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

Vista sencilla de ítem
dc.creatorMasolele, Robert N.
dc.creatorSy, Veronique de
dc.creatorMarcos, Diego
dc.creatorVerbesselt, Jan
dc.creatorGieseke, Fabian
dc.creatorMulatu, Kalkidan Ayele
dc.creatorMoges, Yitebitu
dc.creatorSebrala, Heiru
dc.creatorMartius, Christopher
dc.creatorHerold, Martin
dc.date2022-12-31
dc.date2022-10-13T14:25:33Z
dc.date2022-10-13T14:25:33Z
dc.date.accessioned2026-06-27T13:24:35Z
dc.descriptionNational-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.
dc.identifierhttps://hdl.handle.net/10568/125031
dc.identifier.urihttp://hdl.handle.net/123456789/58483
dc.languageen
dc.publisherInforma UK Limited
dc.rightsOpen Access
dc.sourceMasolele, 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. In GIScience & Remote Sensing (Vol. 59, Issue 1, pp. 1446–1472). Informa UK Limited. https://doi.org/10.1080/15481603.2022.2115619
dc.subjectdeforestation
dc.subjectforestry
dc.subjectgeographical information systems
dc.subjectremote sensing
dc.subjectclimate change
dc.subjectsatellite imagery
dc.titleUsing high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia
dc.typeJournal Article

Archivos

Colecciones

Alianza Bioversity International y CIAT