GEO BON logo Beta version
EBV preview image

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 de ...

Add to map (in progress)

Big DataRemote SensingESALand coverGrid cellTime-seriesGlobal


Relative Magnitude of Fragmentation (RMF)

Publication Year:



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)



Babak Naimi


University of Helsinki



W. Daniel Kissling


Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam

Essential Biodiversity Variables (EBVs)

EBV class

Ecosystem structure

EBV name

Ecosystem distribution

Spatial domain

Spatial extent:


Spatial resolution:

1000 meter

Spatial accuracy:

1000 meter

Temporal domain

Temporal resolution:


Temporal extent:

From 1992-01-01 to 2018-12-31

Biological entity

Entity type:

Ecosystem Types

Environmental domain





Relative Magnitude of Fragmentation (RMF)


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.


No scenario provided

Description of resources:

To be added soon

Resources used to generate the data

Resource 1
Name and version:

To be added soon





User comments:
profile-image Jul 2020
Christian Langer

Please note some information provided for the datasets is still in progress, so the metadata may not be complete yet.

Sign in to post a comment for Relative Magnitude of Fragmentation (RMF)

Related Datasets


Data on Area Of Habitat (AOH) for 5090 mammals from 2015 to 2100, in 5 year intervals.

Download Data (netCDF / 5.35GB)

  • Mammals
  • Species
  • Habitats
  • Global
  • Land cover

Data in this layer were generated using multi-spectral satellite imagery from the Landsat 7 thematic mapper plus (ETM+) sensor. The clear surface observations from over 600,000 images were analysed using Google...

Download Data (netCDF / 59.23MB)

  • Big Data
  • Remote Sensing
  • Forest loss
  • Google
  • AI
  • Machine Learning
  • Global

Global data cube on the yearly extent of ecosystems following the habitat classification scheme of the IUCN Red List, used in the assessment of over 100,000 species. This data cube is composed by 65 ecosystem t...

Download Data (netCDF / 4.76GB)

  • Ecosystem extent
  • Time-series
  • Remote Sensing
  • Global
  • IUCN Red List

Changes in bird diversity at the grid cell level caused by land-use, estimated by the cSAR model (Pereira et al, It reports changes in species number (percentage and ...

Download Data (netCDF / 1.49MB)

  • Big Data
  • Remote Sensing
  • Birds
  • cSAR model
  • LUH 2.0 projections
  • Land-use
  • Global

The modelled suitability for the EUNIS habitat types is an indication of where conditions are favourable for each habitat type based on verified sample plot data (Braun-Blanquet database) and the Maxent softwar...

Download Data (netCDF / 1.85GB)

  • Big Data
  • Remote Sensing
  • Europe
  • Natural area
  • Tundra
  • Terrestrial ecosystem