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Tracking Animals in a Dynamic Environment: Remote Sensing Image Time Series

  • Mathieu BasilleEmail author
  • Ferdinando Urbano
  • Pierre Racine
  • Valerio Capecchi
  • Francesca Cagnacci
Chapter

Abstract

This chapter looks into the spatiotemporal dimension of both animal tracking data sets and the dynamic environmental data that can be associated with them. Typically, these geographic layers derive from remote sensing measurements, commonly those collected by sensors deployed on earth-orbiting satellites, which can be updated on a monthly, weekly or even daily basis. The modelling potential for integrating these two levels of ecological complexity (animal movement and environmental variability) is huge and comes from the possibility to investigate processes as they build up, i.e. in a full dynamic framework. This chapter’s exercise will describe how to integrate dynamic environmental data in the spatial database and join to animal locations one of the most used indices for ecological productivity and phenology, the normalised difference vegetation index (NDVI) derived from MODIS. The exercise is based on the database built so far in  Chaps. 2,  3,  4,  5 and  6.

Keywords

NDVI Raster time series Spatial database Spatiotemporal intersection 

References

  1. Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13:95–120CrossRefGoogle Scholar
  2. Basille M, Fortin D, Dussault C, Ouellet JP, Courtois R (2013) Ecologically based definition of seasons clarifies predator-prey interactions. Ecography 36:220–229CrossRefGoogle Scholar
  3. Cagnacci F, Boitani L, Powell RA, Boyce MS (2010) Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos Trans R Soc B: Biol Sci 365:2157–2162CrossRefGoogle Scholar
  4. Cracknell AP (1997) The advanced very high resolution radiometer (AVHRR). Taylor & Francis, LondonGoogle Scholar
  5. Eerens H, Haesen D, Rembold F, Urbano F, Tote C, Bydekerke L (2014) Image time series processing for agriculture monitoring. Environ Modell Softw 53:154–162Google Scholar
  6. Escadafal R, Bohbot H, Mégier J (2001) Changes in arid mediterranean ecosystems on the long term through earth observation (CAMELEO). Final Report of EU contract IC18-CT97-0155, Edited by Space Applications Institute, JRC, Ispra, ItalyGoogle Scholar
  7. Frair JL, Fieberg J, Hebblewhite M, Cagnacci F, DeCesare NJ, Pedrotti L (2010) Resolving issues of imprecise and habitat biased locations in ecological analyses using GPS telemetry data. Philos Trans R Soc B: Biol Sci 365:2187–2200CrossRefGoogle Scholar
  8. Land Processes DAAC (2008) MODIS reprojection tool user’s manual. USGS Earth Resources Observation and Science (EROS) CenterGoogle Scholar
  9. Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Dorsey Worthy L (2006) Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens Environ 105:142–154CrossRefGoogle Scholar
  10. Maisongrande P, Duchemin B, Dedieu G (2004) VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens 25:9–14CrossRefGoogle Scholar
  11. Manly BFJ, McDonald LL, Thomas DL, McDonald TL, Erickson WP (2002) Resource selection by animals. Kluver Academic Publishers, DordrechtGoogle Scholar
  12. Maselli F, Barbati A, Chiesi M, Chirici G, Corona P (2006) Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy. Remote Sens Environ 100:563–575CrossRefGoogle Scholar
  13. McClain CR (2009) A decade of satellite ocean color observations. Annu Rev Marine Sci 1:19–42Google Scholar
  14. MODIS (1999) MODIS Vegetation Index (MOD 13): Algorithm Theoretical Basis Document Page 26 of 29 (version 3)Google Scholar
  15. Moorcroft P (2012) Mechanistic approaches to understanding and predicting mammalian space use: recent advances, future directions. J Mammal 93:903–916CrossRefGoogle Scholar
  16. Moriondo M, Maselli F, Bindi M (2007) A simple model of regional wheat yield based on NDVI data. Eur J Agron 26:266–274CrossRefGoogle Scholar
  17. Nathan R, Getz WM, Revilla E, Holyoak M, Kadmon R, Saltz D, Smouse PE (2008) A movement ecology paradigm for unifying organismal movement research. Proc Natl Acad Sci 105:19052–19059PubMedCentralPubMedCrossRefGoogle Scholar
  18. Pettorelli N, Gaillard JM, Mysterud A, Duncan P, Stenseth NC, Delorme D, Van Laere G, Toigo C, Klein F (2006) Using a proxy of plant productivity (NDVI) to find key periods for animal performance: the case of roe deer. Oikos 112:565–572CrossRefGoogle Scholar
  19. Townshend JRG, Justice CO (1986) Analysis of the dynamics of African vegetation using the normalized difference vegetation index. Int J Remote Sens 8:1189–1207CrossRefGoogle Scholar
  20. Turchin P (1998) Quantitative analysis of movement: measuring and modeling population redistribution in plants and animals. Sinauer Associates, SunderlandGoogle Scholar
  21. Verhoef W, Menenti M, Azzali S (1996) A colour composite of NOAA-AVHRR–NDVI based on time series analysis (1981–1992). Int J Remote Sens 17:231–235CrossRefGoogle Scholar
  22. Yu XF, Zhuang DF (2006) Monitoring forest phenophases of Northeast China based on MODIS NDVI data. Resour Sci 28:111–117Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Mathieu Basille
    • 1
    Email author
  • Ferdinando Urbano
    • 2
  • Pierre Racine
    • 3
  • Valerio Capecchi
    • 4
  • Francesca Cagnacci
    • 5
  1. 1.Fort Lauderdale Research and Education CenterUniversity of FloridaFort LauderdaleUSA
  2. 2.Università Iuav di VeneziaVeniceItaly
  3. 3.Centre for Forest ResearchUniversity Laval, Pavillon Abitibi-Price, 2405 de la TerrasseQuebec CityCanada
  4. 4.Istituto di BiometeorologiaConsiglio Nazionale delle RicercheSesto FiorentinoItaly
  5. 5.Biodiversity and Molecular Ecology DepartmentResearch and Innovation CentreS.Michele all’AdigeItaly

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