Last updated: 2019-05-22

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Knit directory: 2019-feature-selection/

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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

Predicted defoliation (absolute)

Predicted defoliation by XGBOOST on 20 m spatial resolution in the Basque Country.

With an RMSE of 36 % (for a range of 0 - 100), the model focuses its prediction to the mean of the response range (0 - 100) to achieve acceptable performance results on the test set.

The following vegetation indices have been used, selected by the internal variable importance rating of XGBOOST:

EVI
GDVI4
GDVI3
GDVI2
D1
mNDVI
mSR

Optimization path of hyperparameter tuning

The following shows the optimization path (RMSE) of the hyperparameter tuning for the XGBOOST model using 20 MBO iterations (with a starting design of 30).

 [1] 43.17171 44.46635 43.42093 43.82827 44.53159 43.86555 41.70989
 [8] 37.23756 37.77271 37.66704 39.38304 40.50224 36.66701 36.22678
[15] 38.25126 36.93972 39.12485 42.45889 38.05913 39.47527

Some more details on the optimization path are presented below:

Predicted Defoliation (relative)

Since these absolute values do not reflect the truth, we decided to create a relative Index from the predicted absolute values.

This was done by calling

scale(data$defoliation, center = FALSE,
      scale = max(data$defoliation, na.rm = TRUE)/100)

which re-scales the data from 0 - 100. The code was adapted from this Stackoverflow answer.

Defoliation evaluation

Absolute defoliation

Maps

2017

2018

Histograms

2017

2018

Relative defoliation

Maps

2017

2018

Histograms

2017

2018

R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /opt/spack/opt/spack/linux-centos7-x86_64/gcc-7.3.0/openblas-0.3.5-zncvk4jccaqyfl4z3vszaboeps6hyzta/lib/libopenblas_zen-r0.3.5.so

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] gridExtra_2.3        tidyselect_0.2.5     workflowr_1.3.0     
 [4] here_0.1             ggpubr_0.1.6         ggspatial_1.0.3     
 [7] gdalUtils_2.0.1.7    getSpatialData_0.0.4 knitr_1.21          
[10] rgenoud_5.8-3.0      parallelMap_1.3      mlrMBO_1.1.2        
[13] smoof_1.5.1          checkmate_1.9.1      BBmisc_1.11         
[16] mapview_2.3.0        stringr_1.4.0        fs_1.2.6            
[19] curl_3.3             mlrCPO_0.3.4-2       mlr_2.13.9000       
[22] ParamHelpers_1.12    data.table_1.12.0    magrittr_1.5        
[25] future.apply_1.1.0   future.callr_0.4.0   furrr_0.1.0.9002    
[28] future_1.11.1.1      R.utils_2.8.0        R.oo_1.22.0         
[31] R.methodsS3_1.7.1    glue_1.3.0           purrr_0.3.0         
[34] sf_0.7-3             dplyr_0.8.0.1        hsdar_0.5.2         
[37] caret_6.0-81         ggplot2_3.1.0        lattice_0.20-38     
[40] signal_0.7-6         rootSolve_1.7        rgdal_1.4-3         
[43] raster_2.8-19        sp_1.3-1             drake_7.3.0         

loaded via a namespace (and not attached):
  [1] backports_1.1.3    fastmatch_1.1-0    mapedit_0.4.3     
  [4] plyr_1.8.4         igraph_1.2.4       lazyeval_0.2.1    
  [7] splines_3.5.2      storr_1.2.1        crosstalk_1.0.0   
 [10] listenv_0.7.0      leaflet_2.0.2      usethis_1.4.0     
 [13] digest_0.6.18      foreach_1.4.4      htmltools_0.3.6   
 [16] memoise_1.1.0      base64url_1.4      remotes_2.0.2     
 [19] recipes_0.1.4      globals_0.12.4     gower_0.1.2       
 [22] prettyunits_1.0.2  colorspace_1.4-0   xfun_0.5          
 [25] DiceKriging_1.5.6  callr_3.1.1        crayon_1.3.4      
 [28] jsonlite_1.6       survival_2.43-3    iterators_1.0.10  
 [31] gtable_0.2.0       ipred_0.9-8        webshot_0.5.1     
 [34] pkgbuild_1.0.2     abind_1.4-5        scales_1.0.0      
 [37] GGally_1.4.0       DBI_1.0.0          Rcpp_1.0.0        
 [40] viridisLite_0.3.0  xtable_1.8-3       units_0.6-2       
 [43] stats4_3.5.2       lava_1.6.5         prodlim_2018.04.18
 [46] htmlwidgets_1.3    httr_1.4.0         RColorBrewer_1.1-2
 [49] reshape_0.8.8      pkgconfig_2.0.2    nnet_7.3-12       
 [52] RJSONIO_1.3-1.1    labeling_0.3       rlang_0.3.4       
 [55] reshape2_1.4.3     later_0.8.0        munsell_0.5.0     
 [58] tools_3.5.2        cli_1.1.0          generics_0.0.2    
 [61] devtools_2.0.1     evaluate_0.13      yaml_2.2.0        
 [64] ModelMetrics_1.2.2 processx_3.2.1     satellite_1.0.1   
 [67] nlme_3.1-137       mime_0.6           xml2_1.2.0        
 [70] compiler_3.5.2     prettymapr_0.2.2   plotly_4.8.0      
 [73] png_0.1-7          e1071_1.7-0.1      testthat_2.0.1    
 [76] RPostgreSQL_0.6-2  tibble_2.0.1       lhs_1.0.1         
 [79] stringi_1.3.1      ps_1.3.0           desc_1.2.0        
 [82] plot3D_1.1.1       Matrix_1.2-15      classInt_0.3-1    
 [85] rosm_0.2.4         pillar_1.3.1       httpuv_1.4.5.1    
 [88] R6_2.4.0           promises_1.0.1     sessioninfo_1.1.1 
 [91] codetools_0.2-16   MASS_7.3-51.1      assertthat_0.2.0  
 [94] pkgload_1.0.2      rprojroot_1.3-2    withr_2.1.2       
 [97] parallel_3.5.2     rpart_4.1-13       timeDate_3043.102 
[100] tidyr_0.8.2        class_7.3-15       rmarkdown_1.11    
[103] misc3d_0.8-4       mco_1.0-15.1       git2r_0.24.0      
[106] getPass_0.2-2      shiny_1.2.0        lubridate_1.7.4   
[109] base64enc_0.1-3

Prediction maps of defoliation at trees

Patrick Schratz, Marc Becker, Friedrich-Schiller-University Jena

Predicted defoliation (absolute)

Optimization path of hyperparameter tuning

Predicted Defoliation (relative)

Defoliation evaluation

Absolute defoliation

Maps

2017

2018

Histograms

Relative defoliation

Maps

2017

2018

Histograms