WP 6: Remote sensing and spatial modelling of lake/catchment attributes

Targets     Lead Contractor     Lead Partners    

Task 6.1     Task 6.2     Task 6.3    

Targets

It is impossible to sample all lakes in remote mountain Lake Districts because of their abundance and remoteness. However, to assess the status of these lakes we need to make judgements on the total population. Using a combination of remote sensing and spatial modelling techniques this problem becomes tractable, although an evaluation of the uncertainty that lies behind such extrapolation is also needed. The aim of this workpackage is to develop a methodology that allows the outputs from previous workpackages to be up-scaled to all lakes within the Lake Districts with a minimum of error.

Task 6.1.: Development of regional information systems

We will create a GIS for each Lake District. A common protocol and platform (Arc/Info - Arc/View) will be used to develop a regional GIS that will lead to an eventual amalgamation of data and provide a single integrated system for a pan-European evaluation (WP 7 and 8). A common protocol for regional data collation will be produced. Data sources will include geological maps, vegetation maps, infrared aerial pictures, previous surveys etc. and we will work at the highest resolution possible, usually at 1:25,000 and sometimes at 1:5,000.

Task 6.2.: Remote sensing and modelling of catchment attributes

Remote sensing allows catchment characteristics to be derived for sites not directly studied. Because of the small size of most lakes and the patchiness of vegetation and soils in mountains we will use a combination of SPOT imagery and radar ERRS or JEERS systems for land cover classification. Catchment attributes will be modelled using high quality field data derived from our Experimental sites.

Task 6.3.: Development of an expert system to interface GIS-based information with other models

Some of the data types required by the models, e.g. lake volume, soil depth, soil chemistry, are not easily obtained from documentary sources or remote sensing. Consequently we will develop methods of deriving these data from surrogate variables using transfer functions between the surrogates and measured values. The task will involve two main components: developing sets of statistical models, and developing algorithms to select the most appropriate surrogates to interface between physical, chemical and biological models and the data available in the GIS. It will be planned as a modular system able to be up-dated progressively in phase with new findings and requirements.

Lead Contractor

1. UB-DE - Ecology Department, University of Barcelona, Spain

Lead Partners

• ECRC-UCL - Environmental Change Research Centre, University College London, UK