Mapping Turbidity in the Charles River, Boston Using a High-resolution Satellite
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The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R 2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor’s protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R 2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).
KeywordsCharles River IKONOS Remote sensing Satellite Turbidity Urban Water quality
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- Blake, L. (2003). Lower Charles River Demonstration Project – Gunderboom® Beach Protection System™ – 2002. Lowell, MA: New England Interstate Water Pollution Control Commission (NEIWPCC).Google Scholar
- Breault, R. F., Barlow, L. K., Reisig, K. D., & Parker, G. W. (2000). Spatial distribution, temporal variability, and chemistry of the salt wedge in the lower Charles River, Massachusetts, June 1998 to July 1999. U.S. Geological Survey Water-Resources Investigations Report 00-4124, 1 pl.Google Scholar
- Breault, R. F., Sorenson, J. R., & Weiskel, P. K. (2002). Streamflow, water quality, and contaminant loads in the Lower Charles River Watershed, Massachusetts, 1999–2000. Water-Resources Investigations Report 02-4137, (p. 131)Google Scholar
- Fraser, R. N. (1998). Multispectral remote sensing of turbidity among Nebraska Sand Hill lakes. International Journal of Remote Sensing, 23, 15–35.Google Scholar
- Khorram, S., & Cheshire, H. M. (1985). Remote sensing of water quality in the Neuse River Estuary, North Carolina. Photogrammetric Engineering and Remote Sensing, 51, 329–341.Google Scholar
- Lathrop, R. G., Jr., & Lillesand, T. M. (1986). Use of Thematic Mapper data to assess water quality in Green Bay and central Lake Michigan. Photogrammetric Engineering and Remote Sensing, 52(5), 671–680.Google Scholar
- Lillesand, T. M., Johnson, W. L., Deuell, R. L., Lindstrom, O. M., & Meisner, D. E. (1983). Use of Landsat data to predict the trophic state of Minnesota lakes. Photogrammetric Engineering and Remote Sensing, 49, 219–229.Google Scholar
- Maidment, D. R. (Ed.). (2002). Arc hydro – GIS for water resources. Redlands, CA: ESRI.Google Scholar
- Masopust, P., & Hellweger, F. L. (2006). High-resolution E. coli patterns in an urban river. Journal of the American Water Resouces Association (in preparation).Google Scholar
- MassGIS (1992). MassGIS digital orthophoto. Boston, MA: Massachusetts Geographic Information System (MassGIS), Massachusetts Executive Office of Environmental Affairs.Google Scholar
- Vidot, J., & Santer, R. (2003). Atmospheric correction for inland waters. Application to SeaWiFS and MERIS. Proceedings of SPIE, the International Society for Optical Engineering, 4892, 536–545.Google Scholar