What it Takes to get the Model Running: From a Hydraulic and Habitat Geoprocessor\u27s Standpoint

The analysis of riverine system using 2D hydraulic models and the subsequent simulation of fish habitat has recently increased in sophistication, as development of the methodology continues. With this sophistication comes an associated increase in the quantity and quality of empirical data and preprocessed inputs. With field data collection and preprocessing spatial in nature, Geographic Information Systems (GIS) have been used in the development of the methodology, to streamline preprocessing, and quality control. The author discusses the day-to-day issues of providing data to the modeler, ways to avoid common pitfalls, and problems associated with the effort. When the model results are suspect, it is commonly the spatial and symbolic visualization using a GIS that provides the first tool in discovering errors. Further, the use of enhanced GIS methods allows for further development and insight into the 2D modeling process and results, often contributing to a reduction in errors

Riparian Community and Aquatic Habitat Classification for the Shasta River

This project was undertaken in support of salmon and habitat restoration within the Shasta River drainage. The primary focus was to delineate both riparian and aquatic habitat within the study area based on classification of multi-spectral imagery. Ground truth data were provided by the USFWS for both riparian species and stream habitat types and were used to guide a combination of supervised and unsupervised classification for both riparian and stream habitats. Image classification results are provided for vegetation and stream habitats, including an error assessment of the classifications. A GIS project was also developed to allow resource managers access to both the raw and classified imagery

High Resolution Channel Geometry from Repeat Aerial Imagery

River channel cross sectional geometry is a key attribute for controlling the river energy balances where surface heat fluxes dominate and discharge varies significantly over short time periods throughout the open water season. These dynamics are seen in higher gradient portions of Arctic rivers where surface heat fluxes can dominates river energy balances and low hillslope storage produce rapidly varying hydrographs. Additionally, arctic river geometry can be highly dynamic in the face of thermal erosion of permafrost landscape. While direct in-situ measurements of channel cross sectional geometry are accurate, they are limited in spatial resolution and coverage, and can be access limited in remote areas. Remote sensing can help gather data at high spatial resolutions and large areas, however techniques for extracting channel geometry is often limited to the banks and flood plains adjacent to river, as the water column inhibits sensing of the river bed itself. Green light LiDAR can be used to map bathymetry, however this is expensive, difficult to obtain at large spatial scales, and dependent on water quality. Alternatively, 3D photogrammetry from aerial imagery can be used to analyze the non-wetted portion of the river channel, but extracting full cross sections requires extrapolation into the wetted portion of the river. To bridge these gaps, an approach for using repeat aerial imagery surveys with visual (RGB) and near infrared (NIR) to extract high resolution channel geometry for the Kuparuk River in the Alaskan Arctic was developed. Aerial imagery surveys were conducted under multiple flow conditions and water surface geometry (elevation and width) were extracted through photogrammetry. Channel geometry was extracted by combining water surface widths and elevations from multiple flights. The accuracy of these results were compared against field surveyed cross sections at many locations throughout the study reach and a digital elevation model created under extremely low flow conditions. These extrapolation methods have shown to be promising for estimating detailed channel geometry at large scales