Abstract
Accurate 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.
Highlights
IntroductionUnsustainable utilization of natural resources across drainage basins globally threatens livelihoods and biodiversity
This study aimed to evaluate the significance of integrating spectral bands with indices and climate-based regionalization on the accuracy of LULC based on ML and deep learning (DL) classifiers
This study aimed to evaluate the significance of the integration of spectral indices with bands and of climate-based regionalization for enhance the accuracy of LULC based on Landsat imagery using ML and DL
Summary
Unsustainable utilization of natural resources across drainage basins globally threatens livelihoods and biodiversity. Changes in land use (defined as the function of surface cover) and land cover (defined as the natural and artificial material covering the earth surface) due to anthropogenic activities and climate change affect the supply and distribution of ecosystem services (ES) across basins [1,2]. The situation is more complex for transboundary basins (TDBs) because they provide ES to people across different nations. The exploitation of provisioning services by different nations within TDBs is often not equal due to variations in access to resources as a result of social and ecological structures [3,4]
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