Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information Jason W. Karl a,b, ⁎, Brian A. Maurer b a Jornada Experimental Range, U.S.D.A. Agricultural Research Service, Las Cruces, NM, USA b Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA abstract article info Article history: Received 30 July 2009 Received in revised form 18 February 2010 Accepted 19 February 2010 Keywords: Object-based image analysis Scale Variogram Kriging Geostatistics A significant challenge in ecological studies has been defining scales of observation that correspond to the relevant ecological scales for organisms or processes of interest. Remote sensing has become commonplace in ecological studies and management, but the default resolution of imagery often used in studies is an arbitrary scale of observation. Segmentation of images into objects has been proposed as an alternative method for scaling remotely-sensed data into units having ecological meaning. However, to date, the selection of image object sets to represent landscape patterns has been largely subjective. Changes in observation scale affect the variance and spatial dependence of measured variables, and may be useful in determining which levels of image segmentation are most appropriate for a given purpose. We used observations of percent bare-ground cover from 346 field sites in a semi-arid shrub-steppe ecosystem of southern Idaho to look at the changes in spatial dependence of regression predictions and residuals for 10 different levels of image segmentation. We found that the segmentation level whose regression predictions had spatial dependence that most closely matched the spatial dependence of the field samples also had the strongest predicted-to-observed correlations. This suggested that for percent bare-ground cover in our study area an appropriate scale could be defined. With the incorporation of a geostatistical interpolator to predict the value of regression residuals at unsampled locations, however, we achieved consistently strong correlations across many segmentation levels. This suggests that if spatial dependence in percent bare ground is accounted for, a range of appropriate scales could be defined. Because the best analysis scale may vary for different ecosystem attributes and many inquiries consider more than one attribute, methods that can perform well across a range of scales and perhaps not at a single, ideal scale are important. More work is needed to develop methods that consider a wider range of ways to segment images into different scales and select sets of scales that perform best for answering specific management questions. The robustness of ecological landscape analyses will increase as methods are devised that remove the subjectivity with which observational scales are defined and selected. Published by Elsevier B.V. 1. Introduction Scale is widely recognized as a critical attribute of ecological inquiries that not only defines what patterns and processes can be measured, but also influences observable relationships and governs the inferences that can be made from a set of data (Allen and Starr, 1982; O'Neill et al., 1986b, 1989; Wiens, 1989). In order for data to be useful for management decision-making, it must be collected and analyzed at spatial and temporal scales relevant to processes of interest to managers (O'Neill et al., 1986a) because different patterns can emerge at different scales for almost any ecosystem (Wiens, 1989). Scale is a characteristic of a set of observations, and the choice of scale constrains the patterns and processes that are observable (Burnett and Blaschke, 2003). In general terms, scale refers to the grain and extent of observations made in a study area where grain refers to the finest level of spatial and temporal detail observable and extent refers to the maximum area under consideration (Turner et al., 1989). Grain and extent define the upper and lower limits of inference because elements of patterns below the grain cannot be detected and inferences beyond the extent cannot be made without assuming scale-independent uniformity of patterns and processes (Wiens, 1989). Information at scales finer than the observation grain is filtered out and treated as noise, and information at scales larger than the observation extent is also filtered out and becomes context for Ecological Informatics 5 (2010) 194–202 ⁎ Corresponding author. USDA ARS, Jornada Experimental Range, P.O. Box 80003, MSC 3JER, New Mexico State University, Las Cruces, NM 88003, USA. Tel.: +1 575 646 7015. E-mail address: jkarl@nmsu.edu (J.W. Karl). 1574-9541/$ – see front matter. Published by Elsevier B.V. doi:10.1016/j.ecoinf.2010.02.004 Contents lists available at ScienceDirect Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf