Universität zu Köln

Kantakumar, Lakshmi N. ORCID: 0000-0002-8912-3853, Kumar, Shamita and Schneider, Karl ORCID: 0000-0002-4381-2151 (2019). SUSM: a scenario-based urban growth simulation model using remote sensing data. Eur. J. Remote Sens., 52. S. 26 - 42. ABINGDON: TAYLOR & FRANCIS LTD. ISSN 2279-7254

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Abstract

The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India's urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth. SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km(2). SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Kantakumar, Lakshmi N. UNSPECIFIED orcid.org/0000-0002-8912-3853 UNSPECIFIED Kumar, Shamita UNSPECIFIED UNSPECIFIED UNSPECIFIED Schneider, Karl UNSPECIFIED orcid.org/0000-0002-4381-2151 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-144543
DOI:	10.1080/22797254.2019.1585209
Journal or Publication Title:	Eur. J. Remote Sens.
Volume:	52
Page Range:	S. 26 - 42
Date:	2019
Publisher:	TAYLOR & FRANCIS LTD
Place of Publication:	ABINGDON
ISSN:	2279-7254
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language LAND-USE CHANGE; CELLULAR-AUTOMATA; LOGISTIC-REGRESSION; POTENTIAL MODEL; URBANIZATION; AREA; CITY; PATTERNS; INDIA; INTEGRATION Multiple languages Remote Sensing Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/14454