Abstract
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.
Highlights
The invasion of nonnative plant species has become a global threat to ecosystems due to their competitive advantages in new environments [1]
We evaluated the accuracy of kudzu classification maps based on abundance derived from the linear unmixing method
This paper introduces a three-step classification process that consists of linear unmixing, phenology-based masking, and nonlinear unmixing to identify the presence of kudzu in Knox County
Summary
The invasion of nonnative plant species has become a global threat to ecosystems due to their competitive advantages in new environments [1]. Invasive plants have dramatic effects on biodiversity, ecological functions, and ecosystem structures by outcompeting and reducing the productivity of native plants, changing the dominant vegetation types, and altering soil properties [2,3]. From these perspectives, invasive plants have been recognized as a major non-climatic driver of global change [4,5]. Invasive plants have caused billions of dollars of economic losses each year by reducing agricultural production and damaging infrastructure [6] These economic losses could be much higher under scenarios involving native species extinction, biodiversity reduction, and ecosystem function deterioration [7]. Precise distribution maps are in high demand for local land managers to control and eradicate invasive plants [8]
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