Package: qda-modelos
This repository has the implementation and tests of benchmarked
bio-optic models that evaluates some water quality indexes by analyzing
satellite images.Table of contentsGeneral InfoGetting StartedUsageTestingContributingLinksAuthors and ContributorsLicenseGeneral InfoIn attempt to analyze the water quality of reservoirs and lakes by
remote sensing methods, such as satellites images, bio-optical models
were used. Those models are mathematical and statistical algorithms
which can be used to predict different water quality indexes by
analyzing the water-leaving radiance measured at different bands of
electromagnetic spectrum by sensors onboard satellites.According to the literature there are different approaches used in
bio-optical modeling since simple models, based on empirical and
semi-empirical relations, until most complexes models based on radiative
transfer theory.Currently, this project implements a library of empirical and
semi-empirical models which can be used to predict the concentration of
chlorophyll-a, total suspended solids, water transparency, turbidity,
phycocyanin and the detection of macrophytes in aquatic environments.All the original equations are adapted in order to be applied in images
collected by MSI (Multispectral Instrument) sensor onboardSentinel-2A
and Sentinel-2B
platforms.Getting StartedThese instructions will get you a copy of the project up and running on
your local machine for development and testing purposes.TechnologiesTo execute this project, you’ll need the following technologies:32- or 64-bit computerPython 3SetupThis repository can be used as a complementary library for a main
project and its modules can be used whenever they are necessary.To install the package and dependencies use the following command:pip install qda_modelosIn order to useqda_modelosin your project it’s required to install
therasteriolibrary:pip install rasterioYou can install dependencies directly in your machine or in a virtual
environment of your choice, such asVirtualEnvorConda.UsageThe following example implements the water quality index: turbidity.The chosen method ismiller_mckee_2004which expects satellite
images of 659nm wavelength.This example utilizes Sentinel-2 imagery of which the band 4 has the
central wavelength (665 nm) closest to the models requirement.First, import the required packages and desired methods:importrasterioasriofromqda_modelos.total_suspended_solids_turbidityimportmiller_mckee_2004In this case, we chose the rasterio package to read and write.tifdata files.Set and open the respective satellite images required to analyze the
indexes:reflectance_659nm_wavelength=rio.open("tests/assets/20m/B4_20m_20181224.tif").read()Open the band image file and choose one or more methods to analyze
the desired index:meta=rio.open("tests/assets/20m/B4_20m_20181224.tif").metameta.update(driver="GTiff")meta.update(dtype=rio.float32)miller_mckee_2004=miller_mckee_2004(reflectance_659nm_wavelength)Create and save the new image generated by the respectives bands of
the chosen method:withrio.open("miller_mckee_2004.tif","w",**meta)asdist:dist.write(miller_mckee_2004.astype(rio.float32))The output is a.tiffile containing the processed image by the
chosen method:ReservoirTestingThis repository implementations can be tested by runningpytestcommand.python3-mpytestContributingContributions are always welcome! To fix a bug or enhance an existing
module, follow these steps:Fork the repoCreate a new branch (git checkout-bimprove-feature)Make the appropriate changes in the filesAdd changes to reflect the changes madeCommit your changes (git commit-am'Improve feature')Push to the branch (git push originimprove-feature)Create a Pull RequestWhile contributing, remember to add tests to the new developed methods.LinksA Comprehensive Review on Water Quality Parameters Estimation Using
Remote Sensing
TechniquesBio-optical Modeling and Remote Sensing of Inland
WatersReferencesChlorophyll-aALLAN, M.G, HICKS, B.J., BRABYN, L. (2007). Remote sensing of the
Rotorua lakes for water quality. CBER Contract Report No. 51, client
report prepared for Environment Bay of Plenty. Hamilton, New Zealand:
Centre for Biodiversity and Ecology Research, Department of Biological
Sciences, School of Science and Engineering, The University of Waikato.CHAVULA, G.; BREZONIK, P.; THENKABAIL, P.; JOHNSON, T.; BAUER, M.
