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Remote sensing provides a powerful resource of fundamental data at different spatial resolutions and spectral regions, making it an essential tool for vegetation mapping and biomass management. Due to the ever-increasing availability of free data and software, satellites have been predominantly used to map, analyze, and monitor natural resources for conservation purposes. This study aimed to map vegetation from Sentinel-2 (S2) data in a complex and mixed vegetation cover of the Lous\u00e3 district in Portugal. We used ten multispectral bands with a spatial resolution of 10 m, and four vegetation indices, including Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Enhanced Vegetation Index (EVI), and Soil Adjusted Vegetation Index (SAVI). After applying principal component analysis (PCA) on the 10 S2A bands, four texture features, including mean (ME), homogeneity (HO), correlation (CO), and entropy (EN), were derived for the first three principal components. Textures were obtained using the Gray-Level Co-Occurrence Matrix (GLCM). As a result, 26 independent variables were extracted from S2. After defining the land use classes using an object-based approach, the Random Forest (RF) classifier was applied. The map accuracy was evaluated by the confusion matrix, using the metrics of overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and kappa coefficient (Kappa). The described classification methodology showed a high OA of 90.5% and kappa of 89% for vegetation mapping. Using GLCM texture features and vegetation indices increased the accuracy by up to 2%; however, classification using GLCM texture features and spectral bands achieved the highest OA (92%), indicating the texture features\u2032 capability in detecting the variability of forest species at stand level. The ME and CO showed the highest contribution to the classification accuracy among the GLCM textures. GNDVI outperformed other vegetation indices in variable importance. Moreover, using only S2A spectral bands, especially bands 11, 12, and 2, showed a high potential to classify the map with an OA of 88%. This study showed that adding at least one GLCM texture feature and at least one vegetation index into the S2A spectral bands may effectively increase the accuracy metrics and tree species discrimination.<\/jats:p>","DOI":"10.3390\/rs14184585","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T23:16:36Z","timestamp":1663197396000},"page":"4585","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Vegetation Mapping with Random Forest Using Sentinel 2 and GLCM Texture Feature\u2014A Case Study for Lous\u00e3 Region, Portugal"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8628-3563","authenticated-orcid":false,"given":"Pegah","family":"Mohammadpour","sequence":"first","affiliation":[{"name":"Univ of Coimbra, ADAI, Department of Mechanical Engineering, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"},{"name":"Universidad de Alcala, Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment, Colegios 2, 28801 Alcal\u00e1 de Henares, Spain"}]},{"given":"Domingos Xavier","family":"Viegas","sequence":"additional","affiliation":[{"name":"Univ of Coimbra, ADAI, Department of Mechanical Engineering, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"}]},{"given":"Carlos","family":"Viegas","sequence":"additional","affiliation":[{"name":"Univ of Coimbra, ADAI, Department of Mechanical Engineering, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1080\/01431160500296735","article-title":"Multi-temporal analysis of MODIS data to classify sugarcane crop","volume":"27","author":"Xavier","year":"2006","journal-title":"Int. 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