The measurement and monitoring process of the Total Maximum Daily Load (TMDL) of surface water bodies is an essential part of the TMDL process, as data needs to be collected to state the loads, and consistent monitoring is needed to determine the success of mitigation methods for the listed impairments. Satellite remote sensing can reduce costs of monitoring, yield far denser sampling, and provide greater consistency than the current method of collection. Although there are typically more parameters currently measured per water sample in the laboratory, all but two or three (pigments and nutrients) are irrelevant to harmful algal bloom (cyanobacteria) monitoring, and those few can be measured from satellite, including the phycocyanin, chlorophyll-a, and total phosphorous levels within the water. Satellite data from LANDSAT 5 and 7 can be downloaded for free, which enables monthly monitoring with an eight day repeat cycle of satellite overpass and approximately five measurements per acre over an entire lake.
This study focused on Lake Elsinore, a large recreational lake in southern California, which had a nutrient TMDL created in 2004 due to hypereutrophicaton. Water collection data was provided by the Santa Ana Watershed Protection Authority from 2002 to 2010, and LANDSAT data was downloaded from USGS for overpass dates that fell on the same days as water collection. Preexisting algorithms consisting of linear combination of haze-corrected spectral ratios (which make the algorithms more robust to environmental changes among overpass dates) developed with Lake Erie data, as well as newly created algorithms from Lake Elsinore data, were employed to measure TMDL parameters of total phosphorus, total nitrogen, dissolved oxygen levels, and chlorophyll-a. A preexisting algorithm for mid-range total phosphorus resulted in some degree of correlation with the Lake Elsinore data, but a high bloom phycocyanin algorithm provided accurate insight to the presence of cyanobacteria blooms within the lake. A 6-ratio total phosphorus and 4-ratio turbidity algorithm were successfully created from Lake Elsinore data, using multiple regression analysis, with spectral ratios as independent parameter inputs and the in situ values for the targeted parameter as dependent values. Both algorithms performed well on withheld data sets, which contained data points not used in the algorithm construction process, with R2 values of 85% and 79% respectively, and low RMS errors representing only 14% and 16% of the data ranges, respectively. Images were processed which applied the algorithms to the entire surface area of Lake Elsinore, creating quantitatively scaled color images, as well as scenes that display the amount of TMDL compliance and exceedance within the lake.
This study further proves that remote sensing can be used to create spatially and temporally robust algorithms, which can greatly improve the measurement and monitoring of a TMDL program. Satellites designed specifically for lake monitoring will likely make these improvements even greater in the future.