The links between the natural environment and human well-being have become increasingly highlighted in recent years. Studies that have investigated these links span numerous research agendas including public health [1
], urban planning [5
], landscape ecology [8
], ecosystem services [10
] and environmental justice [12
]. Despite the broad range of perspectives taken, the fundamental metrics through which the natural environmental has been represented in research on the topic have so far paid limited attention to the multi-faceted character of the landscapes in which many people live. In highly managed environments such as urban areas, landscape heterogeneity, function and use are highly modified by human activity. As a result, it has been difficult to describe such landscapes using traditional land use and land cover classification techniques [15
]. With the availability of new data, particularly high-resolution multi-spectral imagery, this situation is now changing. New data availability has also coincided with a greater push from research and practice to better represent urban social–ecological systems as a means to understand the multiple benefits of green and blue spaces for human health and well-being [21
]. In particular, the concept of green infrastructure has emerged as a promising framework to understand, manage and enhance the multiple benefits delivered by green and blue spaces, particularly in highly fragmented landscapes such as those affected by the process of urbanisation [23
A primary aim of a green infrastructure approach involves the maximisation of physical and functional connectivity whilst optimising multi-functionality in terms of social, ecological and economic benefits [24
] and seeking resilience through landscape diversity [26
]. The effective mapping of such attributes therefore necessitates the ability to characterise land cover (form) and land use (function) simultaneously. An appreciation of green infrastructure that takes into account both physical form and functional properties likewise has the potential to consolidate divergent views of what comprises green infrastructure itself. For example, as Mell [25
] argues, environmental practitioners, academics and local social–ecological actors tend to view green assets as either a visual/physical phenomenon or as a functional element in the wider infrastructural landscape. For this reason, green infrastructure typologies vary widely depending on the emphasis placed on either land use (function) or land cover (form). Developing a more social–ecological characterisation of landscape features that contribute to green infrastructure may go some way towards bridging such dichotomous perspectives. The landscape characteristics that may be derived from combining land cover and land use data should be applicable to analyses investigating the environmental benefits afforded by green spaces to urban inhabitants. It is known, for example, that urban green and blue spaces bring a range of health promoting benefits [4
] but that such spaces are unequally distributed, disadvantaging the most socioeconomically deprived communities [14
]. In order to address such inequalities in the planning process, assessments of landscapes and landscape features that relate simultaneously to both social provisions (i.e., function) and environmental quality (i.e., form) could support the design of an urban green infrastructure that promotes social and ecological resilience [28
] in tandem. This is also important given that some aspects of form help to determine some elements of function, such as an enhanced cooling effect from trees over grass or higher aesthetic value of diverse land covers in urban settings [29
Social–Ecological Research and the Representation of Urban Green and Blue Spaces
The association between urban form, landscape and socioeconomic conditions has long been recognised [31
] and research on the topic continues to provide insight in studies focussed on social-ecological dynamics and human well-being, particularly in an urban context [33
]. The underlying premise of a green infrastructure approach relates to the multiple benefits that may be obtained from well-connected ecological networks for human well-being. However, studies into health and well-being benefits of urban nature have come largely from the public health and social sciences. Understandably, these disciplines have tended to pay more attention to human processes and outcomes with relatively little emphasis on characterising the physical and ecological characteristics of the natural environment in their assessments. There has been considerable use of broad density-based metrics. For example, Maas [36
] employed dominant land cover (agricultural, natural and urban green) at a 25 m spatial resolution in their assessments on proximity to greenery and population health in the Netherlands. This resulted in street trees and roadside greenery being largely excluded from the model [37
]. Furthermore, the emphasis was on using categories to estimate a percentage green space cover. Mitchell and Popham’s [2
] seminal work exploring the socioeconomic subtleties in the strength of the relationship between green space and health in the United Kingdom, used 5 m percentage green space cover. However, this indicator did not discriminate between the type, quality or accessibility of spaces that are categorised as green. Similarly, research has failed to consider the relevance of landscape-based metrics as indicators of environmental quality. Therefore, the development of new spatial data that account for the qualities of landscapes in which people live, as opposed to a purely quantitative consideration of green space cover, are needed. Such novel datasets allow more sophisticated approaches to analysing urban human well-being as well as being useful for a range of other purposes, such as urban planning.
