Work Packages

The work is organized into 8 work packages:

  • WP1: Selection of study sites (and species)

 

  • WP2: Data collection

 

  • WP3: Detection and monitoring of plant invasions

WP3 aims at exploring the potential of LiDAR and hyperspectral images to detect and monitor the distribution of invasive plants across the three study sites. To achieve this aim, we are studying the spectral characteristics of native and invasive plants; modelling the 3D structure of the plant canopy; developing an approach to create fine scale baseline maps of the distribution of invasive plants; and validating this approach.

 

  • WP4: Modelling habitat suitability and forecasting future distribution

WP4 aims at assessing current and future distributions of the three studied invasive plants across the three study sites. To achieve this aim, we are: deriving several fine-grained predictor environmental variables for each study site; modelling fine-grained habitat suitability for each invasive plant across its study site; and  forecasting future potential changes in the distribution of each invasive plant across its study site.

 

  • WP5: Assess and characterize the ecosystem impact of invasive species through the combined use of field data and aircraft-based measurements

The species under investigation are known to affect their environment, e.g. micro-climates (Campylopus introflexus) and soils (Prunus serotina) as well as successional processes and community composition (Campylopus introflexus; Prunus serotina) and functional diversity (Prunus serotina). All these impacts are likely to influence ecosystem functioning. The proposed approach offers the opportunity to assess the spatial dimension of functional changes induced by invaders, to quantify these changes pixel-wise and eventually identify unexpected changes. In doing so we are following two approaches: an organismic, indicator-based approach and remote assessments of biochemical and structural stand attributes.

 

  • WP6: Design of toolbox and software package

 

  • WP7: Hands on training