DOI: 10.5281/zenodo.15538497
title: Labelled dataset to classify direct deforestation drivers in Cameroon: NIR-R-G bands

description: Overview

This dataset includes the images (NIR-R-G bands for Landsat-8 or NICFI PlanetScope), auxiliary data (infrared, NCEP, forest gain, OpenStreetMap, SRTM, GFW), and data about forest loss (Global Forest Change) used to train, validate and test a model to classify direct deforestation drivers in Cameroon. The creation of this dataset follows the same structure as: Labelled dataset to classify direct deforestation drivers in Cameroon but with a different set of bands. 

For more details about how this dataset has been created and can be used, please refer to our paper and code: https://github.com/aedebus/Cam-ForestNet. The paper, describing the generation of RGB images, can be found here: https://www.nature.com/articles/s41597-024-03384-z.

Citation:  Debus, A. et al. A labelled dataset to classify direct deforestation drivers from Earth Observation imagery in Cameroon. Sci Data 11, 564 (2024).



Here, the only difference compared with what is described in the paper is that we select NIR-R-G instead of R-G-B bands for our PNG images. 

 


Description of the files and images



'my_examples_landsat_nir.zip': Landsat-8 images (courtesy of the U.S. Geological Survey), auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon. This dataset contains 332x 332 pixels NIR-R-G calibrated top-of-atmosphere (TOA) reflectance with a 30 m resolution (less than 20% cloud cover)

'my_examples_landsat_sr_nir.zip': Same as above, but with surface reflectance (SR) instead of TOA

'my_examples_planet_nir.zip': NICFI PlanetScope images (catalog owner: Planet), auxiliary data and forest loss data used to train, validate and test a model for a detailed classification of deforestation drivers in Cameroon. This dataset contains 332x 332 pixels monthly NIR-R-G composite with a 4.77 m resolution

'my_examples_planet_nir_biannual.zip': Same as above, but with biannual composites instead of monthly composites


For ‘labels_nir.zip’, we have subfolders for Landsat-8 (TOA, SR, groups TOA) and NICFI PlanetScope (monthly, biannual, groups monthly). 

 

For each folder, subfolders named with the coordinates of the centre of the images contain each:•    A folder ‘images’, with a sub-folder ‘visible’ containing the PNG image; and a sub-folder ‘infrared’ containing the infrared bands in a NPY file.•    A folder ‘auxiliary’ with topographic and forest gain information in a NPY format, OpenStreetMap and peat data in a JSON format, and a sub-folder ‘ncep’ containing all data from NCEP in a NPY format.•    The forest loss pickle file delimiting the area of forest loss.

 

Note: The images provided have been filtered to enable a train/validation/test split that ensures a minimum distance of 100 meters between the edges of forest loss areas. 

Details about the auxiliary data



Forest gain from GFC: 30-m resolution, yearly data for 2000-2021, downloaded via Google Earth Engine

Near infrared, shortwave infrared 1 and 2 bands from Landsat-8 TOA: 30-m resolution, data every 16 days for 2013-2023, downloaded via Google Earth Engine and selected using the same process as for Landsat-8 RGB images

From NCEP Climate Forecast System Version 2 (CFSv2) 6-hourly Products: surface level albedo and volumetric soil moisture content (depths: 0.1 m, 0.4 m, 1.0 m, 2.0m) in 0.01%; radiative fluxes (clear-sky longwave flux downward and upward, clear-sky solar flux downward and upward, direct evaporation from bare soil, longwave and shortwave radiation flux downward and upward, latent, ground and sensible heat net flux), potential evaporation rate, and sublimation in W/m²; humidity (specific, maximum specific, minimum specific) in 10-4 kg/kg; ground level precipitation in 0.1 mm; air pressure at surface level in 10 Pa; wind level (u and v component) in 0.01 m/s, water runoff at surface level in 232.01 kg/ m²; temperature in K: 22264-m resolution, available four times a day for 2011-2023, downloaded directly from the NOAA website and selected the mean of the monthly mean over 5 years before the forest loss event, the monthly maximum over 5 years before the forest loss event, and the monthly minimum over 5 years before the forest loss event for each parameter

Closest street and closest city from OpenStreetMap in km: directly downloaded with the Nominatim API

Altitude in m, slope and aspect in 0.01° from  Shuttle Radar Topography Mission (SRTM): 30-m resolution, measured for 2000, downloaded via Google Earth Engine

Presence of peat from GFW: 232-m resolution, measured for 2017, directly downloaded on the GFW website


Details about Global Forest Change

For each image, there is a corresponding 'forest_loss_region' .pkl file delimiting a forest loss region polygon from Global Forest Change (GFC). GFC consists of annual maps of forest cover loss with a 30-m resolution. 

License

The NICFI PlanetScope images fall under the same license as the NICFI data program license agreement (data in 'my_examples_planet_nir.zip', 'my_examples_planet_nir_biannual.zip': subfolders '[coordinates]'>'images'>'visible'). 

OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF) (data in all 'my_examples' folders: subfolders '[coordinates]'>'auxiliary'>'closest_city.json'/'closest_street.json'). The documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0).

The rest of the data is under a Creative Commons Attribution 4.0 International License. The data has been transformed following the code that can be found via this link: https://github.com/aedebus/Cam-ForestNet (in 'prepare_files').