{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T02:34:39Z","timestamp":1767839679325,"version":"3.49.0"},"reference-count":65,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T00:00:00Z","timestamp":1643673600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring forest disturbances has become essential towards the design and tracking of sustainable forest management. Multiple methodologies have been developed to detect these disturbances. However, few studies have focused on the automatic detection of disturbance drivers, an essential task as each disturbance has different implications for the functioning of the ecosystem and associated management actions. Wildfires and harvesting are two of the major drivers of forest disturbances across different ecosystems. In this study, an automated methodology is presented to automatically distinguish between the two once the disturbance is detected, using the properties of its geometry and shape. A cluster analysis was performed to automatically individualize each disturbance and afterwards calculate its geometric properties. Using these properties, a decision tree was built that allowed for the distinction between wildfires and harvesting with an overall accuracy of 91%. This methodology and further research relating to it could pose an essential aid to national and international agencies for incorporating forest-disturbance-driver-related information into forest-focused reports.<\/jats:p>","DOI":"10.3390\/rs14030697","type":"journal-article","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T22:16:18Z","timestamp":1643753778000},"page":"697","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic Identification of Forest Disturbance Drivers Based on Their Geometric Pattern in Atlantic Forests"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8175-5119","authenticated-orcid":false,"given":"Laura","family":"Alonso","sequence":"first","affiliation":[{"name":"Forestry Engineering School, University of Vigo\u2014A Xunqueira Campus, 36005 Pontevedra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2069-8501","authenticated-orcid":false,"given":"Juan","family":"Picos","sequence":"additional","affiliation":[{"name":"Forestry Engineering School, University of Vigo\u2014A Xunqueira Campus, 36005 Pontevedra, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8173-8319","authenticated-orcid":false,"given":"Julia","family":"Armesto","sequence":"additional","affiliation":[{"name":"Forestry Engineering School, University of Vigo\u2014A Xunqueira Campus, 36005 Pontevedra, Spain"},{"name":"CINTECX, GESSMin Group (Safe and Sustainable Management of Mineral Resources), 36310 Vigo, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"Large and Persistent Carbon Sink in the World\u2019s Forests","volume":"333","author":"Pan","year":"2011","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"(2021). 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