GIS eBook: Remote Sensing and GIS Integration (Theories, Methods, and Applications)

Over the past three to four decades, there has been an explosive increase in the use of remotely sensed data for various types of resource, environmental, and urban studies. The evolving capability of geographic information systems (GIS) makes it possible for computer systems to handle geospatial data in a more efficient and effective way. The attempt to take advantage of these data and modern geospatial technologies to investigate natural and human systems and to model and predict their behaviors over time has resulted in voluminous publications with the label integration.  Indeed, since the 1990s, the remote sensing and GIS literature witnessed a great deal of research efforts from both the remote sensing and GIS communities to push the integration of these two related technologies into a new frontier of scientific inquiry. 

Briefly, the integration of remote sensing and GIS is mutually beneficial for the following two reasons: First, there has been a tremendous increase in demand for the use of remotely sensed data combined with cartographic data and other data gathered by GIS, including environmental and socioeconomic data. Products derived from remote sensing are attractive to GIS database development because they can provide cost-effective large-coverage data in a raster data format that are ready for input into a GIS and convertible to a suitable data format for subsequent analysis and modeling applications. Moreover, remote sensing systems usually collect data on multiple dates, making it possible to monitor changes over time for earth-surface features and processes. 

Remote sensing also can provide information about certain biophysical parameters, such as object temperature, biomass, and height, that is valuable in assessing and modeling environmental and resource systems. GIS as a modeling tool needs to integrate remote sensing data with other types of geospatial data. This is particularly true when considering that cartographic data produced in GIS are usually static in nature, with most being collected on a single occasion and then archived. Remotely sensed data can be used to correct, update, and maintain GIS databases. Second, it is still true that GIS is a predominantly data-handling technology, whereas remote sensing is primarily a data-collection technology.

Many tasks that are quite difficult to do in remote sensing image processing systems are relatively easy in a GIS, and vice versa. In a word, the need for the combined use of remotely sensed data and GIS data and for the joint use of remote sensing (including digital image processing) and GIS functionalities for managing, analyzing, and displaying such data leads to their integration.

This book addresses three interconnected issues: theories, methods, and applications for the integration of remote sensing and GIS. First, different theoretical approaches to integration are examined. Specifically, this book looks at such issues as the levels, methodological approaches, and models of integration. The review then goes on to investigate practical methods for the integrated use of remote sensing and GIS data and technologies. Based on theoretical and methodological issues, this book next examines the current impediments, both conceptually and technically, to integration and their possible solutions.

Extensive discussions are directed toward the impact of computers, networks, and telecommunications technologies; the impact of the availability of high-resolution satellite images and light detection and ranging (LiDAR) data; and, finally, the impact of new image-analysis algorithms on integration. The theoretical discussions end with my perspective on future developments. A large portion of this book is dedicated to showcasing a series of application areas involving the integration of remote sensing and GIS. Each application area starts with an analysis of state-of-the-art methodology followed by a detailed presentation of a case study. The application areas include urban landuse and land-cover mapping, landscape characterization and analysis, urban feature extraction, building extraction with LiDAR data, urban heat island and local climate analysis, surface runoff modeling and analysis, the relationship between air quality and land-use patterns, population estimation, quality-of-life assessment, urban and regional development, and public health.

Author: Qihao Weng. Published 2010.
Next Post »