Metadata for files in the zip archive:
Crezee_2022_Central_Congo_Landcover_Probability_6x_layers.zip
This folder contains 6 probability layers, the output of 1000 classification
runs using a random 2/3 subset of training data, which were 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.
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This folder contains 6S layers:
- Probability_2022_HardwoodDominatedPeatSwamp.tif
- Probability_2022_PalmDominatedPeatSwamp.tif
- Probability_2022_NonPeatFormingForest.tif
- Probability_2022_Savanna.tif
- Probability_2022_Water.tif
- Probability_2022_TotalPeatSwamp.tif
Each of the first 5 layers 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.
The 6th layer (Probability_2022_TotalPeatSwamp.tif) was obtained by summing the probabilities of the hardwood and
palm-dominated peat swamp forest files. This layer provides the total probability of the presence of peat for each pixel, irrespective of the aboveground vegetation type (either hardwood- or palm-dominated swamp forest).
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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).
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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