Mapping the spatial distribution of species using airborne and spaceborne imaging spectroscopy: A case study of invasive plants

Abstract

Predicting the spatial distribution of invasive plants remains challenging because of the complex relationships between plant invasion, abiotic, and biotic factors. While conventional species distribution models (SDMs) are often developed using abiotic factors, recent studies have suggested that including biotic factors, particularly plant functional traits, can improve our capability to model the distribution of invasive plants. Remote sensing is capable of estimating plant functional traits across large spatial extents. These remotely-estimated plant functional traits can then be used as predictors in mapping the spatial distribution of species. However, exploring the application of remotely-estimated plant functional traits in mapping the spatial distribution of invasive plants is relatively understudied. In this study, we aimed to (1) develop trait-based approaches for mapping the spatial distribution of an invasive plant, (2) assess the scale-dependency of these trait-based approaches, and (3) determine the capability of spaceborne hyperspectral imagery in mapping the spatial distribution of invasive plants through fusing their data with fine spatial resolution multispectral data. We focused on Lespedeza cuneata (hereafter, L. cuneata), commonly known as sericea lespedeza, an invasive legume threatening grassland ecosystems of the U.S. Southern Great Plains. To achieve our objectives, we collected in situ data, including plant functional traits, such as foliar nitrogen, phosphorus, and potassium, and measured average canopy height, and percent cover of L. cuneata from 900 sampling quadrats. We also collected remote sensing data, including airborne hyperspectral data (400–2500 nm, 1 m spatial resolution), spaceborne hyperspectral data from DLR's DESIS (401.9–999.5 nm, 30 m spatial resolution), and PlanetScope multispectral data (8 bands, 3 m spatial resolution). We also fused DESIS and PlanetScope imagery to produce fine spatial and fine spectral imagery (401.9–999.5 nm, 3 m spatial resolution). We used partial least squares regression (PLSR) to estimate plant functional traits from remotely sensed data and developed approaches for mapping the spatial distribution of invasive plants using remotely-estimated plant functional traits. We developed approaches for mapping the spatial distribution of invasive plants across spatial scales, at 1 m, 3 m, and 30 m spatial resolutions, using (1) abiotic factors only, (2) remotely-estimated plant functional traits only, and (3) remotely-estimated plant functional traits along with abiotic factors. Our findings showed that trait-based approaches for mapping the spatial distribution of invasive plants had higher accuracy than abiotic-based approaches, mapping the spatial distribution of L. cuneata at fine spatial resolution performed better than at coarse spatial resolution, and fusion of coarse spatial resolution hyperspectral imagery with fine spatial resolution imagery had the highest performance at modeling the spatial distribution of L. cuneata. Results showed that fusing forthcoming imagers, such as NASA's Surface Biology and Geology data (SBG) mission, with fine spatial resolution multispectral data, such as PlanetScope data, is a promising approach to modeling the distribution of invasive plants in grasslands.