The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology

By Nathalie Pettorelli, Sadie J. Ryan, Thomas Mueller, N. Bunnefeld, B. Jędrzejewska, M. Lima, Kyrre Kausrud. 2011

771 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/927bf89488fcf5292a9941dfb3fdf928bdcd374e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Markus Neteler
2. Miranda C. Jones
3. T. O'Brien
4. A. Gutierrez
5. Nathalie Pettorelli
6. Harini Nagendra
7. Carlo Ricotta
8. Kate S. He

In Ecology

1. Markus Metz
2. Markus Neteler
3. J. Belnap
4. Miranda C. Jones
5. T. O'Brien
6. A. Gutierrez
7. Nathalie Pettorelli
8. Harini Nagendra

In Nature and Landscape Conservation

1. J. Belnap
2. T. O'Brien
3. Nathalie Pettorelli
4. Harini Nagendra
5. Carlo Ricotta
6. Kate S. He
7. Duccio Rocchini
8. Markus Neteler

Citations in the corpus

• In search of greener pastures: Using satellite images to predict the effects of environmental change on zebra migration (2013, methodology): “While remote sensing data have been used in general mammalmovement and distribution models [Boone et al., 2006; Holdo et al., 2009; Pettorelli et al., 2011], hitherto they have not been used to model initiation of migration and speed of travel in terrestrial mammals.”
• Building capacity in biodiversity monitoring at the global scale (2017, background): “Numerous studies have highlighted the potential key role of satellite data in macroecology, plant ecology, animal population dynamics, habitat-selection and habitat-use studies, movement ecology, and paleoecology (Pettorelli et al. 2011).”
• Will remote sensing shape the next generation of species distribution models? (2015, background): “Many studies have used remotely sensed variables to model habitat suitability for animals, in particular using satellite-derived land cover classifications (Leyequien et al. 2007) as well as continuous metrics of vegetation productivity such as NDVI (Pettorelli et al. 2011).”
• Potential of remote sensing to predict species invasions (2015, background): “NDVI has also proven to be unexpectedly successful in animal ecology for predicting distribution, abundance and life history traits of herbivorous and non- herbivorous species (Pettorelli et al., 2011).”
• Satellite remote sensing for applied ecologists: opportunities and challenges (2014, background): “dynamics (Pettorelli et al. 2011).”
• Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats (2013, methodology): “Recent studies have reported various NDVI applications in animal ecology, confirming the usefulness of this index as a proxy for linking vegetation dynamics with animal distribution, abundance and dynamics (Pettorelli et al. 2011; Olson et al. 2011; Ryan et al. 2012).”