Information on green infrastructure types, such as differentiating formal parks from informal or incidental green spaces, as well as cover, is a necessary step in describing landscapes from an anthropocentric perspective [38
]. These nuances may help to explain negative associations between green space quantity and self-reported health in low-income suburban areas [2
]. It may also challenge the notion that people of different socioeconomic backgrounds experience the same kinds of green spaces and in similar landscapes, an idea contested in research into environmental justice and urban design (see [14
] for a review). Accordingly, it is important to consider how land cover and land use data can be effectively married in refined assessments of urban landscape types for the analysis of associated health and well-being outcomes.
Elsewhere, there have been useful developments that put greater emphasis on physical form and environmental function in the characterisation of urban areas, including a wider consideration of urban function. For example, Urban Morphology Types were designed to provide more homogenous analysis units from the perspective of environmental functionality [39
]. However, the applicability of a UMT approach for health and well-being studies is ultimately hampered due to two main reasons. The first is due to difficulties integrating data on population, demographics, socioeconomic indicators and health which tend to use census tract data. Such statistical units are therefore integral to understanding the social–ecological character of the localities of urban inhabitants. It follows that, in research where social and ecological outcomes are at the fore, there are still strong arguments to make census units the primary analytical and geographical framing. Typologies that seek to characterise neighbourhoods at scales consistent with area-level statistical reporting are therefore a logical step in the advancing of studies into urban health and well-being indicators. Secondly, the methods to develop these datasets have been highly resource-intensive and demanded great sampling effort to achieve desirable levels of accuracy. For this reason the land cover estimates, though detailed, are generalised to a type and not to a location. Furthermore, given that they are time-consuming to conduct, they tend to be updated relatively infrequently. The more recent availability of very fine ≤10 m spatial resolution multi-spectral satellite imagery, has paved the way for semi-automating some of the classification tasks and allowing better classification of heterogeneous urban areas [42
]. While it is still not possible to estimate the full range of urban land covers achieved in the aforementioned studies, there is now the opportunity to develop locally specific urban landscape characterisations for health and well-being studies to a level which was not previously possible. For example, some of the processed datasets available in the U.K. context and their characteristics are shown in Table 1
. Limitations in the use of such data relate to the size of minimum mapping units (MMUs) that provide limited detail for spatial analysis of land use in cities, inconsistencies and the relative infrequency of updating.
The principal advantage of such datasets is their breadth of cover, providing a national and continental repository of thematic land use. They are limited, however, in their ability to offer information on landscape structure and patterns of vegetation. Furthermore, Urban Atlas [43
] and U.K. Land Cover Map [44
] data reveal inconsistencies resulting from variation in mapping units and resolution (see Figures 5 and 7). These inconsistencies, although expected for datasets employing different mapping units and resolution, are relevant given the prevalent use of both of these datasets in international research into urban environments (e.g., [39
]) and policy guidance [22
Most recently in 2017 the U.K. national mapping agency (Ordnance Survey) has produced a fine-scale vector dataset of urban green space using spatial data at the highest available resolution for the United Kingdom. The data are available under licence (OS Mastermap Greenspace Layer [52
]) as well as in open-access format (OS Open Greenspace Layer [53
]). The latter is less detailed, including fewer land use classes, but benefits from a greater extent, covering some peri-urban and rural areas not considered in the Mastermap Greenspace Layer. It overcomes a number of the limitations presented by previous datasets but its focus is on identifying green and blue land parcels and associated land use. It is much less refined in terms of its consideration of form (land cover) and, therefore, the quality of green spaces and how green and blue spaces come together in landscape types. The need to develop more integrated and detailed measures of landscape character than those offered by contemporary measures of land use or land cover presents a current research imperative. A landscape-oriented dataset should provide not only increased interpretability in terms of resolution, but equally a classification schema that supports the creation of meaningful landscape metrics and subsequent typologies. A novel method for incorporating both land use and land cover into a landscape-oriented representation of a large city catchment (Greater Manchester, UK) is presented here as an example of how such a shortcoming can be addressed. The method has three elements: (1) the use of remote sensing and GIS techniques to combine measures of land use, land cover and associated landscape metrics in the characterisation of neighbourhoods according to census units; (2) employing data reduction methods to identify common attributes of urban landscapes for the creation of meaningful typologies for social–ecological research; and (3) a demonstration of the merit of the approach through analysis of social–ecological relationships in a large U.K. urban conurbation.
