ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Enhancing Land Cover Classification in Heterogeneous Landscape by Integrating Auxiliary Data with Sentinel-2 Imagery Using Random Forest Algorithm
Provisionally accepted
- University of KwaZulu-Natal, Durban, South Africa
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Effective and accurate land use and land cover (LULC-C) classification is an indispensable exercise for various environmental management objectives, including future and past land-use dynamics, flood-runoff modelling. However, LULC-C is subject to several limitations, such as labour-intensive derivation of a labelled dataset. So, we aim to enhance LULC-C using auxiliary features: elevation, slope, aspect, distance from-(road, railway stations, rivers, water, and town), global human settlement built-up layer and remote sensing indices in the heterogeneous landscape of eThekwini (EM) and Nelson Mandela Bay Metropolitan (NMBM) using Sentinel-2 and random forest (RF). We compared two classification scenarios: (1) a feature set including bands and indices, and (2) a feature set including bands, indices, and auxiliary features. We trained and tested RF using block cross-validation and random hold-out (70/30) split and validated the classified image using independent validation and a 30% subset through overall accuracy (OA) and F1-score. The study quantified the uncertainty using a 95% confidence interval with bootstrapping samples of 1000 iterations and quantified the significance of scenario 2 using McNemar and p-value. Pixel-wise quantity and allocation disagreement were derived to compare classification scenarios against the two 2020 reference maps for South Africa National Land Cover and Environmental System Research Institute. A class-by-class pixel comparison between classification scenarios underscores the potential of auxiliary features. While classification scenarios achieved comparable accuracy, scenario 2 superseded scenario 1 in all classification schemes. Using an independent validation, the study found confidence intervals (CI) for OA of 83.63% CI: 77.78–88.89 improved to 89.47% CI: 84.79–94.15 for scenario 1 and scenario 2 over EM, respectively. Confirmed by NMBM, where OA of 82.29% CI: 76.57–87.43 stabilised to 88.57% CI: 84.00–93.14 for scenario 1 and scenario 2. The performance improvement was statistically significant, attaining p-values of 0.03 and 0.02, respectively, for EM and NMBM using an independent validation set. However, while using a 30% validation subset, results show insignificant improvement in NMBM attaining p = 0.07, where p-values > 0.5. Overall results proved that an integration of auxiliary features enhances LULC-C. The empirical result of this study is a step forward in effective LULC-C in a complex landscape.
Keywords: Auxiliary feature, Land use and land cover classification, random forest, block-cross validation, global human settlement built-up layer
Received: 02 Sep 2025; Accepted: 06 Nov 2025.
Copyright: © 2025 Shandu, Xulu and Gebreslasie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Irvin Doctor Shandu, idcossa@gmail.com
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