APEX - the Hyperspectral ESA Airborne Prism Experiment.

The airborne ESA-APEX (Airborne Prism Experiment) hyperspectral mission simulator is described with its distinct specifications to provide high quality remote sensing data. The concept of an automatic calibration, performed in the Calibration Home Base (CHB) by using the Control Test Master (CTM), the In-Flight Calibration facility (IFC), quality flagging (QF) and specific processing in a dedicated Processing and Archiving Facility (PAF), and vicarious calibration experiments are presented. A preview on major applications and the corresponding development efforts to provide scientific data products up to level 2/3 to the user is presented for limnology, vegetation, aerosols, general classification routines and rapid mapping tasks. BRDF (Bidirectional Reflectance Distribution Function) issues are discussed and the spectral database SPECCHIO (Spectral Input/Output) introduced. The optical performance as well as the dedicated software utilities make APEX a state-of-the-art hyperspectral sensor, capable of (a) satisfying the needs of several research communities and (b) helping the understanding of the Earth's complex mechanisms.

Forest type mapping using incorporation of spatial models and ETM+ data.

Results of former researches have shown that spectrally based analysis alone could not satisfy forest type classification in mountainous mixed forests. Forest type based on composed different parameters such as topography elements like aspect, elevation and slop. These elements that are affected on occurrences of forest type can be stated as spatial distribution models. Using ancillary data integrated with spectral data could help to separate forest type. In order to find the abilities of using topographic spatial predictive models to improve forest type classification, an investigation was carried out to classify forest type using ETM+ data in a part of northern forests of Iran. The Tasseled Cap, Ratioing transformations and Principal Component Analysis were applied to the spectral bands. The best spectral and predictive data sets for classifying forest type using maximum likelihood classification were chosen using the Bhattacharya seperability index. Primary analysis between forest type and topographic parameters showed that elevation and aspect are most correlated with the occurrences of type. Probability occurrence rates of forest type were extracted in the aspect; elevation, integrated aspect and elevation as well as homogeneous units structured on elevation and aspect classes. Based on occurrence rates of forest type, spatial predictive distribution models were generated for each type individually. Classification of the best spectral data sets was accomplished by maximum likelihood classifier and using these spatial predictive models. Results were assessed using a sample ground truth of forest type. This study showed that spatial predictive models could considerably improve the results compared with spectral data alone from 49 to 60%. Among spatial models used, the spatial predictive models constructed based on the homogeneous units could improve results in comparison to other models. Applying other parameters related to forest type like soil maps would generate accurate spatial predictive models and may improve the results.