Monitoring fine-grained sediment in the Colorado River ecosystem, Arizona — Control network and conventional survey techniques

In 2002, fine-grained sediment (sand, silt, and clay) monitoring in the Colorado River downstream from Glen Canyon Dam was initiated to survey channel topography at scales previously unobtainable in this canyon setting. This report presents the methods used to establish the high-resolution global positioning system (GPS) control network required for this effort as well as the conventional surveying techniques used in the study. Using simultaneous, dual-frequency GPS vector-based methods, the network points were determined to have positioning accuracies of less than 0.03 meters (m) and ellipsoidal height accuracies of between 0.01 and 0.10 m at a 95-percent degree of confidence. We also assessed network point quality with repeated, electronic (optical) total-station observations at 39 points for a total of 362 measurements; the mean range was 0.022 m in horizontal and 0.13 in vertical at a 95-percent confidence interval. These results indicate that the control network is of sufficient spatial and vertical accuracy for collection of airborne and subaerial remote-sensing technologies and integration of these data in a geographic information system on a repeatable basis without anomalies. The monitoring methods were employed in up to 11 discrete reaches over various time intervals. The reaches varied from 1.3 to 6.4 kilometers in length. Field results from surveys in 2000, 2002, and 2004 are described, during which conventional surveying was used to collect more than 3000 points per day. Ground points were used as checkpoints and to supplement areas just below or above the water surface, where remote-sensing data are not collected or are subject to greater error. An accuracy of +or- 0.05 m was identified as the minimum precision of individual ground points. These results are important for assessing digital elevation model (DEM) quality and identifying detection limits of significant change among surfaces generated from remote-sensing technologies.