Landscape fragmentation pressure from urban and transport infrastructure expansion

Indicator Specification
Indicator codes: LSI 004 , CSI 054
Created 14 Feb 2017 Published 26 Apr 2018 Last modified 26 Apr 2018
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This indicator is based on the Effective Mesh Size (Jaeger 2000) method .  For some species, the effective mesh size (meff) can be interpreted as the area that is accessible when beginning to move from a randomly chosen point inside a landscape without encountering man-made barriers such as transport routes or built-up areas. However, it should be stressed that for many species that can fly, or are effective dispersers in others ways, man-made structures may not act as barriers. The combination of all barriers in a landscape is called Fragmentation Geometry (FG) hereafter. The meff value expresses the probability that any two points chosen randomly in an area are connected. Hence, meff is a measure of landscape connectivity, i.e. the degree to which movements between different parts of the landscape are possible. The larger the meff, the more connected the landscape. The indicator addresses structural connectivity of the landscape and does not tackle functional, species specific connectivity. The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by Fragmentation Geometry. It gives the effective number of meshes (or landscape patches) per 1 000 km 2 , in other words, the density of the meshes. The seff value is calculated as 1 000 km 2 /meff, hence, the number of meshes per 1 000 km 2 . The more barriers fragmenting the landscape, the higher the effective mesh density. meff and seff are reported within the cells of a 1 km 2 regular grid. meff is area-proportionally additive, hence it characterises the fragmentation of any region considered, independently of its size, and thus can be calculated for a combination of two or more regions. The meff has several advantages over other metrics: It addresses the entire landscape matrix instead of addressing individual patches. It is independent of the size of the reporting unit and its values can be compared among reporting units of differing sizes. It is suitable for comparing the fragmentation of regions with differing total areas and with differing proportions occupied by housing, industry and transportation structures. It's reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics was limited as they only partially met the following criteria: Intuitive interpretation; Mathematical simplicity; Modest data requirements; Low sensitivity to small patches; Detection of structural differences; Mathematical homogeneity (i.e., intensive or extensive).

Rationale

Justification for indicator selection

Landscape fragmentation, as described in this indicator, is understood to be the physical disintegration of continuous ecosystems, habitats or landscape units, excluding freshwater ecosystems. Such disintegration into smaller sized units, or patches, is most often caused by urban and transport expansion. An important consequence of fragmentation is the increased isolation of these newly formed fragments of ecosystems. Breaking the structural connections results in decreased resilience and ability of habitats to provide various ecosystem services. Furthermore, it prevents access to resources for wildlife, reduces their habitat area and quality, and may isolate some wildlife populations into smaller and more vulnerable fractions. Reducing habitat degradation and fragmentation may ensure that those habitats that remain are more capable of supporting biodiversity. Finally, yet importantly, fragmentation not only directly affects fauna and flora, but also indirectly influences human communities, agriculture, recreation and overall quality of life. Fragmentation decreases landscape quality and changes the visual perception of landscapes, thus decreasing the attractiveness of landscapes for recreational activities.

Scientific references

Indicator definition

This indicator is based on the Effective Mesh Size (Jaeger 2000) methodFor some species, the effective mesh size (meff) can be interpreted as the area that is accessible when beginning to move from a randomly chosen point inside a landscape without encountering man-made barriers such as transport routes or built-up areas. However, it should be stressed that for many species that can fly, or are effective dispersers in others ways, man-made structures may not act as barriers.The combination of all barriers in a landscape is called Fragmentation Geometry (FG) hereafter.

The meff value expresses the probability that any two points chosen randomly in an area are connected. Hence, meff is a measure of landscape connectivity, i.e. the degree to which movements between different parts of the landscape are possible. The larger the meff, the more connected the landscape. The indicator addresses structural connectivity of the landscape and does not tackle functional, species specific connectivity.

The Effective Mesh Density (seff) is a measure of landscape fragmentation, i.e. the degree to which movement between different parts of the landscape is interrupted by Fragmentation Geometry. It gives the effective number of meshes (or landscape patches) per 1 000 km2, in other words, the density of the meshes. The seff value is calculated as 1 000 km2/meff, hence, the number of meshes per 1 000 km2. The more barriers fragmenting the landscape, the higher the effective mesh density.

meff and seff are reported within the cells of a 1 km2 regular grid.

meff is area-proportionally additive, hence it characterises the fragmentation of any region considered, independently of its size, and thus can be calculated for a combination of two or more regions. The meff has several advantages over other metrics:

  • It addresses the entire landscape matrix instead of addressing individual patches.
  • It is independent of the size of the reporting unit and its values can be compared among reporting units of differing sizes.
  • It is suitable for comparing the fragmentation of regions with differing total areas and with differing proportions occupied by housing, industry and transportation structures.
  • It's reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics was limited as they only partially met the following criteria:
    • Intuitive interpretation;
    • Mathematical simplicity;
    • Modest data requirements;
    • Low sensitivity to small patches;
    • Detection of structural differences;
    • Mathematical homogeneity (i.e., intensive or extensive).

