Metadata-Version: 2.1
Name: waffls
Version: 0.1.8
Summary: UNKNOWN
Home-page: http://bitbucket.org/bendv/waffls
Author: Ben DeVries
Author-email: bdv@umd.edu
License: MIT
Description: # waffls v0.1.8
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        **wa**ter **f**raction **f**rom **L**andsat and **S**entinel-2 imagery
        
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        waffls is a collection of algorithms for estimating sub-pixel surface water fraction using medium resolution satellite data. Reader classes for Landsat data (Collection-1 and Pre-Collection) as well as Harmonized Landsat Sentinel-2 (HLS) data are included.
        
        ## Installation
        
        waffls is built on top of gdal and a number of python libraries. The necessary prerequisite libraries must be installed first, and can be easily accessed through conda. See [here](https://conda.io/docs/user-guide/install/index.html) for instructions on installing conda. 
        
        To install waffls in a separate conda environment, run the following:
        
        ```bash
        conda create -n waffls -c conda-forge gdal rasterio cython
        conda activate waffls
        git clone https://github.com/bendv/waffls
        cd waffls
        python setup.py install
        ```
        
        ## References
        
        If you use ```waffls``` in your work, please cite:
        
        DeVries, B., Huang, C-Q., Lang, M.W., Jones, J.W., Huang, W., Creed, I.F. and Carroll, M.L. 2017. Automated quantification of surface water inundation in wetlands using optical satellite imagery. Remote Sensing, 9(8):807.
        
        If you use the Dynamic Surface Water Extent product (ie., using ```waffls.dswe```), please cite:
        
        Jones, J.W., 2015. Efficient wetland surface water detection and monitoring via landsat: Comparison with in situ data from the everglades depth estimation network. Remote Sensing 7, 12503–12538.
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