Estimating chlorophyll concentration in Lake Malawi from MODIS satellite
imagery. Physics and Chemistry of the Earth, Parts A/B/C, [s. l.], v.
34, n. 13–16, p. 755–760, 2009.DALL’OLMO, G.; GITELSON, A. A.; RUNDQUIST, D. C. Towards a unified
approach for remote estimation of chlorophyll-a in both terrestrial
vegetation and turbid productive waters. Geophysical Research Letters,
[s. l.], v. 30, n. 18, 2003.GITELSON, A. A.; SCHALLES, J. F. & HLADIK, C. M. Remote chlorophyll-a
retrieval in turbid, productive estuaries: Chesapeake Bay case study,
Remote Sensing of Environment, v. 109, p. 464 – 472, 2007.GORDON, H. & MOREL, A. Remote Assessment of Ocean Color for
Interpretation of Satellite Visible Imagery: A Review. Lecture Notes on
Coastal and Estuarine Studies, v. 4, Springer Verlag, New York, 114
p. 1983.GONS, H. J. Optical Teledetection of Chlorophyllain Turbid Inland
Waters. Environmental Science & Technology, [s. l.], v. 33, n. 7,
p. 1127–1132, 1999.GOWER, J.; KING, S.; BORSTAD, G.; BROWN, L. Detection of intense
plankton blooms using the 709 nm band of the MERIS imaging spectrometer.
International Journal of Remote Sensing, [s. l.], v. 26, n. 9,
p. 2005–2012, 2005.LE, C.; LI, Y.; ZHA, Y.; SUN, D.; HUANG, C.; LU, H. A four-band
semi-analytical model for estimating chlorophyll a in highly turbid
lakes: The case of Taihu Lake, China. Remote Sensing of Environment, [s.
l.], v. 113, n. 6, p. 1175–1182, 2009.MISHRA, S.; MISHRA, D. R. A novel model for remote estimation of
chlorophyll-a concentration in turbid productive waters. Remote Sensing
of Environment, v. 117, p. 394 - 406, 2012.RODRIGUES, T; ALCÂNTARA, E; WATANABE, F; ROTTA, LUIZ; IMAI, N;
CURTARELLI, M & BARBOSA, C. Comparação entre Métodos Empíricos para
estimativa da concentração de Clorofila-a em Reservatórios em Cascata
(Rio Tietê, São Paulo), Revista Brasileira de Cartografia, v. 68,
p. 181-192, 2016.CyanobacteriaDASH, P., WALKER, N.D., MISHRA, D.R., HU, C., PINCKNEY, J.L., D’SA,
E.J., (2011). Estimation of cyanobacterial pigments in a freshwater lake
using OCM satellite data. Remote Sens. Environ. 115 (12), 3409-3423.SIMIS, S.G.H., PETERS, S.W.M., GONS, H.J., (2005). Remote sensing of the
cyanobacterial pigment phycocyanin in turbid inland water. Limnol.
