Metadata for files in the zip archive: Crezee_2022_Central_Congo_Peat_Probability_Layers.zip This folder contains 5 probability layers, the output of 1000 classification runs using a random 2/3 subset of training data, that was used to produce the peatland extent estimates and probability map (Figure 1b) in: Crezee et al. (2022). Nature Geoscience. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. ------------------------------------------------------------------------------------- This folder contains 5 layers: - Probability_2022_HardwoodDominatedPeatSwamp.tif - Probability_2022_PalmDominatedPeatSwamp.tif - Probability_2022_NonPeatFormingForest.tif - Probability_2022_Savanna.tif - Probability_2022_Water.tif Each layer has pixel values from 0-1000, representing the number of times that class was given as the most likely class for that pixel in 1000 Maximum Likelihood classifications using a random 2/3 subset of the training data. For example, a value of 995 in the Probability_2022_Water.tif file means that in 995 of the 1000 runs this pixel was mapped as 'water'. Conversely, a value of 4 in the Probability_2022_Savanna.tif would mean that that pixel was only mapped as savanna 4 times in the 1000 runs. As an approximation, dividing these values by 1000 gives the probability that that pixel contains a majority of vegetation of that class. Total peat swamp forest probability is obtained by summing the probabilities of the hardwood and palm-dominated peat swamp forest files. ---------------------------------------------------------------------------------------- Projection and file details: The file is a GeoTIFF in a lat/long WGS-84 projection and a pixel size of 0.00044444 degrees (c. 50 m). --------------------------------------------------------------------------------------- You are free to use these data for any purpose provided you cite the original paper. The file was created by Bart Crezee and Edward Mitchard. For questions about these data layers please contact Edward Mitchard at edward.mitchard@ed.ac.uk EPSG:3857