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

Simple item page
cg.authorship.typesCGIAR and developing country instituteen
cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationWageningen University & Researchen
cg.contributor.affiliationUniversity of Münsteren
cg.contributor.affiliationBioversity International and the International Center for Tropical Agricultureen
cg.contributor.affiliationEnvironment, Forest and Climate Change Commission, Ethiopiaen
cg.contributor.affiliationCenter for International Forestry Researchen
cg.contributor.affiliationInternational Center for Tropical Agricultureen
cg.contributor.crpForests, Trees and Agroforestry
cg.contributor.donorFederal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection, Germanyen
cg.contributor.donorCGIAR Trust Funden
cg.contributor.donorNorwegian Agency for Development Cooperationen
cg.contributor.donorEuropean Unionen
cg.contributor.initiativeLow-Emission Food Systems
cg.coverage.countryEthiopia
cg.coverage.iso3166-alpha2ET
cg.coverage.regionAfrica
cg.coverage.regionSub-Saharan Africa
cg.coverage.regionEastern Africa
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.1080/15481603.2022.2115619en
cg.isijournalISI Journalen
cg.issn1548-1603en
cg.issue1en
cg.journalGIScience & Remote Sensingen
cg.reviewStatusPeer Reviewen
cg.subject.actionAreaSystems Transformation
cg.volume58en
dc.contributor.authorMasolele, Robert N.en
dc.contributor.authorSy, Veronique deen
dc.contributor.authorMarcos, Diegoen
dc.contributor.authorVerbesselt, Janen
dc.contributor.authorGieseke, Fabianen
dc.contributor.authorMulatu, Kalkidan Ayeleen
dc.contributor.authorMoges, Yitebituen
dc.contributor.authorSebrala, Heiruen
dc.contributor.authorMartius, Christopheren
dc.contributor.authorHerold, Martinen
dc.date.accessioned2022-10-13T14:25:33Zen
dc.date.available2022-10-13T14:25:33Zen
dc.identifier.urihttps://hdl.handle.net/10568/125031
dc.titleUsing high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopiaen
dcterms.abstractNational-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.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademicsen
dcterms.audienceCGIARen
dcterms.audienceScientistsen
dcterms.available2022-09-07en
dcterms.bibliographicCitationMasolele, 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/10.1080/15481603.2022.2115619en
dcterms.extent1446-1472en
dcterms.issued2022-12-31en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherInforma UK Limiteden
dcterms.replaceshttps://hdl.handle.net/10568/128140en
dcterms.subjectdeforestationen
dcterms.subjectforestryen
dcterms.subjectgeographical information systemsen
dcterms.subjectremote sensingen
dcterms.subjectclimate changeen
dcterms.subjectsatellite imageryen
dcterms.typeJournal Article

Files

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

CGIAR Initiative on Low-Emission Food Systems
Alliance Bioversity CIAT Journal Articles
Alliance Research Lever 2: Multifunctional Landscapes
CIFOR publications
FTA outputs