Next Article in Journal
AHI/Himawari-8 Yonsei Aerosol Retrieval (YAER): Algorithm, Validation and Merged Products
Next Article in Special Issue
Using Google Earth Engine to Map Complex Shade-Grown Coffee Landscapes in Northern Nicaragua
Previous Article in Journal
Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II
Previous Article in Special Issue
Topography and Three-Dimensional Structure Can Estimate Tree Diversity along a Tropical Elevational Gradient in Costa Rica
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(5), 698; https://doi.org/10.3390/rs10050698

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

1
Institut Agronomique néo-Calédonien (IAC), 98800 Noumea, New Caledonia
2
Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou University, Taizhou 317000, China
3
Department of Geography, University of California, Los Angeles, CA 90095, USA
4
UMR AMAP, Université de Montpellier, CIRAD, CNRS, INRA, IRD, 34398 Montpellier, France
*
Author to whom correspondence should be addressed.
Received: 4 February 2018 / Revised: 16 April 2018 / Accepted: 25 April 2018 / Published: 3 May 2018
(This article belongs to the Special Issue Remote Sensing of Tropical Forest Biodiversity)
Full-Text   |   PDF [3791 KB, uploaded 4 May 2018]   |  

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. View Full-Text
Keywords: biodiversity hotspot; multispectral remote sensing; productivity; species richness; tropical forests biodiversity hotspot; multispectral remote sensing; productivity; species richness; tropical forests
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Pouteau, R.; Gillespie, T.W.; Birnbaum, P. Predicting Tropical Tree Species Richness from Normalized Difference Vegetation Index Time Series: The Devil Is Perhaps Not in the Detail. Remote Sens. 2018, 10, 698.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top