Metadata-Version: 2.1
Name: qda-modelos
Version: 1.1.2
Summary: Implement bio-optic models to evaluate water quality indexes with satellite images.
Home-page: https://github.com/fundacaocerti/qda_modelos
Author: CERTI Foundation
Author-email: qda-pypi@certi.org.br
License: BSD
Download-URL: https://github.com/fundacaocerti/qda_modelos/archive/v1.1.2.tar.gz
Description: Bio-Optical Models for Water Quality Analysis
        =============================================
        
        This repository has the implementation and tests of benchmarked
        bio-optic models that evaluates some water quality indexes by analyzing
        satellite images.
        
        Table of contents
        -----------------
        
        -  `General Info <#general-info>`__
        -  `Getting Started <#getting-started>`__
        -  `Usage <#usage>`__
        -  `Testing <#testing>`__
        -  `Contributing <#contributing>`__
        -  `Links <#links>`__
        -  `Authors and Contributors <#authors-and-contributors>`__
        -  `License <#license>`__
        
        General Info
        ------------
        
        In 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 onboard `Sentinel-2A
        and Sentinel-2B
        platforms <https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi>`__.
        
        Getting Started
        ---------------
        
        These instructions will get you a copy of the project up and running on
        your local machine for development and testing purposes.
        
        Technologies
        ~~~~~~~~~~~~
        
        To execute this project, you’ll need the following technologies:
        
        -  32- or 64-bit computer
        -  `Python 3 <https://www.python.org/downloads/>`__
        
        Setup
        ~~~~~
        
        This 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_modelos``
        
        In order to use **qda_modelos** in your project it’s required to install
        the **rasterio** library:
        
        ``pip install rasterio``
        
        You can install dependencies directly in your machine or in a virtual
        environment of your choice, such as
        `VirtualEnv <https://virtualenv.pypa.io/en/latest/>`__ or
        `Conda <https://docs.conda.io/en/latest/>`__.
        
        Usage
        -----
        
        The following example implements the water quality index: turbidity.
        
        The chosen method is ``miller_mckee_2004``\ which 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:
        
        .. code:: python
        
           import rasterio as rio
           from qda_modelos.total_suspended_solids_turbidity import miller_mckee_2004
        
        In this case, we chose the rasterio package to read and write **.tif**
        data files.
        
        -  Set and open the respective satellite images required to analyze the
           indexes:
        
        .. code:: python
        
           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:
        
        .. code:: python
        
           meta = rio.open("tests/assets/20m/B4_20m_20181224.tif").meta
           meta.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:
        
        .. code:: python
        
           with rio.open("miller_mckee_2004.tif", "w", **meta) as dist:
               dist.write(miller_mckee_2004.astype(rio.float32))
        
        -  The output is a **.tif** file containing the processed image by the
           chosen method:
        
        .. figure:: https://i.imgur.com/gOnaIAn.png
           :alt: Reservoir
        
           Reservoir
        
        Testing
        -------
        
        This repository implementations can be tested by running **pytest**
        command.
        
        ``python3 -m pytest``
        
        Contributing
        ------------
        
        Contributions are always welcome! To fix a bug or enhance an existing
        module, follow these steps:
        
        -  Fork the repo
        -  Create a new branch (``git checkout -b improve-feature``)
        -  Make the appropriate changes in the files
        -  Add changes to reflect the changes made
        -  Commit your changes (``git commit -am 'Improve feature'``)
        -  Push to the branch (``git push origin improve-feature``)
        -  Create a Pull Request
        
        While contributing, remember to add tests to the new developed methods.
        
        Links
        -----
        
        -  `A Comprehensive Review on Water Quality Parameters Estimation Using
           Remote Sensing
           Techniques <https://www.researchgate.net/publication/306240486_A_Comprehensive_Review_on_Water_Quality_Parameters_Estimation_Using_Remote_Sensing_Techniques>`__
        -  `Bio-optical Modeling and Remote Sensing of Inland
           Waters <https://www.sciencedirect.com/book/9780128046449/bio-optical-modeling-and-remote-sensing-of-inland-waters>`__
        
        References
        ----------
        
        Chlorophyll-a
        ~~~~~~~~~~~~~
        
        ALLAN, 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.
        
        Cyanobacteria
        ~~~~~~~~~~~~~
        
        DASH, 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.
        
        Macrophytes
        ~~~~~~~~~~~
        
        HUETE, 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 Turbidity
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        DOXARAN, 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 Transparency
        ~~~~~~~~~~~~~~~~~~
        
        GIARDINO, 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 Index
        ~~~~~~~~~~~~~~~~~~~
        
        LAMPARELLI, 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 Index
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        `QUALIDADE DE ÁGUA EM RESERVATÓRIOS
        (IQAR) <http://pnqa.ana.gov.br/indicadores-qualidade-agua.aspx>`__
        
        Authors & Contributors
        ----------------------
        
        Developed 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).
        
        License
        -------
        
        This repository is licensed under the terms of the BSD-style license.
        
Keywords: bio-optic models,water quality,indexes,satellite image,reservoir,lake,water,remote sensing
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/x-rst
