Author(s)

Nadia Trabelsi^1*, Ibtissem Triki², Imen Hentati¹, Nizar Rachdi³

¹Laboratoire Eau, Energie, Environnement, Ecole Nationale d’Ingénieurs de Sfax, Sfax, Tunisia.
²Faculte des Sciences de Gafsa, Campus Universitaire, Gafsa, Tunisia.
³Geoengine, Geomatics and Geosciences Engineering, Rue du lac de Constance, Tunis, Tunisia.

Land use/land cover (LULC) mapping and change detection are fundamental aspects of remote sensing data application. Therefore, selecting an appropriate classifier approach is crucial for accurate classification and change assessment. In the first part of this study, the performance of machine learning classification algorithms was compared using Landsat 9 image (2023) of the Manouba government (Tunisia). Three different classification methods were applied: Maximum Likelihood Classification (MLC), Support Vector Machine (SVM), and Random Trees (RT). The classification aimed to identify five land use classes: urban area, vegetation, bare area, water and forest. A qualitative assessment was conducted using Overall Accuracy (OA) and the Kappa coefficient (K), derived from a confusion matrix. The results of the land cover classification demonstrated a high level of accuracy. The SVM method exhibited the best performance, with an overall accuracy of 93% and a kappa accuracy of 0.9. The ML method is the second-best classifier with an overall accuracy of 92% and a kappa accuracy of 0.88. The Random Trees method yielded the lowest accuracy among the three approaches, with an overall accuracy of 91% and a kappa accuracy of 0.87. The second part of the study focused on analyzing LULC changes in the study area. Based on the classification results, the SVM method was chosen to classify the Landsat 7 image acquired in 2000. LULC changes from 2000 to 2023 were investigated using change detection comparison. The findings indicate that over the last 23 years, vegetation land and urban areas in the study area have experienced significant increases of 31.94% and 5.47%, respectively. This study contributed to a better understanding of the classification process and dynamic LULC changes in the Manouba region. It provided valuable insights for decision-makers in planning land conservation and management.

Remote Sensing, GIS, LULC, SVM, MLC, RT, Change Detection

Trabelsi, N. , Triki, I. , Hentati, I. and Rachdi, N. (2023) Classification and Spatio-Temporal Change Detection of Land Use/Land Cover Using Remote Sensing and Geographic Information System in the Manouba Region, NE Tunisia. Journal of Geographic Information System, 15, 652-668. doi: 10.4236/jgis.2023.156033.