{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:32Z","timestamp":1760146052737,"version":"build-2065373602"},"reference-count":88,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"SILVANUS project","award":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"],"award-info":[{"award-number":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"]}]},{"name":"FCT\/MCTES (PIDDAC)","award":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"],"award-info":[{"award-number":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"]}]},{"name":"FCT\/MCTES (PIDDAC)","award":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"],"award-info":[{"award-number":["10103724","UIDP\/50009\/2020","2020.06277.BD","CEECIND\/04469\/2017","UIDB\/50009\/2020","LA\/P\/0083\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Forests"],"abstract":"<jats:p>Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da Fran\u00e7a (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience\u2014in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, &gt;0%\u201350%, and &gt;50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%\u201388%), recall (77%\u201392%), and F1 score (83%\u201388%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF\u2019s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar\u2019s test indicated statistically significant differences (p value &lt; 0.05) between all models, consolidating RF\u2019s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions.<\/jats:p>","DOI":"10.3390\/f15101739","type":"journal-article","created":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T11:08:47Z","timestamp":1727780927000},"page":"1739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting Fractional Shrub Cover in Heterogeneous Mediterranean Landscapes Using Machine Learning and Sentinel-2 Imagery"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8583-632X","authenticated-orcid":false,"given":"El Khalil","family":"Cherif","sequence":"first","affiliation":[{"name":"Marine, Environment, and Technology Centre\/The Laboratory of Robotics and Engineering Systems, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"given":"Ricardo","family":"Lucas","sequence":"additional","affiliation":[{"name":"Energias de Portugal, S.A., Rua Cidade de Goa, 2, 2685-038 Sacav\u00e9m, Portugal"}]},{"given":"Taha","family":"Ait Tchakoucht","sequence":"additional","affiliation":[{"name":"School of Digital Engineering and Artificial Intelligence, Euromed Research Center, Euromed University of Fes, Meknes Road (Bensouda Roundabout), Fes 30000, Morocco"}]},{"given":"Ivo","family":"Gama","sequence":"additional","affiliation":[{"name":"Terraprima\u2014Servi\u00e7os Ambientais, Sociedade Unipessoal, Lda, 2135-199 Samora Correia, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1879-8922","authenticated-orcid":false,"given":"In\u00eas","family":"Ribeiro","sequence":"additional","affiliation":[{"name":"Marine, Environment, and Technology Centre\/The Laboratory of Robotics and Engineering Systems, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6194-0405","authenticated-orcid":false,"given":"Tiago","family":"Domingos","sequence":"additional","affiliation":[{"name":"Marine, Environment, and Technology Centre\/The Laboratory of Robotics and Engineering Systems, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"},{"name":"Terraprima\u2014Servi\u00e7os Ambientais, Sociedade Unipessoal, Lda, 2135-199 Samora Correia, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8245-357X","authenticated-orcid":false,"given":"V\u00e2nia","family":"Proen\u00e7a","sequence":"additional","affiliation":[{"name":"Marine, Environment, and Technology Centre\/The Laboratory of Robotics and Engineering Systems, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, C., and Li, X. (2022). Land Use and Land Cover Mapping in the Era of Big Data. Land, 11.","DOI":"10.3390\/land11101692"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.isprsjprs.2015.10.004","article-title":"Remote sensing platforms and sensors: A survey","volume":"115","author":"Toth","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"100464","DOI":"10.1016\/j.tfp.2023.100464","article-title":"Post-fire assessment of recovery of montane forest composition and stand parameters using in situ measurements and remote sensing data","volume":"15","author":"Tesha","year":"2024","journal-title":"Trees For. 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