Submitted to: Photogrammetry and Remote Sensing International Archives
Publication Type: Proceedings
Publication Acceptance Date: May 1, 2010
Publication Date: June 29, 2010
Citation: Karl, J.W., Laliberte, A., Rango, A. 2010. Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information. The International Archives of the Photogrammetry, Remote Sensing, and Spatial Information Sciences. June 29 - July 2, 2010, Ghent, Belgium. Vol. No. XXXVIII-4/C7. Technical Abstract: A challenge in ecological studies is defining scales of observation that correspond to relevant ecological scales for organisms or processes. Image segmentation has been proposed as an alternative to pixel-based methods for scaling remotely-sensed data into ecologically-meaningful units. However, to date, selection of image object sets has been largely subjective. Changing scale of image segmentation affects the variance and spatial dependence (amount and range of spatial autocorrelation) of measured variables, and this information can be used to determine appropriate levels of image segmentation. Our objective was to examine how scaling via image segmentation changes spatial dependence of regression-based predictions of landscape features and to determine if these changes could identify appropriate segmentation levels for a given objective. We segmented an Ikonos image for southern Idaho (USA) into successively coarser scales and evaluated goodness-of-fit and spatial dependence of regression predictions of invasive western juniper (Juniperus occidentalis) density. Correlations between juniper density estimates and imagery increased with scale initially, but then decreased as scale became coarser. Scales with highest correlations generally exhibited the most spatial dependence in the regression predictions and residuals. Aggregating original juniper density estimates by image objects changed their spatial dependence, and the point at which spatial dependence began to diverge from the original observations coincided with the highest correlations. Looking at scale effects on spatial dependence of observations may be a simple method for selecting appropriate segmentation levels. The robustness of ecological analyses will increase as methods are devised that remove the subjectivity of selecting scales.