land
Article
Mapping Urban Green Infrastructure: A Novel
Landscape-Based Approach to Incorporating Land
Use and Land Cover in the Mapping of
Human-Dominated Systems
Matthew Dennis 1, *, David Barlow 2 , Gina Cavan 3 , Penny A. Cook 4 , Anna Gilchrist 1 ,
John Handley 1 , Philip James 5 ID , Jessica Thompson 6 , Konstantinos Tzoulas 3 ID , Phil Wheater 3
and Sarah Lindley 1
1
2
3
4
5
6
*
School of Environment Education and Development, University of Manchester, Oxford Road,
Manchester M13 9PL, UK; Anna.Gilchrist@manchester.ac.uk (A.G.);
John.Handley@manchester.ac.uk (J.H.); Sarah.Lindley@Manchester.ac.uk (S.L.)
Manchester City Council, Manchester Town Hall, Albert Square, Manchester M60 2LA, UK;
d.barlow@manchester.gov.uk
School of Science and the Environment, Manchester Metropolitan University, Oxford Road,
Manchester M15 6BH, UK; G.Cavan@mmu.ac.uk (G.C.); K.Tzoulas@mmu.ac.uk (K.T.);
P.Wheater@mmu.ac.uk (P.W.)
School of Health Sciences, University of Salford, The Crescent, Manchester M5 4WT, UK;
p.a.cook@salford.ac.uk
School of Environment and Life Sciences, University of Salford, The Crescent,
Manchester M5 4WT, UK; P.James@Salford.ac.uk
City of Trees, 6 Kansas Avenue, Salford M50 2GL, UK; JessicaT@cityoftrees.org.uk
Correspondence: matthew.dennis@manchester.ac.uk
Received: 22 December 2017; Accepted: 22 January 2018; Published: 25 January 2018
Abstract: Common approaches to mapping green infrastructure in urbanised landscapes invariably focus
on measures of land use or land cover and associated functional or physical traits. However, such onedimensional perspectives do not accurately capture the character and complexity of the landscapes in
which urban inhabitants live. The new approach presented in this paper demonstrates how open-source,
high spatial and temporal resolution data with global coverage can be used to measure and represent the
landscape qualities of urban environments. Through going beyond simple metrics of quantity, such as
percentage green and blue cover, it is now possible to explore the extent to which landscape quality
helps to unpick the mixed evidence presented in the literature on the benefits of urban nature to human
well-being. Here we present a landscape approach, employing remote sensing, GIS and data reduction
techniques to map urban green infrastructure elements in a large U.K. city region. Comparison with
existing urban datasets demonstrates considerable improvement in terms of coverage and thematic detail.
The characterisation of landscapes, using census tracts as spatial units, and subsequent exploration
of associations with social–ecological attributes highlights the further detail that can be uncovered by
the approach. For example, eight urban landscape types identified for the case study city exhibited
associations with distinct socioeconomic conditions accountable not only to quantities but also qualities
of green and blue space. The identification of individual landscape features through simultaneous
measures of land use and land cover demonstrated unique and significant associations between the
former and indicators of human health and ecological condition. The approach may therefore provide
a promising basis for developing further insight into processes and characteristics that affect human
health and well-being in urban areas, both in the United Kingdom and beyond.
Keywords: health and well-being; GIS; remote sensing; urban ecosystems; social–ecological systems
Land 2018, 7, 17; doi:10.3390/land7010017
www.mdpi.com/journal/land
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1. Introduction
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 [1–4], urban planning [5–7], landscape ecology [8,9], ecosystem services [10,11]
and environmental justice [12–14]. 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 [15–20]. 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 [21,22]. 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 [24,25] 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 [4,11,26] but that such spaces are unequally distributed, disadvantaging
the most socioeconomically deprived communities [14,27]. 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 [29,30].
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 [31,32] 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 [33–35].
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
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the natural environment in their assessments. There has been considerable use of broad density-based
metrics. For example, Maas [36,37] 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 [2,3] 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 [39–41]. 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.
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Table 1. Attributes of commonly used processed datasets covering urban areas in the United Kingdom.
