Tropical cities are more susceptible to the suggested fall outs from projected global warming scenarios as they are located in the Torrid Zone and growing at rapid rates. Therefore, research on the mitigation of urban heat island (UHI) effects in tropical cities has attained much significance and increased immensely over recent years. The UHI mitigation strategies commonly used for temperate cities need to be examined in the tropical context since the mechanism of attaining a surface energy balance in the tropics is quite different from that in the mid-latitudes. The present paper evaluates the performance of four different mitigation strategies to counterbalance the impact of UHI phenomena for climate resilient adaptation in the Kolkata Metropolitan Area (KMA), India. This has been achieved by reproducing the study sites, selected from three different urban morphologies of open low-rise, compact low-rise and mid-rise residential areas, using ENVI-met V 4.0 and simulating the effects of different mitigation strategies-cool pavement, cool roof, added urban vegetation and cool city (a combination of the three former strategies), in reducing the UHI intensity. Simulation results show that at a diurnal scale during summer, the green city model performed best at neighborhood level to reduce air temperature (Ta) by 0.7 °C, 0.8 °C and 1.1 °C, whereas the cool city model was the most effective strategy to reduce physiologically equivalent temperature (PET) by 2.8°-3.1 °C, 2.2°-2.8 °C and 2.8°-2.9 °C in the mid-rise, compact low-rise and open low-rise residential areas, respectively. It was observed that (for all the built environment types) vegetation played the most significant role in determining surface energy balance in the study area, compared to cool roofs and cool pavements. This study also finds that irrespective of building environments, tropical cities are less sensitive to the selected strategies of UHI mitigation than their temperate counter parts, which can be attributed to the difference in magnitude of urbanness.

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

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