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Designing a traffic monitoring program using landuse change detection
The opportunity for GIS and remote sensing
Multi temporal dataset archives of terrestrial areas were historically maintained by USGS after the launch of Landsat series of earth observing satellites. The Landsat datasets have limited utility in landuse analysis due to low spatial resolution. However, with the availability of multi-temporal datasets in a standardized format they can be very useful in understanding changes at a regional level. A more common form of remotely sensed data is the aerial photograph, which can be acquired at the project, city, county or state level. Until recently, acquisition of aerial photos requires lower tasking time and acquisition cost than satellite imagery, Also, the spatial resolution of aerial photographs was better than available satellite imagery and they could be collected and stored by public and even private agencies for use with various transportation and environmental projects. Improvement in storage technology also made the dia-positives of the aerial photographs available in electronic formats, which aided data access and storage. Data are available at many spatial and spectral resolutions. Many state, city and county agencies have archives of remote sensing data, which can be utilized for improving planning processes without the need for significant further investment. Change detection Change detection for determining the change in the landuse is based on identifying the regions, which have undergone growth and development during the analysis period. This is done by determining the difference between the landuse classes in the datasets acquired at different epochs for a study area. The procedure allows an analyst to identify the areas where the landuse has changed and the manner in which it has changed. Methods of change detection:
This procedure involves pixel level operation wherein the imagery from one epoch is subtracted from that of another. The changes in the radiance values are grouped to detect the areas with appreciable change. The determination of the threshold value to determine the areas of appreciable change based on the change in radiance values is important in getting the results. The imagery from each dataset is independently classified by using supervised classification and then the landuse types in the earlier epoch are compared with the later epoch to determine the direction of change. This method is more descriptive as the type of landuse change can also be determined. The available remotely sensed datasets could be categorized with respect to their aerial extent, spatial and spectral resolution. Panchromatic aerial photos available with USGS and the yearly road vector database prepared by DOT were used in this change detection study. 
The older imagery from 1992 had a spatial resolution of 1 meter and the recent imagery from 2002 had a spatial resolution of 0.3 meter. Table 2 shows the data typology for the state of Iowa. In table 2 "X" marks the datasets used for change detection the city of Maquoketa. Implementation of a conventional change detection procedure: An image differencing method for change detection was applied to identify the changes in the city of Maquoketa during the analysis period. These preliminary results were not very useful, as the aerial photo from the two epochs did not match accurately. To address this issue, a post classification comparison procedure was applied. This was done by first classifying the aerial photographs from two different time periods into five landuse classes: thick vegetation, cultivated land, dense residential, sparse residential and water bodies. However, the results of classification were again disappointing. Image differencing was then applied to find the regions with landuse change. The end result continued to lack utility due to the low spectral resolution (8 bit panchromatic) of the available imagery. This resulted in the selection of impure training pixels for supervised classification. While high spectral resolution imagery could have improved the results of supervised classification, it is likely that there would be problems using the data for automated change detection. For example, color infrared imagery will soon be available for the study area. However, older imagery would still have low spatial and spectral resolution and there would continue to be problems with image-to-image registration. Finally, the analyst has to manually identify the change in landuse in the corresponding images after comparing the regions, making the process difficult when large areas are considered. Therefore, we propose the procedure for use with available datasets of low spatial and spectral information in the following section. |