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

Robin Pouteau,Philippe Birnbaum,Thomas Gillespie

doi:10.3390/rs10050698

Abstract

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.

Highlights

IntroductionImproving predictions of biodiversity, which encompasses the variety of life at all levels of organization (from genetic diversity within a species to diversity within entire regions or ecosystems), is essential if we are to meet the challenges posed by global change
Improving predictions of biodiversity, which encompasses the variety of life at all levels of organization, is essential if we are to meet the challenges posed by global change
Plant species richness measured in the field has been successfully estimated using normalized difference vegetation index (NDVI) measurements derived from a range of sensors including Advanced Very High Resolution Radiometer (AVHRR) [5,6], Landsat [5,7,8,9], radar [10] and LIDAR [11,12] in both temperate [5,6,13] and tropical ecosystems [7,8,9,11,12,13]

Summary

Introduction

Improving predictions of biodiversity, which encompasses the variety of life at all levels of organization (from genetic diversity within a species to diversity within entire regions or ecosystems), is essential if we are to meet the challenges posed by global change. Remote sensing is acknowledged as a promising technique that will shape the generation of biodiversity models such as correlative species distribution models and macro-ecological models [1,2]. The NDVI-species richness relationship remains poorly understood as it is highly context- and scale-dependent and because of correlated biotic and abiotic factors that influence NDVI [17,18]

Methods

Results

Conclusion