Collecting and analysing bathymetric information is
essential for lake management. This is particularly true
regarding Lake Nasser/Nubia in Egypt, where accumulated
sediment in the lake must be examined. This is typically
accomplished through field measurements, which are time
consuming and costly. However, remotely sensed imagery
provides wide coverage, low cost, and time-saving solutions
for bathymetric measurements, especially in shallow areas
with high erosion or sediment accumulation, such as at the
entrance of Lake Nasser/Nubia. In this study, bagging (Bag)
and least square boosting (LSB) fitting algorithms that use
reflectance of green and red band logarithms, green/red band
logarithms ratio, and blue/red band logarithms ratio are pro-
posed for bathymetry detection. For validation, the proposed
approaches were compared with the ratio method (RM) and
neural network (NN) conventional methods. Bathymetric
data obtained from all methods using SPOT-6 imagery were
evaluated by means of global positioning system (GPS) and
echo sounder data field measurements. The Bag ensemble
outperformed all methods with 0.85
m RMSE, whereas RM,
LSB, and NN yielded 1.03, 0.99, and 0.97
m respectively.
The results showed that the proposed approaches outper
-
form and are more accurate than RM conventional method
and the Bag approach is more accurate than the NN model
when applied over shallow water depths of up to 6.5 m. |