The methodology presented here succeeds in tackling some of the specific limitations of existing datasets on land use and land cover in a U.K. context through the combination and interpretation of available spatial data towards an integrated landscape approach. For example, the under-representation of green and blue space by the LCM 2015 and Urban Atlas 2012 is reflected in the distribution of percentage cover values per LSOA (Figure 5
a–d), which were highly skewed and included many values close to or at zero. In the United Kingdom, the improvement on such data in terms of coverage made by the OS Mastermap Greenspace layer is clear from the much higher frequency of values at greater levels of green space cover for land use (Figure 5
c). However, the distribution of land cover within the same dataset (Figure 5
d) shows a similar pattern of under-representation as for the UA and LCM. Conversely, the ILM (Integrated Landscape Map) exhibited near-normal distribution for these values. Such differences in distribution highlight the shortcomings of currently available datasets for mapping city region-level green infrastructure, mainly a result of large minimum mapping units and spatial extent. In this paper, we have shown the improvements that can be made through the creation of composite datasets and their use to generate new landscape data, such as in the ILM.
Distinction between datasets in terms of the distribution of green and blue space cover that they report is important as it has implications for research on environmental justice and human well-being. For example, the distribution of percentage green and blue space cover described in Figure 5
a–d and Figure 6
shows great variation between datasets. It follows, therefore, that the conclusions drawn from such patterns, for example on inequalities in green space provision throughout an urban landscape, would likewise vary greatly depending on the data source used. Moreover, given the widespread use of both the LCM and Urban Atlas data programs in environmental research, the analysis developed here is of particular note and highlights the degree of uncertainty created when large minimum mapping units are employed.
a–d highlight the inconsistencies that result in the variability of both mapping units and terminology employed by the UA 2012 and UK LCM 2015. The OS Mastermap Greenspace layer is a significant improvement in terms of detail and interpretability and, through its incorporation in the ILM, the latter is able to identify accurately small pockets of land such as allotments and community growing spaces and their land cover. Under the classification schemes of the UA and LCM, however, it is not possible to identify such sites as consisting of green and blue space at all. Such spaces provide important social [69
] and ecological [71
] benefits and present a pertinent example of how smaller but highly productive urban green spaces have hitherto been overlooked in urban mapping classification schemes. The ability to capture such spaces and their associated landscape features is a key improvement made possible through the mapping approach developed here.
The final classification scheme of the ILM into seven thematic land use types coupled with five land cover values revealed that individual landscape features exhibit significant and unique associations with both ecological and socioeconomic indicators (Table 2
). The stronger correlation exhibited between the years of potential life lost indicator with individual landscape features (e.g., higher plants and shrubs in private gardens) over others (e.g., amenity trees), controlling for income, presents a landscape approach as a promising avenue for investigations into quality of life in urban areas. Therefore, the capture and classification of landscape features appears to be a valid approach to investigating social–ecological relationships and represents a key consideration in landscape assessments of both social and ecological dynamics in urban areas. The preliminary relationships explored herein suggest a significant improvement to mapping urban landscapes through the current study.
