Landscape resource mapping for wildlife research using very high resolution satellite imagery

Methods in Ecology and Evolution

Volumn 4, Issue 10, 2013, Pages 982-992

  Recio, Mariano R ^a   Mathieu, Renaud ^b   Hall, G Brent ^a   Moore, Antoni B ^a   Seddon, Philip J ^a  

^a UNIVERSITY OF OTAGO   (New Zealand)

^b Future production Chemicals Cluster   (South Africa)

Author keywords

Fine scale; Object based image analysis; Quickbird; Resource selection; Very high spatial resolution; Wildlife research

Indexed keywords

EID: 84885601486     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/2041-210X.12094     Document Type: Article

References (43)

1. Addink, E.A., de Jong, S.M. & Pebesma, E.J. (2007) The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogrammetric Engineering & Remote Sensing, 73, 905-912.
2. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I. & Heynen, M. (2004) Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, 239-258.
3. Blaschke, T. (2010) Object based image analysis for remote sensing. ISPRS International Journal of Photogrammetry and Remote Sensing, 65, 2-16.
4. Boyce, M.S. (2006) Scale for resource selection functions. Diversity and Distributions, 12, 269-276.
5. Boyce, M.S. & McDonald, L.L. (1999) Relating populations to habitats using resource selection functions. Trends in Ecology and Evolution, 14, 268-272.
6. Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Multimodel Inference. Springer, New York, USA.
7. Cameron, B., van Heezik, Y., Maloney, R., Seddon, P. & Harraway, J. (2005) Improving predator capture rates: analysis of river margin trap site data in the Waitaki basin, New Zealand. New Zealand Journal of Ecology, 29, 117-128.
8. Chen, G., Hay, G.J., Castilla, G., St-Onge, B. & Powers, R. (2011) A multiscale geographic object-based image analysis to estímate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographical Information Science, 25, 877-893.
9. Chuvieco, E. (2006) Teledeteccion Ambiental. La Observacion de la Tierra Desde el Espacio. 2nd edn. Ariel Ciencia, Barcelona.
10. Congalton, R. (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
11. Culbert, P.D., Radeloff, V.C., St-Louis, V., Flather, C.H., Rittenhouse, C.D., Albright, T.P. & Pidgeon, A.M. (2012) Modeling broad-scale patterns of avian species richness across the Midwestern United States with measures of satellite image texture. Remote Sensing of Environment, 118, 140-150.
12. Definiens (2010) ECognition Developer 8.0.1. - User Guide. 1.2.1 edn. Definiens AG, Munich.
13. Estes, L.D., Reillo, P.R., Mwangi, A.G., Okin, G.S. & Shugart, H.H. (2010) Remote sensing of structural complexity indices for habitat and species distribution modeling. Remote Sensing of Environment, 114, 792-804.
14. Forester, D.J., Im, H.K. & Rathouz, P.J. (2009) Accounting for animal movement in estimation of resource selection functions: sampling and data analysis. Ecology, 90, 3554-3565.
15. Frair, J., Fieberg, J., Hebblewhite, M., Cagnacci, F., DeCesare, N. & Pedrotti, L. (2010) Resolving issues of imprecise and habitat-biased locations in ecological analyses using gps telemetry data. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 2187-2200.
16. Franklin, S.E. (2010) Remote Sensing for Biodiversity and Wildlife Management. McGraw-Hill, New York.
17. Fretwell, T.J., LaRue, M.A., Paul Morin, P., Kooyman, G.L., Wienecke, B., Ratcliffe, N. et al. (2012) An emperor Penguin population estimate: the first global, synoptic survey of a species from space. PLoS ONE, 7, e33751.
18. Gillies, C. (2001) Advances in New Zealand mammalogy 1990-2000: House cat. Journal of the Royal Society of New Zealand, 31, 205-218.
19. Hebblewhite, M. & Haydon, D. (2010) Distinguishing technology from biology: A critical review of the use of GPS telemetry data in ecology. Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 2303-2312.
