Monika Kuffer

Understanding heterogeneity in metropolitan India : the added value of remote sensing data for analyzing sub - standard residential areas

Abstract Analyzing the heterogeneity in metropolitan areas of India utilizing remote sensing data can help to identify more precise patterns of sub-standard residential areas. Earlier work analyzing inequalities in Indian cities employed a constructed index of multiple deprivations (IMDs) utilizing data from the Census of India 2001 ( http://censusindia.gov.in ). While that index, described in an earlier paper, provided a first approach to identify heterogeneity at the citywide scale, it neither provided information on spatial variations within the geographical boundaries of the Census database, nor about physical characteristics, such as green spaces and the variation in housing density and quality. In this article, we analyze whether different types of sub-standard residential areas can be identified through remote sensing data, combined, where relevant, with ground-truthing and local knowledge. The specific questions address: (1) the extent to which types of residential sub-standard areas can be drawn from remote sensing data, based on patterns of green space, structure of layout, density of built-up areas, size of buildings and other site characteristics; (2) the spatial diversity of these residential types for selected electoral wards; and (3) the correlation between different types of sub-standard residential areas and the results of the index of multiple deprivations utilized at electoral ward level found previously. The results of a limited number of test wards in Delhi showed that it was possible to extract different residential types matching existing settlement categories using the physical indicators structure of layout, built-up density, building size and other site characteristics. However, the indicator ‘amount of green spaces’ was not useful to identify informal areas. The analysis of heterogeneity showed that wards with higher IMD scores displayed more or less the full range of residential types, implying that visual image interpretation is able to zoom in on clusters of deprivation of varying size. Finally, the visual interpretation of the diversity of residential types matched the results of the IMD analysis quite well, although the limited number of test wards would need to be expanded to strengthen this statement. Visual image analysis strengthens the robustness of the IMD, and in addition, gives a better idea of the degree of heterogeneity in deprivations within a ward.

Slums from Space: 15 Years of Slum Mapping Using Remote Sensing

The body of scientific literature on slum mapping employing remote sensing methods has increased since the availability of more very-high-resolution (VHR) sensors. This improves the ability to produce information for pro-poor policy development and to build methods capable of supporting systematic global slum monitoring required for international policy development such as the Sustainable Development Goals. This review provides an overview of slum mapping-related remote sensing publications over the period of 2000–2015 regarding four dimensions: contextual factors, physical slum characteristics, data and requirements, and slum extraction methods. The review has shown the following results. First, our contextual knowledge on the diversity of slums across the globe is limited, and slum dynamics are not well captured. Second, a more systematic exploration of physical slum characteristics is required for the development of robust image-based proxies. Third, although the latest commercial sensor technologies provide image data of less than 0.5 m spatial resolution, thereby improving object recognition in slums, the complex and diverse morphology of slums makes extraction through standard methods difficult. Fourth, successful approaches show diversity in terms of extracted information levels (area or object based), implemented indicator sets (single or large sets) and methods employed (e.g., object-based image analysis (OBIA) or machine learning). In the context of a global slum inventory, texture-based methods show good robustness across cities and imagery. Machine-learning algorithms have the highest reported accuracies and allow working with large indicator sets in a computationally efficient manner, while the upscaling of pixel-level information requires further research. For local slum mapping, OBIA approaches show good capabilities of extracting both area- and object-based information. Ultimately, establishing a more systematic relationship between higher-level image elements and slum characteristics is essential to train algorithms able to analyze variations in slum morphologies to facilitate global slum monitoring.

Extraction of Slum Areas From VHR Imagery Using GLCM Variance

Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.

