Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia


Masolele,  Robert N.
External Organizations;

De Sy,  Veronique
External Organizations;

Marcos,  Diego
External Organizations;

Verbesselt,  Jan
External Organizations;

Gieseke,  Fabian
External Organizations;

Mulatu,  Kalkidan Ayele
External Organizations;

Moges,  Yitebitu
External Organizations;

Sebrala,  Heiru
External Organizations;

Martius,  Christopher
External Organizations;


Herold,  Martin
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

External Ressource
No external resources are shared
Fulltext (public)

(Publisher version), 21MB

Supplementary Material (public)
There is no public supplementary material available

Masolele, R. N., De Sy, V., Marcos, D., Verbesselt, J., Gieseke, F., Mulatu, K. A., Moges, Y., Sebrala, H., Martius, C., Herold, M. (2022): Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia. - GIScience & Remote Sensing, 59, 1, 1446-1472.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5012994
National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy.