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At least 109 records · Page 6Linked to original sources
USGS2016

Hanson Russian River Ponds floodplain restoration: Feasibility study and conceptual design; Appendix G: Physical evaluation of the restoration alternatives

Appendix G: Hanson Russian River Ponds Floodplain Restoration: Feasibility Study and Conceptual Design |G-1Appendix GPhysical Evaluation of the Restoration AlternativesRichard McDonald and Jonathan Nelson, PhDU.S. Geological Survey Geomorphology and Sediment Transport Laboratory, Golden, ColoradoIntroductionTo assess the relative and overall impacts of the scenarios proposed in Chapters 7 and 9,(Stage I-A–I-D and Stage II-A –II-E), each of the topographic configurations were evaluated over a range of flows. Thisevaluation was carried out using computational flow modeling tools available in the iRIC public-domain river modeling interface (www.i-ric.org, Nelsonet al.in press). Using the iRIC modeling tools described in more detail below, basic hydraulic computations of water-surface elevation, velocity, shear stress, and other hydraulic variables were carried out for the alternatives in the reach surrounding the project area, from the confluence of Dry Creek upstream to the Wohler road bridge downstream, for the full range of observed flows. This methodology allows comparison of the current channel configuration with the proposed alternatives in terms of inundation period and frequency, depth, water velocity, and other hydraulic information. By integrating this kind of information over the reach of interest and the flow record, critical metrics assessing the impacts of various topographic modifications can be compared to those same metrics for the existing condition or other modification scenarios. In addition, because the iRIC tools include predictions of sediment mobility, suspension of fines, and the potential evolution of the land surface in response to flow, these methods provide evaluation of sediment transport, stability of current and proposed surfaces, and evaluation of how these surfaces might evolve into the future. This hydraulic and sediment transport information is critically important for understanding theimpacts of various proposed alternatives on the physical system; perhaps even more importantly given the objectives of the proposed restoration, this information can be related to biological impacts, as is discussed in subsequent chapters of this document.

Book chapter↗
USGS2016

Biological soil crusts as an organizing principle in drylands

Biological soil crusts (biocrusts) have been present on Earth’s terrestrial surfaces for billions of years. They are a critical part of ecosystem processes in dryland regions, as they cover most of the soil surface and thus mediate almost all inputs and outputs from soils in these areas. There are many intriguing, but understudied, roles these communities may play in drylands. These include their function in nutrient capture and transformation, influence on the movement and distribution of nutrients and water within dryland soils, ability to structure vascular plant communities, role in creating biodiversity hotspots, and the possibility that they can be used as indicators of soil health. There are still many fascinating aspects of these communities that need study, and we hope that this chapter will facilitate such efforts.

Book chapter↗
USGS2007

Volcano deformation--Geodetic monitoring techniques

This book describes the techniques used by volcanologists to successfully predict several recent volcanic eruptions by combining information from various scientific disciplines, including geodetic techniques. Many recent developments in the use of state-of-the-art and emerging techniques, including Global Positioning System and Synthetic Aperture Radar Interferometry, mean that most books on volcanology are out of date, and this book includes chapters devoted entirely to these two techniques.

Book↗
USGS2005

Introduction to the handbook

In September of 1802, Pierre Simon Laplace (1749–1827) used a capture– recapture type of approach to estimate the size of the human population of France (Cochran 1978; Stigler 1986). At that time, live births were recorded for all of France on an annual basis. In the year prior to September 1802, Laplace estimated the number of such births to be approximately X = 1,000,000. These newly born individuals constituted a marked population. Laplace then obtained census and live birth data from several communities “with zealous and intelligent mayors” across all of France. Recognizing some variation in annual birth rates, Laplace summed the number of births reported in these sample communities for the three years leading up to the time of his estimate, and divided by three to determine that there were x = 71,866 births per year (marked individuals) in those communities. The ratio of these marked individuals to the total number of individuals in the sampled communities, y = 2,037,615, was then the estimate p = 71,866/2,037,615 = 0.0353 of the proportion of the total population in France that was newly born. On this basis, the one million marked individuals in the whole of France is related to the total population N as N p ≈ 1,000,000 so that N ≈ 1,000,000/0.0353 =28,328,612 This estimation procedure is equivalent to the Lincoln-Peterson capture-recapture estimator described in chapter 2.

Book chapter↗
USGS2012

Incorporating spatial context into the analysis of salmonid habitat relations

In this response to the chapter by Lapointe (this volume), we discuss the question of why it is so difficult to predict salmonid-habitat relations in gravel-bed rivers and streams. We acknowledge that this cannot be an exhaustive treatment of the subject and, thus, identify what we believe are several key issues that demonstrate the necessity of incorporating spatial context into the analysis of fish-habitat data. Our emphasis is on spatial context (i.e., scale and location), but it is important to note that the same principles may be applied with some modification to temporal context, which is beyond the scope of this chapter.

Oregon↗
USGS2021

Spatial data reduction through element -of-interest (EOI) extraction

Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.

Book chapter↗