An integrated Markov Chain and Cellular Automata modelling (CA MARKOV), multicriteria evaluation techniques have been applied to produce transition probability. The unsupervised method was employed to classify the satellite images of year 1985, 1995, 2005 and 2015 to meet the magnitude of LULC change. Results showing the spatial pattern of the sub-basin is largely influenced by the biophysical and socio-economic drivers leading to growth of agricultural lands and built-up area in the basin. Simulated plausible future LULC changes for 2025 which is based on a CA MARKOV that integrates Markovian transition probabilities computed from satellite-derived LULC maps and a CA contiguity spatial filter (5 × 5). Further, the fragmentation analysis was performed to check the fragmentation scenario in the year 2025. The result for year 2025 with reasonably good accuracy will be useful to the planners, policy- and decision-makers.

This study aims to analyse the processes and patterns of peri-urbanization using diurnal earth observation data-sets from onboard DMSP–Operational Linescan System. In this study, multiple correlation, simple and conditional linear regression are used to find out the degree of relationship and spatial behavioural pattern of the factors responsible for the urbanization. All the factors are standardized using the Analytical Hierarchy Process (AHP) coupled fuzzy membership functions. AHP is used to derive the weighting of the factors to produce the urbanity index. In total three functional zones – urban, rural and urban shadow are generated based on factor standardization and spatial contiguity index. Urban fringe is sharing ≥ 60% of Urbanity Index followed by rural fringe (39.50–60% of urbanity index) and urban shadow <39.50% of urbanity index. Shape index indicates that the city is going through unplanned development following cross to star shape growth.

Urbanization is a growing challenge for city planners and policymakers who are continuously focusing on computer-based statistical models, and machine learning for a sustainable and livable city. The main objectives of this article were to develop a robust artificial intelligence-based hybrid geo-simulation model to support multi-scenario urban growth modeling for urban policy improvement. In this study, earth observation datasets, Artificial Neural Network-Multilayer Perceptron coupled with Markov Chain (MLP-Markov) and Cellular Automata and Markov Chain (CA-Markov) were applied and the best performance was measured for urban growth modeling. The result shows that the urban land use was 25.79, 31.40, 45.19, 89.22 and 147.96 square km in 1971, 1981, 1991, 2001 and 2011 which has been predicted for 2021, 2031, 2041 and 2051 based on the planned and unplanned development scenarios. The predicted urban land use of the planned development scenario is 242.10, 312.69, 363.80 and 400.72 square km while 242.91, 314.31, 366.23 and 403.98 square km of the unplanned development scenario during 2021, 2031, 2041 and 2051. The uncertainty result shows that overall agreement (84.99%) and other indices are higher, and disagreement is lower (15.01%) for MLP-Markov than the CA-Markov for the urban land use prediction. The hybrid geo-simulation models were tested over multiple urban planning indicators to understand urban growth patterns and related scenarios. The result shows that the geo-simulation model is extremely sensitive to the complex pattern of urban growth and disperse indicators over space and time. This study provides a promising guideline for urban planners and conservation scientists to implement a robust artificial intelligence-based hybrid geo-simulation model for compact, organized, and integrated land use-transportation development.

The correction of atmospheric effects is very essential because visible bands of shorter wavelength are highly affected by atmospheric scattering especially of Rayleigh scattering. The objectives of the paper is to find out the haze values present in the all spectral bands and to correct the haze values for urban analysis. In this paper, Improved Dark Object Subtraction method of P. Chavez (1988) is applied for the correction of atmospheric haze in the Resoucesat-1 LISS-4 multispectral satellite image. Dark object Subtraction is a very simple image-based method of atmospheric haze which assumes that there are at least a few pixels within an image which should be black (% reflectance) and such black reflectance termed as dark object which are clear water body and shadows whose DN values zero (0) or Close to zero in the image. Simple Dark Object Subtraction method is a first order atmospheric correction but Improved Dark Object Subtraction method which tends to correct the Haze in terms of atmospheric scattering and path radiance based on the power law of relative scattering effect of atmosphere. The haze values extracted using Simple Dark Object Subtraction method for Green band (Band2), Red band (Band3) and NIR band (band4) are 40, 34 and 18 but the haze values extracted using Improved Dark Object Subtraction method are 40, 18.02 and 11.80 for aforesaid bands. Here it is concluded that the haze values extracted by Improved Dark Object Subtraction method provides more realistic results than Simple Dark Object Subtraction method.