Characterizing phenological differences of invasive shrubs in a forest matrix using high resolution VENµS time series

Abstract

Many invasive shrubs in the eastern deciduous forests of the United States use the temporal niche before and after the native tree canopy leaf-on period (leafing out prior to most native species and retaining leaves after most natives senesce) to establish in the light-limited environment of the understory. To support an increased understanding of invasive shrub species’ ecology and distribution patterns and inform better management plans, this key phenological difference needs to be characterized in detail. Here we leveraged the high-resolution observations from the French-Israel VENµS mission to examine the phenological characteristics of a widespread invasive shrub species—Amur honeysuckle (AH; Lonicera maackii (Rupr.) Herder)—compared to native deciduous trees in Robinson Forest, Kentucky. VENµS offered daily superspectral (12 narrow bands) observations at 4 m resolution in a limited number of global sites, providing us with crucial data for the analysis. We identified three forest communities with respect to AH presence through field surveys (i.e., uninvaded forest stands, forest stands with AH understory, and AH shrub thickets) and compared their VENµS-derived spectral signatures and time series of vegetation indices. In 2023, AH shrub thickets greened up one month earlier than uninvaded forest stands (mid-March vs. mid-April). AH leaf growth advanced into full green before the canopy tree greenup started in early April, marking an optimal window for isolating areas with AH understory from the uninvaded forest using remote sensing. Based on the phenological differences identified, we predicted the distribution of AH in the study area using a two-date differencing model and a spectral mixture analysis. Our detailed findings using VENµS data offer insights into the temporal dynamics of invasive shrubs and native trees in a typical eastern deciduous forest. While our prediction of the AH distribution was confounded by the presence of native early greening and/or evergreen understory plants at a few locations, it was still moderately accurate (overall accuracy ∼ 70 %) and its abundance estimates agreed with observations in forest stands with minimal native understory growth. Moving forward, high-resolution remote sensing observations combined with a phenology-based approach will likely support more precise monitoring and management of invasive understory plants in native forest ecosystems.