| Management of semi-arid shrub-steppe ecosystems (i.e., rangelands) requires accurate information over large landscapes, and remote sensing is an attractive option for collecting such data. To successfully use remotely-sensed data in landscape-level rangeland management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis (OBIA), which segments image pixels into homogeneous regions, or objects, has been suggested as a way to increase accuracy of remotely-sensed products, but little research has gone into how to determine sizes of image objects with regard to scaling of ecosystem properties. The purpose of my dissertation was to determine if OBIA could be used to generate observational scales to match ecological scales in rangelands and to explore the potential for OBIA to generate accurate and repeatable remote-sensing products for managers. The work presented here was conducted in southern Idaho's Snake River Plain region. By comparing OBIA segmentation of satellite imagery into successively coarser objects to pixel-based aggregation methods, I found that canonical correlations between field-collected and image data were similar at the finest scales, but higher for image segmentation as scale increased. I also detected scaling thresholds with image segmentation that were confirmed via semi-variograms of field data. This approach proved useful for evaluating the overall utility of an image to address an objective, and identifying scaling limits for analysis. I next used observations of percent bare-ground cover from 346 field sites to consider how hierarchies of image objects created through OBIA could be used to discover appropriate scales for analysis given a specific objective. Using a regression-based approach, I found that segmentation levels whose predictions of bare-ground cover had spatial dependence that most closely matched the spatial dependence of the field samples had the highest predicted-to-observed correlations. When combined with geostatistical predictors, these changes in spatial variance with scale led to robust predictions across a range of scales. Third, I demonstrated an application of OBIA with the technique of regression kriging (RK), a geostatistical interpolator, to make spatial predictions for three aspects of rangeland condition (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]). Comparing spatial predictions from generalized least-squares (GLS) regression to RK, I found that RK implemented with OBIA produced more accurate results than GLS regression alone for all three variables measured by cross-validated root mean-squared error. Finally, I considered why techniques like OBIA, and remote sensing in general, are not more widely used in routine rangeland management. Bolstering decision-making through (1) better information tools and data to support management and (2) adaptive management has been proffered as a means for making sound management decisions, but two recent lawsuits in southern Idaho suggest that neither of these solutions is likely to be effective at managing rangelands at scales commensurate with their threats unless there are changes to the underlying management paradigm governing how the public participates in the management process. |