The results of the k
-means clustering of LSOAs into landscape types demonstrated both visually (Figure 9
, Figure 10
and Figure 11
) and statistically (Table 4
, Figure 12
) that combining data on land cover and land use, even when limited to a small number of categories, offers an effective means to describe urban environments using only a minimal amount of geoprocessing time. Such analyses can be conducted over large areas and more frequently than has been possible in the past. There are further datasets that can be used to replicate some of the local datasets used here, such as the U.K. National Tree Map produced via Lidar although, as in the case of the latter, not all of these are open-source. Table 4
shows the range of combinations of land use and land cover that can be observed for LSOAs in the landscape of Greater Manchester as an example of a large urban city region. The results illustrate the heterogeneity in urban landscapes, which can be captured and used in a data-driven delineation of neighbourhood types. Figure 12
a–d demonstrate that classification of neighbourhoods according to these combinations may reveal greater levels of nuance in the associations between landscape configurations and social–ecological conditions. The simple stratification of the study area according to overall green cover was closely mirrored by an inverse trend in IMD score (Figure 12
b). However, stratifying by a typology based on amount, use and cover revealed that IMD was sensitive to configurations of green space qualities as well as total cover. This suggests that a simple one dimensional metric such as overall percentage green space, as used in numerous social-ecological health and well-being studies to date, may fail to capture the true relationship between landscape and social–ecological conditions.
The ILM therefore provides a versatile mapping approach to evaluating the relationship between physical, socioeconomic, health and landscape characteristics. The nature of the final classification of Greater Manchester presented here, combining cover and a designation of use, offers the opportunity to explore a range of combinations reflecting urban form as well as investigating the cover and distribution of individual landscape features (e.g., residential trees). An assessment of their contribution to factors such as landscape connectivity in urban environments may, thereby, be permitted, which offers greater interpretive power than coarse density metrics such as percentage green space alone. For example, the positive relationship between domestic green cover and both canopy connectivity and landscape cohesion seen in Table 3
presents the former as a potentially important structural component and a landscape feature worthy of further exploration and consideration in planning policy. Conversely, the ability of the ILM to delineate landscape features, combining data on use and cover reveals that individual cover types e.g., tree cover can exhibit contradictory relationships with landscape fragmentation depending on the land use in which they are situated (e.g., amenity versus private garden functions, Table 3
Given the known relationship that exists between the natural environment, socioeconomic conditions and health, the use of composite datasets such as the Integrated Landscape Map presented here, and the analyses that are permitted, may contribute to sophisticated landscape-focussed assessments of factors influencing urban well-being. For example, landscape types that exhibit local connectivity but consist of smaller patches of principally domestic green space (e.g., Leafy Residential) may, due to their distribution, provide important connectivity to larger open patches. Therefore, the creation of landscape types, and mapping of their spatial distribution may also facilitate studies across scales. Knowledge of the spatial contiguity of landscape features and types may open up analyses of spatially dependent relationships where non-linear approaches are required to understand social–ecological processes [74
]. Moreover, the creation of landscape types could be tailored to particular research questions by including a range of variables of interest selected by the analyst. The method presented here represents a template for landscape explorations of social–ecological dynamics, the strength of which stems ostensibly from its ability to combine information on land use and land cover but may ultimately be applicable to a wide range of datasets and research agendas. The real merit of applying such an approach lies in the viewing of highly managed landscapes as lived environments. The consideration of land use, land cover and socio-geographic elements, in combination rather than exclusion, supports a social–ecological perspective that could be applied to the characterisation of city regions and their catchments around the world. In the coming years, the emergence of even finer sub-10 m spatial resolution imagery should allow even more refined assessments, both of landscape type and also of associated ecosystem and landscape characteristics. The integration of globally available, open-source and high-resolution imagery—such as that used here—with accurate land use data is therefore becoming increasingly viable. National land survey agencies provide land use data across the globe and, in a European context, for example, many datasets are freely available [75
], further supporting the replication of the approach presented here.