Units

meff values are positive real numbers including 0 for grid cells completely covered by urban areas and infrastructure (i.e. the landscape is covered by impermeable surfaces). The lower threshold for meff is 0.000001 km2 (= 1 m2), and smaller values are rounded to this latter value. The highest possible value of meff is limited by the area of the landscape patches as well as by the area of the Fragmentation Geometry affecting the landscape patches. A landscape patch is defined as a continuous area with the barriers of the Fragmentation Geometry as boundaries. Hence, the largest meff value will be assigned to the largest continuous landscape patch with the smallest area taken up by the Fragmentation Geometry (see illustration of meff calculation).

The seff values are positive real numbers. If meff = 0.000001 km2, then seff = 1 000 000 000 meshes per 1 000 km2. For grid cells completely covered by built-up areas and infrastructure (i.e. where meff = 0 km2), the seff value is set to -2, i.e. -2 represents positive infinity.

For convenience and practical considerations, meff values < 0.01 km2 = 10 000 m2 are rounded to 0 as these values are too small to be measurable without noise on a European scale. As a consequence, the largest reported seff values are 100 000 (= 1 000 km2/0.01 km2) meshes per 1 000 km2.

The indicator presents the effective mesh density (seff) values because these are more intuitive to understand as fragmentation. For the assessment, the continuous seff values were grouped into 5 fragmentation classes (very low, low, medium, high, and very high) according to the following steps:

(1)                Selecting 95 % of the seff value range (ignoring the upper and lower 5th percentiles)

(2)                Running the geometric interval classification.

(3)                Rounding threshold values for straightforward comparison and change detection.

The thresholds for the fragmentation classes are:

seff values  [number of meshes per  1000 km2]

Fragmentation class

(0 – 1.5]

Very low

(1.5 – 10]

Low

(10 – 50]

Medium

(50 – 250]

High

> 250 seffs

Very high

Policy context and targets

Context description

Priority objective 1, paragraph 23, of the Seventh Environment Action Programme (7th EAP) explicitly lists fragmentation as one of the key elements to protect, conserve and enhance the Union’s natural capital: 'The degradation, fragmentation and unsustainable use of land in the Union is jeopardizing the provision of several key ecosystem services, threatening biodiversity and increasing Europe’s vulnerability to climate change and natural disasters. It is also exacerbating soil degradation and desertification.'

Furthermore, Priority objective 7 (To improve environmental integration and policy coherence), paragraph 87, offers ample space for fragmentation to play a role in more effective integration of environmental and climate-related considerations into other policies: 'Incorporation of the Green Infrastructure can also help to overcome the fragmentation of habitats, preserve and restore ecological connectivity, enhance ecosystem resilience and thereby ensure the continued provision of ecosystem services, including carbon sequestration, and climate adaptation, while providing healthier environments and recreational spaces for people to enjoy.'

The EU 2020 Biodiversity Strategy, specifically Target 2, indirectly addresses fragmentation of ecosystems and habitats as it requires that 'by 2020, ecosystems and their services are maintained and enhanced by establishing green infrastructure and restoring at least 15% of degraded ecosystems'.

Reducing fragmentation also contributes to all other targets of the EU Biodiversity strategy, such as to Target 1 concerning the full implementation of the Birds and the Habitats Directives. In particular, paragraph 1 of Article 3 of the Habitats Directive sets up the legal framework for the Natura 2000 network, whereas paragraph 3 states that 'Where they consider it necessary, Member States shall endeavour to improve the ecological coherence of Natura 2000 by maintaining, and where appropriate developing, features of the landscape which are of major importance for wild fauna and flora, as referred to in Article 10.'

In addition, Article 6.4 of the Habitats Directive stipulates that Member States are to take 'all compensatory measures necessary to ensure that the overall coherence of the Natura 2000 network is protected'. Article 10 of the Habitats Directive and Article 3 of the Birds Directive also include more general connectivity provisions that relate to land use planning and development policies. Work on fragmentation of ecosystems and habitats also contributes to targets 3 and 4 of the EU 2020 Biodiversity Strategy concerning maintaining and enhancing biodiversity in the wider countryside (and the marine environment).

