Data Integration, as the term specifies, concerns with the combination of and further integration of spatial data. Basically Data Integration concentrates our attention on the combination of data from multiple sources to derive more geospatial information of higher quality for our consumption. Data Integration brings with several issues that play a role in the processing of the data, key amongst them include: *data models (Raster, Vectors, TIN etc); *data conversion; * resampling and (dis)aggregation, *gap filling and interpolation; * spectral, angular and temporal effects; *change detection; *visualization techniques; *data assimilation in process models; *multi-sensor approaches.
This learning path will introduce you to some of these distinctive ways of combining data from multiple sources to derive geospatial information. It starts with the term - Data Integration, for the understanding of why the need for data integration.
Earth Observation is the next item this learning path will introduce you to, as part of the process of data integration because satellite remote sensing is an important source of spatial data.
The next concept worth considering in this learning path is the term Database.
In this learning path, we will equally look at the concept of GIS, which is a computerised system which facilitates the handling of georeferenced data, including: *data capture and preparation, *data management (storage and maintenance), *data manipulation and analysis, and *data presentation.
It is important to note that as data from multiple sources are being integrated, there is need for a common platform to allow such integration correctly, hence the scales of processes and observations come to mind as the subsequent concept in this learning path.
This learning path will also introduce to you the concept of Earth system processes, to understand the complex phenomena which take place in space and in time -- in four dimensions.
The term Process Model will also be introduced under this learning path, which describes the evolution of geo(bio)physical surface properties in time, independently from remote sensing observations. Examples of process models include: *numerical weather prediction (NWP) model; *hydrological model; *oceanogrpahic model; * vegatation growth model; and * climate model.
Aside process models, there is also observation models which this learning path seeks to introduce you to as its next concept.
This learning path concludes by introducing you to the concept of retrievability.