Simulating future land-use within the uThukela and uMngeni catchments in KwaZulu-Natal

Moodley, Kimara; Toucher, Michele; Lottering, Romano

doi:10.1016/j.sciaf.2023.e01666

Cited by 1 publication

(1 citation statement)

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“…Water and wetland classes cover 1.69% and 0.12% of the study area, respectively. The wetlands are found in the north-west periphery and similar findings were also reported by [85]. Both water and wetlands are the major role players in ensuring the health of the ecosystem in general [86].…”

Section: Discussionsupporting

confidence: 78%

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

Bhungeni,

Ramjatan,

Gebreslasie

2024

Remote Sensing

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Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data.

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Section: Discussionsupporting

confidence: 78%