Environmental Monitoring of Harbour Dredging Using EO data Liis Sipelgas, Rivo Uiboupin, Laura Raag, Priidik Lagemaa, Victor Alari

Marine Systems Institute at Tallinn University of Technology, ESTONIA

Corresponding author: Liis Sipelgas e-mail: liis.sipelgas@msi.ttu.ee

In 2011 the Estonian PECS program supported the project “Environmental Monitoring of Harbor Dredging”. The goal of the project was to develop a tool for environmental monitoring during harbour dredging activities using Earth Observation (EO) data, in situ measurements and hydrodynamical modeling. Second objective of the project was to analyze the suspended matter distribution in the vicinity of the ports during 10 years period to asses the background levels of suspended matter concentration and to evaluate the environmental impact of port development and maintenance. Sub-objectives of the project were: (a) Validation of and development of site specific algorithms for retrieval of water quality parameters (e.g. total suspended matter) from multi sensor EO data. (b) Integration of EO data, in situ measurements and numerical model outputs to characterize and forecast water quality during the dredging activities. (c) Creation of an online GIS based user interface where the EO data, in situ measurements and model outputs are presented/updated operationally to provide overview of the water quality in the dredging region for interested parties (decision makers, port authorities, environmental agencies).

Validation and development of site specific algorithms for retrieval of TSM from MODIS nad MERIS data We used MERIS Full Swath Geo-located (FSG) products with 300m resolution and MODIS band data with 250m resolution from years 2006-2010 in our analysis. Validation of the two processors C2R and FUB available in BEAM software with in situ measurements of TSM at the time of dredging operation was done (Figure 1). Analysis showed reliable correlation between satellite data and in situ TSM measurements, r2 was 0.43 for FUB processor and 0.47 for C2R processor. Althouth both processors underestimete the TSM concnetration (Figure 1). For conversion of MODIS band 1 (B1) reflectance data to TSM concentration empirical algorithm was established. Firstly we used “dark pixel” methodology for correction of atmospheric disturbances. Statistically reliable correlation (r2=0.43) between water sample TSM and MODIS B1 reflectance was obtained using the hole dataset (Table 1). Our analysis of in situ measured scattering coefficient data had shown that increase in Chlorophyll a (Chl a) concentration (when the phytoplankton particles become dominant in water) changes the shape of in situ scattering spectrum and also the relationship between in situ scattering and TSM. Therefore we also found correlations separately for two situations 1) MODIS B1 vs TSM in case Chl a <5mg m-3 and 2) MODIS B1 vs TSM in case Chl a >5mg m-3 (Table 1). This kind of separation relaying on dominant particles origin gave slightly better result (Tabel 1). Figure 1. TSM estimated with C2R (green line), FUB (red line processors and TSM determined from water samples (blue line).

GIS based user interface where the EO data, in situ measurements and model outputs are displaied The environmental parameters that are provided in the web interface are total suspended matter, wind speed, wind direction, current speed, current direction and significant wave height. The data are presented in the form of 2D maps and graphs of time series. TSM maps The TSM data originates from satellite imagery and from in situ measurements. The TSM concentration values obtained from satellite imagery area displayed as maps on the web interface. Three types of TSM maps are displayed on the web interface: -The daily TSM maps calculated operationally from MODIS images and in situ data. -The reference TSM maps of 5 year monthly mean calculated from MERIS data and describing the natural background conditions at the dredging site. - Maps describing the difference between current condition and 5 year monthly mean TSM values. In order to compare the situation at the time of dredging operations with the natural background conditions the 5 year monthly average is provided as a reference. A map describing the TSM concentration difference between the situation at the time of dredging and mean conditions is calculated and displayed after the operational acquisition and processing of each satellite image. Maps based on in situ measurements are generated 2-3 times during dredging period (3-6 months) – usually in the beginning of the dredging activity. The in situ measurements can be used to validate the TSM products retrieved from satellite data. An example of the visualization of TSM concentration calculated from satellite imagery is given on Figure 5 Time series graphs of wind, wave, currents and TSM data Wind, wave and current data calculated by operational models are displayed as graphs on the web interface. Each parameter is shown on different graph. Also the TSM data collected from autonomous buoy measurements near dredging site is displayed as TSM time series on the web interface. Screen shot of the web interface displaying time series’ is given on Figure 5 In total 6 graphs are displayed: -wind speed -wind direction -current speed -current direction -significant wave height -total suspended matter Also the value of each parameter corresponding to the moment of satellite overpass is displayed in the information window located below the 2D TSM maps and above time series graphs .

