KNOWLEDGE BASED METHODS FOR THE CLASSIFICATION OF FOREST PARAMETERS WITH QUICKBIRD DATA

This study makes a contribution to the automated pixel based identification of tree species and the age of forest stands with Quickbird imagery. The method used follows a hierarchical knowledge based procedure developed for vegetation monitoring in the project SARA’04(i). As the complex nature of very high resolution satellite data complicates the manual delineation of spectrally pure training areas for supervised classifications, a knowledge base is set to automatically assign the training pixels for the following maximum likelihood classification. The rules and thresholds for the knowledge base are derived from statistical investigations of a forest area in Brandenburg/ Germany. The transferability of the knowledge base to forest areas with similar natural characteristics is evaluated in other test sites in Brandenburg. To achieve transferability the knowledge base does not refer to absolute pixel values which are influenced by the conditions of illumination and date of acquisition, but uses synthetic channels like ratios and indices which are more stable against these factors. As textural measures perform strongly for the separability of forest parameters (ii) the knowledge base also includes texture channels and a choice of Haralick features (iii). The first results show a good separability of approximately 80 to 90% overall accuracy for the tree species beech, oak, robinia, larch and pine. Additionally homogeneous forest stands can be separated into age classes. The accuracy of the classification can be further improved by the integration of additional data like soil maps or old habitat maps. INTRODUCTION The use of satellite imagery has already been common practice in forest monitoring for a long time. The increasing geometric resolution of satellite images opens up new possibilities but also requires different methods of interpretation. With a geometric resolution of approximately 0.6m in the panchromatic channel and 2.5m in the four multispectral bands, Quickbird is the satellite with the highest geometrical resolution available at the moment and its spectral resolution is suitable for environmental applications. Especially the regional Nature Conservation Administration in Germany is in need of efficient procedures to cover the requirements for large-area monitoring tasks to comply with the guidelines of the EC Habitat Directive. In this context the interpretation of very high resolution satellite data could be a timeand cost-efficient alternative to the commonly used CIRairphoto interpretation. As environmental surveys require standardized, objective and repeatable proceedings, the implementation of a knowledge base is especially suitable for this task. The use of knowledge bases and additional data has proved to increase the accuracy and number of separable classes (iv), (v). The main intention of this study is to evaluate the potentials of knowledge based classifications for Quickbird-data in forest monitoring tasks. Proceedings of the 2 Workshop of the EARSeL SIG on Land Use and Land Cover 270 METHODS The main study area is the ‘Koenigswald’, a forested area near Potsdam/Germany, which is dominated by pine forests as well as oak and beech stands. The main part of the approximately 1000 hectares covering study area is designated as ‘Site of Community Importance’. The acquisition of the Quickbird scene is dated to the End of July in 2004. All statistical measures are derived from this area. The transferability of the knowledge base is tested on two other areas in the federal state Brandenburg: ‘Potsdam/Wildpark’ and ‘Jueterbog’. ‘Potsdam/Wildpark’ resembles the ‘Koenigswald’ in terms of natural conditions and date of satellite-data acquisition. ‘Jueterbog’ is a former military training area which is still not accessible to public. Pine, oak and birch woodlands with a sparse canopy cover are left to natural succession. The satellite scene was captured on approximately the same date but one year earlier in 2003. The off nadir view angle of 14.6° is distinctly higher than in the study areas in Potsdam (5.6°). The ground truth data is mainly gathered from the ‘Datenspeicher Wald’, an eastern German forest inventory which contains area-wide information concerning habitat characteristics of forested areas. According to the interpretation key of Brandenburg, age classes are established depending on their stem diameter and height. In order to keep them distinguishable, the 8 classes are reduced to four classes. Concerning tree species, the five classes pine (pinus sylvestris), larch (larix decidua), beech (fagus sylvatica), robinia (robinia pseudoacacia) and oak (quercus petraea and quercus robur) are separated. The image pre-processing includes georeferencing, conversion to spectral radiance and a resolution merge of the panchromatic and the multispectral bands (vi). Due to the lack of relief in the study areas, the use of a digital elevation model is not