Minimum mapping units
Input data resolution
Available years
Number of land use/land cover classes
Thematic accuracy
Urban Atlas
Corine Land Cover
Land Cover Map
0.25 ha
2.5 m
2006; 2012
27
≥80%
25 ha
25 m
1990; 2000; 2006; 2012
44
≥85%
0.5 ha
25 m
2007; 2015
21
-
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., [39,45–51]) 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.
2. Materials and Methods
The methodology presented here demonstrates the possibility of integrating currently available
land use data such as those published by the U.K. Ordnance Survey with a land cover classification
derived from high-resolution satellite imagery. The resulting composite dataset exhibits the ability
to capture landscape features (integrating land use and land cover), indices, and a related typology
congruent with existing socio-geographic units (U.K. national census tracts). Use of the latter as spatial
extents for processing and analysis is particularly advantageous given that they reflect statistical units
at which population, socioeconomic and health-related data are regularly reported. The primary
use of recently available high-resolution remotely sensed data with global coverage (Sentinel 2A
satellites, launched 2015 [54]), combined with a universally applicable classification scheme based on
simple ecological stratification, highlights the potential of the method for work in other urban and
human-dominated landscapes in a range of climates. The capacity to integrate elements of function
and form in human-dominated landscapes and reflect multiple social and ecological dimensions of
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use and quality presents a key opportunity for explorations of human health and ecological condition
in social–ecological systems.
2.1. Overview
Greater Manchester comprises an extensive conurbation (1276 km2 ) with a population of
approximately 2.8 million [55] that covers multiple areas of urban to rural transition but that is
essentially defined by the distribution of urban centres within the 10 local authorities that make up the
coterminous landscape of the city region. A novel composite spatial dataset covering the conurbation
of Greater Manchester was achieved through a combination of remote sensing and GIS techniques that
drew on the strengths of separately but freely available spatial data. The resulting dataset was then
compared with other open-source and widely used datasets covering the same study area (Urban Atlas
2012 and Land Cover Map 2015). The methodology may provide a useful template for developing
refined green infrastructure maps for other cities, particularly from the perspective of informing more
detailed analyses of links between the urban environment and health and well-being. In order to assess
the potential uses of the data, a number of example analyses are also carried out, namely to consider
gradients observed in associations with indicators of health and social deprivation. The method
consisted of three practical stages. Stages one and two achieved the processing and integration of
land use and land cover data towards the characterisation of discrete landscape features (element
one described in the previous section). Stage three involved the subsequent computing of landscape
indices and, through data reduction techniques, the creation of a landscape typology (element two)
towards the validation of the dataset with social–ecological analyses (element three).
2.2. Stage One: Automatic Land Cover Classification of Sentinel 2A Data and Data Processing
Copernicus Sentinel S2A (available since 2015) data were obtained from the Copernicus Scientific
Data Hub (scihub.copernicus.eu/dhus). S2A multi-spectral imagery consists of 13 spectral bands
with a swath width of 290 km. The spatial resolution of the bands are 10 m (for visible and near
infrared bands), 20 m (for 6 red-edge and shortwave infrared bands) and 60 m (for 3 atmospheric
correction bands). False colour infrared images were processed using bands 3 (green), 4 (red) and
8 (near infrared), all at 10 m resolution (Red:Green:Blue: 8:4:3). The Sentinel 2A mission has a re-visit
time of five days.
A supervised classification approach was then employed to train a maximum liklihood automatic
classifier. Around 100 training samples were used (after e.g., [42,56]). The results of the automatic
classification divided the study area into five classes based on cover by water and levels of vegetative
succession: built/impervious; a ground layer consisting of grasses and ground vegetation; a field
layer consisting of forbs and shrubs; a canopy layer and a fifth class for areas of water. A simplified
woodland stratification scheme was chosen as a widely acknowledged succession-related classification
and, being common to temperate and tropical biomes, should be widely replicable for measures of
greenness, biomass and structure and therefore suitable for a variety of environmental applications.
The accuracy of the resulting classification was improved by incorporating digitised tree canopy
data available from a local environmental NGO: City of Trees [57], which served principally to
correct misclassification of Calluna vulgaris (ling heather) as tree cover in upland areas. Areas that
showed evidence of such misclassification were clipped, re-classified and mosaicked with the original
dataset. Additionally, a data layer for canals, rivers and open water from the Ordnance Survey
Open Rivers layer 2017 [58] was added to improve cohesion of water-classified pixels in more urban
areas. These vector datasets were rasterised and combined with existing pixel classes using map
algebra and reclassification. All spatial processing and analysis were carried out using ArcMap 10.4.1.
