Using the satellite-derived NDVI to assess ecological responses to environmental change

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Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.

Section snippets

Using the NDVI to monitor vegetation and plant responses to environmental change

Because the NDVI correlates directly with vegetation productivity [21], there are numerous possible applications of this index for ecological purposes. The NDVI provides information about the spatial and temporal distribution of vegetation communities [21], vegetation biomass [21], CO2 fluxes 22, 23, vegetation quality for herbivores (because the rate of greening can be correlated with food quality [24]) and the extent of land degradation in various ecosystems 25, 26.

The NDVI was used

Using the NVDI to assess trophic interactions

So far, the NDVI has been used predominantly in studies focusing on the effects of environmental change on plants. However, the NDVI could also be used to gain novel insight into trophic inter-linkages. The use of the NVDI as a covariate rather than as a response variable has opened up new areas of research in trophic interactions. Recently, studies have coupled vegetation dynamics, as assessed by the NVDI, with biodiversity 35, 40, 41, animal species distribution 42, 43, 44, the movement

Ecologically relevant measures that can be derived from NDVI time-series

It can be complicated to couple information from two trophic levels when each is assessed at different temporal or spatial scales. For example, it is possible to get up to 365 NDVI pictures a year, whereas some animal responses can be gathered only once a year. However, depending on the biological question asked (e.g. the impact of a delayed or early start or end of the growing season, or vegetation availability in summer, on the performance of an animal population), only part of the

Other vegetation indices

The NVDI is a vegetation index that has demonstrated its usefulness in many ecological studies. However, in some situations, other vegetation indexes might be more appropriate.

The relationship between the NVDI and vegetation can be biased in sparsely vegetated areas (e.g. arid to semi-arid zones in Australia) and dense canopies (e.g. Amazonian Forest [55]). In sparsely vegetated areas with a leaf area index (LAI) of <3, the NVDI is influenced mainly by soil reflectance, whereas for LAI>6 (i.e.

Conclusions and perspectives

The NVDI has already been successfully applied to research on temporal and spatial trends and variation in vegetation distribution, productivity and dynamics, to monitor habitat degradation and fragmentation, and the ecological effects of climatic disasters such as drought or fire. The encouraging results reviewed here suggest that NDVI will become an extremely useful tool for terrestrial ecologists aiming to gain a better understanding of how vegetation dynamics and distribution affect

Acknowledgements

We thank the Distributed Active Archive Center (Code 902.2) at the Goddard Space Flight Center, Greenbelt, MD, 20771, for producing the data in their present form and distributing them. The original data products were produced under the NOAA/NASA Pathfinder program, by a processing team headed by Mary James of the Goddard Global Change Data Center; and the science algorithms were established by the AVHRR Land Science Working Group, chaired by John Townshend of the University of Maryland.

Glossary

AVHRR:
Advanced Very High Resolution Radiometer.
BISE:
Best Index Slope Extraction. This is a method aiming at smoothing NDVI time-series.
BRDF:
Bidirectional Reflectance Distribution Function. This gives the reflectance of a target as a function of illumination geometry and viewing geometry. The BRDF depends on wavelength and is determined by the structural and optical properties of the surface, such as shadow-casting, mutiple scattering, mutual shadowing, transmission, reflection, absorption and

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