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Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for monitoring rangeland health or progress toward management objectives because of its importance for assessing riparian areas, post-fire recovery, wind erosion, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are also often too expensive, require specialized sensors, or are not of high enough resolution to meet monitoring objectives. An alternative remote sensing approach that is potentially more accessible to managers is to measure vegetation heights from digital stereo aerial photographs. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. Individual shrub heights were generally underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot height averages were also underestimated compared with the field measurements. Grasses and forbs in our plots were generally too small to be adequately modeled with the resolution of imagery we used. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.
