The script used to generate DEMs for UK flightlines (nextmapdem.sh) currently only takes into account the start and end position of the aircraft on each line (because it reads these from the header files). This means that on long lines where there is an entire BNG tile between the start and the end it may not include DEM data for the whole flightline.

Possible solutions:

1. (Preferred): Read SBET file in (using sbet_handler.py, unless you've got a pressing reason to do it another way), track position of aircraft and do maths on swath width to work out which BNG tile the aircraft is over (ensuring it's in the UK as well).
2. (Possibly easier): Adapt existing nextmapdem.sh script to be more intelligent about what tiles might be in the middle. May be easier but might be complicated by corner case and/or case where rolls over tile edge (9->0).

Data location: ~arsf/arsf_data/2010/flight_data/western_europe/EUFAR10_02-2010_199a-Santander

Data arrived from ARSF via network transfer on 26/07/2010.

Scientific objectives:

1 Development of a reliable tool to estimate coastal water quality indicators by using remote sensing techniques, both airborne and satellite.

1.1. Development of a set of semi-empirical algorithms based on hyperspectral airborne and in-situ data for estimating the concentrations of the water constituents; 1.2. Migration of the developed algorithms into the satellite workflow.

2. Implementation of an automatic warning system based on satellite imagery processing;

3. Inclusion of the information obtained by means of remote sensing in the dataset used for the calibration of regional hydrodynamic models.

Priority: a4m - awaiting confirmation

PI: Castillo-Lopez

Sensors:

• Eagle (28/08/2010)
• Hawk (21/12/2010)
• Leica LIDAR (02/02/2011)
• RCD Digital Photography (28/08/2010)

Data location: ~arsf/arsf_data/2010/flight_data/western_europe/EUFAR10_02-2010_199b-Santander

Data arrived from ARSF via network transfer on 26/07/2010.

Scientific objectives:

1 Development of a reliable tool to estimate coastal water quality indicators by using remote sensing techniques, both airborne and satellite.

1.1. Development of a set of semi-empirical algorithms based on hyperspectral airborne and in-situ data for estimating the concentrations of the water constituents; 1.2. Migration of the developed algorithms into the satellite workflow.

2. Implementation of an automatic warning system based on satellite imagery processing;

3. Inclusion of the information obtained by means of remote sensing in the dataset used for the calibration of regional hydrodynamic models.

Priority: a4m - awaiting confirmation

PI: Castillo-Lopez

Sensors: ¶

• Eagle (27/08/2010)
• Hawk (21/12/2010)
• Leica LIDAR (21/12/2010)
• RCD Digital photography (27/08/2010)