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General Union Environment Action Programme to 2020 - Living well, within the limits of the planet

Policy Document
7th EAP. ISBN 978-92-79-34724-5 doi:10.2779/66315

Related content

Related indicators

Imperviousness and imperviousness change in Europe The imperviousness indicator is defined as the yearly average imperviousness change between two reference years, as measured by imperviousness change products. The change is aggregated for a certain reference unit and expressed relative to the size of that unit (as a percentage). The imperviousness change value for a 100 m raster cell is based on 100 m imperviousness change products. The default reference unit is the country, but the indicator can be aggregated based on different spatial units. For example, for a certain country, a n imperviousness indicator value of 0.2 %, means that on average, an additional 0.2 % of this country's area has been sealed annually during the period between the two reference years in question. If above a certain rate of increase (threshold values) , this value can be used as a warning sign. The aggregation of imperviousness values to reference units is performed using the integrated spatial data platform at EEA.
Vegetation productivity The indicator addresses trends in land surface productivity derived from remote sensing observed time series of vegetation indices. The vegetation index used in the indicator is the Plant Phenology Index (PPI, Jin and Eklundh, 2014). PPI is based on the MODIS Nadir BRDF-Adjusted Reflectance product (MODIS MCD43 NBAR. The product provides reflectance data for the MODIS “land” bands (1 - 7) adjusted using a bi-directional reflectance distribution function. This function models values as if they were collected from a nadir-view to remove so called cross-track illumination effects. The Plant Phenology Index (PPI) is a new vegetation index optimized for efficient monitoring of vegetation phenology. It is derived from radiative transfer solution using reflectance in visible-red (RED) and near-infrared (NIR) spectral domains. PPI is defined to have a linear relationship to the canopy green leaf area index (LAI) and its temporal pattern is strongly similar to the temporal pattern of gross primary productivity (GPP) estimated by flux towers at ground reference stations. PPI is less affected by presence of snow compared to commonly used vegetation indices such as Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI). The product is distributed with 500 m pixel size (MODIS Sinusoidal Grid) with 8-days compositing period.  References: Jönsson P., Eklundh L., 2004. TIMESAT—a program for analyzing time-series of satellite sensor data. Computers & Geosciences 30 (2004) 833–845. Eklundh L., Jönsson P., 2015. TIMESAT: A Software Package for Time-Series Processing and Assessment of Vegetation Dynamics. In: Kuenzer C., Dech S., Wagner W. (eds) Remote Sensing Time Series. Remote Sensing and Digital Image Processing, vol 22. Springer, Cham Jin, H., Eklundh, L. 2014. A physically based vegetation index for improved monitoring of plant phenology, Remote Sensing of Environment, 152, 512 – 525. Karkauskaite, P., Tagesson, T., Fensholt, R., 2017. Evaluation of the Plant Phenology Index (PPI), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone, Remote Sensing, 9 (485), 21 pp. Jin, H.X.; Jönsson, A.M.; Bolmgren, K.; Langvall, O.; Eklundh, L., 2017. Disentangling remotely-sensed plant phenology and snow seasonality at northern Europe using MODIS and the plant phenology index. Remote Sensing of Environment 2017,198, 203-212. Abdi, A. M., N. Boke-Olén, H. Jin, L. Eklundh, T. Tagesson, V. Lehsten and J. Ardö (2019). First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. International Journal of Applied Earth Observation and Geoinformation 78: 249-260. Jin, H., A. M. Jönsson, C. Olsson, J. Lindström, P. Jönsson and L. Eklundh (2019). New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. International Journal of Biometeorology 63(6): 763-775.
Landscape fragmentation pressure and trends in Europe This indicator measures landscape fragmentation due to transport infrastructure and sealed areas. Unlike the previous indicator on fragmentation status, this updated version uses the TeleAtlas® Multinet data set to ensure the statistical comparability of the time series. While the Open Street Map data set is a valuable source of the street network available for the general public, there are still inconsistencies in this data set for some regions of Europe, which render it secondary to the TeleAtlas data set. As in the previous version, this indicator is based on the effective mesh size method  (Jaeger, 2000) . 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 anthropogenic 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 referred to as the 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 the 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 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. The values of meff and seff are reported within the cells of a 1 km 2 regular grid. The value of 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. It 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. Its reliability has been confirmed on the basis of suitability criteria through a systematic comparison with other quantitative measures. The suitability of other metrics is limited, as they only partially meet 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).
Landscape fragmentation pressure from urban and transport infrastructure expansion 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).
Archived
Imperviousness and imperviousness change The imperviousness indicator is defined as the yearly average imperviousness change between two reference years, as measured by imperviousness change products. The change is aggregated for a certain reference unit and expressed as relative to the size of that unit (as a percentage). The imperviousness change value for a 100 m raster cell is based on 100 m imperviousness change products. The default reference unit is the country, but the indicator can be aggregated based on different spatial units. For example, for a certain country, a n imperviousness indicator value of 0.2 %, means that on average, an additional 0.2 % of this country's area has been sealed annually during the period between the two reference years in question. If above a certain rate of increase (threshold values) , this value can be used as a warning sign. The aggregation of imperviousness values to reference units is performed using the LEAC CUBE method.
Archived
Imperviousness and imperviousness change The imperviousness indicator is defined as the yearly average imperviousness change between two reference years, as measured by imperviousness change products. The change is aggregated for a certain reference unit, and expressed and relative to the size of said unit (as a percentage). The imperviousness change value for a 100 m raster cell is based on 100 m imperviousness change products, corresponding to the difference in imperviousness status values (e.g. IMD2009-IMD2006). The default reference unit is the country, but the indicator can be aggregated based on different spatial units. For example, for a certain country, a n imperviousness indicator value of 0.2 %, means that an additional 0.2 % of this country's area has been sealed annually during the period between the two reference years in question. If above a certain rate of increase (threshold values) , this value can be used as a warning sign. The aggregation of imperviousness values to reference units is performed using the LEAC CUBE method.

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