Abstract
We present a novel and innovative automated processing environment for the derivation of land cover (LC) and land use (LU) information. This processing framework named TWOPAC (TWinned Object and Pixel based Automated classification Chain) enables the standardized, independent, user-friendly, and comparable derivation of LC and LU information, with minimized manual classification labor. TWOPAC allows classification of multi-spectral and multi-temporal remote sensing imagery from different sensor types. TWOPAC enables not only pixel-based classification, but also allows classification based on object-based characteristics. Classification is based on a Decision Tree approach (DT) for which the well-known C5.0 code has been implemented, which builds decision trees based on the concept of information entropy. TWOPAC enables automatic generation of the decision tree classifier based on a C5.0-retrieved ascii-file, as well as fully automatic validation of the classification output via sample based accuracy assessment.Envisaging the automated generation of standardized land cover products, as well as area-wide classification of large amounts of data in preferably a short processing time, standardized interfaces for process control, Web Processing Services (WPS), as introduced by the Open Geospatial Consortium (OGC), are utilized. TWOPAC’s functionality to process geospatial raster or vector data via web resources (server, network) enables TWOPAC’s usability independent of any commercial client or desktop software and allows for large scale data processing on servers. Furthermore, the components of TWOPAC were built-up using open source code components and are implemented as a plug-in for Quantum GIS software for easy handling of the classification process from the user’s perspective.
Highlights
Land cover describes the physical cover of the Earth’s surface including vegetation, non-vegetation and man-made features, while land use is characterized by anthropogenic activities to modify, manage and use certain types of land cover [1,2,3,4,5]
Land cover (LC) and land use (LU) are two examples of value added information, which can be derived from Earth Observation (EO) data
Eye image tile has a size of 25 × 25 km whereas the presented 4 × 4 tile mosaic covers an area of 100 × 100 km with spatial resolution of 6.5 m
Summary
Land cover describes the physical cover of the Earth’s surface including vegetation, non-vegetation and man-made features, while land use is characterized by anthropogenic activities to modify, manage and use certain types of land cover [1,2,3,4,5]. Land cover (LC) and land use (LU) are two examples of value added information, which can be derived from Earth Observation (EO) data. Land cover information from satellite imagery is a widely-used tool for large area environmental monitoring and analyses of change processes on the Earth’s surface [6,7,8,9]. Global networks for the assessment of the impact of climate change (e.g., GTOS (Global Terrestrial Observing System), GCOS (Global Climate Observing System), etc.) consider land cover as one of 13 essential climate variables beside biomass, leaf area index, fire disturbance, water use, etc. Innovative environmental planning and management tools, such as Decision Support Systems, require high-quality land cover and land use information derived from high and very high resolution data at a regional scale [6]. With the expanding community of scientific and non-scientific users not necessarily experts in remote sensing image analysis techniques (e.g., hydrologists, socio-economists, or political stakeholders and decision makers), the requirements for simplified, automated processing environments yielding the above mentioned LC and LU products are increasing
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