Accuracy assessment was enabled through ground-truthing (200 sampling points) based on 2017 Edina
Digimap aerial photography [59] and cross-tabulated.
An overview of the data processing workflow for the landscape assessment is presented in
Figure 1.
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workflow.
Figure 1. Data processing workflow.
2.3. Stage Two: Generalisation and Incorporation of Land Use Data
The land cover classification was subsequently enhanced with data on land use from the OS
Mastermap Greenspace Layer (downloaded through the Edina Digimap Service from http://digimap.
edina.ac.uk) through a four-step process. Firstly, the OS Mastermap Greenspace Layer was converted
to 10 m raster cells to render it compatible with the land cover data. Secondly, the resulting raster layer
was mosaicked with data from the rasterised OS Open Greenspace Layer, which has a wider spatial
extent but less detail (data on publically accessible green spaces available at: https://www.os.uk/
opengreenspace) and with data for private gardens extracted from the OS Mastermap Topography
Layer ([60] also available from http://digimap.edina.ac.uk). This served to increase the spatial extent
of data on these respective land use types. Thirdly, the items in the Primary function (land use)
attribute of the resulting raster layer were generalised to represent common themes. The data were
generalised in order to highlight common functional traits related to elements of green infrastructure
(e.g., to combine similar usage types such as playing fields and other sports facilities) and also to reduce
the number of classes for use in the statistical analyses presented in this paper. A fully dissagregated
version of the dataset, covering all 18 land uses and with attributes denoting urban and peri-urban
contexts is described in the supplementary materials. An open-access version of the dataset is available
through the following link: http://huckg.is/d/ILM_Open.zip.
The 18 land use categories presented in the land use data were aggregated into the following
five classes: amenity, public parks and recreation, private gardens, institutional land, and land
use changing. The latter class was renamed brownfield land in the final classification as a more
commonly used signifier of this type of land use in the United Kingdom. An additional two classes,
urban other and peri-urban other, comprised urban and non-urban areas outside the extent of the OS
Mastermap Greenspace Layer. The latter were classified according to their inclusion in the 2015 U.K.
Land Cover Map. The sequential classification of land use and attribution of land cover is summarised
in Figure 2. The fourth step employed map algebra to create a final layer with values corresponding to
all possible combinations of land use and land cover, as individual landscape features. This resulted in
a classification scheme consisting of 35 unique values. The classification scheme of the final Integrated
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Landscape Dataset (ILM) provided a suitably aggregated dataset that reduced the number of values
to near zero for each landscape feature at the LSOA level whilst providing an easily transferable
thematic structure. This schematic approach, based on designations used within the OS urban land
use classification and Land Cover Map, and land cover derived from freely available remotely sensed
data, renders the data and the methodology employed immediately transferable at a national scale
and to international contexts where comparable levels of land use data exist.
Figure
treeofofland
land
use
aggregation.
Figure 2.
2. Decision
Decision tree
use
aggregation.
2.4. Consideration of Spatial Units
Much data are already available in spatial units that seek to capture local socio-geographic
homogeneity, such as in the case of U.K. census data. The boundaries that are described by such units
provide a useful spatial template for the development of effective landscape metrics and associated
datasets. Given the increasing centrality of the natural environment in approaches to understanding
human well-being, the generation of landscape typologies coterminous with spatial units employed in
the gathering and dissemination of socio-demographic data is desirable from a spatial analysis point
of view. Not only do such geographical units provide the basis for the reporting of a range of local
area statistics, such as national census data, they also commonly delineate socioeconomically coherent
spatial units. For example, in England and Wales, census reporting units (known as Lower Super
Output Areas (LSOAs)) are designed to be socially homogenous in nature reflecting tenure, dwelling
type and socioeconomic status [61]. They are also determined according to population and accordingly
their spatial extent is a reflection of local population density. The spatial character of LSOAs is,
furthermore, often shaped by elements of green (e.g., woodland), blue (e.g., watercourses) and grey
infrastructure (e.g., major roads) in the landscape (Figure 3). Accordingly such socio-geographic
units present a useful spatial template for the development of a landscape typology related to
human-dominated systems, such as those in urban areas.
