{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T13:36:52Z","timestamp":1768829812429,"version":"3.49.0"},"reference-count":64,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:00:00Z","timestamp":1731715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UNIVERSIDAD DEL AZUAY","award":["2020-0125"],"award-info":[{"award-number":["2020-0125"]}]},{"name":"UNIVERSIDAD DEL AZUAY","award":["2022-0159"],"award-info":[{"award-number":["2022-0159"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1\u2013SC6, combining 5 m resolution data, and SC7\u2013SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.<\/jats:p>","DOI":"10.3390\/rs16224271","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T06:06:54Z","timestamp":1731996414000},"page":"4271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mapping Polylepis Forest Using Sentinel, PlanetScope Images, and Topographical Features with Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8432-0559","authenticated-orcid":false,"given":"Diego","family":"Pacheco-Prado","sequence":"first","affiliation":[{"name":"Instituto de Estudios de R\u00e9gimen Seccional del Ecuador (IERSE), Universidad del Azuay, Cuenca 010204, Ecuador"},{"name":"Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1812-5613","authenticated-orcid":false,"given":"Esteban","family":"Bravo-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Instituto de Estudios de R\u00e9gimen Seccional del Ecuador (IERSE), Universidad del Azuay, Cuenca 010204, Ecuador"},{"name":"Department of Cartographic, Geodetic and Photogrammetric Engineering, Photogrammetric and Topometric Systems Research Group, Centre for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaen, 23071 Jaen, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0073-7259","authenticated-orcid":false,"given":"Luis \u00c1.","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Geo-Environmental Cartography and Remote Sensing Group (CGAT), Universitat Polit\u00e8cnica de Val\u00e8ncia, Cam\u00ed de Vera s\/n, 46022 Valencia, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1017\/S0030605315000198","article-title":"Regional Red List Assessment of Tree Species in Upper Montane Forests of the Tropical Andes","volume":"49","author":"Newton","year":"2015","journal-title":"Oryx"},{"key":"ref_2","first-page":"148","article-title":"La Evaluaci\u00f3n Del Estado de Conservaci\u00f3n de Los Bosques Montanos En Los Andes Tropicales","volume":"21","author":"Garavito","year":"2012","journal-title":"Ecosistemas"},{"key":"ref_3","first-page":"1","article-title":"Diversity, Composition and Structure of Andean High Forest in Ecuador, South America","volume":"10","author":"Castillo","year":"2017","journal-title":"Bull. 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