An Urban Heat Island Study of the Colombo Metropolitan Area, Sri Lanka, Based on Landsat Data (1997–2017)

One of the major impacts associated with unplanned rapid urban growth is the decrease of urban vegetation, which is often replaced with impervious surfaces such as buildings, parking lots, roads, and pavements. Consequently, as the percentage of impervious surfaces continues to increase at the expense of vegetation cover, surface urban heat island (SUHI) forms and becomes more intense. The Colombo Metropolitan Area (CMA), Sri Lanka, is one of the rapidly urbanizing metropolitan regions in South Asia. In this study, we examined the spatiotemporal variations of land surface temperature (LST) in the CMA in the context of the SUHI phenomenon using Landsat data. More specifically, we examined the relationship of LST with the normalized difference vegetation index (NDVI) and the normalized difference built-up index (NDBI) at three time points (1997, 2007 and 2017). In addition, we also identified environmentally critical areas based on LST and NDVI. We found significant correlations of LST with NDVI (negative) and NDBI (positive) (p < 0.001) across all three time points. Most of the environmentally critical areas are located in the central business district (CBD), near the harbor, across the coastal belt, and along the main transportation network. We recommend that those identified environmentally critical areas be considered in the future urban planning and landscape development of the city. Green spaces can help improve the environmental sustainability of the CMA

An Internet-Based GIS Platform Providing Data for Visualization and Spatial Analysis of Urbanization in Major Asian and African Cities

Rapid urbanization in developing countries has been observed to be relatively high in the last two decades, especially in the Asian and African regions. Although many researchers have made efforts to improve the understanding of the urbanization trends of various cities in Asia and Africa, the absence of platforms where local stakeholders can visualize and obtain processed urbanization data for their specific needs or analysis, still remains a gap. In this paper, we present an Internet-based GIS platform called MEGA-WEB. The Platform was developed in view of the urban planning and management challenges in developing countries of Asia and Africa due to the limited availability of data resources, effective tools, and proficiency in data analysis. MEGA-WEB provides online access, visualization, spatial analysis, and data sharing services following a mashup framework of the MEGA-WEB Geo Web Services (GWS), with the third-party map services using HTML5/JavaScript techniques. Through the integration of GIS, remote sensing, geo-modelling, and Internet GIS, several indicators for analyzing urbanization are provided in MEGA-WEB to give diverse perspectives on the urbanization of not only the physical land surface condition, but also the relationships of population, energy use, and the environment. The design, architecture, system functions, and uses of MEGA-WEB are discussed in the paper. The MEGA-WEB project is aimed at contributing to sustainable urban development in developing countries of Asia and Africa

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

AbstractFor most sub-Saharan African (SSA) cities, in order to control the historically unplanned urban growth and stimulate sustainable future urban development, there is a need for accurate identification of the past and present urban land use (ULU). However, studies addressing ULU classification in SSA cities are lacking. In this study, we developed an integrated approach of remote sensing and Geographical Information System (GIS) techniques to classify ULU in the developing SSA city of Lusaka. First, we defined six ULU classes (i.e., unplanned high density residential; unplanned low density residential; planned medium-high density residential; planned low density residential; commercial and industrial; public institutions and service areas). ULU parcels, created using road networks as homogenous units separating ULU classes, were used to classify ULU. We utilised the combined detail of cadastral and land use data plus high-resolution Google Earth imagery to infer ULU and classify the parcels. For residential ULU, we also created density thresholds for accurate separation of the classes. We then used the classified ULU parcels for post-classification sorting of built-up pixels extracted from three Landsat TM/ETM+ imageries (1990, 2000, and 2010) into respective ULU classes. Three ULU maps were produced with overall accuracy values of 84.09% to 85.86%. The maps provide information that is relevant to urban planners and policy makers for sustainable future urban planning of Lusaka City. The study also provides an insight for ULU classification in SSA cities with complex urban landscapes similar to Lusaka