Spatial and Temporal GIS Analysis Using Change Detection

Nowadays, remotely sensed images are used for various purposes in different applications. One of them is change detection using high resolution satellite imagery of city areas. Urbanization with rapid land use and land cover change has taken place in many cities of the world in the last 50 years. In this context, comparison of extraction results from these images and existing vector data is the most important issue. The availability of high resolution optical imagery appears to be interesting for geo-spatial database applications, namely for the capturing and maintenance of geodata.  

Developments in remote sensing and image processing technologies have specifically provided the opportunity for determination of large areas in detail and in this respect, production of reliable and extended recent data quickly. Thus, the rapid developments in urban areas can be followed and strategies of directing those developments can be formulated. In this respect, automatic object extraction approaches have recently become necessary for large-scale topographic mapping from the images, determining the changes in topography and revising the existing map data. For mapping from high resolution imagery or GIS database construction and its update, automatic object-based image analysis has been generally used for remote sensing applications in recent years. Besides, as the products obtained by automatic object-based extractions are GIS-based, they can be integrated to GIS, queried and various strategic analysis can be made.

                                    Fig 1  Change Detection for determining Land Use/Land Cover 

GIS is the systematic introduction of numerous different disciplinary spatial and statistical data, that can be used in environment monitoring, observation of change and constituent processes and prediction based on current practices and management plans. Remote Sensing helps in acquiring multi-spectral, spatial and temporal data through space- borne remote sensors. Image processing techniques helps in analyzing the dynamic changes associated with the earth resources such as land and water using remote sensing data. Thus, spatial and temporal analysis technologies are very useful in generating scientifically based statistical spatial data for understanding the land ecosystem dynamics. Successful utilization of remotely sensed data for land cover and land use (LULC) change detection requires careful selection of appropriate data set.

                                                Fig 2  Change Detection using Imagery 

Generally speaking, the inclusion of Digital Elevation Model (DEM) increases the ability to differentiate objects with significant height compared to other spectrally similar classes, such as bridges and highways, buildings and skyscrapers, as well as trees and ground level vegetation. 

Application of Landsat TM imagery for Change Detection

One of the major applications for moderate-resolution remote sensing data such as Landsat TM is to detect landcover changes between two different dates of images. In forestry, disturbances due to forest operations such as thinning, soil preparation, and road construction are often visible in images from two different dates. This is because these changes may occur over areas that cover at least several pixels and because these disturbances can cause large differences in spectral reflectance of the surfaces. 

There are many environmental parameters that should be considered when performing change detection. Failure to understand the impact of these factors can lead to errors in the change detection analysis. Ideally, the remotely sensed data should be acquired by a system that holds spatial (pixel size), spectral (wavelengths recorded by a sensor), and radiometric resolutions that are the same between the two images used. The temporal (date of acquisition, time of day) factor is also important in change detection. All four of these factors should also be appropriate to the application.

                                                          Fig 3  Land Cover Change Detection Using Landsat TM Imagery 

Summary
Change detection is the measurement of the distinct data framework and thematic change information that can guide to more tangible insights into underlying process involving land cover and land use changes, than the information obtained from continuous change. Digital change detection is the process that helps in determining the changes associated with landuse and land cover properties with reference to geo-registered multi-temporal remote sensing data. It helps in identifying change between two (or more) dates that is uncharacterized of normal variation. Change detection is useful in many applications such as landuse changes, habitat fragmentation, the rate of deforestation, coastal change, urban sprawl, and other cumulative changes through spatial and temporal analysis techniques such as GIS (Geographic Information System) and Remote Sensing along with digital image processing techniques.