20. Horning, N., Robinson, J., Sterling, E., Turner, W. & Spector, S. (2010) Remote Sensing for Ecology and Conservation. Oxford University Press, Oxford.
21. Hosmer, D.W. & Lemeshow, S. (2000) Applied Logistic Regression. Wiley, New York.
22. Huete, A. (1988) A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295-309.
23. TM satellite imagery to detect the nests of masked boobies Sula dactylatra. Wildlife Biology, 17, 210-216.
24. IUCN (2012) IUCN Red List of Threatened Species. Version 2012.2. URL http://www.iucnredlist.org [accessed 25 March 2013].
25. Jones, C. & Norbury, G. (2006) Habitat use as a predictor of nest raiding by individual hedgehogs Erinaceus europaeus in New Zealand. Pacific Conservation Biology, 12, 180-188.
26. Lahoz-Monfort, J.J., Guillera-Arroita, G., Milner-Gulland, E.J., Young, R.P. & Nicholson, E. (2010) Satellite imagery as a single source of predictor variables for habitat suitability modelling: how Landsat can inform the conservation of a critically endangered lemur. Journal of Applied Ecology, 47, 1094-1102.
27. Laliberte, A., Rango, A., Havstad, K., Paris, J., Beck, R., McNeely, R. & Gonzalez, A. (2004) Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment, 93, 198-210.
28. Landis, J.R. & Kock, G.G. (1977) The measurement of observer agreement for categorical data. Biometrics, 33, 159-174.
29. Marzluff, J.M., Millspaugh, J.J., Hurvitz, P. & Handcock, M.S. (2004) Relating resources to a probabilistic measure of space use: forest fragments and Steller's Jays. Ecology, 85, 1411-1427.
30. Mathieu, R., Freeman, C. & Aryal, J. (2007) Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery. Landscape and Urban Planning, 81, 179-192.
31. Möller, M., Lymburner, L. & Volk, M. (2007) The comparison index: a tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9, 311-321.
32. Moorcroft, P.J. & Barnett, A. (2008) Mechanistic home range models and resource selection analysis: a reconciliation and unification. Ecology, 89, 1112-1119.
33. Mysterud, A. & Ims, R.A. (1998) Functional responses in habitat use: availability influences relative use in trade-off situations. Ecology, 88, 56-62.
34. Pohl, C. & van Genderen, J. (1998) Multisensor image fusion in remote sensing: concepts, methods and applications. International Journal of Remote Sensing, 19, 823-854.
35. Recio, M.R. (2011) Spatial Ecology of Introduced Mammalian Predators in New Zealand: Using Satellite Technology to Quantify Resource Selection. PhD thesis, University of Otago, Dunedin.
36. Recio, M.R., Mathieu, R., Denys, P., Sirguey, P. & Seddon, P.J. (2011) Lightweight GPS-Tags, one giant leap for wildlife tracking? An Assessment Approach PLoS ONE, 6, e28225.
37. Recio, M.R., Mathieu, R., Latham, M.C., Latham, A.D.M. & Seddon, P.J. (2013) Quantifying fine-scale resource selection by introduced European hedgehogs (Erinaceus europaeus) in ecologically sensitive areas. Biological Invasions, 15, 1807-1818.
38. Richter, R. (1998) Correction of satellite imagery over mountainous terrain. Applied Optics, 37, 4004-4015.
39. Rubio, J.L. & Carrascal, L.M. (1994) Habitat selection and conservation of an endemic Spanish lizard (Algyroides marchi) (Reptilia, Lacertidae). Biological Conservation, 70, 245-250.
40. Skidmore, A. (1999). Accuracy assessment of spatial information. Spatial Statistics for Remote Sensing (eds. A. Stein, F. van der Meer & B. Gorte), pp. 197-209. Kluwer Academic Publishers, Dordrecht.
41. Toutin, T. (2004) Geometric processing of remote sensing images: models, algorithms and methods. International Journal of Remote Sensing, 25, 1893-1924.
42. Tucker, C.J. (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of the Environment, 8, 127-150.
43. Zhan, Q., Martien, M., Tempfli, L. & Shi, W. (2005) Quality assessment for geospatial objects derived from remotely sensed data. International Journal of Remote Sensing, 26, 2953-2974.