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Figure 3. Boundaries of socio-geographic statistical units (Lower Super Output Areas) in South
Figure 3. Boundaries of socio-geographic statistical units (Lower Super Output Areas) in South
Manchester, UK. Source: Boundary data, ONS (2011); Aerial imagery, Edina (2017).
2.5. Stage 3: Validation of the Dataset
2.5.1. Statistical Comparison with Existing Urban Land Use Data
Validation was carried out based on a comparison with existing datasets and the reporting of
classification error statistics. The selected comparators were Urban Atlas 2012, LCM 2015 and OS
Mastermap Greenspace Layer 2017 for the same area (see Table 1). To investigate more closely the
relative efficacy of the integrated landscape dataset for capturing vegetation and water cover across
the Greater Manchester city region as indicators of its green infrastructure, histograms were generated
of total percentage green and blue space cover per Lower Super Output Area [62] for the ILM, LCM,
Urban Atlas, and the Primary Function and Form layers of the OS Mastermap Greenspace Layer. In the
case of the latter two datasets, all land use values comprising water or vegetation classes according to
the available technical reports were included in the calculation. For the ILM, all cells classed as water or
vegetation were included.
2.5.2. Calculation of Landscape Indices and Correlations with Socioeconomic and
Landscape Characteristics
The stratified approach to land cover using simple vegetative and water layers allowed for the
generation of a range of landscape indices with which to explore the study area. The QGIS Landscape
Ecology plugin LecoS 2.0.7 [63] was used to calculate values across a range of metrics using a vector
overlay method that permitted the rapid computation of indices matching the neighbourhood-scale
LSOAs. Values were computed for landscape indices reflecting diversity, Shannon’s Index (SHDI),
and connectivity, landscape division (LD) and canopy patch Euclidean distance nearest neighbour
(ENN). These metrics were chosen given the centrality of landscape heterogeneity and connectivity in
productive and resilient green infrastructure networks [12,64].
To assess the capacity of the ILM to reflect ecological and social variability in the study area
landscape values for percentage cover at the LSOA-level for individual landscape feature were tested
as correlates (Spearman’s Rank) with landscape indices (LD and canopy ENN) and social deprivation
indices related to premature mortality. The latter was quantified using the indicator years of potential
life lost, an age- and sex-standardised measure of premature death, which is a sub-domain of the
English Index of Multiple Deprivation [65]. Correlations were computed controlling for the sub-domain
income deprivation from the same dataset to give a more accurate impression of the distinct relationship
between landscape features and local health status.
In the case of landscape indices, landscape features describing tree cover for each of the seven land
use classes were tested for correlations with measures of connectivity and, in the case of socioeconomic
deprivation indices, all landscape features associated with amenity, private gardens and public
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parks and recreation land use classes were included in the analysis. The hypothesis here was that
individual landscape features, as unique green infrastructure elements, should similarly exhibit unique
relationships with social-ecological characteristics in a given landscape. Accordingly, if the hypothesis
holds, the creation of an integrated landscape approach to mapping green infrastructure can inform
analysis and decision-making in human-dominated landscapes.
2.5.3. Generation and Assessment of Landscape Types
The 35 landscape features of the final ILM layer were used to group LSOAs into landscape types.
In order to generate a landscape type for each LSOA, values for percentage cover by each of the
landscape features as well as values for the landscape indices SHDI and LD were entered into a data
reduction (k-means clustering ([66]) algorithm to group LSOAs. In order to test the ability of the method
to create landscape types that effectively delineate social and ecological characteristics, types resulting
from the k-means clustering were explored for patterns of variation in percentage green and blue cover
per LSOA, Shannon’s Diversity Index (SHDI) and Index of Multiple Deprivation scores. The same
analysis was also performed for comparison on LSOAs grouped into quantiles (corresponding to the
same number of percentile groups as for landscape types) according to total green and blue cover.