Oceanogr. 50, 237-245.WOZNIAK, M., BRADTKE, K.M., DARECKI, M., KREZEL, A., (2016). Empirical
model for phycocyanin concentration estimation as an indicator of
cyanobacterial bloom in the optically complex coastal waters of the
Baltic Sea. Remote Sens. 8 (3), 212-234.MacrophytesHUETE, A. A comparison of vegetation indices over a global set of TM
images for EOS-MODIS. Remote Sensing of Environment, [s. l.], v. 59,
n. 3, p. 440–451, 1997.TUCKER, C. J. Red and photographic infrared linear combinations for
monitoring vegetation. Remote Sensing of Environment, [s. l.], v. 8,
n. 2, p. 127–150, 1979.VILLA, P.; LAINI, A.; BRESCIANI, M.; BOLPAGNI, R. A remote sensing
approach to monitor the conservation status of lacustrine Phragmites
australis beds. Wetlands Ecology and Management, [s. l.], v. 21, n. 6,
p. 399–416, 2013.VILLA, P.; MOUSIVAND, A.; BRESCIANI, M. Aquatic vegetation indices
assessment through radiative transfer modeling and linear mixture
simulation. International Journal of Applied Earth Observation and
Geoinformation, [s. l.], v. 30, p. 113–127, 2014.Total Suspended Solids and TurbidityDOXARAN, D.; FROIDEFOND, J.-M.; CASTAING, P. Remote-sensing reflectance
of turbid sediment-dominated waters Reduction of sediment type
variations and changing illumination conditions effects by use of
reflectance ratios. Applied Optics, [s. l.], v. 42, n. 15, p. 2623,
2003.DOXARAN, D.; FROIDEFOND, J.-M.; CASTAING, P.; BABIN, M. Dynamics of the
turbidity maximum zone in a macrotidal estuary (the Gironde, France):
Observations from field and MODIS satellite data. Estuarine, Coastal and
Shelf Science, [s. l.], v. 81, n. 3, p. 321–332, 2009.LIU, C. D., HE, B. Y., LI, M. T., REN, X. X. (2006). Quantitative
modeling of suspended sediment in middle Changjiang river from MODIS.
Chinese Geographical Science, v. 16, pp. 79–82.MILLER, R. L.; MCKEE, B. A. Using MODIS Terra 250 m imagery to map
concentrations of total suspended matter in coastal waters. Remote
Sensing of Environment, [s. l.], v. 93, n. 1–2, p. 259–266, 2004.TANG, S.; LAROUCHE, P.; NIEMI, A.; MICHEL, C. Regional algorithms for
remote-sensing estimates of total suspended matter in the Beaufort Sea.
International Journal of Remote Sensing, [s. l.], v. 34, n. 19,
p. 6562–6576, 2013.TARRANT, P. E.; AMACHER, J. A.; NEUER, S. Assessing the potential of
Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution
Imaging Spectroradiometer (MODIS) data for monitoring total suspended
matter in small and intermediate sized lakes and reservoirs. Water
Resources Research, [s. l.], v. 46, n. 9, 2010.ZHANG, Y.; LIN, S.; LIU, J.; QIAN, X.; GE, Y. Time-series MODIS
Image-based Retrieval and Distribution Analysis of Total Suspended
Matter Concentrations in Lake Taihu (China). International Journal of
Environmental Research and Public Health, [s. l.], v. 7, n. 9,
p. 3545–3560, 2010.Water TransparencyGIARDINO, C. et al. (2001). Detecting chlorophyll, Secchi disk depth and
surface temperature in a sub-alpine lake using Landsat imagery. The
Science of Total Environment, v. 268, pp. 19-29.GUIMARÃES, V. S. et al. (2016). Desenvolvimento de modelo empírico para
determinação de transparência de Secchi na Lagoa da Conceição – SC, a
partir de imagens multiespectrais do sensor Operational Land Imager
(OLI) -Landsat-8. Anais do XXI Simpósio Brasileiro de Recursos Hídricos.HÄRMÄ, P. et al. (2001). Detecting chlorophyll, Secchi disk depth and
surface temperature in a sub-alpine lake using Landsat imagery. The
Science of Total Environment, v. 268, pp. 107-121.Trophic State IndexLAMPARELLI, M.C. (2004) Grau de trofia em corpos d’água do estado de São
Paulo: avaliação dos métodos de monitoramento. Thesis (Phd) – University
of São Paulo, São Paulo.Reservoir Water Quality IndexQUALIDADE DE ÁGUA EM RESERVATÓRIOS
(IQAR)Authors & ContributorsDeveloped by CERTI Foundation.This research was supported by FOZ DO CHAPECÓ ENERGIA S.A research and
technological development program,through the PD-02949-2405/2019 project, regulated by Brazilian
Electricity Regulatory Agency (ANEEL).LicenseThis repository is licensed under the terms of the BSD-style license.