Enhancing land cover classification in the heterogeneous landscape by integrating auxiliary data with Sentinel-2 imagery using the random forest algorithm

Effective and accurate land use and land cover classification (LULC-C) is an indispensable exercise for various environmental management objectives, including past and future land-use dynamics, flood and 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 forests (RF). We compared two classification scenarios: (1) feature set including bands and indices, and (2) 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 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 quantify 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 African 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 While classification scenarios achieved comparable accuracy, scenario 2 superseded scenario 1 in all classification scheme. Using an independent validation, the study found confidence interval (CI) for OA of 83.63% CI: 77.78–88.89 improved to 89.47% CI: 84.79–94.15, respectively, for scenario 1 and scenario 2 over EM. 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 30% validation subset, results show in-significant improvement in NMBM attaining p-value = <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m1"> <mml:mrow> <mml:mn>0.07</mml:mn> </mml:mrow> </mml:math> , where p-value &amp;gt;0.05. Overall results proved that an integration of auxiliary features enhance LULC-C. The empirical result of this study is a step forward in effective LULC-C in a complex landscape.