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Relative Magnitude of Fragmentation (RMF)

Dataset Creator: Babak Naimi
Publication Year: 2020

We use an existing spatially contiguous, global remote-sensing data product (i.e. the 27-year annual ESA CCI land cover maps which can be categorized as an EBV ‘Ecosystem Distribution’) to derive an annual (27 year) time-series of the Relative Magnitude of Fragmentation (RMF) at a global scale and with a spatial resolution of 300m. From this derived EBV data product, we can calculate a RMF indicator of ecosystem degradation, i.e. the change, ...

ESALand covergrid celltime-series

Title:

Relative Magnitude of Fragmentation (RMF)

Publication Year:

2020

Description:
License:

CC BY 4.0

Additional Info:

We define the ‘forest’ class by aggregating all 14 tree cover related land cover types from the ESA CCI product into one class. We further define eight non-forest classes (agriculture, grassland, wetland, settlement, sparse vegetation, bare area, water, permanent snow and ice) that we use as multinomial categorical data, or as binary categorical data (to define forest vs. non-forest). This classes follow the reclassification used by Mousivand & Arsanjani 2019 (Applied Geography 106: 82-92). For deriving the RMF, we either calculate ELSA using the binary categorical data (forest vs. non-forest) or the multinomial categorical data (forest vs. the eight non-forest classes)

Creator

Name:

Babak Naimi

E-mail: naimi.b@gmail.com
Organisation:

University of Helsinki

Contact

Name:

W. Daniel Kissling

E-mail: wdkissling@gmail.com
Organisation:

University of Amsterdam

Essential Biodiversity Variables (EBVs)

EBV class

Ecosystem structure

EBV name

Ecosystem distribution

Metric

Metric 1
Name:

Relative Magnitude of Fragmentation (RMF)

Description:

The Relative Magnitude of Fragmentation (RMF) measures the fragmentation of specific land cover types using the entropy-based local indicator of spatial association (ELSA). This metric quantifies the degree of fragmentation at each location (grid cell) relative to neighbouring locations, and simultaneously incorporates both the spatial composition and the configuration of land cover types. The values of ELSA vary between 0 and 1, denoting lowest and highest fragmentation. The RMF values are calculated for 300 m pixels worldwide and can be aggregated at any coarser spatial resolution to summarize trends and the magnitude of ecosystem fragmentation for any terrestrial area. The RMF is based on the annual ESA CCI land cover maps and thus provides time series of fragmentation with a starting year of 1992. Definition and clustering of classes in variables: We define the ‘forest’ class by aggregating all 14 tree cover related land cover types from the ESA CCI product into one class. We further define eight non-forest classes (agriculture, grassland, wetland, settlement, sparse vegetation, bare area, water, permanent snow and ice) that we use as multinomial categorical data, or as binary categorical data (to define forest vs. non-forest). This classes follow the reclassification used by Mousivand & Arsanjani 2019 (Applied Geography 106: 82-92). For deriving the RMF, we either calculate ELSA using the binary categorical data (forest vs. non-forest) or the multinomial categorical data (forest vs. the eight non-forest classes).

Unit:

None


Spatial domain

Spatial extent:

Global

W: -180.0 S: -90.0 E: 180.0 N: 90.0
Spatial resolution:

1000 meter

Spatial accuracy:

1000 meter

Temporal domain

Temporal resolution:

Yearly

Temporal extent:

From 1992 to 2018

Biological entity

Entity type:

Ecosystem Types

Classification System Name:

Forests

Environmental domain

Realm:

Terrestrial

Description:

To assess the change in ecosystem fragmentation, we calculate, for every 300 m pixel and its neighbourhood (e.g. all eight adjacent grid cells or all grid cells in a given radius, such as 1 km around a focal cell), a metric that is called the entropy-based local indicator of spatial association (ELSA). This ELSA metric quantifies the relative magnitude of fragmentation of a specific land cover type for each landscape around a 300 m pixel (Naimi et al. 2019, Spatial Statistics 29: 66-88). We focus on forest land cover types by aggregating the 14 tree cover related land cover types from the ESA CCI product into ‘forest’ and by measuring its fragmentation relative to eight other (non-forest) land cover types (following the reclassification of Mousivand & Arsanjani 2019, Applied Geography 106: 82-92). The ELSA metric can be used for both binary and multinomial categorical spatial data and quantifies the degree of fragmentation at each location relative to neighbouring locations, simultaneously incorporating both the spatial composition and the configuration of land cover types. The values of ELSA vary between 0 and 1, denoting lowest and highest fragmentation, respectively. The calculated RMF values of each 300 m pixel can be aggregated (e.g. averaged) at any coarser spatial resolution (e.g. country, national park, region) to summarize trends and the magnitude of ecosystem fragmentation for a specific area. The values are also comparable across regions and national boundaries, and thus scalable, because they are relative on a scale from 0 to 1. Values in the data have been scaled to integer with scale factor 0.0001 to reduce the size of the files.

Sources used to generate the data

Source 1
Name and version:

27-year annual ESA CCI land cover maps

Type:

Dataset


Access:

Open


Link:

https://climate.esa.int/en/projects/land-cover/


Reference:


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