Distinguishing Early Successional Plant Communities Using Ground-Level Hyperspectral Data

Abstract

Abandoned agricultural fields have recently become more abundant in the U.S. and remain susceptible to species invasions after cultivation disturbance. As invasive species become more widespread with increases in anthropogenic activities, we need more effective ways to use limited resources for conservation of native ecosystems. Remote sensing can help us monitor the spread and effects of invasive species, and thus determine the species and locations to target for conservation. To examine this potential, we studied plant communities dominated by exotic invasive plant species in secondary successional fields in northern Virginia using ground-level hyperspectral data. Within these communities, ordination analyses of vegetation surveys revealed differences in species compositions among plots and fields. These differences among communities were also observed in the spectral data. Stepwise multiple linear regression analyses to determine which species influenced the ordination axes revealed that many of the influential species are considered invasive, again underscoring the influence of invasive species on community properties. Stepwise regression analyses also revealed that the most influential wavelengths for discrimination were distributed along the spectral profile from the visible to the near-infrared regions. A discriminant analysis using wavelengths selected with a principal components analysis demonstrated that different plant communities were separable using spectral data. These spectrally observable differences suggest that we can use hyperspectral data to distinguish among invasive-dominated successional plant communities in this region.

Identification of the Best Hyperspectral Indices in Estimating Plant Species Richness in Sandy Grasslands

“…Similar results were observed and reported in a previous study of prairie species richness [31]. Spectral differences related to the diversity in pigment content [35,36], water content, structural elements, cell size, intercellular space, and cell wall thickness can assist in differentiating among plant species and communities [5,47,48]. Wavebands that characterize these plant physiological and structure traits were employed in our study (Table 1) to extract plant diversity information [4,35,47].…”

“…The reflectance values at 465 nm (within the range of 420-480 nm) and 583 nm indicate the contents of carotenoids and chlorophyll, while the 531 nm wavelength mainly indicates anthocyanins, rather than carotenoids and chlorophylls. Chlorophyll b is indicated at the wavelengths that are near 681 nm [48]. The wavelengths of 606, 681, and 750 nm are sensitive indicators of leaf nitrogen contents (LNC) and chlorophyll [56].…”

“…A wavelength at 750 nm is a better indicator to estimate chlorophyll content, because it is less affected by leaf and canopy structure [57]. The near-infrared bands, such as bands between 760 and 900 nm, have been used to assess leaf structure [48]. Contents of chlorophyll, carotenoids, anthocyanins, and proteins in leaves, and the leaf structure of green plants, are the key information reflecting variations different plant species [58].…”

Correlating species and spectral diversities using hyperspectral remote sensing in early‐successional fields

“…The normalized reflectance was then subtracted from one to obtain normalized absorption 550, and 700 nm, respectively. These species were selected because they were present in many of the community plots and were prevalent in several of those plots [see figure 5 in Aneece and Epstein (2015)]. Reflectance spectra were used for these calculations because the equations are tailored toward reflectance measurements, rather than band depth.…”

“…(), and Gitelson, Merzlyak, and Chivkunova (), respectively, where R 770 , R 705 , R 515 , R 565 , R 550 , and R 700 are reflectance values at 770, 705, 515, 565, 550, and 700 nm, respectively. These species were selected because they were present in many of the community plots and were prevalent in several of those plots [see figure 5 in Aneece and Epstein ()]. Reflectance spectra were used for these calculations because the equations are tailored toward reflectance measurements, rather than band depth.…”