Produce diversity maps from optical images with biodivMapR

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.

Jean-Baptiste Féret, Florian de Boissieu

2023-12-05