The rationale for this was to test whether the landscape typology, ranked according to green/blue
cover extent, exhibited patterns of socioeconomic deprivation and landscape diversity that simply
replicated those generated as a result of a simple stratification of LSOAs by percentage green/blue
space cover. If the latter were so, then the creation of such a typology would be invalidated as simply
reflecting a coarse quantitative view of the relationship between green space and social–ecological
characteristics. However, if exhibiting different patterns to those of a simple quantile stratification
a landscape typology approach can, thereby, be validated as one that goes beyond the broad linear
interpretation of social–ecological dynamics. All statistical operations were carried out in SPSS v. 23.
3. Results
Initial interrogation of the five-category land cover classification (see Figure 4) demonstrated
satisfactory levels of accuracy (overall accuracy 85%) consistent with internationally recognised
standards [67,68]. The dataset resulting from this initial classification is presented in Figure 4.
Figure 4. Result of the initial classification (Overall Accuracy 85%; Cohen’s Kappa 0.86). Contains City
of Trees (2011), Ordnance Survey (2017) and European Space Agency (2016) data.
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3.1. Statistical Assessment of the Dataset
Figure 5a–d give the frequency distributions of total percentage green space cover per LSOA
within Greater Manchester (N = 1673) derived from Urban Atlas 2012, UK LCM 2015 (as the sum of all
green and blue land use classes), and the OS Mastermap Greenspace Layer values for land use and
land cover. Figure 6 shows the distribution of values for green and blue space per LSOA for the ILM.
In addition to this comparison of frequency distributions of percentage green and blue cover,
examples of the UA, LCM and OS Mastermap Greenspace Map with the Integrated Landscape Map
are shown for an area of mixed land use in South Manchester. Figure 7a–e further compares with a
high-resolution (25 cm) Edina 2017 aerial image given of the same area for reference in Figure 7f.
(a)
(b)
(c)
(d)
Figure 5. Frequency distribution of green space cover in local urban neighbourhoods. Top left, (a):
Figure 5. Frequency distribution of green space cover in local urban neighbourhoods. Top left, (a): Urban
Atlas (EEA, 2012); Top right (b): Land Cover Map (Rowland et al. 2017); Bottom left (c): OS Mastermap
Greenspace Layer, land use (Ordnance Survey, 2017); Bottom right, (d): OS Mastermap Greenspace
Layer, land cover (Ordnance Survey, 2017).
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Figure 6. Distribution of green land cover values: Integrated Landscape Map.
(a)
(b)
Figure 7. (a) (Top left) UK LCM 2015 (Rowland et al., 2017); (b) (top right) Urban Atlas 2012 (EEA, 2012); (c) (centre left) OS Mastermap Greenspace: land use (Ordn
Figure 7. Cont.
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(c)
(d)
(e)
(f)
Figure 7. (a) (Top left) UK LCM 2015 (Rowland et al., 2017); (b) (top right) Urban Atlas 2012 (EEA, 2012); (c) (centre left) OS Mastermap Greenspace: land use
(Ordnance Survey, 2017); (d) (centre right) OS Mastermap Greenspace: land cover (Ordnance Survey, 2017); (e) (bottom left) composite land cover map from stage one
of the method and (f) aerial photograph (25 cm) of the same area (Edina, 2017).
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3.2. Final Classification and Exploration of Social–Ecological Relationships
The results of the final classification of pixels into a 35 landscape features is presented in Figure 8.
In the classification scheme, cells denote land use and land cover combinations where different colours
(e.g., yellow, green, red) delineate use and the tone from light to dark indicates built, water, grasses,
forbs and shrubs, and canopy cover, in that order. Table 2 presents correlations observed between
selected landscape features and an indicator of health deprivation (premature mortality). Table 3
documents correlations with landscape indices reflecting connectivity.
Figure 8. Final classification of the ILM dataset into 35 classes of landscape features (contains Ordnance
Survey, 2017; European Space Agency, 2016 and City of Trees 2011 data).
The correlations shown in Table 2 demonstrate the different associations that can be observed
between the discrete landscape features identified in the ILM and an indicator of local health status.
