Modelling Urban Sprawl Using Remotely Sensed Data: A Case Study of Chennai City, Tamilnadu

Abstract

Urban sprawl (US), propelled by rapid population growth leads to the shrinkage of productive agricultural lands and pristine forests in the suburban areas and, in turn, adversely affects the provision of ecosystem services. The quantification of US is thus crucial for effective urban planning and environmental management. Like many megacities in fast growing developing countries, Chennai, the capital of Tamilnadu and one of the business hubs in India, has experienced extensive US triggered by the doubling of total population over the past three decades. However, the extent and level of US has not yet been quantified and a prediction for future extent of US is lacking. We employed the Random Forest (RF) classification on Landsat imageries from 1991, 2003, and 2016, and computed six landscape metrics to delineate the extent of urban areas within a 10 km suburban buffer of Chennai. The level of US was then quantified using Renyi's entropy. A land change model was subsequently used to project land cover for 2027. A 70.35% expansion in urban areas was observed mainly towards the suburban periphery of Chennai between 1991 and 2016. The Renyi's entropy value for year 2016 was 0.9, exhibiting a two-fold level of US when compared to 1991. The spatial metrics values indicate that the existing urban areas became denser and the suburban agricultural, forests and particularly barren lands were transformed into fragmented urban settlements. The forecasted land cover for 2027 indicates a conversion of 13,670.33 ha (16.57% of the total landscape) of existing forests and agricultural lands into urban areas with an associated increase in the entropy value to 1.7, indicating a tremendous level of US. Our study provides useful metrics for urban planning authorities to address the social-ecological consequences of US and to protect ecosystem services.

Assessment of Component Selection Strategies in Hyperspectral Imagery

“…These features represent high-level information that can be used to describe the objects in and structure of images [10,35], and in consequence can be applied to select the components providing important information. An entropy first-order texture filter is applied based on a co-occurrence matrix [31].…”

Spatial analysis of cities using Renyi entropy and fractal parameters

“…Entropy, especially spatial Renyi entropy, is an important spatial measurement of urban and regional systems. Renyi entropy has been applied to measuring regional land use and urban sprawl (Fan et al, 2017;Padmanaban et al, 2017), and the interesting results show that the entropy measurement effect is similar to the fractal dimension. Just based on the Renyi entropy, the general correlation dimension of multifractals is defined to describe the scaling phenomena (Feder, 1988).…”

“…More general spatial entropy is the spatial Renyi entropy (Chen and Wang, 2013;Fan et al, 2017). Both Renyi entropy and fractal dimension can be employed to measure urban sprawl (Padmanaban et al, 2017;Terzi and Kaya, 2011), and the similar functions suggest the intrinsic relation between entropy and fractal dimension. The traditional spatial entropy is quantified by means of geographical systems of zones.…”

Mapping of Coastal Cities Using Optimized Spectral–Spatial Features Based Multi-Scale Superpixel Classification

“…Accordingly, the pre-classification results at each single scale are generated using the Random Forest (RF) classifier and the majority vote scheme. The RF classifier is selected as the classier mainly because of the random and rotation forest classifiers having achieved outstanding performances in classification of multi-source and multi-temporal images [38][39][40][41][42], such as urban sprawl modelling [43], agriculture water management [44], and mapping of the wetland park [45]. Finally, these pre-classification results are fused to obtain the ultimate land-cover map of coastal cities.…”