Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Humeda Biosphere Reserve in Central Spain

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{dona:2016:Remote_Sensing,

• author = "Carolina Dona and Ni-Bin Chang and Vicente Caselles and Juan Manuel Sanchez and Lluis Perez-Planells and Maria Del Mar Bisquert and Vicente Garcia-Santos and Sanaz Imen and Antonio Camacho",
• title = "Monitoring Hydrological Patterns of Temporary Lakes Using Remote Sensing and Machine Learning Models: Case Study of La Mancha Humeda Biosphere Reserve in Central Spain",
• journal = "Remote Sensing",
• year = "2016",
• volume = "8",
• number = "8",
• keywords = "genetic algorithms, genetic programming",
• ISSN = "2072-4292",
• URL = "https://www.mdpi.com/2072-4292/8/8/618",
• DOI = "doi:10.3390/rs8080618",
• abstract = "The Biosphere Reserve of La Mancha Humeda is a wetland-rich area located in central Spain. This reserve comprises a set of temporary lakes, often saline, where water level fluctuates seasonally. Water inflows come mainly from direct precipitation and runoff of small lake watersheds. Most of these lakes lack surface outlets and behave as endorheic systems, where water withdrawal is mainly due to evaporation, causing salt accumulation in the lake beds. Remote sensing was used to estimate the temporal variation of the flooded area in these lakes and their associated hydrological patterns related to the seasonality of precipitation and evapotranspiration. Landsat 7 ETM+ satellite images for the reference period 2013-2015 were jointly used with ground-truth datasets. Several inverse modelling methods, such as two-band and multispectral indices, single-band threshold, classification methods, artificial neural network, support vector machine and genetic programming, were applied to retrieve information on the variation of the flooded areas. Results were compared to ground-truth data, and the classification errors were evaluated by means of the kappa coefficient. Comparative analyses demonstrated that the genetic programming approach yielded the best results, with a kappa value of 0.98 and a total error of omission-commission of 2percent. The dependence of the variations in the water-covered area on precipitation and evaporation was also investigated. The results show the potential of the tested techniques to monitor the hydrological patterns of temporary lakes in semiarid areas, which might be useful for management strategy-linked lake conservation and specifically to accomplish the goals of both the European Water Framework Directive and the Habitats Directive.",
• notes = "also known as \cite{rs8080618}",

}

Genetic Programming entries for Carolina Dona Monzo Ni-Bin Chang Vicente Caselles Miralles Juan Manuel Sanchez Perez Lluis Perez-Planells Maria Del Mar Bisquert Vicente Garcia-Santos Sanaz Imen Antonio Camacho Gonzalez

Citations