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Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation

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Abstract

To successfully use remotely-sensed data in landscape-level management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis, segmenting images into homogeneous regions called objects, has been suggested for increasing accuracy of remotely-sensed products, but little research has gone into determining image object size with regard to scaling of ecosystem properties. We looked at how segmentation of high-resolution Ikonos and medium-resolution Landsat images into successively coarser objects affected multivariate correlations between image data and eight percent-cover measurements of a sagebrush ecosystem. We also looked at changes in correlation as imagery was aggregated into larger square pixels. We found similar canonical correlations between field and image data at the finest scales, but higher for image segmentation than pixel aggregation for both images when scale increased. For image segmentation, correlations between the canonical variables and original field variables were invariant with respect to size of the image objects, suggesting linear scaling of vegetation cover in our study system. We detected a scaling threshold with the Ikonos segmentation and confirmed with a semi-variogram of the sample data. Below the threshold interpretation of the canonical variables was consistent: scale levels differed primarily in the amount of detail portrayed. Above the threshold, meaning of the canonical variables changed. This approach proved useful for evaluating overall utility of images to address an objective, and identified scaling limits for analysis. Selection of appropriate scale for analysis will ultimately depend on the objective being considered.

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References

  • Addink EA, de Jong SM, Pebesma EJ (2007) The importance of scale in object-based mapping of vegetation parameters with hyperspectral imagery. Photogramm Eng Remote Sens 73:905–912

    Google Scholar 

  • Allen TFH, Starr TB (1982) Hierarchy: perspectives for ecological complexity. University of Chicago Press, Chicago

    Google Scholar 

  • Atkinson PM (2004) Resolution manipulation and sub-pixel mapping. In: de Jong SM, van der Meer FD (eds) Remote sensing image analysis: including the spatial domain. Kluwer, Dordrecht, pp 51–70

    Chapter  Google Scholar 

  • Baatz M, Schäpe A (2000) Multiresolution segmentation—an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Griesebner G (eds) Angewandte Geographische Informationsverarbeitung XII. Wichmann-Verlag, Heidelberg, pp 12–23

    Google Scholar 

  • Bellier E, Monestiez P, Durbec J, Candau J (2007) Identifying spatial relationships at multiple scales: principal coordinates of neighbor matrices (PCNM) and geostatistical approaches. Ecography 30:385–399

    Google Scholar 

  • Bulman D (2000) Is the application of remote sensing to weed mapping just “S-pie in the sky”? Weed Science Society of Victoria Annual Conference. Victoria, Australia

    Google Scholar 

  • Burnett C, Blaschke T (2003) A multi-scale segmentation/object relationship modeling methodology for landscape analysis. Ecol Model 168:233–249

    Article  Google Scholar 

  • Chavez PS Jr (1996) Image-based atmospheric corrections—revisited and improved. Photogramm Eng Remote Sens 62:1025–1036

    Google Scholar 

  • Chen W, Henebry GM (2009) Change of spatial information under rescaling: a case study using multi-resolution image series. ISPRS J Photogramm Remote Sens 64:592–597

    Article  Google Scholar 

  • Crist EP, Kauth RJ (1986) The tasseled cap de-mystified. Photogramm Eng Remote Sens 52:81–86

    Google Scholar 

  • Crist EP, Laurin R, Cicone RC (1986) Vegetation and soils information contained in transformed thematic mapper data. European Space Agency, Paris, pp 1465–1470

    Google Scholar 

  • Dark SJ, Bram D (2007) The modifiable areal unit problem (MAUP) in physical geography. Prog Phys Geogr 31:471–479

    Article  Google Scholar 

  • Feitosa RQ, Costa GAOP, Cazes TB, Feijo B (2006) A genetic approach for the automatic adaption of segmentation parameters. Salzburg University, Salzburg

    Google Scholar 

  • Fisher P (1997) The pixel: a snare and a delusion. Int J Remote Sens 18:679–685

    Article  Google Scholar 

  • Foody GM (2004) Sub-pixel methods in remote sensing. In: de Jong SM, van der Meer FD (eds) Remote sensing image analysis: including the spatial domain. Kluwer, Dordrecht, pp 71–92

    Google Scholar 

  • Fortin MJ, Dale M (2005) Spatial analysis: a guide for ecologists. Cambridge University Press, Cambridge

    Google Scholar 

  • Goodin DG, Henebry GM (2002) The effect of rescaling on fine spatial resolution NDVI data: a test using multi-resolution aircraft sensor data. Int J Remote Sens 23:3865–3871

    Article  Google Scholar 

  • Hay GJ, Marceau DJ, Dube P, Bouchard A (2001) A multiscale framework for landscape analysis: object-specific analysis and upscaling. Landscape Ecol 16:471–490

    Article  Google Scholar 

  • Herrick JE, Van Zee JW, Havstad KM, Burkett LM, Whitford WG (2005) Monitoring manual for grassland, shrubland, and savanna ecosystems. USDA-ARS Jornada Experimental Range, Las Cruces

