Detecting an invasive shrub in deciduous forest understories using remote sensing
Remote sensing has been used to directly detect and map invasive plants, but has not been used for forest understory invaders because they are obscured by a canopy. However, if the invasive species has a leaf phenology distinct from native forest species, then temporal opportunities exist to detect the invasive. Lonicera maackii, an Asian shrub that invades North American forests, expands leaves earlier and retains leaves later than native woody species. I explored whether Landsat 5 TM and Landsat 7 ETM+ imagery could predict L. maackii cover across woodlots in Darke and Preble Counties in south western Ohio and Wayne County, Indiana. The best predictor of L. maackii cover was Normalized Difference Vegetation Index (NDVI) from November 2005, with a quadratic function providing a better fit (R2 = 0.75) than a linear function. This predictive model was verified with 15 other woodlots. With refinement, this approach can map understory invasion by L. maackii.
School Location:USA - Ohio
Source Type:Master's Thesis
Keywords:lonicera maackii invasive species remote sensing landsat etm deciduous forest understory normalized difference vegetation index ndvi
Date of Publication:01/01/2008