Remote sensing research

Spatial and temporal accuracy of tree species mapping with Satellite Image Time Series

After several years of work, we are proud with my advisors to publish our paper about the tree species mapping using nine one-year Satellite Image Time Series from the Formosat-2 sensor. 🥂

📖 Read the article directly on Remote Sensing journal.

📎 Our main contributions can be synthesized as follows

  • Spatial autocorrelation in the dataset highly overestimate the quality and so the extrapolation capabilites of the algorithm
  • 📅 Optimal dates to map tree species are highly instable from one year to another
  • Monospecific broadleaf plantations (Aspen, Red Oak and Eucalyptus) have high quality prediction, whereas conifer are difficult to map.
  • ⛅ The use of the full Satellite Image Time Series can seriously decrease the quality due to clouds, cloud shadows

🗺️ Explore the map of tree species from the south of Toulouse

A web map allow you to explore the tree species map from 9 one-year Satellite Image Time Series of Formosat-2.

♻️ Reproducible research

In order to reproduce this study or to have tree species ground references suitable for remote sensing, we decided to share our reference samples publicly on Zenodo, the Open Science platform at the CERN Data.

Moreover, my own python library Museo ToolBox, allows to reproduce the whole paper and much more. For now, the code used for the paper is kept private, but it will be published at the end of my thesis.

Remote sensing research

Who uses the dzetsaka QGIS plugin ? A brief inventory

I identified about 24 works, including 8 in peer-reviewed journals, that use the dzetsaka classification plugin. When I made this plugin, it was only to reproduce my work at the Guiana Amazonian Park. I didn’t expect it would be so much used and have so many downloads (about 55k today !).

I think must thank the Brazilian community because I found the majority of the people using dzetsaka is from there (Olá !). I don’t know why there are so much users in the Brazil then in the rest of the world, maybe because some researchers or teachers teach this plugin to their students. I notice that there also have been some great tutorials about dzetsaka published in youtube (Andre Q. Almeida UFS in 2017, Geoaplicada in 2018 and Anderson Medeiros of ClickGeo in 2019).

Algorithms used and dzetsaka evolution

The number of works per year is surprising. As we can see, 2019 is a great year with 15 works published online. The other years (2016,2017 and 2018) had about 3 to 4 works per year, so that’s quite a huge increase !

Number of published works per year (peer-reviewed, seminar, or report)

What algorithm is used in dzetsaka ? The wide majority performs the classification with the Gaussian Mixture Model implemented by the great Mathieu Fauvel. It is a efficient algorithm, with amazing speed capacities. However, Random-Forest often has better results, but takes longer to perform the fit and the predict process. GMM is provided by default in dzetsaka, you don’t need to install scikit-lit python library. I think that is the main reason for this gap.

Number of times an algorithm is used according to number of works.

Detailed of this work is available in Google docs.

Detailed list of the works (not limited), ordered by year