Entropy-Based Approach for the Analysis of Spatio-Temporal Urban Growth Dynamics

Relative Entropy (RE) is defined as the measure of the degree of randomness of any geographical variable (i.e., urban growth). It is an effective indicator to evaluate the patterns of urban growth, whether compact or dispersed. In the present study, RE has been used to evaluate the urban growth of Dehradun city. Dehradun, the capital of Uttarakhand, is situated in the foothills of the Himalayas and has undergone rapid urbanization. Landsat satellite data for the years 2000, 2010 and 2019 have been used in the study. Built-up cover outside municipal limits and within municipal limits was classified for the given time period. The road network and city center of the study area were also delineated using satellite data. RE was calculated for the periods 2000–2010 and 2010–2019 with respect to the road network and city center. High values of RE indicate higher levels of urban sprawl, whereas lower values indicate compactness. The urban growth pattern over a period of 19 years was examined with the help of RE

Hyperspectral and multispectral data fusion using fast discrete curvelet transform for urban surface material characterization

The objective of the present study is to analyze the quality of hyperspectral data fusion using low spatial hyperspectral (LSH) Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) 8 m data and high spatial multispectral (HSM) WorldView-3 image at 1.24 m remote sensing images with spectral unmixing technique. The resultant HSH data shows new prospects for urban surface material characterization with spectrally distinct classes. The spatial resolution of LSH is enhanced by injecting the high-frequency details from the corresponding HSM bands in fast discrete curvelet transform domain. The image fusion-based products’ quality has been analyzed by endmembers extraction and fractional maps generated using Piecewise Convex Multiple-Model Endmember Detection (PCOMMEND) method. Experimental results showed that the fusion has improved the spatial as well as spectral separability to extract the endmembers, particularly for the urban surface materials like the combination of water and asphalt, and bare soil and roof tiles

Simulation of peri-urban growth dynamics using weights of evidence approach

The study aims to simulate the peri-urban growth dynamics in a growing region of India using Weights of Evidence (WOE) based cellular automata model. The growth process was expressed as a function of four causative variables corresponding to which seven data layers were generated in a Geographic Information Systems environment. The model was calibrated for the period 2000–2005 using Kappa indices and fuzzy set theory based two way comparison method. The Kappa value was 0.7, while the value of Klocation and Khisto were 0.81 and 0.93, respectively. The fuzzy similarity values increased for small to large neighbourhood sizes which showed that the model was able to simulate the contiguous and dense growth. However, for dispersed and isolated growth the model showed less accuracy. The model was validated for period 2005–2010 and revealed a Kappa value of 0.88, while value of Klocation and Khisto were 0.91 and 0.96, respectively

Urban growth dynamics of an Indian metropolitan using CA Markov and Logistic Regression

The inescapable phenomenon of growth due to urbanization is witnessed by urban centres. The rate of growth of an urban area can be attributed to a number of factors that play a pivotal role in depicting the land use dynamics. The need to identify, quantify and analyse the drivers of growth is essential to understand the phenomenon of urban growth in a fast growing agglomeration like Lucknow, capital of the most populous state, Uttar Pradesh in India. In this study the urban growth within the planning area was analysed for the year 1993, 2003 and 2013 using certain bio-physical and proximity factors affecting the growth pattern of the city. Factors maps were generated for the various years and the growth was predicted for the year 2023 using integrated Logistic Regression based CA-Markov analysis embedded in the LULC Dynamics Modelling Platform v1.0 developed under the ISRO Geosphere Biosphere Programme at IIRS, ISRO, Dehradun. The predictions show that the city is expected to grow manifolds to 441.2 sq. km in 2023 from mere 53.6 sq. km in 1993. Results show that the model was successful in depicting the infill growth but could not completely predict the expansion phenomenon. The results indicate that integration of remote sensing, GIS and growth models provide important information related to the process of urban expansion useful for planners preparing vision documents for cities. Keywords: Urbanization, Urban growth, Drivers of growth, Logistic Regressio

Development of Decadal (1985–1995–2005) Land Use and Land Cover Database for India

India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study