412
T. J. Ballatore et al.
lake height, and suspended solids among others allowed the authors to tie the cause
of the abnormal proliferation from October 2006 to January 2008 to an unusually
heavy rainy season and the consequent large inflows of nutrients and sediment from
surrounding land.
Rebelo (2010) used blue, red and NIR bands from Landsat TM to calculate an
Enhanced Vegetation Index (EVI) for Lake Chilwa (Malawi, Mozambique) for May
2006. This was combined with SAR data to achieve a high classification accuracy.
20.5.2 Microwave Sensing of Macrophytes
Rebelo (2010) used Fine Beam Single, Fine Beam Dual, and polarimetric images (all
12.5 m spatial resolution) from the L-band ALOS-PALSAR instrument to classify
vegetative cover in and around lakes Chilwa and Urema to an accuracy of 89 and
84 %, respectively, compared with field observations. PALSAR was notably useful
in these two cases because of the ability of L-band SAR to penetrate frequent cloud
cover and smoke, especially in Mozambique where visible and NIR images were
limited during the study period (2006–2008).
20.5.3 Optical Sensing of Trophic Conditions
One of the most challenging areas of remote sensing for lake management is the
application of optical (visible wavelength) images for the determination of trophic
conditions. The presence of elevated concentrations of Colored Dissolved Organic
Matter (CDOM) and Total Suspended Matter (TSM) as well as the difficulty of
atmospheric correction over African lakes with a range of elevations makes the
process inherently more difficult than typical ocean color approaches. Nevertheless
new algorithms and techniques promise to make optical sensing of trophic conditions
a growing application.
Bergamino et al. (2007) used SeaWiFS imagery to assess the changes in chlorophyll concentration in Lake Tanganyika during a 7-year period using the OC4.v4
algorithm. Empirical Orthogonal Function (EOF) analysis was used to define regions
of the lake with the same temporal dynamics of phytoplankton biomass. Three covarying regions (north, south and central) were identified which displayed different
seasonal and inter-annual dynamics in estimated chlorophyll concentrations.
Sørensen et al. (2008) evaluated the performance of MERIS products processed
using the Case2R and the EUL processors, including signal depth (Z90_max), to
estimate chlorophyll-a concentrations and TSM in Lake Victoria. Reference data
consisted of actual Secchi Disk Depth (SDD) measurements. The Z90_max was
found to correlate well with measured SDD when using both the Case2R and EUL
processors, but the relationship was different in each case. For the few chlorophyll-a
data available, the OC4.v4 algorithm analyzed with the Case2R processor produced
T. J. Ballatore et al.
lake height, and suspended solids among others allowed the authors to tie the cause
of the abnormal proliferation from October 2006 to January 2008 to an unusually
heavy rainy season and the consequent large inflows of nutrients and sediment from
surrounding land.
Rebelo (2010) used blue, red and NIR bands from Landsat TM to calculate an
Enhanced Vegetation Index (EVI) for Lake Chilwa (Malawi, Mozambique) for May
2006. This was combined with SAR data to achieve a high classification accuracy.
20.5.2 Microwave Sensing of Macrophytes
Rebelo (2010) used Fine Beam Single, Fine Beam Dual, and polarimetric images (all
12.5 m spatial resolution) from the L-band ALOS-PALSAR instrument to classify
vegetative cover in and around lakes Chilwa and Urema to an accuracy of 89 and
84 %, respectively, compared with field observations. PALSAR was notably useful
in these two cases because of the ability of L-band SAR to penetrate frequent cloud
cover and smoke, especially in Mozambique where visible and NIR images were
limited during the study period (2006–2008).
20.5.3 Optical Sensing of Trophic Conditions
One of the most challenging areas of remote sensing for lake management is the
application of optical (visible wavelength) images for the determination of trophic
conditions. The presence of elevated concentrations of Colored Dissolved Organic
Matter (CDOM) and Total Suspended Matter (TSM) as well as the difficulty of
atmospheric correction over African lakes with a range of elevations makes the
process inherently more difficult than typical ocean color approaches. Nevertheless
new algorithms and techniques promise to make optical sensing of trophic conditions
a growing application.
Bergamino et al. (2007) used SeaWiFS imagery to assess the changes in chlorophyll concentration in Lake Tanganyika during a 7-year period using the OC4.v4
algorithm. Empirical Orthogonal Function (EOF) analysis was used to define regions
of the lake with the same temporal dynamics of phytoplankton biomass. Three covarying regions (north, south and central) were identified which displayed different
seasonal and inter-annual dynamics in estimated chlorophyll concentrations.
Sørensen et al. (2008) evaluated the performance of MERIS products processed
using the Case2R and the EUL processors, including signal depth (Z90_max), to
estimate chlorophyll-a concentrations and TSM in Lake Victoria. Reference data
consisted of actual Secchi Disk Depth (SDD) measurements. The Z90_max was
found to correlate well with measured SDD when using both the Case2R and EUL
processors, but the relationship was different in each case. For the few chlorophyll-a
data available, the OC4.v4 algorithm analyzed with the Case2R processor produced