Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

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.

Detecting Connectivity and Spread Pathways of Land Use/Cover Change in a Transboundary Basin Based on the Circuit Theory

“…The workflow of the study design is depicted in Figure 2. This study builds from our previous work [41,46]. In Kavhu et al [46], we found that post-feature selected and climate-based regionalization improved the accuracy of LULC classification for both Machine Learning and Deep learning techniques within the Okavango, a transboundary basin.…”

“…This study builds from our previous work [41,46]. In Kavhu et al [46], we found that post-feature selected and climate-based regionalization improved the accuracy of LULC classification for both Machine Learning and Deep learning techniques within the Okavango, a transboundary basin. The most accurate LULC product was based on the Deep neural network (DNN) classification.…”

“…The most accurate LULC product and the social-ecological drivers generated in previous studies are used in this study. For more detailed descriptions of methods for LULC classification, change detection, and the characterization of social-ecological drivers, readers should refer to Kavhu et al [46] and Kavhu et al [41].…”

“…The Deep neural network (DNN was used for LULC classification in R statistics (See Supplementary Figure S1 for LULC change results). Details of how the DNN was parameterized and the study site was segmented are found in Kavhu et al [46].…”

Optimal Land-cover Classification Feature Selection in Arid Areas based on Sentinel-2 Imagery and Spectral Indices

“…Each tree in FR model acts as a decision in the classification or regression process and the number of these decision trees is known as ntree and determined by the selected features from the user. In this study, the ntree was set to 500 as recommended by [27]. The mtry parameter refers to the predictors number that are randomly sampled when creating the trees at each split.…”