Predicting Tropical Tree Species Richness from Normalized Difference Vegetation Index Time Series: The Devil Is Perhaps Not in the Detail

Robin Pouteau (1, 2, 3) , Thomas Gillespie (4) , Philippe Birnbaum (3)

1 IAC - Institut Agronomique Néo-Calédonien
2 Taizhou University
3 UMR AMAP - Botanique et Modélisation de l'Architecture des Plantes et des Végétations
4 Department of Geography

The normalized difference vegetation index (NDVI) derived from remote sensing is a common explanatory variable inputted in correlative biodiversity models in the form of descriptive statistics summarizing complex time series. Here, we hypothesized that a single meaningful remotely-sensed scene can provide better prediction of species richness than any usual multi-scene statistics. We tested this idea using a 15-year time series of six-day composite MODIS NDVI data combined with field measurements of tree species richness in the tropical biodiversity hotspot of New Caledonia. Although some overall, seasonal, annual and monthly statistics appeared to successfully correlate with tree species richness in New Caledonia, a range of individual scenes were found to provide significantly better predictions of both the overall tree species richness (|r| = 0.68) and the richness of large trees (|r| = 0.91). A preliminary screening of the NDVI-species richness relationship within each time step can therefore be an effective and straightforward way to maximize the accuracy of NDVI-based correlative biodiversity models

Mots clés: Biodiversity hotspot; multispectral remote sensing; tropical forests; species richness; productivity

Mots clés: nouvelle calédonie

Domaines: Sciences du Vivant [q-bio]

Robin Pouteau, Thomas Gillespie, Philippe Birnbaum. Predicting Tropical Tree Species Richness from Normalized Difference Vegetation Index Time Series: The Devil Is Perhaps Not in the Detail. Remote Sensing, 2018, 10 (5), pp.698. ⟨10.3390/rs10050698⟩. ⟨hal-01918114⟩