Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas

Abstract

Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map.

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

“…These improvements in classification accuracy for this study may be due to the use of vegetation indices and topographic factors as additional variables in refining the classification. In this regard, consistently with this study, a number of studies have shown the importance of vegetation indices and topographic factors in improving LULC classification accuracy [55,76,77].…”

“…Among these variables, elevation was found to be the most important. Consistently with these findings, several studies have reported the importance of vegetation indices and topographic factors in improving LULC classification [55,76]. Among the vegetation indices, the NDVI was more important than the SAVI in most cases.…”

Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Landscape

“…Similarly, the study observed confusion between the grassland and farmland. Mapping LULC with Sentinel-2 data in the semi-arid region is quite promising [34] but challenging because most crops are planted during the rainy season, and their growing season is in July and August, during which the cloud cover is high in the area. And the reliance on dry season imagery may not be feasible as there is a transition from cropland to barren land in the area, especially from early November.…”

“…The spatial resolution of these data is 20 m. The data were pre-processed and atmospherically corrected by the providers. Sentinel-2 has promise in LULC mapping in semiarid/agriculturally dominant landscapes based on RF feature selection [33,34].…”

Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data

“…According to Yang et al [19], the random forest (RF) [20] algorithm has been widely utilized in previous LULC studies [16,19,[21][22][23][24][25][26][27][28][29], followed by support vector machine (SVM) [30][31][32][33][34][35]. Li et al [36] conducted a comparison between the performance of the RF classification algorithm and other algorithms such as SVM and artificial neural network (ANN).…”