[1] European Environment Agency, 2014, Fragmentation: Overview of the knowledge base in the field of habitat and landscape fragmentation

Targets

None of the existing EU policies set quantitative targets for reducing and/or measuring the harmful impacts of fragmentation of ecosystems. The EU 2020 Biodiversity Strategy, specifically Target 2, directly addresses fragmentation of ecosystems and habitats as it requires that 'by 2020, ecosystems and their services are maintained and enhanced by establishing green infrastructure and restoring at least 15 % of degraded ecosystems'. 

But combating fragmentation contributes to all other targets of the EU Biodiversity strategy as well, such as to Target 1 concerning the full implementation of the Birds and the Habitats Directives. In particular, paragraph 1 of Article 3 of the Habitats Directive sets up the legal framework for the Natura 2000 network, whereas paragraph 3 states that 'Where they consider it necessary, Member States shall endeavour to improve the ecological coherence of Natura 2000 by maintaining, and where appropriate developing, features of the landscape which are of major importance for wild fauna and flora, as referred to in Article 10.'

In addition, Article 6.4 stipulates that Member States are to take 'all compensatory measures necessary to ensure that the overall coherence of Natura 2000 is protected'. Article 10 of the Habitats Directive and Article 3 of the Birds Directive also include more general connectivity provisions that relate to land use planning and development policies. Work on fragmentation of ecosystems and habitats also contributes to targets 3 and 4 of the EU 2020 Biodiversity Strategy concerning maintaining and enhancing biodiversity in the wider countryside and the marine environment.

Related policy documents

Key policy question

What is the fragmentation pressure of urban and transport expansion on the European landscape?

Specific policy question

How strong is the link between landscape fragmentation and urbanisation and population density?

Specific policy question

How severe is the pressure exerted by fragmentation on ecosystems in EEA member countries?

Specific policy question

How strong is the pressure exerted by fragmentation within and outside the Natura 2000 network?

Methodology

Methodology for indicator calculation

Calculation of the effective mesh size (meff) is based on three spatial datasets: 1) the 'landscape' extent, 2) the Fragmentation Geometry (FG) (landscape elements representing man-made barriers) and 3) reporting units (spatial units for which meff is calculated). The following steps are followed in computing the indicator.

1) Landscape extent:

The 'landscape' for the calculation of meff is the seamless area of Europe. The input to this step is the Copernicus High Resolution Layer (HRL) on Imperviousness Density (IMD) from 2012[1].

2) Fragmentation Geometry:

Fragmentation Geometries are man-made landscape elements, which divide the landscape into unconnected patches. Only anthropogenic elements are considered because the indicator addresses fragmentation of the landscape from urban areas and transport infrastructure (roads and rails).

2.1 Fragmentation Geometry — built up areas:

The build-up areas are excluded during the 'landscape extent' preparation step. From this layer, a binary mask is created and pixels with IMD value > 30 % are deleted from the dataset. 

2.2 Fragmentation Geometry — road network:

The dataset representing the transportation network must meet the following technical requirements:

  1. has to be methodologically stable, so that changes in time represent real changes and not the level of dataset completion;
  2. nomenclature/classes of roads must be clearly defined, and consistent over time, in order to allow different levels of fragmentation details;
  3. must be topologically correct i.e. must not contain discontinuities;
  4. shall enable the differentiation of landscape elements that have a major impact on the resulting connectivity or isolation of patches, such as tunnels, overpasses etc. (where such elements occur, landscape patches may be in fact interconnected and thus the value of fragmentation can be considerably different);
  5. shall be based on regularly updated and if possible open source data streams to ensure sustainability of indicator.

The Open Street Maps (OSM) dataset[1]  was selected as input to process the road network Fragmenting Geometry. The following roads/rails classes were included:

  • motorways and motorways links;
  • trunk and trunk links;
  • primary roads and primary roads links;
  • secondary roads and secondary roads links;
  • tertiary roads and tertiary roads links;
  • railroads;
  • tunnels are removed from the transportation network geometry.

Definition of OSM road classes:

Class 0 — a motorway is a major highway with restricted access to adjacent properties, designed for motorised vehicles, normally equipped with a minimum of four or more lanes. In most cases, the motorway is a dual carriageway, which means that the traffic for each direction is separated by a central barrier or strip of land and the whole infrastructure is often fenced.

Class 1 — trunks are important roads that aren’t motorways. Often suitable for long journeys with relatively high speed.

