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landmap package for R

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Package provides methodology for automated mapping i.e. spatial interpolation and/or prediction using Ensemble Machine Learning (extends functionality of the mlr package). Key functionality includes:

  • train.spLearner --- train a spatial prediction and/or interpolation model using Ensemble Machine Learning (works with numeric, binomial and factor-type variables),
  • buffer.dist --- derive buffer (geographical) distances that can be used as covariates in spLearner,
  • spc --- derive Principal Components using stack of spatial layers,
  • tile --- tile spatial layers so they can be used to run processing in parallel,
  • spsample.prob --- determine inclusion probability / representation of a given point sample based on feature space analysis (maxlike function) and kernel density analysis,
  • download.landgis --- access and download LandGIS layers from,

Warning: most of functions are optimized to run in parallel by default. This might result in high RAM and CPU usage.

Spatial prediction using Ensemble Machine Learning with geographical distances is explained in detail in:

Use of geographical distances and nearest neighbors as features in machine learning is also explained in detail in:

A detailed tutorial on how to use landmap package to generate predictions / interpolated point data sets is available here.


Install development versions from github:


Note: functions not recommended for large datasets.


Automated mapping using Ensemble Machine Learning

First, we need to install number of packages as Ensemble Machine Learning uses several independent learners:

ls <- c("rgdal", "raster", "plotKML", "geoR", "ranger", "mlr", "forestError", 
        "xgboost", "glmnet", "matrixStats", "kernlab", "deepnet")
new.packages <- ls[!(ls %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)

The following examples demostrates spatial prediction using the meuse data set. Note that we only have to specify the target point data set, covariate layers (object of class SpatialPixelsDataFrame) and that the target variable needs a transformation lambda = 1, which is only required for the fitting of variogram using the geoR package:

demo(meuse, echo=FALSE)
m <- train.spLearner(meuse["zinc"], covariates=meuse.grid[,c("dist","ffreq")], lambda = 1)

this runs several steps:

Converting ffreq to indicators...
Converting covariates to principal components...
Deriving oblique coordinates...TRUE
Fitting a variogram using 'linkfit' and trend model...TRUE
Estimating block size ID for spatial Cross Validation...TRUE
Starting parallelization in mode=socket with cpus=32.
Using learners: regr.ranger, regr.xgboost, regr.nnet, regr.ksvm, regr.cvglmnet...TRUE
Fitting a spatial learner using 'mlr::makeRegrTask'...TRUE
Exporting objects to slaves for mode socket: .mlr.slave.options
Mapping in parallel: mode = socket; cpus = 32; elements = 5.
Exporting objects to slaves for mode socket: .mlr.slave.options
Mapping in parallel: mode = socket; cpus = 32; elements = 5.
Exporting objects to slaves for mode socket: .mlr.slave.options
Mapping in parallel: mode = socket; cpus = 32; elements = 5.
# weights:  103
initial  value 54927206.667240 
final  value 20750447.509677 
Fitting a quantreg model using 'ranger::ranger'...TRUE
Exporting objects to slaves for mode socket: .mlr.slave.options
Mapping in parallel: mode = socket; cpus = 32; elements = 5.
Stopped parallelization. All cleaned up.

In the landmap framework, variogram model is only fitted to estimate effective range of spatial dependence, which is then used to determine the size of blocks for spatial block Cross-Validation. Spatial Prediction models are based only on fitting the Ensemble Machine Learning (by default landmap uses c("regr.ranger", "regr.xgboost", "regr.ksvm", "regr.nnet", "regr.cvglmnet"); see a complete list of learners available via mlr) with oblique coordinates (rotated coordinates) as described in Moller et al. (2019) "Oblique Coordinates as Covariates for Digital Soil Mapping" to account for spatial auto-correlation in values. In the landmap package, geographical distances to ALL points can be added by specifying buffer.dist=TRUE; this is however not recommended for large point data sets. The meta-learning i.e. the SuperLearner model shows which individual learners are most important:

stats::lm(formula = f, data = d)

    Min      1Q  Median      3Q     Max 
-478.73 -107.15  -30.85   67.52 1201.31 

                Estimate Std. Error t value Pr(>|t|)    
(Intercept)   1358.41882  622.23470   2.183 0.030592 *  
regr.ranger      0.74741    0.20159   3.708 0.000295 ***
regr.xgboost     0.08317    0.41544   0.200 0.841606    
regr.nnet       -2.89762    1.33319  -2.173 0.031326 *  
regr.ksvm        0.41938    0.22837   1.836 0.068283 .  
regr.cvglmnet   -0.14177    0.19576  -0.724 0.470071    
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 217.1 on 149 degrees of freedom
Multiple R-squared:  0.6616,	Adjusted R-squared:  0.6503 
F-statistic: 58.27 on 5 and 149 DF,  p-value: < 2.2e-16

in this case regr.ranger seems to be most important for predicting zinc concentration (highest absolute t value), while regr.ksvm and regr.cvglmnet are the least important. Overall, this ensemble model explains ca 65% of variance (based on repeated 5-fold cross-validation).

Next we can generate predictions by using:

meuse.zinc <- predict(m)
Predicting values using 'getStackedBaseLearnerPredictions'...TRUE
Deriving model errors using ranger package 'quantreg' option...TRUE

Note that, based on the current set-up with method = "", so every time we re-run the model training we might get somewhat different models / different betas. On the other hand, the final ensemble predictions (map) should visually not differ too much (see below). In practice, as the number of training points and features increases, the predictions should not differ significantly.

