A Novel approach for monitoring cyanobacterial blooms using an ensemble based system from MODIS imagery downscaled to 250 metres spatial resolution

Thursday, 18 December 2014
Anas El Alem1, Karem Chokmani1, Isabelle Laurion1 and Sallah-Eddine El-Adlouni2, (1)Institut National de la Recherche Scientifique-Eau Terre Environnement INRS-ETE, Quebec City, QC, Canada, (2)Université de moncton, Mathématiques et de Statistique, Moncton, Canada
In reason of inland freshwaters sensitivity to Harmful algae blooms (HAB) development and the limits coverage of standards monitoring programs, remote sensing data have become increasingly used for monitoring HAB extension. Usually, HAB monitoring using remote sensing data is based on empirical and semi-empirical models. Development of such models requires a great number of continuous in situ measurements to reach an acceptable accuracy. However, Ministries and water management organizations often use two thresholds, established by the World Health Organization, to determine water quality. Consequently, the available data are ordinal «semi-qualitative» and they are mostly unexploited. Use of such databases with remote sensing data and statistical classification algorithms can produce hazard management maps linked to the presence of cyanobacteria. Unlike standard classification algorithms, which are generally unstable, classifiers based on ensemble systems are more general and stable. In the present study, an ensemble based classifier was developed and compared to a standard classification method called CART (Classification and Regression Tree) in a context of HAB monitoring in freshwaters using MODIS images downscaled to 250 spatial resolution and ordinal in situ data. Calibration and validation data on cyanobacteria densities were collected by the Ministère du Développement durable, de l'Environnement et de la Lutte contre les changements climatiques on 22 waters bodies between 2000 and 2010. These data comprise three density classes: waters poorly (< 20,000 cells mL‑1), moderately (20,000 - 100,000 cells mL‑1), and highly (> 100,000 cells mL‑1) loaded in cyanobacteria. Results were very interesting and highlighted that inland waters exhibit different spectral response allowing them to be classified into the three above classes for water quality monitoring. On the other, even if the accuracy (Kappa-index = 0.86) of the proposed approach is relatively lower than that of the CART algorithm (Kappa-index = 0.87), but its robustness is higher with a standard-deviation of 0.05 versus 0.06, specifically when applied on MODIS images. A new accurate, robust, and quick approach is thus proposed for a daily near real-time monitoring of HAB in southern Quebec freshwaters.