Velocity field information The information about wind and velocity fields is extracted from the HIROMB model (High Resolution Operational Model for the Baltic Sea. Combined with wave field information, the velocity field data can be used for forecasting the drift of dredging bloom and thus plan the dredging works according to the environmental conditions. In the current project Hiromb-EST model setup (Table 2) has been used for extracting current field information near dredging sites. The model setup HIROMB-EST is operated by the MSI and it has been in operational use since the May 2009. The setup has no nested grids and the horizontal resolution is 0.5 NM. It covers mostly Estonian coastal waters, including the entire Gulf of Finland and the Gulf of Riga. The current fields calculated with Hiromb model have been compared with in situ measurements carried out in the NW part of Gulf of Finland. In general the 0.5 nm model prediction agrees reasonably well with the observed subsurface currents and can be used in the dredging monitoring system for forecasting the drift of dredging bloom. The forecast is initiated once per day starting from midnight and calculating a 48 h forecast with a 1 h time step. The daily forecasts are freely available at open access website http://emhi.ee/?ide=19,1304, presenting forecasts of the currents, sea level, sea surface temperature, sea surface salinity etc. Today the HIROMB-EST setup uses model code version 4.5, but in other aspects it has not changed since the beginning of the operational runs.

Figure 4. Example of velocity field and sea surface temperature calculate by Hiromb model.

Wave field information The wave field information (significant wave height) with 1 km horizontal resolution is provided by SWAN model (www.swan.tudelft.nl) which is running operationally at Marine Systems Institute and covers the entire Baltic Sea (example wave field on Figure 3). The 1 km resolution is currently the state of the art for the Baltic Sea region as no other institution is providing operational wave information with better resolution. The model results have been validated with in situ wave measurements collected in coastal areas of Estonia and the agrement is good between measured and modelled wave paremeters. Considering the good agreement of the model results and measurements it can be concluded that the model results are suitable for monitoring wave height near dredging sites. Subsets of wave information near dredging sites are extracted from the model outputs for the harbour dredging monitoring purposes. The significant wave height (SWH) data is extracted from the model outputs and displayed on the web interface as time series which shows the historical SWH values at the dredging site and provides 24 h wave forecast. Figure 3. Example image of significant wave height on 5 September 2012 in the

Baltic Sea calculated with operational SWAN model.

MODIS B1 vs TSM (all data)

TSM=787.7*B1 r2=0.43

MODIS B1 vs TSM in case Chl a <5mg m-3

TSM=571.8*B1 r2=0.67

MODIS B1 vs TSM in case Chl a >5mg m-3

TSM=517*B1+2.6 r2=0.63

Tabel 1. Algorithms for conversion MODIS B1 data to TSM concentration.

Figure 2. Montly average TSM concnetration calculated from MERIS images processed with C2R water processor for years 2006-2010.

Interface input: Averaged TSM maps for years 2006-2010 (Figure 2) were calculated from MERIS data using C2R processor. These maps were used as input data for natural backgound level of TSM along the Estonian coasline.

Area

Around Estonia (GoF,

GoR)

Grid 0.5 nautical mile grid 1’E

by 1/2’N

Nr of grid points 529 x 455

Vertical resolution 39 vertical layers, 3 m

down to 90 m

Forcing

by HIRLAM-EMHI 11 km

Boundry conditions daily from HIROMB-BS01

Initialization date May 1, 2009

Tabel 2. Facts about HIROMB-EST model

Figure 5. Screen snapshot of web interface

Acknowledgement This work was done by support of Estonian PECS program

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TSM

mg

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measurement number

TSM (water samples)

TSM (FUB)

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Average TSM concentration in April

Average TSM concentration in May

Average TSM concentration in June

Average TSM concentration in July

Average TSM concentration in August

Average TSM concentration in

September

Average TSM concentration in October