{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T08:10:16Z","timestamp":1772007016721,"version":"3.50.1"},"reference-count":53,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100008526","name":"University of Tennessee at Knoxville","doi-asserted-by":"publisher","award":["Open Publishing Support Fund"],"award-info":[{"award-number":["Open Publishing Support Fund"]}],"id":[{"id":"10.13039\/100008526","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an invasive plant species, kudzu has been spreading rapidly in the Southeastern United States in recent years. Accurate mapping of kudzu is critical for effective invasion control and management. However, the remote detection of kudzu distribution using multispectral images is challenging because of the mixed reflectance and potential misclassification with other vegetation. We propose a three-step classification process to map kudzu in Knox County, Tennessee, using multispectral Sentinel-2 images and the integration of spectral unmixing analysis and phenological characteristics. This classification includes an initial linear unmixing process to produce an overestimated kudzu map, a phenological-based masking to reduce misclassification, and a nonlinear unmixing process to refine the classification. The initial linear unmixing provides high producer\u2019s accuracy (PA) but low user\u2019s accuracy (UA) due to misclassification with grasslands. The phenological-based masking increases the accuracy of the kudzu classification and reduces the domain for further processing. The nonlinear unmixing further refines the kudzu classification via the selection of an appropriate nonlinear model. The final kudzu classification for Knox County reaches relatively high accuracy, with UA, PA, Jaccard, and Kappa index values of 0.858, 0.907, 0.789, and 0.725, respectively. Our proposed method has potential for continuous monitoring of kudzu in large areas.<\/jats:p>","DOI":"10.3390\/rs13224551","type":"journal-article","created":{"date-parts":[[2021,11,14]],"date-time":"2021-11-14T20:51:53Z","timestamp":1636923113000},"page":"4551","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0566-7950","authenticated-orcid":false,"given":"Ming","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Geography, The University of Tennessee, Knoxville, TN 37996, USA"}]},{"given":"Maofeng","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, The University of Tennessee, Knoxville, TN 37996, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3722-8960","authenticated-orcid":false,"given":"Yingkui","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Tennessee, Knoxville, TN 37996, USA"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1997","DOI":"10.1016\/j.rse.2011.04.002","article-title":"Characterizing spatial patterns of invasive species using sub\u2013pixel classifications","volume":"115","author":"Frazier","year":"2011","journal-title":"Remote Sens. 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