Climate-based regionalization and inclusion of spectral indices for enhancing transboundary land-use/cover classification using deep learning and machine learning
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
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.
Description
CITATION: 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.
The original publication is available at https://www.mdpi.com
The original publication is available at https://www.mdpi.com
Keywords
Climate, Machine learning, Remote sensing, Spectral imaging, Climatology, Climatic zones, Koppen-Geiger climate regionalization, Landscape changes, Land-use, Okavango River Delta (Botswana), Wetlands -- Okavango River
Citation
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.