The sciences, technologies, and practices of remote sensing and of geographic information systems (GIS) arose separately, developed in parallel, intersected, and are now inextricably linked. Nearly all the features in most GIS are collected by means of satellite imagery or aerial photogrammetry, and GIS is the application where this imagery is most commonly visualized. “All the foundation elements of GIS come from remote sensing: cultural features, roads, buildings, water features, topography, terrain, soils, slopes, geology, and many more,” points out Lawrie Jordan, Director of Imagery at Esri.
Merging Two Approaches
It was not always this way. In the 1970s, 1980s, and early 1990s, remote sensing and image processing, on the one hand, and GIS on the other, were separate worlds—each with its own culture and software. The former stored data in a raster format and used multispectral classification; the latter stored data in a vector format and used topology. Software vendors specialized in one or the other—even though their customers were acquiring and using both types of data. Until recently, in a GIS context, imagery was thought of only as a background or a base map to the information that was being analyzed.
Over the last decade, however, remote sensing and GIS have become increasingly integrated. “Now people are seeing imagery as a source of a lot of GIS information,” says Jennifer Stefanacci, Director of Product Management at Exelis. “So, the analysis workflows that our users are doing incorporate both analysis of the imagery and analysis of their GIS data.” While GIS gives you the information about ‘where,’ through information extraction routines, remote sensing gives you the information about ‘what,’ explains Mladen Stojic, V.P. of Geospatial at Intergraph, “By merging the two, we now have the opportunity to do modeling with raster data, vector data, and, on top of that, terrain data.”
Today, GIS is the most practical and efficient platform to combine remote sensing with other layers of information. “People do not acquire and process imagery just to make a pretty picture out of it,” says Jordan. “They want to combine it with other spatial information to solve problems and create meaningful results.”