Jerry D. Tagestad

Use of Synthetic Aperture Radar (SAR) to Identify and Characterize Overwintering Areas of Fish in Ice-Covered Arctic Rivers: A Demonstration with Broad Whitefish and Their Habitats in the Sagavanirktok River, Alaska

Abstract In northern climates, locating overwintering fish can be very challenging due to thick ice cover. Areas near the coast of the Beaufort Sea provide valuable overwintering habitat for both resident and anadromous fish species; identifying them and understanding their use of overwintering areas is of special interest. Synthetic aperture radar (SAR) imagery from two spaceborne satellites was examined as an alternative to radiotelemetry for identifying anadromous fish overwintering. The presence of water and ice were sampled at 162 sites, and fish were sampled at 16 of these sites. From SAR imagery alone, we successfully identified large pools inhabited by overwintering fish in the ice-covered Sagavanirktok River, Alaska. In addition, the imagery was able to identify all of the larger pools (mean minimum length = 138 m, SD = 131, range = 15–470 m) of water located by field sampling. The effectiveness of SAR in identifying these pools varied from 31% to 100%, depending on imagery polarization, the inci...

Sage-Grouse and Wind Energy: Biology, Habits, and Potential Effects from Development

Proposed development of domestic energy resources, including wind energy, is expected to impact the sagebrush steppe ecosystem in the western United States. The greater sage-grouse relies on habitats within this ecosystem for survival, yet very little is known about how wind energy development may affect sage-grouse. The purpose of this report is to inform organizations of the impacts wind energy development could have on greater sage-grouse populations and identify information needed to fill gaps in knowledge.

Macroalgae Analysis A National GIS-based Analysis of Macroalgae Production Potential Summary Report and Project Plan

The overall project objective is to conduct a strategic analysis to assess the state of macroalgae as a feedstock for biofuels production. The objective in FY11 is to develop a multi-year systematic national assessment to evaluate the U.S. potential for macroalgae production using a GIS-based assessment tool and biophysical growth model developed as part of these activities. The initial model development for both resource assessment and constraints was completed and applied to the demonstration areas. The model for macroalgal growth was extended to the EEZ off the East and West Coasts of the United States, and a plan to merge the findings for an initial composite assessment was developed. In parallel, an assessment of land-based, port, and offshore infrastructure needs based on published and grey literature was conducted. Major information gaps and challenges encountered during this analysis were identified. Also conducted was an analysis of the type of local, state, and federal requirements that pertain to permitting land-based facilities and nearshore/offshore culture operations

Assessment of Technologies Used to Characterize Wildlife Populations in the Offshore Environment

Wind energy development in the offshore environment can have both direct and indirect effects on wildlife, yet little is known about most species that use near-shore and offshore waters due in part to the difficulty involved in studying animals in remote, challenging environments. Traditional methods to characterize offshore wildlife populations include shipboard observations. Technological advances have provided researches with an array of technologies to gather information about fauna from afar. This report describes the use and application of radar, thermal and optical imagery, and acoustic detection technologies for monitoring birds, bats, and marine mammals in offshore environments.

A GIS-Based Adaptive Management Decision Support System to Develop a Multi-Objective Framework: A Case Study Utilizing GIS Technologies and Physically-Based Models to Achieve Improved Decision Making for Site Management

ABSTRACT The notion of Adaptive Management (AM) allows for the realization and adjustment of management practices in response to elements of uncertainty. In terms of natural resource management, this will typically integrate monitoring, databases, simulation modeling, decision theory, and expert judgment to evaluate management alternatives and adapt them as necessary to continually improve the natural resource condition as defined by stakeholders. Natural resource management scenarios can often be expressed, viewed, and understood as a geospatial and temporal problem. The integration of Geographic Information System (GIS) technologies and physically-based models provide an effective state-of-the-art solution for deriving, understanding, and applying AM scenarios for land use and remediation. A recently developed GIS-based adaptive management decision support system is presented for the U.S. Department of Defense Yakima Training Center near Yakima, Washington.

Landscape Measures of Rangeland Condition in the BLM Owyhee Pilot Project: Shrub Canopy Mapping, Vegetation Classification, and Detection of Anomalous Land Areas

In 2006, the BLM tasked PNNL to collaborate in research being conducted under the Owyhee Uplands Pilot Project to assess rangeland condition. The objective of this effort was to provide Owyhee Uplands Pilot Project with a sophisticated suite of data and tools to assist in evaluating the health and condition of the Owyhee Uplands study area. We focused on three technical areas. The first involved enhancing existing algorithms to estimate shrub canopy cover in the Lower Reynolds Creek Watershed. The second task involved developing and applying a strategy to assess and compare three vegetation map products for the Idaho portion of the Owyhee study area. The third task developed techniques and data that can be used to identify areas exhibiting anomalous rangeland conditions (for example exotic plants or excessive bare soil exposure). This report documents the methods used, results obtained, and conclusions drawn.

Role of texture analysis in multisensor data fusion

The Pacific Northwest National Laboratory is involved in the design and development of algorithms to improve feature identification and detection using multisensor imagery. This research is funded jointly by the National Imagery and Mapping Agency (NIMA) and the U.S. Department of Energy. A process has been designed that exploits the spatial discontinuities in a scene as revealed by the reflectance variation in a given frequency. We believe that by mapping the discontinuities in a scene, man-made objects can be better distinguished from natural objects. The process involves the generation of a texture map for each of the multisensor data sets; this facilitates the fusion of data from different sources with different physical characteristics. The advantage of this approach is that texture seems to reduce image data to a common base. This common base becomes important when using data of variable quality, resolution, and geometry. Texture analysis has applicability to a wide variety of feature identification and extraction applications. This paper focus on demonstrating how the classification of texture maps derived from multisensor imagery can be used to automatically extract major roads from multisensor imagery, a requirement from NIMA under its comprehensive and integrated geospatial information generation strategy. Automatic/assisted road extraction is a particularly challenging task given the need for global coverage, accurate positioning, and sophisticated attribution.