Of particular note is the strength of association between higher plants and shrubs in private garden
settings and years of potential life lost in comparison with, for example, correlations between the same
land cover in public parks and this health indicator. Likewise, markedly different correlations can be
seen between the latter and discrete land cover types (e.g., forbs/shrubs versus trees) occurring within
the same land use (e.g., private gardens).
Table 3 demonstrates the variety in both strength and direction of correlations that occurs between
landscape features of the ILM and different measures of connectivity in the landscape. For example,
tree cover in the Other Urban category, which primarily describes incidental cover outside of the
18 land use categories of the OS Mastermap Greenspace Layer, shows a significant association with
habitat fragmentation (Landscape Division) whilst simultaneously contributing to canopy connectivity.
By contrast, tree cover situated in amenity areas bears the inverse relationship with both measures of
landscape cohesion.
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Table 2. Correlation matrix: selected ILM landscape features with years of potential life lost indicators. * denotes p < 0.05.
Public Parks
and Recreation
Grasses
Public Parks and
Recreation Forbs
and Shrubs
Public Parks
and Recreation
Tree Canopy
Amenity
Grasses
Amenity
Forbs and
Shrubs
Amenity
Trees
Private
Garden
Grasses
Private
Garden Forbs
and Shrubs
Private
Garden
Trees
Correlation
−0.066 *
−0.074 *
−0.026
−0.102 *
−0.109 *
−0.036
−0.176 *
−0.263 *
−0.132 *
Significance
(2-tailed)
0.007
0.002
0.285
0.000
0.000
0.139
0.000
0.000
0.000
Control Variable: Income
Deprivation Score
Years of potential
life lost indicator
Table 3. Correlation matrix: selected ILM landscape features with landscape division and canopy connectivity (Euclidean nearest neighbour) * denotes p < 0.05.
Peri-Urban Other:
Tree Canopy
Brownfield:
Tree Canopy
Institutional Land:
Tree Canopy
Private Domestic
Garden:
Tree Canopy
Amenity:
Tree Canopy
Public Parks and
Recreation:
Tree Canopy
Other Urban:
Tree Canopy
LD
Correlation Coefficient
Sig. (2-tailed)
−095 *
0.000
0.062 *
0.012
0.082 *
0.001
−0.201 *
0.000
−0.218 *
0.000
0.257 *
0.000
0.253 *
0.000
Canopy ENN
Correlation Coefficient
Sig. (2-tailed)
0.066 *
0.007
0.158 *
0.000
−0.049*
0.046
−0.313 *
0.000
0.274 *
0.000
0.274*
0.000
−0.283 *
0.000
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3.3. Development of the Landscape Typology
Figure 9 presents examples of the landscape typology resulting from the k-means clustering of
LSOAs according to cover by landscape features. Table 4 offers descriptive statistics for all eight types,
highlighting the differences in mean values of land use, land cover and landscape indices upon which
the typology is established. Figure 10 shows their spatial distribution.
Figure
9. The
eight
landscapes
thecase
casestudy
studyarea
area
with
indicative
labels
based
Figure
9. The
eight
landscapestypes
typesresulting
resulting from
from the
with
indicative
labels
based
on
the characteristics in Table 4 (Edina, 2017).
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Table 4. Description and basic characteristics of landscape types generated for the case study area.
