Overview

FOTO for FOurier transform Textural Ordination

Predicting stand structure parameters for tropical forests from remotely sensed data by defining an index of canopy texture from the ordination of the Fourier spectra computed for tropical rain forest images.

References:
  • Couteron, P., Pelissier, R., Nicolini, E. A., & Paget, D. (2005). Predicting tropical forest stand structure parameters from Fourier transform of very high‐resolution remotely sensed canopy images. Journal of applied ecology, 42(6), 1121-1128.
    https://doi.org/10.1111/j.1365-2664.2005.01097.x
  • Proisy, C., Couteron, P., & Fromard, F. (2007). Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sensing of Environment, 109(3), 379-392.
    https://doi.org/10.1016/j.rse.2007.01.009
  • Barbier, N., & Couteron, P. (2015). Attenuating the bidirectional texture variation of satellite images of tropical forest canopies. Remote Sensing of Environment, 171, 245-260.
    https://doi.org/10.1016/j.rse.2015.10.007

: README

FOTOTEX

Fourier Transform Textural Ordination in Python

License: MIT
Maintenance
PyPI version

Freely adapted from https://github.com/CaussesCevennes/FOTO.py

List of authors

Tutorial

See here

Description

FOTO (Fourier Textural Ordination) is an algorithm allowing texture
characterization and comparison, and is fully
described in Textural ordination based on Fourier spectral
decomposition: a method to analyze and compare landscape patterns

(Pierre Couteron, Nicolas Barbier and Denis Gautier, 2006)

FOTOTEX is to date the most complete Python implementation of this
algorithm. It is (really) fast and optimized to get the best of
FOTO on any computer.

Installation

Use pip in a terminal to install fototex:
shell script
$ pip install fototex

Note on GDAL

Installing GDAL through pip might be tricky as it only gets
the bindings, so be sure the library is already installed on
your machine, and that the headers are located in the right
folder. Another solution may to install it through a third-party
distribution such as conda.

See here for the steps
you should follow to install GDAL/OGR and the GDAL Python libraries
on your machine.

Contributing

Development and improvement

  • Benjamin Pillot
  • Dominique Lyszczarz
  • Claire Teillet
  • Pierre Couteron
  • Nicolas Barbier
  • Philippe Verley
  • Marc Lang
  • Thibault Catry
  • Laurent Demagistri

Conceptualization and Coordination

  • Benjamin Pillot
  • Thibault Catry
  • Nadine Dessay

Scientific projects

  • TOSCA APUREZA project, funded by CNES (TOSCA 2017-2020)
  • TOSCA DELICIOSA project, funded by CNES (TOSCA 2020-2022)
  • PCIA PROGYSAT project, funded by Interreg Amazon Cooperation Program (Urban axis) - (2021-2023)