Integrated satellite data fusion and mining for monitoring lake water quality status of the Albufera de Valencia in Spain
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- @Article{Dona:2015:JEM,
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author = "Carolina Dona and Ni-Bin Chang and
Vicente Caselles and Juan M. Sanchez and Antonio Camacho and
Jesus Delegido and Benjamin W. Vannah",
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title = "Integrated satellite data fusion and mining for
monitoring lake water quality status of the {Albufera
de Valencia in Spain}",
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journal = "Journal of Environmental Management",
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volume = "151",
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pages = "416--426",
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year = "2015",
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keywords = "genetic algorithms, genetic programming, Water
quality, Lake management, Remote sensing, Data fusion,
Data mining, Machine learning",
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ISSN = "0301-4797",
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DOI = "doi:10.1016/j.jenvman.2014.12.003",
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URL = "http://www.sciencedirect.com/science/article/pii/S0301479714005805",
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abstract = "Lake eutrophication is a critical issue in the
interplay of water supply, environmental management,
and ecosystem conservation. Integrated sensing,
monitoring, and modelling for a holistic lake water
quality assessment with respect to multiple
constituents is in acute need. The aim of this paper is
to develop an integrated algorithm for data fusion and
mining of satellite remote sensing images to generate
daily estimates of some water quality parameters of
interest, such as chlorophyll a concentrations and
water transparency, to be applied for the assessment of
the hypertrophic Albufera de Valencia. The Albufera de
Valencia is the largest freshwater lake in Spain, which
can often present values of chlorophyll a concentration
over 200 mg m-3 and values of transparency (Secchi
Disk, SD) as low as 20 cm. Remote sensing data from
Moderate Resolution Imaging Spectroradiometer (MODIS)
and Landsat Thematic Mapper (TM) and Enhance Thematic
Mapper (ETM+) images were fused to carry out an
integrative near-real time water quality assessment on
a daily basis. Landsat images are useful to study the
spatial variability of the water quality parameters,
due to its spatial resolution of 30 m, in comparison to
the low spatial resolution (250/500 m) of MODIS. While
Landsat offers a high spatial resolution, the low
temporal resolution of 16 days is a significant
drawback to achieve a near real-time monitoring system.
This gap may be bridged by using MODIS images that have
a high temporal resolution of 1 day, in spite of its
low spatial resolution. Synthetic Landsat images were
fused for dates with no Landsat overpass over the study
area. Finally, with a suite of ground truth data, a few
genetic programming (GP) models were derived to
estimate the water quality using the fused surface
reflectance data as inputs. The GP model for
chlorophyll a estimation yielded a R2 of 0.94, with a
Root Mean Square Error (RMSE) = 8 mg m-3, and the GP
model for water transparency estimation using Secchi
disk showed a R2 of 0.89, with an RMSE = 4 cm. With
this effort, the spatiotemporal variations of water
transparency and chlorophyll a concentrations may be
assessed simultaneously on a daily basis throughout the
lake for environmental management.",
- }
Genetic Programming entries for
Carolina Dona Monzo
Ni-Bin Chang
Vicente Caselles Miralles
Juan Manuel Sanchez Toma
Antonio Camacho Gonzalez
Jesus Delegido
Benjamin W Vannah
Citations