{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:55:55Z","timestamp":1773809755752,"version":"3.50.1"},"reference-count":122,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T00:00:00Z","timestamp":1712966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012528","name":"Kazan Federal University","doi-asserted-by":"publisher","award":["FZSM-2024-0004"],"award-info":[{"award-number":["FZSM-2024-0004"]}],"id":[{"id":"10.13039\/501100012528","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study examines the application of unsupervised classification techniques in the mapping of forest vegetation, aiming to align vegetation cover with the Braun-Blanquet classification system through remote sensing. By leveraging Landsat 8 and 9 satellite imagery and advanced clustering algorithms, specifically the Weka X-Means, this research addresses the challenge of minimizing researcher subjectivity in vegetation mapping. The methodology incorporates a two-step clustering approach to accurately classify forest communities, utilizing a comprehensive set of vegetation indices to distinguish between different types of forest ecosystems. The validation of the classification model relied on a detailed analysis of over 17,000 relev\u00e9s from the \u201cFlora\u201d database, ensuring a high degree of accuracy in matching satellite-derived vegetation classes with field observations. The study\u2019s findings reveal the successful identification of 44 forest community types that was aggregated into seven classes of Braun-Blanquet classification system, demonstrating the efficacy of unsupervised classification in generating reliable vegetation maps. This work not only contributes to the advancement of remote sensing applications in ecological research, but also provides a valuable tool for natural resource management and conservation planning. The integration of unsupervised classification with the Braun-Blanquet system presents a novel approach to vegetation mapping, offering insights into ecological characteristics, and can be good starter point for sequestration potential of forest communities\u2019 assessment in the Republic of Tatarstan.<\/jats:p>","DOI":"10.3390\/rs16081371","type":"journal-article","created":{"date-parts":[[2024,4,15]],"date-time":"2024-04-15T03:56:13Z","timestamp":1713153373000},"page":"1371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Forest Community Spatial Modeling Using Machine Learning and Remote Sensing Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0812-1750","authenticated-orcid":false,"given":"Artur","family":"Gafurov","sequence":"first","affiliation":[{"name":"Institute of Environmental Sciences, Kazan Federal University, 5 Tovarisheskaya Str., 420097 Kazan, Russia"}]},{"given":"Vadim","family":"Prokhorov","sequence":"additional","affiliation":[{"name":"Institute of Environmental Sciences, Kazan Federal University, 5 Tovarisheskaya Str., 420097 Kazan, Russia"}]},{"given":"Maria","family":"Kozhevnikova","sequence":"additional","affiliation":[{"name":"Institute of Environmental Sciences, Kazan Federal University, 5 Tovarisheskaya Str., 420097 Kazan, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9354-1348","authenticated-orcid":false,"given":"Bulat","family":"Usmanov","sequence":"additional","affiliation":[{"name":"Institute of Environmental Sciences, Kazan Federal University, 5 Tovarisheskaya Str., 420097 Kazan, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,13]]},"reference":[{"key":"ref_1","unstructured":"Executive Steering Committee for Australian Vegetation Information (2003). 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