388
M. Ahmed and V. Barale
GIS form, to determine the spatial and temporal patterns that characterize the coastal
lagoons region (Ahmed et al. 2009).
Finally, a regional assessment of water properties in the near-coastal area adjacent
to the Egyptian coastal lagoons was performed using a time series of Sea-viewing
Wide Field-of-view Sensor (SeaWiFS) data (McClain et al. 2004), covering 10 full
annual cycles, from September 1997 to August 2007. In this case, the analysis focused on water quality parameters such as CHL, in order to highlight the possible
impact of exchanges between enclosed and near-coastal water bodies. Climatological and monthly composite images were obtained directly from web-based services
2 ,
which provide a suite of data products that includes SeaWiFS imagery, fully processed to correct top-of-the-atmosphere radiances from atmospheric noise; to derive
normalized water-leaving radiances; and to compute from these a score of derived
parameters (including CHL). In the web services’ version used in the present case,
the parameter CHL was computed using the OC4 empirical algorithm for SeaWiFS data (O’Reilly et al. 2000). The compositing was obtained by re-mapping on
a common equal-area grid and then averaging with a grid cell, or “bin”, ranging
in size from 2 × 2 to 9 × 9 km
2 , for various time intervals. For each space/time
bin, mean values (weighted by the number of valid pixels used in the binning process) are coupled to data quality statistics (Campbell et al. 1995). The resulting
data products should be considered with caution, owing to the impact of various
colored water constituents, or to direct bottom reflection, which may significantly
alter the CHL derived estimates, particularly in shallow coastal areas. The presence
of optically active materials (other than phytoplankton and related pigments) with
partially overlapping spectral signatures—i.e. dissolved organic matter or suspended
inorganic particles—can prevent the computation of reliable CHL absolute values
(Sathyendranath et al. 2000). However, when these limitations are accounted for, the
analysis of such data can provide interesting, if qualitative, information on recurrent
or anomalous features, and related environmental boundary conditions.
19.4 Observed Features of Lagoon and Coastal Waters
19.4.1 Lake Maryut
A series of Landsat MSS/TM, SPOT HRV and EgyptSat-1 data, collected over the
past four decades, was assembled to monitor costal margins and water surface properties of Lake Maryut. The time series, provided by the different sensors in 1972,
1985, 1999 and 2007 (see samples in Fig. 19.2), allowed the identification of changes
in the lagoon shape and extension.
2 i.e. the Goddard Earth Sciences (GES) and Data Information Services Center (DISC) Interactive Online Visualization ANd aNalysis Infrastructure, (GIOVANNI), available at the address
http://disc.sci.gsfc.nasa.gov/giovanni/overview/, by the National Aeronautic and Space Administration (NASA). This web services portal allows for the extraction of fully processed data, from
multiple sensors, and the use of various data analysis and visualization options.
M. Ahmed and V. Barale
GIS form, to determine the spatial and temporal patterns that characterize the coastal
lagoons region (Ahmed et al. 2009).
Finally, a regional assessment of water properties in the near-coastal area adjacent
to the Egyptian coastal lagoons was performed using a time series of Sea-viewing
Wide Field-of-view Sensor (SeaWiFS) data (McClain et al. 2004), covering 10 full
annual cycles, from September 1997 to August 2007. In this case, the analysis focused on water quality parameters such as CHL, in order to highlight the possible
impact of exchanges between enclosed and near-coastal water bodies. Climatological and monthly composite images were obtained directly from web-based services
2 ,
which provide a suite of data products that includes SeaWiFS imagery, fully processed to correct top-of-the-atmosphere radiances from atmospheric noise; to derive
normalized water-leaving radiances; and to compute from these a score of derived
parameters (including CHL). In the web services’ version used in the present case,
the parameter CHL was computed using the OC4 empirical algorithm for SeaWiFS data (O’Reilly et al. 2000). The compositing was obtained by re-mapping on
a common equal-area grid and then averaging with a grid cell, or “bin”, ranging
in size from 2 × 2 to 9 × 9 km
2 , for various time intervals. For each space/time
bin, mean values (weighted by the number of valid pixels used in the binning process) are coupled to data quality statistics (Campbell et al. 1995). The resulting
data products should be considered with caution, owing to the impact of various
colored water constituents, or to direct bottom reflection, which may significantly
alter the CHL derived estimates, particularly in shallow coastal areas. The presence
of optically active materials (other than phytoplankton and related pigments) with
partially overlapping spectral signatures—i.e. dissolved organic matter or suspended
inorganic particles—can prevent the computation of reliable CHL absolute values
(Sathyendranath et al. 2000). However, when these limitations are accounted for, the
analysis of such data can provide interesting, if qualitative, information on recurrent
or anomalous features, and related environmental boundary conditions.
19.4 Observed Features of Lagoon and Coastal Waters
19.4.1 Lake Maryut
A series of Landsat MSS/TM, SPOT HRV and EgyptSat-1 data, collected over the
past four decades, was assembled to monitor costal margins and water surface properties of Lake Maryut. The time series, provided by the different sensors in 1972,
1985, 1999 and 2007 (see samples in Fig. 19.2), allowed the identification of changes
in the lagoon shape and extension.
2 i.e. the Goddard Earth Sciences (GES) and Data Information Services Center (DISC) Interactive Online Visualization ANd aNalysis Infrastructure, (GIOVANNI), available at the address
http://disc.sci.gsfc.nasa.gov/giovanni/overview/, by the National Aeronautic and Space Administration (NASA). This web services portal allows for the extraction of fully processed data, from
multiple sensors, and the use of various data analysis and visualization options.