Class 2 — primary road, generally linking larger towns.

Class 3  secondary road, generally linking smaller towns and villages

A tertiary road class is generally used for roads wider than 4 metres in width that is not included in the previous classes.

The line vectors were buffered according to the road classes in order to create polygon objects. Buffer sizes were selected according to the road class they represent. Buffering was also applied to prevent small topological inconsistencies in the OSM dataset.

Table 1: Buffers applied to the various OSM road and railroad classes:

OSM road class

Buffer width [in m] on either side of the roads

motorways and motorways links

15

trunk and trunk links

10

primary roads and primary roads links

7.5

secondary roads and secondary roads links

5

tertiary roads and tertiary roads links

2.5

Railroads

2


The result of step 2 is the Fragmentation Geometry layer that contains landscape patches (i.e. polygons representing the remaining non fragmented areas) and gaps (no value), in locations where Fragmentation Geometries were deleted from the landscape.

 

Step 3) Calculation of meff

The meff values are calculated for all reporting units. The reporting units are 1 km2 grid cells corresponding to the EEA’s accounting grid. Note, any regular (i.e. larger or smaller grids) or irregular (e.g. NUTS regions) reporting units can be chosen for the calculation as long as the spatial detail is satisfactory for the topic the indicator should support. To calculate meff, the Cross Boundary Calculation (CBC) procedure is used (Moser et al., 2007). In the CBC process, not only the area of the landscape patch that falls inside the reporting unit is input to the computation, but the whole area of that given landscape patch is accounted for (see image below). Hence, the borders of analytical units themselves do not influence meff values (see detailed explanation in Moser et al. 2007).

Schematic illustration for calculating the meff index: 

Methodology for gap filling

The Copernicus High Resolution Layer (HRL) is based on satellite imagery classification. As such, there are areas assigned with no IMD values due to cloud coverage (satellite datasets are sometimes not cloud-free). These gaps in data are filled using the Corine Land Cover (CLC) dataset using the corresponding build up mask derived from CLC classes. These are:

  • 1.1. Continuous urban fabric, discontinuous urban fabric
  • 1.2. Industrial and commercial units, road and rail networks and associated land, port areas and airports
  • 1.3. Mineral extraction sites, dump sites and construction sites
  • 1.4.2. Sport and leisure facilities (only included if they were completely surrounded by the previous classes)
  • 4.2.2. Salines

Methodology references

Data specifications

EEA data references

External data references

Data sources in latest figures

Uncertainties

Methodology uncertainty

The methodology is without any major uncertainty. Some critique might arise regarding the Fragmentation Geometries, which were included (or not included) as barriers. This is however not a methodological uncertainty of meff and seff, but is rather a matter of consciously addressing the spatial detail of the indicator.

Data sets uncertainty

Uncertainty of the Copernicus High Resolution Layer – Imperviousness (HRL IMD): clouds are contained in the data layer. Corresponding Copernicus Corine Land Cover (CLC) data are used for the map filling (see Methodology for gap filling section). Because the spatial resolutions of the HRL IMD and CLC data are different, the spatial detail of the indicator may be influenced for the cloudy area. The metadata layer is part of the indicator dataset indicating HRL IMD cloud areas. 

Uncertainty of the Open Street Map (OSM): maturity, completeness and classification stability of the OSM dataset are critical features for monitoring indicator changes and trends. Based on the OSM stability analysis done in 2016 (see link below), these qualities have been confirmed. Nevertheless, as the OSM is a collaborative project providing crowd sourced data under the Open Database Licence, the dataset has to be carefully analysed before any subsequent indicator update.

https://forum.eionet.europa.eu/etc-urban-land-and-soil-systems/library/10.-ap-2018/1.8.1.4-re-analysis-landscape-fragmentation-time-series/osm-stability-analysis-document

Rationale uncertainty

Without rationale uncertainty

Further work

Short term work

Work specified here requires to be completed within 1 year from now.

Long term work

Work specified here will require more than 1 year (from now) to be completed.

General metadata

Responsibility and ownership

EEA Contact Info

Eva Ivits-Wasser

Ownership

European Environment Agency (EEA)

Identification

Indicator code
LSI 004
CSI 054
Specification
Version id: 1
Primary theme: Land use Land use

Frequency of updates

Updates are scheduled every 3 years

Classification

DPSIR: Pressure
Typology: Descriptive indicator (Type A - What is happening to the environment and to humans?)

Related content

Data references used

Data used

Latest figures and vizualizations

Relevant policy documents

Document Actions
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Denmark
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