necessary. In order to minimize the influence of the atmospheric conditions and date of acquisition, the classification does not refer to the original satellite data but uses relative values like ratios and indices which are more stable against these factors. As texture has proved to be an important measure for the differentiation of forest parameters in several studies, texture is also included into the classification process (ii). Therefore a large number of synthetic channels are calculated (Table 1). Since this amount of input channels is not feasible for image classification, statistical tests like the discriminant analysis were used to evaluate which combination of synthetic channels provides the best results for the classification of tree species and age classes. Only this choice of channels is used as input for the knowledge base and has to be calculated for every image that is classified with this method. In the next step, the thresholds for the different classes in the knowledge base were set up. The 5th and the 95th percentiles of the mean pixel value in homogeneous stands were taken as benchmark for the thresholds. Table 1: Synthetic channels which were calculated for the evaluation Calculated synthetic channels all combinations of subtractions of two original channels at a time (IR minus Red, etc.) all combinations of divisions (IR / Red, etc) the NDVI the four tasselled cap bands (vi) the variance of all bands with different filter sizes from 3 to 11 pixels the GLCM features Homogeneity, Contrast, Dissimilarity, Standard Deviation, Entropy, Angular Second Moment, Correlation, and Mean for all bands Center for Remote Sensing of Land Surfaces, Bonn, 28-30 September 2006 271 Figure 1: Box & Whiskers diagram showing the distribution of the mean values of the infrared minus green channel for the different tree species. All these investigations have to be done only once since the resulting knowledge base is supposed to be transferable to other Quickbird scenes. Before the actual classification begins, all areas which are not forest are masked out. Shadow areas are detected by a local minima filter and broadleaved and needleleaved species are separated from broadleaved by means of the infrared minus green channel. The complex structure of high-resolution satellite data makes a manual delineation of spectrally pure training areas difficult, thus, this procedure is automated. The automation is realized with the Erdas Imagine Expert Classifier and facilitates the use for monitoring purposes, since it minimizes the editor’s influence on the interpretation and increases its transparency. All pixels that can be assigned to a class by the knowledge base are marked as training pixels and are subsequently used for the signature extraction for the respective class. Undersized training areas are sieved out, since they are a major source of error. The spectral signatures of the training pixels show a mostly gaussian shape, which is a necessary precondition for the following maximum likelihood classification (see Fig.2). Figure 2: Spectral distribution of pixel values for the classes ‘pine/ young’ and ‘pine/ middle aged’ in the Correlation channel Using these spectral signatures, the synthetic channels undergo a fuzzy maximum-likelihood classification with two classes, which assigns the remaining unclassified pixels to a class. The result passes through further treatment. On the basis of the fuzzy distance picture, single pixels and small groups of pixels with a relatively insecure category assignment can be grouped to a more suitable class with a fuzzy convolution filter. Classes that are not separable by spectral and textural means are likely to be better separable with additional data, e.g. soil data or digital elevation models. Proceedings of the 2 Workshop of the EARSeL SIG on Land Use and Land Cover 272 Figure 3: Original Quickbird Image (a), Trainingspixels (b) and Classification Result (c) RESULTS & DISCUSSION For the classification of the examined tree species a combination of 9 synthetic channels has proved to be suitable: Table 2: Combination of channels used for the classification Synthetic Chanel Main Separation Tasks in Knowledge Base NDVI Broadleaved species from each other IR – Green Broadleaved from needle leaved Homogeneity Red Young pine from pine Correlation IR Age classes in general Variance of Pan Needle leaved coniferous, larch and broadleaved 4th Tasselled Cap Band Beech from oak Standard deviation of red green Beech Green Blue Robinia Red / Green Robinia from oak For each polygon created through a visual habitat survey, the percentage of the single classes can be calculated as shown in the following figure. Until now accuracy assessment has been realized for homogeneous forest stands only but it will be extended to mixed stands in the course of further investigation. For homogeneous stands the majority of the classified pixels was calculated and compared to the classes of the ‘Datenspeicher Wald’ in an error matrix (Table 3). Figure 4: