High-Resolution Bathymetry Dataset for the Columbia River through the Hanford ReachFinal Report, 2010 iii
Executive SummaryA bathymetric and topographic data collection and processing effort involving existing and newly collected data has been performed for the Columbia River through the Hanford Reach in central Washington State, extending 60-miles from the tailrace of Priest Rapids Dam (river mile 397) to near the vicinity of the Interstate 182 bridge just upstream of the Yakima River confluence (river mile 337).The contents of this report provide a description of the data collections, data inputs, processing methodology, and final data quality assessment used to develop a comprehensive and continuous merged bathymetric and topographic surface dataset for the Columbia River through the Hanford Reach. This work is a continuation of FY2009 work that focused on retrieving, assembling, and processing existing bathymetry and terrestrial topographic data (Coleman, 2009). At the conclusion of the FY2009 work, it was determined and recommended that additional data be collected to supplement existing bathymetric and topographic data to fill significant data gaps in the central portion of the Hanford Reach. In FY2010, hydrographic surveys were conducted and resulting data were cleaned, processed, quality checked against other sources, and incorporated into a multi-source data fusion process to produce a single high-resolution dataset to support the various DOE Hanford missions.
High-Resolution Bathymetry Dataset for the Columbia River through the Hanford ReachFinal Report, 2010 v
AcknowledgmentsWe appreciate the contributions to this study from the following PNNL staff:• Travis Yeik• Vibhav Durgesh
• Erin HamiltonWe acknowledge John Skalicky of the U.S. Fish and Wildlife Service, for without his support in providing key existing data products, this effort could not have progressed to the level it did.We express our sincere appreciation to Dr. Scott Petersen at CHPRC for funding this fundamental data development effort and realizing the many values this data will have in the near and long-term for multidisciplinary research and river protection throughout the Hanford Reach.
Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.
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