Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in [2006][2007]24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82-0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.
The timely identification of vehicles involved in an accident, such as a hit-and-run situation, bears great importance in forensics. To this end, procedures have been defined for analyzing car paint samples that combine techniques such as visual analysis and Fourier transform infrared spectroscopy. This work proposes a new methodology in order to automate the visual analysis using image retrieval. Specifically, color and texture information is extracted from a microscopic image of a recovered paint sample, and this information is then compared with the same features for a database of paint types, resulting in a shortlist of candidate paints. In order to demonstrate the operation of the methodology, a test database has been set up and two retrieval experiments have been performed. The first experiment quantifies the performance of the procedure for retrieving exact matches, while the second experiment emulates the real-life situation of paint samples that experience changes in color and texture over time.
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