Produce diversity maps from optical images with biodivMapR

Jean-Baptiste Féret, Florian de Boissieu

2024-08-30

This tutorial aims at describing the processing workflow and providing a script aiming at producing diversity maps on a Sentinel-2 subset image acquired over the Cameroonese forest. The workflow is divided into three steps:

• Definition of the processing parameters:

  • input / output files paths
  • output spatial resolution
  • preprocessing and processing options

• Computation of the diversity maps. This includes several diversity metrics which can be computed independently:

  • $\alpha$ and $\beta$ diversity maps as defined in Feret & Asner (2014) and Feret & de Boissieu (2019)
  • Functional diversity maps, including Richnes, Evenness and Divergence as defined in Villeger et al. (2008)

• Validation of the resulting diversity metrics if field plots measurements are available

The computation of the diversity maps is based on a certain number of prepocessing steps including: * Spectral normalization with continuum removal (relevant if woring with multi or hypersepctral images) * dimensionality reduction based on PCA, SPCA or MNF and component selection

Below is the typical flow chart of the computation of diversity maps with biodivMapR :

Please check the full tutorial pages to get instructions, data and code examples to run biodivMapR can be applied.