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.