Percentage Cover LSOA− 1
Landscape Type with Indicative Labels
Public
Amenity Gardens
Peri-Urban
Institutional Brownfield
Land
Land
Urban
Other
Grasses
Forbs &
Shrubs
Canopy
Green/Blue
Cover
SHDI
LD
Dense
Greyscape
Highly urbanised; very high population
density; very low green space cover, high
proportion amenity
Mean
SD
N
4.32
4.32
171
10.71
6.86
171
14.53
8.95
171
1.27
2.62
171
3.75
4.79
171
1.04
2.37
171
64.38
9.55
171
4.8
2.32
171
6.3
3.96
171
12.55
5.19
171
25.04
9.53
171
1.051
0.148
171
0.783
0.16
171
Garden city
High population density, moderate cover by
domestic gardens, high surface sealing
Mean
SD
N
9.17
7.67
309
12.86
7.64
309
28.09
9.15
309
2.97
5.51
309
4.16
5.3
309
0.71
2.37
309
42.05
7.33
309
9.93
4.06
309
13.14
5.4
309
19.33
6.08
309
43.36
9.49
309
1.078
0.106
309
0.596
0.207
309
Leafy
Residential
Leafy residential areas, high canopy cover
Mean
SD
N
5.75
6.54
445
9.51
7.06
445
53.55
8.84
445
2.34
4.14
445
3.79
4.71
445
0.16
0.7
445
24.91
6.85
445
14.42
5.51
445
16.89
5.74
445
25.11
8.51
445
56.75
11.37
445
1.05
0.076
444
0.391
0.273
444
Peri-urban
Fringe
Urban fringe; low density residential;
moderate canopy and overall green cover
Mean
SD
N
7.42
7.98
151
9.69
8.3
151
22.06
9.63
151
42.78
10.68
151
1.71
2.59
151
0.27
1.05
151
16.08
8.53
151
18.46
9.56
151
22.69
9.42
151
22.7
8.59
151
65.29
17.69
151
1.091
0.083
151
0.325
0.274
151
Encapsulated
Countryside
Peri-urban vegetation; extensive informal
grassland and wooded areas; very low
garden; amenity of formal green space.
Mean
SD
N
2.15
2.93
88
4.02
4.66
88
7.32
4.26
88
80.1
10.17
88
0.45
0.8
88
0.16
1.12
88
5.8
4.28
88
16.4
8.78
88
26.11
11.95
88
21.47
8.99
88
65.83
18.7
88
1.088
0.102
88
0.321
0.277
88
Amenity
Suburbs
Very high provision of amenity greenery;
low density residential areas with high cover
by trees and higher plants.
Mean
SD
N
7.23
6.42
287
36.76
10.61
287
25.86
10.38
287
7.81
7.62
287
2.81
3.99
287
0.62
3.18
287
18.9
7.78
287
15.01
6.53
287
28.57
9.61
287
25.76
9.66
287
70.78
10.17
287
1.075
0.09
287
0.259
0.203
287
Parklands
High formal public green space provision;
extensive canopy and overall green cover
Mean
SD
N
39.13
12.28
168
10.97
7.6
168
25.92
10.69
168
4.56
6.78
168
2.6
4.31
168
0.26
0.83
168
16.55
7.3
168
17.67
7.22
168
22.93
7.29
168
30.34
9.49
168
73.51
10.89
168
1.112
0.092
168
0.214
0.193
168
Rural
Hinterland
Semi-rural areas dominated by open pasture
and grassland
Mean
SD
N
7.91
8.24
54
17.53
11.44
54
11.43
4.75
54
51.7
14.38
54
0.99
1.63
54
0.23
0.67
54
10.22
5.47
54
18.91
7.89
54
57.36
14.36
54
12.41
5.36
54
90.01
6.97
54
0.939
0.136
54
0.104
0.166
54
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Figure 10. Examples of landscape types in the southwest of Greater Manchester. Source: LSOA boundary data from ONS, 2011. Key: Type 1: Dense Greyscape; Type 2:
Figure 10. Examples of landscape types in the southwest of Greater Manchester. Source: LSOA boundary data from ONS, 2011. Key: Type 1: Dense Greyscape;
Type 2: Garden City; Type 3: Leafy Residential; Type 4: Peri-urban Fringe; Type 5: Encapsulated Countryside; Type 6: Amenity Suburbs; Type 7: Parklands; Type 8:
Rural Hinterland.
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The distribution of all types included in the final landscape classification throughout the Greater
Manchester area is presented in Figure 11.
Figure
Figure11.
11.Distribution
DistributionofofLSOAs
LSOAsininGreater
GreaterManchester
Manchesteraccording
accordingtototheir
theirlandscape
landscapetype
typewith
with
indicative labels (see Table 4; source: LSOA boundary data from ONS, 2011).
A comparison of the distribution of index of multiple deprivation scores (IMD) and Shannon’s
diversity index (SHDI) according to both the eight landscape types and eight quantile groups for green
and blue cover is presented in Figure 12a–d.
(a)
(b)
Figure 12. Cont.
