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Description:

fractionalcover3 1.0.12

Fractionalcover version 3
Vegetation fractional cover package.
Description
Vegetation fractional cover represents the exposed proportion of green, non-green, and bare cover within each pixel.
Landsat-scale ground cover information is important for soil erosion and nutrient flux estimates into the stream
network, as well as assessing the impact of human activities.
The fractional cover v3.0 model is a Multi Layer Perceptron (neural network) model architecture that uses surface
reflectance to estimate the three cover fractions of bare ground, photosynthetic vegetation (PV) and non-photosynthetic
vegetation (NPV). The MLP model was trained with Tensorflow using Landsat TM, ETM+ and OLI surface reflectance and
a collection of 4000 field observations of overstorey and ground cover. The field observations covered a wide
variety of vegetation, soil and climate types across Australia, collected between 1997 and 2018 following the
procedure outlined in:
Muir, J., Schmidt, M., Tindall, D., Trevithick, R., Scarth, P. and Stewart, J.B., 2011. Field measurement of
fractional ground cover: a technical handbook supporting ground cover monitoring for Australia.
Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES): Canberra, Australia.
Installation:
This package requires numpy and tflite-runtime
If those packages are available, then installation should be straightforward.
Here is an example:
python -m pip install fractionalcover3

The package comes with two scripts to produce fractional cover images on RSC standard
landsat surface reflectance and Sentinel2 Surface Reflectance. To use these scripts, you
will require some additional dependencies, which you can install if you have access
to them using the rsc option. For example:
# install numpy, then gdal first
python -m pip install numpy
python -m pip install gdal==$(gdal-config --version)
python -m pip install fractionalcover3[rsc]

Not all of the dependencies are on PyPI, so you may need to manually install
these from source first. The Raster Processing package rios, is
available from github (check their
documentation ), but the rsc package
is internal to the JRSRP. Contact the authors for
more information on access.
While the scripts won't function without these packages, they are included in
case they are useful templates for writing similar scripts to operate on
complete images.
Basic Usage
The main function in the package is unmix_fractional_cover, which takes
a numpy 3d array for surface reflectance (scaled between 0 and 1), and
produces a 3d array of fractional cover. The output has 3 bands, one for
each bare, green and non-green components.
The unmixing uses a tensorflow model. This is supplied as a
tflite.Interpreter object. There are four models provided with the package,
each at varying levels of complexity. These can be selected by number, with 1
the simplest and 4 the most complex.
The simplest example might look like:
from fractionalcover3 import unmix_fractional_cover
from fractionalcover3 import data
import numpy as np
inref = np.array([562, 825, 1088, 2056, 2951, 2187]) * 0.0001
inref.shape = (6, 1, 1)

# use the default model provided
fractions = unmix_fractional_cover(inref,
fc_model=data.get_model()
)

How to Cite this Package
You can cite this package using the DOI:
@misc{scarthp2022,
title={JRSRP} {F}ractional {C}over 3.0,
author={Peter Scarth, Robert Denham, Fiona Watson},
year={2022},
month={08},
howpublished={\url{https://gitlab.com/jrsrp/themes/cover/fractionalcover3}},
doi={10.5281/zenodo.7008343}
}