This study investigates the potential of very high resolution (VHR) optical and radar data for olive grove landscape mapping. VHR data were fed into a four-step processing chain performing an object-based land-use classification. The four steps included (i) image segmentation, (ii) object feature calculation, (iii) object-based classification and (iv) land-use map evaluation. First, the optical (ADS40) and radar (RAMSES SAR and TerraSAR-X) data were applied to the processing chain separately. As supported by two segmentation evaluation measures, the stand purity index (PI) and the potential mapping accuracy (PMA), the optical data thereby led to a significantly better segmentation and a more accurate olive cover map (Kruskal?Wallis test, ). Second, synergy models were developed combining data from the different sensors at different stages of the object-based classification process, namely, (1) during the segmentation step, (2) during the feature calculation step and (3) after the object classification step. The combined use of features from the different sensors resulted in a considerable improvement in mapping accuracy, with correctly classified objects supported by high probabilities. The assessment of feature importance revealed that optical data were most important for successful object-based olive grove mapping; however, features related to object shape and texture of the radar imagery added to its success. Comparison of the object-based synergy model with a pixel-based synergy model indicated a limited classification improvement. This research showed that the integrated use of VHR optical and radar data is appropriate in an object-based classification framework, leading towards more accurate olive grove landscape mapping.
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