Metadata-Version: 2.1
Name: earthlib
Version: 0.2.6
Summary: Spectral unmixing library for satellite land cover mapping
Home-page: https://github.com/earth-chris/earthlib
Author: Christopher Anderson
Author-email: cbanders@stanford.edu
License: MIT
Description: # The Earth Library
        
        <img src="docs/img/earth-unmixed.png">
        
        <p align="center">
          <em>A global reference library for land cover spectra, and tools for working with it.</em>
        </p>
        
        ![GitHub](https://img.shields.io/github/license/earth-chris/earthlib)
        ![PyPI](https://img.shields.io/pypi/v/earthlib)
        ![GitHub Workflow Status](https://img.shields.io/github/workflow/status/earth-chris/earthlib/run-pytest)
        ![PyPI - Downloads](https://img.shields.io/pypi/dm/earthlib)
        ![GitHub last commit](https://img.shields.io/github/last-commit/earth-chris/earthlib)
        
        
        ---
        
        **Documentation**: [earth-chris.github.io/earthlib](https://earth-chris.github.io/earthlib)
        
        **Source code**: [earth-chris/earthlib](https://github.com/earth-chris/earthlib)
        
        ---
        
        ## Introduction
        
        `earthlib` is a spectral library and a set of software tools for satellite-base land cover mapping.
        
        The library contains several thousand unique spectral endmembers representing green vegetation, soil, non-photosynthetic vegetation, urban materials, and burned materials. The reflectance data cover the visible to the shortwave infrared wavelengths (400-2450 nm) at 10 nm band widths.
        
        The software tools (1) resample these data to match the wavelenths of popular satellite and airborne earth observing sensors and (2) run [spectral mixture analysis](docs/introduction.md) in Google Earth Engine via the `earthengine` python package.
        
        The goal is to quantify spatial and temporal patterns of change in global vegetation cover, as well as patterns of soil cover, burned area, non-photosynthetic vegetation, and impervious surfaces. With `earthlib`, you can do this using most public satellite [data sources](docs/sources.md).
        
        
        ## Installation
        
        This library can be installed via `pip`.
        
        ```bash
        pip install earthlib
        ```
        
        You can also clone the source repository and install it locally.
        
        ```bash
        git clone https://github.com/earth-chris/earthlib.git
        cd earthlib
        pip install -e .
        ```
        
        ### conda
        
        I recommend working with `earthlib` in `conda` (download from [here](https://docs.conda.io/en/latest/miniconda.html)). The `environment.yml` file in this repository contains a series of packages that are either required (`earthengine-api`) or just convenient (`jupyter`, `folium`) to have.
        
        ```bash
        git clone https://github.com/earth-chris/earthlib.git
        cd earthlib
        conda env update
        ```
        
        Once the environment has been created, you can activate it with `conda activate earthlib`.
        
        
        ## Contact
        
        ![Twitter Follow](https://img.shields.io/twitter/follow/earth_chris)
        
        This work is supported by the Stanford Center for Conservation Biology and the Natural Capital Project.
        
Keywords: ecology,conservation,remote sensing,earth engine,spectral unmixing
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.4
Description-Content-Type: text/markdown
