## 2 Observation Systems and their Integration
### Design of the Integration Approach  
Our integrative approach focuses on the value-chain of generating environmental information, in which raw measurements are processed step-by-step. The processing chains for generating value-added information traverse the layers in Figure 2, where the center represents the initial content and each surrounding layer represents the results of one processing step. For example, the raw data might be air temperature measures (in intervals of one minute), and the first processing step might provide daily averages, the next weekly averages, _etc._ (Figure 2a). We may also think of data coming from different sources, for example measured by diverse sensor networks, such as air temperature, wind-direction, cloud-cover and humidity values. In this case, the first processing step might be a merger of pieces of information into a complex measure, such as the fire risk index (Figure 2b).  
Alternatively, contents provided by two different sources, e.g., satellite images from a conventional physical sensor and VGI posts on photo-sharing web sites, may be provided separately. As the information is processed--_i.e._, value-added information is created--resulting layers might overlap. The example from the previous section illustrates this nicely. Satellite images could be analyzed for temperature hotspots, and some of these hotspots might be categorized as forest fires. At the same time, VGI posted through social media platforms could be analyzed for hotspots as well. In social media, these hotspots could be purely thematic in nature, such as an increase of words like 'fire' in messages, but in the case of sufficiently accurate VGI as in our example case, the hotspots would correspond to spatio-temporal clusters [21] and subsequently, some of these hotspots might also be categorized as forest fires (Figure 3). Two case studies for France in 2010 and 2011 have shown that the CONAVI system can detect the same forest fires that the EFFIS system reports, and some that were not reported but confirmed by looking up news reports [28]. Thus, one can arrive at the same kind of (forest fire) observation result using different information channels. This is an important result in itself, because it can help verify the respective results from multiple sources. However, usually the integration of the results depends on manual domain expert investigation, which is always time-consuming and might be in scarce supply. With our integration approach presented here, we aim to facilitate semantic observation integration by providing a sound formalization of the overall system.  
For the following, especially for Sections 4 and 5, it is worth remembering three characteristics:  
* We can only move from the inner layers to the outside.
* The information becomes more specialized with distance to the center, _i.e._, application specific context is introduced increasingly.
* Information can only be integrated on a shared layer.  
Figure 3: Detecting forest fires using satellite images and VGI.  
Figure 2: Layers of value-added information, (**a**) averages of air temperature; (**b**) fire risk index.  
In Section 4 we will use these characteristics, together with an ontology for observations, to formalize a system for information integration. Before, _i.e._, in the next section, we discuss the use of algebra as a tool for (formal) system engineering, whereas possible generalization, re-use and further improvements are discussed in Section 5.