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Journal Article

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

Authors

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;

/persons/resource/herold

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

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5012994.pdf
(Publisher version), 21MB

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Citation

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.
https://doi.org/10.1080/15481603.2022.2115619


Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5012994
Abstract
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.