new ways of defining mixed pixel analysis. Also it becomes very interesting to redefine image texture analysis, not only as analysis of variance among neighbouring pixels (a filter operation), but also as spatial relationship among image-objects on different levels of resolution (Baatz,1999). In the forestry application inside the difficult area of the Bavarian Alps, GIS updating with cheap data is becoming increasingly important. Extreme snowfall and rain conditions in this and past year, including the loss of infrastructure and property, reaching into the amounts of billions of German marks shows again how important it is to gain a proper grip on the management of geo-factors in the region. Accurate and up to date maps are therefore a must and only modern sensors allow the deliverance of quick and up to date material to acquire the proper parameters to construct an optimal decision support system for the Bavarian state officials. Cheap data in this sense is not only the acquisition of quality satellite imagery, but also cheap methods of processing them with reliable accuracy. In this study, the potential of object based image analysis with a seamless forest-GIS synergy is shown. Typical forest parameters such as stand closure, erosion hazards and forest species composition can be derived from images that have 1 meter panchromatic and 5 meter multispectral resolution, the resolution expected from the new satellite generations .