The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
https://doi.org/10.5194/isprsarchives-XL-7-W3-707-2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-707-2015
29 Apr 2015
 | 29 Apr 2015

New Methods in Acquisition, Update and Dissemination of Nature Conservation Geodata - Implementation of an Integrated Framework

G. Tintrup gen. Suntrup, T. Jalke, L. Streib, N. Keck, S. Nieland, N. Moran, B. Kleinschmit, and M. Trapp

Keywords: automated processing, interoperability, nature conservation, INSPIRE, remote sensing, land cover classification, biodiversity monitoring, ontology

Abstract. Within the framework of this project methods are being tested and implemented a) to introduce remote sensing based approaches into the existing process of biotope mapping and b) to develop a framework serving the multiple requirements arising from different users’ backgrounds and thus the need for comprehensive data interoperability. Therefore state-wide high resolution land cover vector-data have been generated in an automated object oriented workflow based on aerial imagery and a normalised digital surface models.These data have been enriched by an extensive characterisation of the individual objects by e.g. site specific, contextual or spectral parameters utilising multitemporal satellite images, DEM-derivatives and multiple relevant geo-data. Parameters are tested on relevance in regard to the classification process using different data mining approaches and have been used to formalise categories of the European nature information system (EUNIS) in a semantic framework. The Classification will be realised by ontology-based reasoning. Dissemination and storage of data is developed fully INSPIRE-compatible and facilitated via a web portal. Main objectives of the project are a) maximum exploitation of existing “standard” data provided by state authorities, b) combination of these data with satellite imagery (Copernicus), c) create land cover objects and achieve data interoperability through low number of classes but comprehensive characterisation and d) implement algorithms and methods suitable for automated processing on large scales.