Bathymetric modelin from satellite imagery via Single Band Algorithm (SBA) and Principal Components Analysis (PCA) in southern Caspian Sea

Remotely sensed imagery is proving to be a useful tool to estimate water depths in coastal zones. Bathymetric algorithms attempt to isolate water attenuation and hence depth from other factors by using different combinations of spectral bands. In this research, images of absolute bathymetry using two different but related methods in a region in the southern Caspian Sea coasts has been produced. The first method used a Single Band Algorithm (SBA) and assumed a constant water attenuation coefficient throughout the blue band. The second method used Principal Components Analysis (PCA) to adjust for varying water attenuation coefficients without additional ground truth data. PCA method (r=-0.672394) appears to match our control points slightly better than single band algorithm (r=-0.645404). It is clear that both methods can be used as rough estimates of bathymetry for many coastal zone studies in the southern Caspian Sea such as near shore fisheries, coastal erosion, water quality, recreation siting and so forth. The presented methodology can be considered as the first step toward mapping bathymetry in the southern Caspian Sea. Further research must investigate the determination of the nonlinear optimization techniques as well as the assessment of these models’ performance in the study area

Influence of vertical distribution of phytoplankton on remote sensing signal of Case II waters : southern Caspian Sea case study

Reliable monitoring of coastal waters is not possible without using remote sensing data. On the other hand, it is quite difficult to develop remote sensing algorithms that allow one to retrieve water characteristics (like chlorophyll-a concentration) in optically complex coastal and inland waters (called also Case II waters) as the concentrations of optically active substances (phytoplankton, suspended matter, and colored dissolved organic matter) vary independently from each other and the range of variability is often high. Another problem related to developing remote sensing algorithms for retrieving concentrations of optically active substances in such complex waters is vertical distribution of these substances. For example, phytoplankton distribution in the water column is often characterized with maxima just below the surface mixed layer, and some phytoplankton species even have the capability to migrate in the water column and tend to form layers at depths optimal for their growth. Twenty-three field campaigns were performed during the spring-summer period in the coastal waters of the southern Caspian Sea where vertical distribution of phytoplankton was measured by means of chlorophyll-a fluorometer. There results showed that there is usually a chlorophyll-a maximum between 10 and 20 m where the concentration is about one order of magnitude higher than in the top mixed layer. The Hydrolight 5.0 radiative transfer model used to estimate if the vertical distribution of biomass have detectable impact on remote sensing signal in these waters. For that purpose, several stations with distinctly different chlorophyll-a profiles were selected and two simulations for each of those measuring stations was carried out. First the Hydrolight was run with the actual chlorophyll-a vertical distribution profile and second a constant chlorophyll-a value (taken as an average of measured chlorophyll-a in the surface layer) was used in the model simulation. The modelling results show that the “deep” chlorophyll maximum has negligible effect on the remote sensing reflectance spectra. Consequently, there is no need to take into account the vertical distribution of phytoplankton while developing remote sensing algorithms for the Caspian Sea coastal water

Remotely sensed empirical modeling of bathymetry in the southeastern Caspian Sea

Remotely sensed imagery is proving to be a useful tool in estimating water depths in coastal zones. On the other hand, many coastal zone studies in the southern part of the Caspian Sea are only concerned with areas of shallow water and would benefit from easily updated bathymetric estimates. In this study, we tested three different methods for extracting bathymetry information from Landsat 5 data in the southeastern Caspian Sea, Iran. The first method used was a single band algorithm (SBA), utilizing either blue or red bands. The second method was principal components analysis (PCA), and the third method was the multi-layer perceptron (back propagation) neural network between visible bands and one output neuron (bathymetry). This latter MLP-ANNs method produced the best depth estimates (r = 0.94). The single band algorithm utilizing a red band also produced reasonably accurate results (r = 0.66), while the blue band algorithm and PCA did not perform (correlation between the estimated and measured depths 0.49 and 0.21, respectively). Furthermore, the shallow waters have negative influences on the accuracy of bathymetric modeling, thus the correction of data in these shallow waters is challenged by the presence of continental aerosols, bottom reflectance, and adjacency of land