Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning

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dc.contributor.authorKavhu, Blessingen_ZA
dc.contributor.authorMashimbye, Zama Ericen_ZA
dc.contributor.authorLuvuno, Lindaen_ZA
dc.date.accessioned2021-12-22T07:15:53Z
dc.date.available2021-12-22T07:15:53Z
dc.date.issued2021
dc.descriptionCITATION: Kavhu, B., Mashimbye, Z. E. & Luvuno, L. 2021. Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning. Remote Sensing, 13:5054, doi:10.3390/rs13245054.
dc.descriptionThe original publication is available at https://www.mdpi.com
dc.description.abstractAccurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs.en_ZA
dc.description.urihttps://www.mdpi.com/2072-4292/13/24/5054
dc.description.versionPublisher's version
dc.format.extent23 pages : illustrations
dc.identifier.citationKavhu, B., Mashimbye, Z. E. & Luvuno, L. 2021. Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning. Remote Sensing, 13:5054, doi:10.3390/rs13245054.
dc.identifier.issn2072-4292 (online)
dc.identifier.otherdoi:10.3390/rs13245054
dc.identifier.urihttp://hdl.handle.net/10019.1/123564
dc.language.isoen_ZAen_ZA
dc.publisherMDPI
dc.rights.holderAuthors retain copyright
dc.subjectClimateen_ZA
dc.subjectMachine learningen_ZA
dc.subjectRemote sensingen_ZA
dc.subjectSpectral imagingen_ZA
dc.subjectClimatologyen_ZA
dc.subjectClimatic zonesen_ZA
dc.subjectKoppen-Geiger climate regionalizationen_ZA
dc.subjectLandscape changesen_ZA
dc.subjectLand-useen_ZA
dc.subjectOkavango River Delta (Botswana)en_ZA
dc.subjectWetlands -- Okavango Riveren_ZA
dc.titleClimate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learningen_ZA
dc.typeArticleen_ZA
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