Process satellite remote sensing data to produce biodiversity indicators

What it does

Féret and Asner (2014) developed a method for tropical forest diversity mapping based on very high spatial resolution airborne imaging spectroscopy.

The goal of this tool using the package biodivMapR is to compute diversity indices over each spatial polygon of a shapefile of plots, if available, in order to compare field inventories with diversity indices estimated from remotely-sensed images.

Input description

It expects an image file as input, with a specific data format. ENVI HDR image with BIL interleave required. The image is an ENVI raster including :

• A binary file (which has no extension here).
• A header file (with .hdr extension).

The header file is a text file including all necessary metadata which can be read with a text editor. It includes image dimensions, projection, and the name and central wavelength for each spectral band.

In order to get such input we advise to use the tool preprocessing sentinel 2 data.

Output

• Two tabulars :

  • One matrix for Bray-Curtis indicator
  • One table for the following indicators; Species richness, shannon, fisher, simpson, richness, eveness, divergence

• One comparison png plot in the Pcoa space that summarizes α- and β-diversity in scatterplots and illustrates that the combination of the three components computed with PCoA allows proper differentiation among vegetation types:

  • PCoA#1 allows differentiating medium and high diversity forests from low diversity forest and low vegetation, but does not discriminate medium and high diversity forests.
  • PCoA#2 allows differentiating low diversity forest from medium/high diversity forests and low vegetation
  • PCoA#3 allows differentiating medium diversity forests from high diversity forests and low vegetation.