The development of a morphological unplanned settlement index using very-high-resolution (VHR) imagery

Spatial metrics combined with spectral information extracted from very-high-resolution (VHR) imagery allow quantification of the general spatial characteristics of urban areas, as well as specific morphological features (i.e., density, size, and pattern) of unplanned settlements. Such morphological features are visible in VHR imagery, but they are challenging to quantify. Still, quantification of the morphological differences between planned and unplanned areas is an important step towards automatic extraction of unplanned areas from VHR imagery. In this work, we discuss how image segmentation assists in the extraction of homogenous urban patches (HUPs), and use spatial metrics to quantify the morphological differences between planned and unplanned HUPs. A set of spatial metrics meaningful to describe morphological features of unplanned areas is selected and combined into an unplanned settlement index (USI) using a multi-criteria evaluation approach. Two case study areas are used to test the USI, i.e., Dar es Salaam, Tanzania, and New Delhi, India. The ability of the developed USI to extract unplanned areas is confirmed via visual comparison with existing land use data, and a quantitative accuracy assessment shows that areas of high USI coincide well with unplanned areas in the reference data. The quantitative accuracy assessment presents an accuracy of greater than 70% for five selected test areas in both cities.

An integrative methodology to improve brownfield redevelopment planning in Chinese cities: A case study of Futian, Shenzhen

Brownfields are real property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant (USEPA, 2002). In recent years, there have been a rising number of brownfield redevelopment practices in Chinese cities. However, some redevelopment practices have been unsuccessful in spite of cautious planning whereas others have been successful in the absence of any planning. It is largely due to China’s rapid urbanization on one hand, and inadequate information on the locations and conditions of brownfield on the other. To address the gaps, an integrative methodology is devised based on two frameworks, one for identifying potential instead of actual brownfields, and one for establishing priorities for redevelopment. The first framework identifies potential brownfields through four steps: (1) define input sites; (2) verify environmental liability; (3) confirm tax delinquency; and (4) cross-check with industrial classification code. The second framework prioritize the identified potential brownfield sites with a set of criteria which are selected and weighed based on key interviews and the study of local reference cases. The utility of this methodology is exemplified with the case study of Futian in Shenzhen. Local data including land use data, tax and environmental records of 2005 and the development plan for 2006–2010 are utilized. We conclude that this methodology properly responds to the increasing need of urban planners in making proactive plans for brownfield redevelopment in Chinese cities.

The spatial and temporal nature of urban objects

The purpose of this chapter is to examine, from an application perspective, the utility of remote sensing to collect data on urban and suburban areas for Urban Planning and Management (UPM). Specifically, the chapter discusses the use of remote sensing at two different spatial levels, the information needs with respect to monitoring planned and unplanned development, and the optimal spatial and temporal requirements for images used in this regard.

Capturing the Diversity of Deprived Areas with Image-Based Features : The Case of Mumbai

Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

The utility of the co-occurrence matrix to extract slum areas from VHR imagery

Many cities in developing countries lack detailed information on the emergence and growth of highly dynamic slum developments. Available statistical data are often aggregated to large administrative units that are heterogeneous and geographically rather meaningless in terms of pro-poor policy development. Such general base information neither allows a spatially disaggregated analysis of deprivations nor are settlement dynamics easily monitored, while slums are rapidly developing in particular in megacities. This paper explores the utility of the co-occurrence matrix (GLCM) and NDVI to distinguish between slums and formal built-up areas in very high spatial and spectral resolution satellite imagery (i.e., 8-Band images of WorldView-2). For this study, an East-West cross-section of Mumbai in India was used. We employed image segmentation to extract homogenous urban patches (HUPs) for which the information extracted from the GLCM was aggregated. The result was evaluated using collected ground-truth information and visual image interpretation. The results showed that the variance of the GLCM combined with the NDVI separate formal built-up and slum areas very well (overall accuracy of 86.7%).

An exploration of natural capital in the context of multiple deprivations

Urban poverty research has moved from income-consumption perspective to a more informed and multi-dimensional approach. Issues such as powerlessness, insecurity, exclusion and many others which did not find much acceptance in this domain earlier are increasingly being considered the key to understanding the multiple deprivations that the poor face. Urban environmental issues, which may affect the poor in their own way, still get little attention. This research, using remote sensing techniques is an attempt to understand the environmental aspects of deprivation for the Indian city of Kalyan-Dombivli. An index of environmental quality was constructed using thermal information, vegetation and building density. The final index shows that environmental quality is worst in areas occupied by slums.