Hidden Markov Model for Land Cover

This repo includes R code for

• simulating land cover data generated by a hidden Markov model, and
• estimating parameters using maximum likelihood (ML using the expectation-maximization algorithm) and minimum distance (MD) estimators.

Citations

Please cite "Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach" by Adrian L. Torchiana, Ted Rosenbaum, Paul T. Scott, and Eduardo Souza-Rodrigues.

How to Run the Code

We run our code in a Docker container so that our environment is reproducible. Start by installing Docker, cloning this repo, and changing directory into the root of the repo:

cd hidden_markov_model

At this point your working directory should be the root of the repo, and if you ls you should see several .R files as well as the README.md file that you are currently reading.

Start by running

sudo docker build -f Dockerfile . --tag=hidden_markov_model

The build command above will use the Dockerfile to create an image with R version 4.2.1 and several R packages installed. We can now use that image to run scripts:

sudo docker run -it -v /home/$USER/hidden_markov_model:/home/hidden_markov_model hidden_markov_model bash
Rscript simulation_simple.R

Note that the -v in the docker run command above is mounting a volume, and assumes that you cloned this repo into /home/$USER/hidden_markov_model. If you cloned into some other location, replace /home/$USER/hidden_markov_model with the correct path.

The code in simulation_simple.R runs simple simulations that are used to confirm that the estimation functions are working correctly. Its output is not used in the paper.

All R scripts should be run inside the container, i.e. following the docker run command as above. Most of the simulations will save output files to the output directory.

Baseline Results

To reproduce section D.2 Baseline Results, run

Rscript simulation_baseline.R --n_simulations 100
Rscript describe_simulation_baseline.R --simulation_date yyyy-mm-dd

where yyyy-mm-dd is the date on which you ran simulation_baseline.R.

Spatial Dependence and Serial Correlation

To reproduce section D.4 Spatial Dependence and Serial Correlation, run

Rscript simulation_spatial_corr.R --n_simulations 100
Rscript simulation_spatial_corr.R --n_simulations 100 --z_constant_over_time

Embrapa Validation

The code for section 6 Validation Exercise Using Land Cover Data is fit_embrapa_validation_hmm.R. While we have made the Embrapa validation code public, the input dataset used in this section is private and is not included in the repo.

Atlantic Forest

The code for section 7 Empirical Exercise: Carbon Stocks in the Atlantic Forest has several steps:

mkdir -p atlantic_forest_output
./run_mapbiomas_all_windows.sh
Rscript combine_mapbiomas_estimates.R
./run_viterbi.sh
Rscript atlantic_forest_carbon_stock.R

Like section 6, the code for section 7 is public but the input files (the "raw" mapbiomas rasters) are private.