Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago |
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Authors: | H Santin-Janin M Garel J-L Chapuis D Pontier |
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Institution: | 1.Laboratoire de Biométrie et Biologie évolutive,Université de Lyon, Université Lyon 1, CNRS, UMR 5558,Villeurbanne,France;2.Office National de la Chasse et de la Faune Sauvage,Centre National d’étude et de Recherche Appliquée Faune de Montagne,Montpellier Cedex 05,France;3.Muséum National d’Histoire Naturelle, Département Ecologie et Gestion de la Biodiversité,Conservation des espèces, restauration et suivi des populations, UMR 5173 MNHN-CNRS-P6, CP 53,Paris,France |
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Abstract: | Numerous ecological studies, including of the polar environment, are now using the remotely sensed normalized difference vegetation
index (NDVI, e.g. PAL-NDVI or MODIS-NDVI) as a proxy of vegetation productivity rather than performing direct vegetation assessments.
Even though previous data strongly suggested a saturation of NDVI at high biomass values, few studies have explicitly included
this characteristic in the modelling process. Here, we developed a generalized non-linear model to explicitly model the relationship
between temporal variations of NDVI (Pathfinder AVHRR Land 8 km dataset) and empirical field data. We illustrated our approach
on the Kerguelen archipelago by using a green biomass index (point-intercept protocol) sampled at a small scale relative to
PAL-NDVI data, and in presence of spatial (water) and temporal (cloud contamination, snow) heterogeneity, i.e. field conditions
encountered in many ecological studies. We showed a strong relationship (r
pred.obs = 0.89 0.77; 0.95]95%) between this index and the seasonal component of NDVI time series (NDVIcomp). Despite the absence of lignified species in the stand, the NDVIcomp reached an asymptote (0.54 ± 0.05) for high values of green biomass index stressing the need to account for non-linearity
when relating NDVI and plant measurements. We provided here a new methodological framework to standardize comparisons between
studies assessing performance of NDVI as a proxy of vegetation data.
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Keywords: | Generalized non-linear model Negative binomial distribution NDVI Predictive model Sub-antarctic Validation study Vegetation biomass |
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