Resampling using Ensemble ML (classification)

[oliviafraserusda]

Do you have an example of resampling using ensemble ml (section 2.4) with classification (instead of regression)?

[thengl]

Yes, example is at: https://opengeohub.github.io/spatial-prediction-eml/spatial-interpolation-using-ensemble-ml.html#spatial-prediction-of-soil-types-factor-variable It works the same way - you just have to prepare the spatial grid for spatial blocking or use the landmap::train.spLearner function.

[oliviafraserusda]

Is it possible to predict landmap::train.spLearner across an independent geographic area SpatialPixelsDataFrame for model validation?

[thengl]

Yes just use argument predictionLocations. See: https://rdrr.io/cran/landmap/man/predict.spLearner.html

[jpgannon]

I’m having trouble using the predict() function for the purpose above (applying the model to a new geographic area). I’m hoping you can provide some guidance.

The line to create the model I’m using: mC <- train.spLearner(plots["hpu"], covariates = spdf_all_layers[,c('tpi20', 'tpi100', 'tpi200m', 'mrvbf', 'hbuaab', 'EDb', 'twid')], SL.library = SL.library, super.learner = "classif.glmnet", parallel = FALSE, oblique.coords = TRUE)

Then I am trying to use predict() to apply the model to a new geographic location. Both of the following syntax options return the same error (spdf_all_layers2 is an spdf of the new area):

BPhpu <- predict(mC, predictionLocations = spdf_all_layers2[,c('tpi20', 'tpi100', 'tpi200m', 'mrvbf', 'hbuaab', 'EDb', 'twid')])

Or

BPhpu <- predict(mC, predictionLocations = spdf_all_layers2)

Error in [.data.frame(predictionLocations@data, , object@spModel$features) : undefined columns selected

I’m using the same code to create spfd_all_layers2 as to create the initial one. All columns are named the same, the spdf has the same dimensions, projection, etc.

Are there any known frequent issues with this that I could use to help me troubleshoot? Thank you!

[jpgannon]

One more thing: I tried to run predict() with the exact spatial points dataframe used to build the model "spdf_all_layers" above, and it threw the same error.

[oliviafraserusda]

OH!!! Are your Cone/Black Pond rasters all exactly the same footprint? I've gotten a similar error when my rasters weren't all the same exact geographic extent.

For example, a topo metric stacked with NDVI derived from NAIP.

@. Olivia Fraser (she/her) National Reforestation and Nursery System Analyst Forest Service WO Forest, Range Management, & Vegetation Ecology National Forest System Cell: 603-290-3882 @*.**@*.***> www.fs.fed.ushttp://www.fs.fed.us/ [USDA Logo]https://usda.gov/[Forest Service Twitter]https://twitter.com/forestservice[USDA Facebook]https://www.facebook.com/pages/US-Forest-Service/1431984283714112 Caring for the land and serving people

From: JP Gannon @.> Sent: Friday, March 22, 2024 7:23 AM To: OpenGeoHub/spatial-sampling-ml @.> Cc: Fraser, Olivia - FS, NH @.>; Author @.> Subject: Re: [OpenGeoHub/spatial-sampling-ml] Resampling using Ensemble ML (classification) (Issue #1)

One more thing: I tried to run predict() with the exact spatial points dataframe used to build the model "spdf_all_layers" above, and it threw the same error.

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[jpgannon]

Yeah they are all derived from the same DEM. They are built in the exact same way as the input for the model.