Tools for Analysing Time Series of Satellite Imagery (TATSSI)

Install using Anaconda

You can install TATSSI on your favourite Linux distro or if you want to run it on Windows here you can follow the instructions to do it.

• Download conda

  • wget https://repo.continuum.io/archive/Anaconda3-2019.07-Linux-x86_64.sh
    • Install conda bash ./Anaconda3-2019.07-Linux-x86_64.sh
      • Accept the default settings. When asked: Do you wish the installer to initialize Anaconda3 by running conda init? [yes|no] Say: yes
    • Close that shell and open a new one

• Clone this repo

  • git clone https://github.com/GerardoLopez/TATSSI

• Install the required libraries:

  • cd TATSSI
  • conda install --file tatssi-package-list.txt
  • If you wanto to use the changepoint R package:
    • Install R
      • sudo apt update
      • sudo apt-get install r-base
    • Install the changepoint package
      • Run R with the following command: /usr/bin/R
      • install.packages('changepoint')
      • install.packages('changepoint.np')
      • Exit R with the following command: quit()

• Run TATSSI

  • If you want to use the Jupyter Notebooks:
    • Go to the TATSSI/notebooks directory and run jupyter notebook
  • If you prefer to use the UI:
    • Go to the TATSSI/TATSSI/UI directory and run python tatssi.py

Downloading products from LP DAAC with TATSSI

Downloading products from the LP DAAC requires a NASA EarthData login. Please, first register as an EarthData user to get login credentials.

• If gedit is not installed in your system:

  • sudo apt install gedit

• Update config.json file with login credentials:

  • cd TATSSI/TATSSI/download
  • gedit config.json
  • Replace USERNAME and PASSWORD with login credentials, save and close

Description

TATSSI is a set of software tools to analise Earth Observation (EO) data. It allows you to:

• Download data from the Land Processes Distributed Active Archive Center (LP DAAC)
• Transform to/from diverse EO raster data formats using GDAL
• Decode the QA-SDS associated to diverse MODIS & VIIRS data.
• Create time series of the aforementioned products masking by the user-defined QA parameter selection
• Perform basic gap-filling using the interpolation methods used in SciPy.
• Smooth time series using robust spline smoothing following Garcia. 2010
• Analyse time series using different tools such as decomposition, climatologies, trends, change point detection, etc.

There are some Jupyter Notebooks associated to each module, here you can find a description of each one.

First workshop presentations (In Spanish)

• Introducción al manejo de calidad de datos
• Introducción a TATSSI
• Aplicaciones del análisis de series de tiempo
• Análisis de algunos métodos de interpolación

Second workshop videos (In Spanish)

• First day showing the Downloaders, Time Series Generation, QA Analytics, Interpolation and Smoothing TATSSI modules.
• Second day showing the Time Series Analysis TATSSI module.

Some plots and presentations...

• A quick glimpse of a simple plot for EVI and associated QAs
• Presentations at the 2020 Joint Statistical Meetings: Gerardo Lopez Saldana; Inder Tecuapetla

Funding

TATSSI is funded by "Convocatoria de Proyectos de Desarrollo Científico para Atender Problemas Nacionales 2016" Project No. 2760; P.I.: Inder Tecuapetla. Collaborators: Gerardo Lopez Saldana, Rainer Ressl and Isabel Cruz.