Step-wise Land-class Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity

Abstract

The extraction of urban built-up areas is an important aspect of urban planning and understanding the complex drivers and biophysical mechanism of urban climate processes. However, built-up area extraction using Landsat data is a challenging task due to spatio-temporal dynamics and spatially intermixed nature of Land Use and Land Cover (LULC) in the cities of the developing countries, particularly in tropics. In the light of advantages and drawbacks of the Normalized Difference Built-up Index (NDBI) and Built-up Area Extraction Method (BAEM), a new and simple method i.e. Step-wise Land-class Elimination Approach (SLEA) is proposed for rapid and accurate mapping of urban built-up areas without depending exclusively on the band specific normalized indices, in order to pursue a more generalized approach. It combines the use of a single band layer, Normalized Difference Vegetation Index (NDVI) image and another binary image obtained through Logit model. Based on the spectral designation of the satellite image in use, a particular band is chosen for identification of water pixels. The Double-window Flexible Pace Search (DFPS) approach is employed for finding the optimum threshold value that segments the selected band image into water and non-water categories- the water pixels are then eliminated from the original image. The vegetation pixels are similarly identified using the NDVI image and eliminated. The residual pixels left after elimination of water and vegetation categories belong either to the built-up areas or to bare land categories. Logit model is used for separation of the built-up areas from bare lands. The effectiveness of this method was tested through the mapping of built-up areas of the Kolkata Metropolitan Area (KMA), India from Thematic Mapper (TM) images of 2000, 2005 and 2010, and Operational Land Imager (OLI) image of 2015. Results of the proposed SLEA were 95.33% accurate on the whole, while those derived by the NDBI and BAEM approaches returned an overall accuracy of 83.67% and 89.33%, respectively. Comparisons of the results obtained using this method with those obtained from NDBI and BAEM approaches demonstrate that the proposed approach is quite reliable. The SLEA generates new patterns of evidence and hypotheses for built-up areas extraction research, providing an integral link with statistical science and encouraging trans-disciplinary collaborations to build robust knowledge and problem solving capacity in urban areas. It is also brings landscape architecture, urban and regional planning, landscape and ecological engineering, and other practice-oriented fields to bear in processes for identifying problems and analyzing, synthesizing, and evaluating desirable alternatives for urban change. This method produced very accurate results in a more efficient manner compared to the earlier built-up area extraction approaches for the landscape and urban planning

Estimating population and urban areas at risk of coastal hazards, 1990–2015: how data choices matter

“…It is worth noting that this represents an important improvement from estimates in McGranahan et al (2007a) and that newer, more recent estimates of global populations in the LECZ (Kulp and Strauss, 2019, which showcases Coastal-DEM) do not stratify by any urban-rural classes. Other studies have highlighted population in case-study cities (Small et al, 2018b;Ahmed et al, 2018;Khan et al, 2019), but these are not global in extent; others have focused on types of cities (such as ports, De Sherbinin et al, 2007, or megacities, Nicholls, 1995 at risk.…”

Remotely-Sensed Derived Built-up Area as an Alternative Indicator in the Study of Thailand’s Regional Development

“…The Normalized Difference Built-up Index (NDBI) proposed by Zha et al (2003) was developed by analyzing the spectral response of built-up elements in different bands of the Landsat TM image. Due to its simple mathematical computation and quick mapping, the index has been widely used for automatically mapping urban built-up areas from Landsat TM imagery (Zha et al, 2003;Mwakapuja et al, 2013;Khan et al, 2019;Hidayati et al, 2018). The Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) formulated by McFeeters (1996) and Xu ( 2006) respectively, were based on the principle that built-up areas (impervious surfaces) are prone to have a lower moisture or water content than barren areas (pervious surfaces).…”

“…The results showed that among several tests, the merging of NDBI and MNDWI produced the highest accuracy of 90.3%. Khan et al (2019) compared the results of the NDBI index and the Built-up Area Extraction Method (BAEM) index through the mapping of built-up areas of Kolkata Megacity in India. The BAEM index, developed by Bhatti and Tripathi (2014), is an amalgamation and integration of Land Surface Temperature (LST), NDVI, and MNDWI.…”

“…The accuracy in the extraction of built-up areas from satellite imagery still requires improvements. Noted by Khan et al (2019), there has not been a single technique to classify the built-up areas. Integrating the intrinsic aspect of two or more techniques to build a hybrid approach is one solution.…”

Estimating Population and Urban Areas at Risk of Coastal Hazards, 1990–2015: How data choices matter

“…560 It is worth noting that this represents an important improvement from estimates inMcGranahan et al, 2007b, and that newer, more recent estimates of global populations in the LECZ (Kulp and Strauss, 2019 which showcases CoastalDEM), do not stratify by any urban-rural classes. Other studies have highlighted population in case-study cities(Small et al, 2018b;Ahmed et al, 2018;Khan et al, 2019) but these are not global in extent; others have focused on types of cities (such as ports, De Sherbinin et al, 2007 or megacities,…”