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
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UB-DE - Ecology Department, University of Barcelona, Spain
Lead Partners
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