Published March 6, 2024 | Version v2
Dataset Open

Land use following deforestation in Ethiopia

  • 1. ROR icon Wageningen University & Research
  • 2. Research Centre Inria Sophia Antipolis - Méditerranée: Montpellier

Description

These datasets were generated from the research article "Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia".

Here we publish the yearly time series maps showing land use following deforestation across Ethiopia produced at 10m resolution covering the year 2001 to 2015 (15 years). The land use maps are derived from sentinel-2 images using  U-Net deep neural network architecture enhanced with attention.

The dataset has eleven land use classes with values ranging from 1 -11, where 1: Larger-scale cropland, 2: Pasture, 3: Mining, 4: Small-scale cropland, 5: Roads, 6: Other land with tree cover, 7: Plantation forest, 8: Coffee, 9: Settlement, 10: Tea plantation,  and 11: Water.

The data is accompanied by the sld  and qml file  in a zip folder (Visualisation_layer_descriptor_sld_and_qml) to aid in visualisation or legend creation.

 

Data Visualisation at 10m resolution: https://robertnag82.users.earthengine.app/view/deforestationdriverethiopia

Zoom to this location (Longitude:35.27458, Latitude: 7.3291) to visualize expansion of coffee plantation, small and large scale croplands in a degraded forest.

Zoom to this location  (Longitude:35.48129, Latitude: 7.84093) to visualize expansion of Tea plantation after deforestation.

File Naming:  Landuse_dl_10m_s_20010101_20011230_af_epsg.4326_v20240201.tif

  • Generic variable name: Landuse
  • Method of data production: dl (Deep learning)
  • Position in the probability distribution / variable type: c
  • Spatial support in m10m
  • Depth reference at surface ("s"): s
  • Time reference begin time (YYYYMMDD):  i.e. 20010101
  • Time reference end time: 20011230
  • Bounding box (2 letters max): af (Refering to Africa)
  • EPSG code: epsg.3035
  • Version code i.e. creation date: v20240201

 

JSON field representing the legend. This is important for the visualization of the legend. 

[ { "Large-scale cropland": "1", "color": "#FFFF00" }, { "Pasture": "0", "color": "#808080" }, { "Mining": "1", "color": "#FFC0CB" }, { "Small-scale cropland": "120", "color": "#F39C12" }, { "Roads": "1", "color": "#800000" }, { "Other land with tree cover/Regrowth ": "0", "color": "#008000" }, { "Plantation forest": "1", "color": "#808000" }, { "Coffee": "120", "color": "#008080" }, { "Settlement": "1", "color": "#FF0000" }, { "Tea plantation": "0", "color": "#3CB371" }, { "Water": "1", "color": "#0000FF" } ]

Files

Landuse_dl_10m_s_20010101_20011230_af_epsg.4326_v20240201.tif

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Additional details

Related works

Is published in
Journal article: 10.1080/15481603.2022.2115619 (DOI)

Funding

European Commission
OEMC - Open-Earth-Monitor Cyberinfrastructure 101059548

Software

References

  • Masolele, R. N. et al. Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia. GISci. Remote Sens. 59(1), 1446–1472. https://doi.org/10.1080/15481603.2022.2115619 (2022).