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(c)
(d)
Figures
12. (a):
(a): Landscape
Landscape types
Figure 12.
types arranged
arranged in
in ascending
ascending order
order of
of green
green space
space cover
cover and
and mean
mean IMD
IMD
score; (b): Eight quantile groups by percentage green/blue cover and respective mean IMD scores;
(c): Landscape types arranged in ascending order of green/blue space cover and mean Shannon’s
diversity index; (d): Eight quantile groups by percentage green space cover and Shannon’s diversity
index (SHDI). Key: Type 1: Dense Greyscape; Type 2: Garden City; Type 3: Leafy Residential; Type 4:
Peri-urban Fringe; Type 5: Encapsulated Countryside; Type 6: Amenity Suburbs; Type 7: Parklands;
Type 8: Rural Hinterland.
The association between green and blue space cover and both the SHDI and IMD 2015 indicators
for LSOAs in Greater Manchester exhibit markedly different patterns when stratifying LSOAs by
landscape type (Figure 12a,c) compared to a quantile stratification of according to green and blue space
cover (Figure 12b,d). The negative association that can be observed between IMD and green/blue
space cover in Figure 12b stands in contrast to the non-linear trend exhibited when stratifying the study
area according to the landscape typology derived from the ILM. Likewise, although in neither case
was a linear relationship observed between green and blue space cover and SHDI, the stratification of
the study area by landscape type (Figure 12c) reflects greater variance in landscape diversity than that
described by a quantile grouping of LSOAs based on green and blue space cover (Figure 12d).
4. Discussion
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 5a–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 5c). However, the distribution of
land cover within the same dataset (Figure 5d) 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 5a–d and
Figure 6 shows great variation between datasets. It follows, therefore, that the conclusions drawn from
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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.
Figure 7a–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 [69–71] and ecological [71–73] 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
(Figures 9–11) 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 12a–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 12b).
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
Land 2018, 7, 17
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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.
5. Conclusions
The creation of a spatial dataset incorporating freely available remote sensing data and
cartographic layers is a useful step towards a green infrastructure dataset for a wide range of uses for
research, policy and practice. The work in this paper takes this dataset further through the development
of a characterisation that encompasses elements of both land cover (form) and land use (function)
culminating in a new urban landscape-oriented dataset. We have generated indicative labels based on
our case study results. Labels can be modified to align with other country contexts, i.e., in line with
the results for other cases study applications and to account for specific policy/practice perspectives,
i.e., to give classifications local meaning and relevance. The landscape-oriented dataset provides insight
into relationships between landscape features and social–ecological factors relevant to research into
health and well-being and, moreover, does so in a way that goes beyond crude singular (i.e., percentage
green/blue space or biomass measures, such as NDVI) or dichotomous (land use versus land cover)
descriptions of landscape quality. In the case study city region, we have demonstrated that the use of
high-resolution data was effective in capturing total green and blue cover in greater detail than other
available sources (LCM 2015, Urban Atlas 2012 and OS Mastermap Greenspace 2017 datasets used for
comparison). The associated methodology can be replicated in other urban areas, giving the potential
Land 2018, 7, 17
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to generate urban landscape characteristics that are meaningful in the context of social–ecological
systems and that help with consideration of the quality of the natural environment in analyses of
health and well-being. To this end, the typology developed here, as described in Table 4, provides a
promising point of departure that could be used as a template in city regions across the globe.
Supplementary Materials: The following are available online at http://www.mdpi.com/2073-445X/7/1/17/s1.
Acknowledgments: This work was carried out as part of the Green Infrastructure and the Health and Well-Being
Influences on an Ageing Population (GHIA) project (2016–2019) www.ghia.org.uk. Funders: Natural Environment
Research Council, the Arts and Humanities Research Council and the Economic and Social Research Council
under the Valuing Nature Programme. NE/N013530/1. We gratefully acknowledge input from the GHIA team
and its partners and advisors.
Author Contributions: Matthew Dennis and Sarah Lindley conceived and designed the work and co-wrote the
manuscript; Matthew Dennis performed the data processing and analysis; Philip James, Konstantinos Tzoulas,
Gina Cavan, Penny A. Cook and Phil Wheater provided commentary and final editing on the paper; David Barlow,
Anna Gilchrist, John Handley and Jessica Thompson had an advisory role in the development and planning of
the research.
Conflicts of Interest: The authors declare no conflict of interest.
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