Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

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...

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Autores principales:	Ming Shen, Maofeng Tang, Yingkui Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	kudzu linear spectral unmixing nonlinear spectral unmixing phenology Sentinel-2 Science Q
Acceso en línea:	https://doaj.org/article/1ffe695dfc664900ac5d6706dc1b70a7
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spelling	oai:doaj.org-article:1ffe695dfc664900ac5d6706dc1b70a72021-11-25T18:54:18ZPhenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee10.3390/rs132245512072-4292https://doaj.org/article/1ffe695dfc664900ac5d6706dc1b70a72021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4551https://doaj.org/toc/2072-4292As 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’s accuracy (PA) but low user’s 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.Ming ShenMaofeng TangYingkui LiMDPI AGarticlekudzulinear spectral unmixingnonlinear spectral unmixingphenologySentinel-2ScienceQENRemote Sensing, Vol 13, Iss 4551, p 4551 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	kudzu linear spectral unmixing nonlinear spectral unmixing phenology Sentinel-2 Science Q
spellingShingle	kudzu linear spectral unmixing nonlinear spectral unmixing phenology Sentinel-2 Science Q Ming Shen Maofeng Tang Yingkui Li Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
description	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’s accuracy (PA) but low user’s 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.
format	article
author	Ming Shen Maofeng Tang Yingkui Li
author_facet	Ming Shen Maofeng Tang Yingkui Li
author_sort	Ming Shen
title	Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
title_short	Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
title_full	Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
title_fullStr	Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
title_full_unstemmed	Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee
title_sort	phenology and spectral unmixing-based invasive kudzu mapping: a case study in knox county, tennessee
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1ffe695dfc664900ac5d6706dc1b70a7
work_keys_str_mv	AT mingshen phenologyandspectralunmixingbasedinvasivekudzumappingacasestudyinknoxcountytennessee AT maofengtang phenologyandspectralunmixingbasedinvasivekudzumappingacasestudyinknoxcountytennessee AT yingkuili phenologyandspectralunmixingbasedinvasivekudzumappingacasestudyinknoxcountytennessee
_version_	1718410574869561344

Phenology and Spectral Unmixing-Based Invasive Kudzu Mapping: A Case Study in Knox County, Tennessee

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