Assessing Water Quality in Catawba River Reservoirs Using Landsat Thematic Mapper Satellite Data
Open Access
- 1 December 1998
- journal article
- research article
- Published by Taylor & Francis in Lake and Reservoir Management
- Vol. 14 (4) , 405-416
- https://doi.org/10.1080/07438149809354347
Abstract
This study investigates the potential of satellite-based remote sensing to assess water quality in the 11 reservoirs of the Catawba River basin. Near-simultaneous acquisition of both Landsat TM (Thematic Mapper) data and in situ water quality observations (turbidity, secchi disk depth, chlorophyll and surface temperature), in May of 1995, provided a statistical foundation for the development of algorithms that convert TM reflectance to each water quality parameter. The conversion models defined for turbidity, secchi disk depth, chlorophyll (power law function) and temperature (linear function) were used to produce digital cartographic products that depict the distribution of each parameter in the 11 reservoirs. A analysis of error demonstrates that accurate quantitative data products can be produced from Landsat TM imagery for the surface waters of the major reservoirs in this system with a spatial resolution of 30 m for turbidity and secchi disk depth, and a 120-m resolution for surface temperature. Landsat TM appeared to have an inadequate spectral resolution for the quantitative assessment of chlorophyll. In this study, an additional experiment was performed in October 1995 to evaluate the applicability of the reflectance-based conversion algorithms to TM data acquired at other times and from other reservoirs. Though not quantitatively conclusive, the predicted values derived by the conversion algorithms were as consistent with ground observations for all water quality parameters except chlorophyll as that seen in the initial experiment.Keywords
This publication has 4 references indexed in Scilit:
- Remote sensing of Lake Chicot, Arkansas: Monitoring suspended sediments, turbidity, and Secchi depth with Landsat MSS dataRemote Sensing of Environment, 1992
- The relationship of MSS and TM digital data with suspended sediments, chlorophyll, and temperature in Moon Lake, MississippiRemote Sensing of Environment, 1990
- Modeling inland water quality using Landsat dataRemote Sensing of Environment, 1983
- Criteria for the use of regression analysis for remote sensing of sediment and pollutantsRemote Sensing of Environment, 1982