Data files belonging to the paper "Dealing with clustered samples for assessing map accuracy by cross-validation"

Mapping of environmental variables often relies on map accuracy assessment through cross-validation with the data used for calibrating the underlying mapping model. When the data points are spatially clustered, conventional cross-validation leads to optimistically biased estimates of map accuracy. Several papers have promoted spatial cross-validation as a means to tackle this over-optimism. Many of these papers blame spatial autocorrelation as the cause of the bias and propagate the widespread misconception that spatial proximity of calibration points to validation points invalidates classical statistical validation of maps. In the paper related to these data, we present and evaluate alternative cross-validation approaches for assessing map accuracy from clustered sample data. 

The study area is western Europe, constrained in the north at 52° latitude and at -10° and 24° longitude The projection is IGNF:ETRS89LAEA (Lambert azimuthal equal area projection).

Files:

agb.tif  = above ground biomass (AGB) map from version 3 of the 2017 CCI-Biomass product (https://catalogue.ceda.ac.uk/uuid/5f331c418e9f4935b8eb1b836f8a91b8)
AGBstack.tif  = covariates used for predicting AGB
aggArea.tif  = coarse grid used for simulation in the model-based methods
ocs.tif  = soil organic carbon stock (OCS) map (0-30 cm) from Soilgrids (https://www.isric.org/explore/soilgrids)
OCSstack.tif  = covariates used for predicting OCS
strata.xxx = 100 compact geo-strata (ESRI shape) created with the spcosa package; used for generating clustered samples
TOTmask.tif  = mask of the area covered by the covariates

Details and data sources of the covariates in AGBstack.tif and OCSstack.tif:

Name

Description

Source

Note

ai

Aridity Index

https://chelsa-climate.org/downloads/

Version 2.1

bio1

Mean annual air temperature [°C]

https://chelsa-climate.org/downloads/
Version 2.1

bio5

Mean daily maximum air temperature of the warmest month [°C]

https://chelsa-climate.org/downloads/
Version 2.1

bio7

Annual range of air temperature [°C]

https://chelsa-climate.org/downloads/
Version 2.1

bio12

Annual precipitation [kg/m2]

https://chelsa-climate.org/downloads/
Version 2.1

bio15

Precipitation seasonality [kg/m2]

https://chelsa-climate.org/downloads/
Version 2.1

gdd10

Growing degree days heat sum above 10°C

https://chelsa-climate.org/downloads/
Version 2.1

clay

Clay content [g/kg] of the 0-5cm layer

https://soilgrids.org/

Only used for AGB

sand

Sand content [g/kg] of the 0-5cm layer

https://soilgrids.org/
as above

pH

Acidity (Ph(water)) of the 0-5cm layer

https://soilgrids.org/
as above

glc2017

Landcover 2017

https://land.copernicus.eu/global/products/lc, reclassified  to: closed forest, open forest,  natural non-forest veg., bare & sparse veg. cropland, built-up, water

Categorical variable

dem

Elevation

https://www.eea.europa.eu/data-and-maps/data/copernicus-land-monitoring-service-eu-dem

cosasp

Cosine of slope aspect

Computed with the terra package from elevation

Computed @25m resolution; next aggregated to 0.5km

sinasp

Sine of slope aspect

Computed with the terra package from elevation
as above

slope

Slope

Computed with the terra package from elevation
as above

TPI

Topographic position index

Computed with the terra package from elevation
as above

TRI

Terrain ruggedness index

Computed with the terra package from elevation
as above

TWI

Topographic wetness index

Computed with SAGA from 500m resolution (aggregated) dem

gedi

Forest height

https://glad.umd.edu/dataset/gedi

Zone: NAFR

xcoord

X coordinate

Using a mask created from the other covariates

ycoord

Y coordinate

Using a mask created from the other covariates

Dcoast

Distance from coast

Using a land mask created from the other covariates