The Earth´s land surface is undergoing rapid and manifold changes. Among the most drastic land use changes is the sealing of land surface by infrastructure or building constructions, generating so-called „impervious surfaces“ (Seto et al., 2011). Such areas are core elements of builtup areas, settlements, villages, and urban areas. Estimated at about 0.45% of the habitable land area in 2010 (Liu et al., 2014), the global total of settlement and urban area is relatively small in comparison to agricultural land cover (about 50%) and forests (about 35%), but urban areas are constantly and rapidly expanding. A well-defined and unambiguous definition is therefore a must when reliable land use maps shall be produced, also due to the substantial impacts on sustainability of urban areas (Elmqvist et al., 2021). There is a multitude of possible definitions for the degree of urbanization: the Statistical Office of the European Union (Eurostat) uses, for example, a criterion of geographical contiguity in combination with a minimum population threshold within 1km² square grid cells (European Commission. Statistical Office of the European Union., 2021). For remote sensing image interpretation, however, one needs in the first place to resort to observable biophysical features, since social and economic variables cannot be identified directly from the imagery. The most typical biophysical feature of settlements and urban areas is the presence of built-up elements, including houses, industrial areas, roads, and parking lots. In a hierarchical classification framework, it appears straightfoward to start out with an identification of impervious surfaces and proceed with a classification into classes characterizing the degree of urbanity, either in categories (such as “urban”, “transition”, “rural”) or on a continuous scale of, for example, percent of “urbanity” on the second level.
Research on quantification of urban impervious surface and its spatial pattern is commonly based on remote sensing analysis (Elvidge et al., 2007, Ma, 2016). Yet mapping the class “urban” is the central element in urban studies, and research has considered various spatial resolutions as well as different sensors and classification methods (Weng, 2012). High-resolution satellite imagery such as IKONOS, QuickBird or WorldView are the premier data source for detailed thematic mapping of urbanity (Goetz et al., 2003; Hu and Weng, 2011; Cablk and Minor, 2003). Impervious surface is used as proxy variable as the class “urban” cannot be directly classified from remote sensing
Within the assessment of urbanization, urban green spaces play an important role. Urban green spaces, also referred to as green infrastructure (Hansen et al., 2019), are composed of parks, gardens, roadside trees or alleys, and other “green” landscape elements and have a crucial importance for regulating air temperature (Herath et al., 2018) and air quality (Nowak et al., 2006) and also for providing habitats to animals and plants (Lepczyk et al., 2017). Various studies have shown that the occurrence of green spaces in the neighbourhood has a positive effect on health and well-being (Groenewegen, 2006; Maas et al., 2009; Troy and Grove, 2008), in particular where trees form the main element of green infrastructure.
Conventional methods for assessing and analysing urbanization and its complexity are usually based on gradient analysis or on the use of concentric circles but may fail to assess a new type of environment, which is found around many of the rapidly urbanising megacities of the Global South.. Such environments are characterised by both urban and rural features. For example, despite intensive development of traffic and settlement infrastructure, they are often characterised by a high presence of green spaces which could be either historical or newly created. The megacity of Bangalore in the South of India is one example of such new environment, with e.g. old-growth tree structures overrun by urbanization, so that historic green spaces or remnants of agricultural use are frequently found next to new and very dense settlements. An enormous complexity of grey and green infrastructure emerges here, both in terms of (two-dimensional) area and the (three-dimensional) space-filling built-up volume.
High-resolution satellite imagery allows assessing and categorizing the complex pattern of grey and green infrastructure at the rural-urban interface through quantitative analyses. Previous studies have commonly used data derived in a two-dimensional domain in combination with hard thresholding, frequently ignoring the important fact that urbanization takes also place in a three-dimensional space. Therefore, it may be indicated to integrate the third dimension when doing a classification of urbanity. The aim of this study is to fill this gap by developing a novel, transparent, and unambiguous approach to extract quantitative and comparable 2D/3D information on the degree of urbanity and as basis for a categorization of grey and green infrastructure. The approach we propose here uses a small set of biophysical variables, extracted from high-resolution remote sensing imagery, as indicators.