Early detection of GPP-related regime shifts after plant invasion by integrating imaging spectroscopy with airborne LiDAR

摘要: Abstract Invasive plant species can have high, self-reinforcing impacts on ecosystem structure and functioning that induce permanent changes of properties. Therefore, early detection timely management is required to alleviate consequences invasion. Integrating airborne hyperspectral imagery with LiDAR data deliver spatially explicit information invader occurrence transformations even at stages However, relevant “model invaders” well-characterized ecosystems need be identified both increase predictive power invasion theory prioritize management. In addition, there still a knowledge gap regarding sensor-based approaches are valid in space time assess the impact invasive engineers as well potential regime shifts. this study, spatio-temporal N2-fixing shrub, Acacia longifolia, was assessed heterogeneous, Mediterranean dune ecosystem. The mapped using vegetation indices derived from images Random Forest classification Sensitivity 0.79, Positive Predicted Value (PPV) 0.81, Cohen's Kappa 0.77. Invaded sites varied between low cover, where isolated patches were detected, heavily infested A. longifolia thickets. Analysis historical showed could establish under harsh conditions open plains, possibly triggered by human interference. recently developed Near-Infrared Vegetation Index (NIRV), which related Gross Primary Production (GPP), increased linearly significantly increasing cover. This indicated GPP-related shift induced invader, changing productivity representative shrublands forests. Such Thus, NIRV index may provide an appropriate metric” engineers. offers opportunity predict anticipate shifts basis for