    Google Scholar 

  • Horne JH (2003) A tasseled cap transformation for IKONOS images. (May 5, 2003). ASPRS

  • Hunt ER Jr, Hamilton R, Everitt JH (2005) What weeds can be remotely sensed? USDA Forest Service, Remote Sensing Applications Center A weed manager’s guide to remote sensing and GIS

  • Jelinski DE, Wu J (1996) The modifiable areal unit problem and implications for landscape ecology. Landscape Ecol 11:129–140

    Article  Google Scholar 

  • Jensen JR (1996) Introductory digital image processing, 2nd edn. Prentice-Hall, Inc., Upper Saddle River

    Google Scholar 

  • Johnson RA, Wichern DW (2002) Applied multivariate statistical analysis, 5th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Kim M, Madden M (2006) Determination of optimal scale parameter for alliance-level forest classification of multispectral IKONOS images. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Salzburg

    Google Scholar 

  • Lass LW, Callihan RH (1997) Effects of phenological stage on detectability of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol 11:248–256

    Google Scholar 

  • Luscier JD, Thompson WL, Wilson JM, Gorham BE, Dragut LD (2006) Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots. Front Ecol Environ 4:408–413

    Article  Google Scholar 

  • Navulur K (2007) Multi-spectral image analysis using the object-oriented paradigm. CRC Press, Taylor and Francis Group, LLC, Boca Raton

    Google Scholar 

  • Neubert M, Herold H, Meinel G (2006) Evaluation of remote sensing image segmentation quality—further results and concepts. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Salzburg

    Google Scholar 

  • O’Neill RV, Deangelis DL, Waide JB, Allen TFH (1986) A hierarchical concept of ecosystems. Princeton University Press, Princeton

    Google Scholar 

  • Openshaw S, Taylor PJ (1981) The modifiable areal unit problem. In: Wrigley N, Bennett RJ (eds) Quantitative geography: a British view. Routledge & Kegan Paul Ltd, London, pp 60–69

    Google Scholar 

  • Oppenshaw S (1984) Ecological fallacies and the analysis of areal census data. Environ Plan A 16:17–31

    Article  Google Scholar 

  • Owen DE (2008) Geology of Craters of the Moon. Craters of the Moon National Monument. National Park Service, Arco

    Google Scholar 

  • Radeloff VC, Miller TF, He HS, Mladenoff DJ (2000) Periodicity in spatial data and geostatistical models: autocorrelation between patches. Ecography 23:81–91

    Article  Google Scholar 

  • Strahler AH, Woodcock C, Smith JA (1986) On the nature of models in remote sensing. Remote Sens Environ 20:121–139

    Article  Google Scholar 

  • Svancara LK, Garton EO, Chang K, Scott JM, Zager P, Gratson M (2002) The inherit aggravation of aggregation: an example with elk aerial survey data. J Wildl Manag 66:776–787

    Article  Google Scholar 

  • Wang L, Sousa WP, Gong P (2004) Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens 25:5655–5668

    Article  Google Scholar 

  • Wiens JA (1989) Spatial scaling in ecology. Funct Ecol 3:385–397

    Article  Google Scholar 

  • Woodcock C, Harward VJ (1992) Nested-hierarchical scene models and image segmentation. Int J Remote Sens 13:3167–3187

    Article  Google Scholar 

  • Wu J (1999) Hierarchy and scaling: extrapolating information along a scaling ladder. Can J Remote Sens 25:367–380

    Google Scholar 

  • Wu J (2004) Effects of changing scale on landscape pattern analysis: scaling relations. Landscape Ecol 19:125–138

    Article  Google Scholar 

Download references

Acknowledgments

This research was supported by funding from the M.J. Murdock Charitable Trust, The Nature Conservancy’s Rodney Johnson/Katherine Ordway Science Endowment, and the Lava Lake Institute for Science and Conservation. P. Murphy and A. Lucas collected the field data used in this study. A. Asada digitized field forms and performed data quality-control. G. Henebry, J. Qi, S. Riley, G. Roloff, R. Unnasch, and two anonymous reviewers provided valuable comments and edits to this manuscript.

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  1. Jason W. Karl

    Present address: USDA Agricultural Research Service, Jornada Experimental Range, New Mexico State University, P.O. Box 30003, MSC 3JER, Las Cruces, NM, 88003-8003, USA

Authors and Affiliations

  1. Idaho Chapter of The Nature Conservancy, 116 1st Ave North, Hailey, ID, 83333, USA

    Jason W. Karl

  2. Department of Fisheries and Wildlife, Michigan State University, 13 Natural Resources, East Lansing, MI, 48824, USA

    Jason W. Karl & Brian A. Maurer

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  1. Jason W. Karl
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Correspondence to Jason W. Karl.

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Karl, J.W., Maurer, B.A. Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation. Landscape Ecol 25, 591–605 (2010). https://doi.org/10.1007/s10980-009-9439-4

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  • DOI: https://doi.org/10.1007/s10980-009-9439-4

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