Categorization of green and grey infrastructure complexity in the rural–urban interface of Bengaluru, India: an unsupervised volumetric approach with relevance for urban quality

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 built-up 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 of urban areas is therefore a must when reliable land use maps shall be produced, also due to their multiple and substantial sustainability impacts (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 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, thus showing “rurban” characteristics. For example, despite intensive development of traffic and settlement infrastructure, they are often characterised by a high presence of green spaces that may either have a long historical continuity or be newly created. The megacity of Bengaluru 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 (Nagendra and Gopal 2011). 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 is time 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.

Considering an expected number of five billion people living in urban environments by 2030 (Venugopal et al. 2010), keeping urbanization within sustainable limits, and harnessing green infrastructure such as urban trees, are grand future challenges (Elmqvist and Maddox 2018). Decision processes in urban planning should be based on meaningful information, most notably regarding the future distribution of green infrastructures (Haaland and van den Bosch 2015).

With the aim of contributing to satisfying these information needs, the present study provides a data-driven approach that characterizes rural–urban gradients in terms of urbanity and grey/green infrastructure complexity. The produced map combines the percent impervious surface on a 2D scale together with the presence of trees and buildings and their corresponding volumes on a 3D scale as indicators to quantify the degree of urbanity, with implications for urban quality. Considering the space filling 3D volume of buildings and trees is a new approach that helps to uncover important characteristics of urban quality that might be overseen by a purely two-dimensional view. Such a classification might also provide a closer linkage to social-ecological complexity and helps to identify social-ecological systems (SESs) as described in Pacheco-Romero et al. (2021).

In our analysis we discriminated five clusters of similar characteristics; each of these five clusters characterizes urban quality of life in specific ways, and these contributions can be either positive or negative. The clustering used here is based on the similarity of variable profiles of three metrics that are informative if considered all together. Compared to a classification based on hard thresholds of single- or multiple variables, this clustering approach is less influenced by the magnitude of absolute values that might vary over the study area. Clustering approaches used in other studies (e.g. Pacheco-Romero et al. 2021) often use the normed differences of several indicators in a weighted distance metric (e.g. Euclidean or Mahatten distance). Contrary to such approaches, our approach of pattern recognition is based on the similarity of variable profiles (Q-correlation), which makes it a more general metric that is applicable in different environments. In our analysis we limited the number of clusters to k = 5 based on their statistical “separability”, which turned out to be well in line with the number of classes that can be clearly distinguished by visual interpretations. A larger number of clusters would lead to lower separability and ambiguous classification results.

Cluster #4 is characterized by a high coverage of impervious surface and only minor tree volume, and is the dominant type in urban Bengaluru. Relatively few trees with low green volume occurring in combination with a high coverage of impervious surface coverage may have negative effects on the quality of life and a healthy urban community. In terms of air quality, McPherson et al. (1994) mentioned that particularly the large trees, like those huge alley trees in Bengaluru, remove up to 70 times more air pollution than small trees. Street trees play a key role for quality of life (Turner-Skoff and Cavender 2019) and therefore, there should be minimum criteria for the amount of tree cover available. For example, Konijnendijk van den Bosch (2021) recently proposed a 3-30-300 m rule, according to which one should be able to see three trees from each house, the neighbourhood canopy coverage should be at least 30% to provide environmental, economic or social benefits (Mullaney et al. 2015), and lastly there should be a green space at 300 m distance.

Regions where neighbouring villages are categorized differently in terms of the green and grey infrastructure complexity may be an indicator for an ongoing transformation towards a more urban environment. The presented cluster approach provides a first indication for new type of environment that could be named “rurban”. Rurban environments do not necessarily represent an intermediate state along a linear transition from urban to rural, but often lead to highly dynamic, complex, and novel land cover constellations due to multiple development processes and tensions.

Tree cover (and tree volume) is a very broad and general land cover class. Cluster #2 in particular includes mango plantations as well as urban parks - although these different types of tree cover provide very different ecosystem services and thus also contribute to the quality of life in very different ways (one point is the question of public access, which is not necessarily guaranteed in the case of plantations, for example). As a future extension, we plan to implement a more detailed classification scheme which may provide more linkages to the different ecosystem services and to quality of life.

The three indicators used in the clustering process may have some shortcomings, such as misclassification. Although the classification is based on very high-resolution imagery, mainly pure pixels per land cover class and a state-of-the-art classifier, it cannot be avoided that some objects or land cover types have been incorrectly classified. This in turn may have only a minor impact due to the coarser grid resolution but will nonetheless misallocate some clusters. The grid resolution of the presented continuous map of 100 m x 100 m is in line with Schoepfer et al. (2005) who created a 100 m grid after an exchange with city planners. In comparison, the resolution of the maps used for the analysis is very high with 0.3 m. The reference areas (sizes of overlapping moving windows) we used to derive the mean PIC per pixel were adjusted to the expected size and spatial scale of relevant objects or structures, like single houses or properties, wards and small villages. For other applications different spatial scales of neighborhoods might be more appropriate. However, for an area of 250 km², the analysis is computationally intensive. Down sampling to 1 ha during cluster analysis is a compromise of computational requirements, preservation of spatial variability, and interpretability of the map. The down sampling also offers the possibility to upscale this approach by integrating Sentinel-2 satellite imagery and monitor changes in categorization over time.

Rapid urbanization creates new and complex configurations of land that are not captured by conventional dichotomies of “urban “ and “rural “ – in particular in the Global South. At the same time there is evidence of the important role of green infrastructure in mitigating the negative impacts of urbanization on sustainability. We make use of 3D stereo satellite imagery and develop a data-driven approach to characterize a rural–urban interface regarding its green and grey infrastructure complexity. The approach is novel in terms of the method following an unsupervised and objective approach and the integration of 3D information.

Our analysis shows that the three-step procedure of 1) classifying “imperviousness” at the pixel level, 2) determining “urbanity” based on a relative proportion of impervious surface in a defined reference area in the 2D space, and 3) adding information on complexity by considering the 3D volume of green and grey infrastructure helps distinguishing different clusters representing different intensities of urbanization. Adding a third dimension of build-up and green volume, however, requires high resolution stereo imagery or other 3D data sources (e.g. airborne LiDAR data) that can be used as basis to derive a digital surface model (DSM).

The presented method could be considered in urban planning and in particular in green infrastructure development as it provides spatial information and a categorization of green infrastructure. For further development of the approach, an assessment of ecosystem services from green infrastructure would complement the information basis for decision making. Our study contributes to the existing recommendations from Hansen et al. (2019) for planning green infrastructure in cities and allows planning and site development by using a wall-to-wall map of green infrastructure categories. Our clustering approach may also advance integration of green infrastructure into ongoing efforts to identify and map land system archetypes, which are a hot topic in landscape and sustainability science (Pacheco-Romero et al. 2021). Using clustering approaches to identify land system archetypes facilitates the understanding of social-ecological complexity and context by categorizing systems with similar variable profiles that can be correlated or enriched by socio-economic and ecological data (Rocha et al. 2020).