Figure: Predicted zinc content for the Meuse data set. Model error is derived using quantile regression from multiple model predictions.

Figure: Repeated predictions for zinc content using the same settings.

As a default setting, we use the method of Lu & Hardin (2021) implemented in the forestError package to derive the prediction intervals i.e. the estimated uncertainty around a single predicted value. It can be derived as:

  • upper and lower quantiles, and/or
  • standard deviation (assumes symmetric distribution of errors),

As a default value for prediction intervals, landmap uses quantiles = c((1-.682)/2, 1-(1-.682)/2) so that s.d. can also be derived from the upper and lower 68% quantiles by using:

pred.error <- (q.upr-q.lwr)/2

Figure: Lower and upper prediction intervals based on the 68% probability.

Animated predictions by 9 models (3x independently fitted random forest, SVM and Xgboost) shows the following patterns:

_Figure: Examples of independently generated predictions for lead concentration. The coefficients are beta coefficients from the meta-learner fit: the higher the coefficient, more important the model for the ensemble merge._

The predictions shown in the image above incorporate spatial correlation between values, and hence can be used as a possible replacement for kriging methods (Hengl et al. 2018). Automation comes, however, at the high computing and RAM usage costs.

In the following example we use somewhat larger data set from the SIC1997 exercise.

X <- sic1997$swiss1km[c("CHELSA_rainfall","DEM")]
mR <- train.spLearner(sic1997$daily.rainfall, covariates=X, lambda=1)
rainfall1km <- predict(mR)

The processing is now much more computational because the data set consists from 467 points and size of grid / features is higher. This will make the regression matrix becoming extensive, and also 5x5 models need to be fitted. At the moment, using train.spLearner for point data set with >>1000 points should be done with caution.

The final results also shows quite similar results to universal kriging in geoR. The model error map above, however, shows more spatial contrast and helps detect areas of especially high errors.

Figure: Predicted daily rainfall for the SIC1997 data set.

The same function can also be used to interpolate factor-type variables:

gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
coordinates(eberg) <- ~X+Y
proj4string(eberg) <- CRS("+init=epsg:31467")
X <- eberg_grid[c("PRMGEO6","DEMSRT6","TWISRT6","TIRAST6")]
mF <- train.spLearner(eberg["TAXGRSC"], covariates=X)
TAXGRSC <- predict(mF)
plot(stack(TAXGRSC$pred[grep("prob.", names(TAXGRSC$pred))]), 
     col=SAGA_pal[["SG_COLORS_YELLOW_RED"]], zlim=c(0,1))

Figure: Predicted Ebergotzen soil types (probabilities).

For each class we can also derive a standard deviation of predicted probabilities by multiple independently fitted learners. This shows where the model is in average most uncertain per class. In the landmap package we in general recommend that log-loss measure is used to evaluate mapping accuracy, so that one can see which classes and where are the most problematic.

Figure: Predicted errors of the Ebergotzen soil types (probabilities).

Note that in the case of factor variables, prediction are based on ensemble stacking based on the following three classification algorithms c("regr.ranger", "regr.xgboost", "regr.nnet"). See mlr documentation on how to add additional learners.

In summary: package mlr provides a comprehensive environment for Machine Learning:

  • Ensemble predictions are based on the mlr::makeStackedLearner function,
  • Additional learners can be added,
  • Processing can be parallelized using the parallelMap package,

Ensemble Machine Learning is also available via the subsemble and the SuperLearner packages (not used here). For more info about Ensemble Machine Learning refer to this tutorial.

Accessing LandGIS layers

Landmap package also provides functionality to access and download LandGIS layers from Recommend process is to first search the coverage ID names and file names e.g.:

search.landgis(pattern=c("clay", "10..10cm"))

This shows that a clay map at 10 cm depth of the world is available via:


[1] "" 
[2] ""

Web Coverage Service functionality and API are explained in detail here. Next we can download only clay map for Switzerland using the Web Coverage Service functionality of LandGIS:

coverageId = "predicted250m:sol_clay.wfraction_usda.3a1a1a_m_250m_b10..10cm_1950..2017_v0.2"
swiss1km.ll <- raster::projectRaster(raster(sic1997$swiss1km[2]), crs = "+init=epsg:4326", res=c(1/120, 1/120))
s1 = paste0("Lat(", swiss1km.ll@extent@ymin, ",", swiss1km.ll@extent@ymax,")")
s2 = paste0("Long(", swiss1km.ll@extent@xmin, ",", swiss1km.ll@extent@xmax,")")
sf = (1/480)/(1/120)
download.landgis(coverageId, filename = "clay_ch1km.tif", subset = c(s1,s2), scalefactor = sf)
swiss1km.ll1km <- as(swiss1km.ll, "SpatialGridDataFrame")
swiss1km.ll1km$clay_10..10cm <- readGDAL("clay_ch1km.tif")$band1
swiss1km.ll1km$clay_10..10cm <- ifelse($DEM), NA, swiss1km.ll1km$clay_10..10cm)

Figure: Clay content map for Switzerland.

This takes few steps because you have to determine:

  • bounding box,
  • scaling factor,
  • mask out pixels of interest,

For smaller areas (<500Mb in size) download of data using WCS is fast and efficient. For accessing and using global layers larger than 1GB we recommend directly downloading data from


  • Contributions to landmap are welcome. Issues and pull requests are the preferred ways of sharing them.
  • We are interested in your results and experiences with using the train.spLearner function for generating spatial predictions with your own data. Share your data sets, code and results either using github issues and/or R-sig-geo mailing list.