As pointed out by Farquhar, the simplest way to evaluate urban sprawl is change detection by land use and land cover categories using MLH or any other method of classification. However, land use/land cover at initial point of time is to be processed using simple percentages under different categories or using common place entropy method. The same methods of classification and analysis are to be applied to the final point of time and difference either in percentages of categories or entropy values may be compared to detect change. If one has a historical series of maps and images of an urban area, simulation after classification may be carried out using cellular automa or in case of a few images or maps genetic algorithm with a Morkovian chain process which could be calibrated. Perhaps, i would be able to send you a paper of one of my colleagues because I have been consulted during preparation of that paper.

simplest way is to perform a simple change detection over time with two coregistered images and look for areas of vegetation becoming urban. Using a tool like NDVI to identify green vegetation and if it is removed then there is no vegetation there. With ENVI there are quite a few tutorials on the ittvis.com website.

As pointed out by Farquhar, the simplest way to evaluate urban sprawl is change detection by land use and land cover categories using MLH or any other method of classification. However, land use/land cover at initial point of time is to be processed using simple percentages under different categories or using common place entropy method. The same methods of classification and analysis are to be applied to the final point of time and difference either in percentages of categories or entropy values may be compared to detect change. If one has a historical series of maps and images of an urban area, simulation after classification may be carried out using cellular automa or in case of a few images or maps genetic algorithm with a Morkovian chain process which could be calibrated. Perhaps, i would be able to send you a paper of one of my colleagues because I have been consulted during preparation of that paper.

Thanks for liking my reply.

Hi Vijay,

Sorry for belated response. I was on long winter holidays.

As you know analysis of satellite data for urban sprawl includes registration, classification and change detection using supervised and unsupervised classification. Change detection analysis encompasses a broad range of methods used to identify, describe and quantity differences between images of the some scene at different times or under different conditions. One may use any from a range, the simplest quantification being percentage change. Higher versions of both ENVI and ERDAS Imagine have advance functionalities and numerous of the tools which can be used independently or in combination as part of a change detection analysis. ENVI includes new, automated tools for linear feature extraction, anomaly detection and spectral image classification that allow analysts to get information from imagery quickly without extensive image processing experience. Feature extraction can be done by enhancing contrast and visual interpretation, maximum likelihood and ANN (artificial neural network) methods. The maximum likelihood (ML) procedure is commonly employed as classification method because it does not require an extended training process. Image classification using neural networks is done by texture feature extraction and then applying the back propagation algorithm.

In ERDAS Imagine, image fusion is the spatial enhancement technique to make the best use of different complementary information from multi-source and multi-temporal imagery. Before running the fusion, it has to be kept in mind that both images should have been registered in a common reference system and they should matched one-to-one without any displacement of the same features within the images. Various techniques of image fusion (sometimes called merging or sharpening) have been developed. The most common are IHS (Intensity-Hue-Saturation), HPF (High Pass Filter), Colour Normalized, PC (Principal Component) Spectral, Gram-Schmidt Spectral, Wavelet etc. The IHS method is known as modified IHS in ERDAS Imagine. The process requires an optimum and specifically designed classification algorithm for precise application purpose because it largely varies depending upon the type and objective of the work. Typical method for RS image classification is so called pixel based method in which the classifier considers different pixel values and group them into classes solely based on their spectral properties. This practice is based on conventional statistical techniques such as supervised and unsupervised classification where the classes are supervised by analyst and are not supervised. The expediency of traditional pixel based image classification often proves very effective especially in case of low and medium resolution satellite images. The enhancement approach involves the mathematical combination of imagery from different dates such as subtraction of bands, rationing, image regression or principal components analysis (PCA). Thresholds are applied to the enhanced image to isolate the pixels that have changed. The direct multi-date classification is based on the single analysis of a combined dataset of two or more different dates, in order to identify areas of changes. The post-classification comparison is a comparative analysis of images obtained at different moments after previous independent classification. In this method, registered images acquired at different times are subtracted to produce a residual image which represents the change between the two dates. Pixels of no radiance change are distributed around the mean, while pixels of radiance change are distributed in the tails of the distribution. Apart from pixel based, object oriented classification is also available in ERDAS. This classification process is based upon the fuzzy set theory, used in the nearest neighbour method of classification and in the membership function.

As you know, groundtruthing, and accuracy analysis are must for production of a reliable thematic map or further analysis as modelling or simulation. For, one who is not much familiar with ENVI and ERDAS Imagine, it would be better to use ENVI Tutorials and ERDAS Field Guide.

https://www.researchgate.net/publication/230738894_Peri-Urban_to_Urban_Landscape_Patterns_Elucidation_through_Spatial_Metrics

https://www.researchgate.net/publication/229826816_Empirical_patterns_of_the_influence_of_Spatial_Resolution_of_Remote_Sensing_Data_on_Landscape_Metrics