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At least 37 records · Page 2Linked to original sources
USGS2021

High-resolution soil-moisture maps over landslide regions in northern California grassland derived From SAR backscattering coefficients

Slow-moving landslides are destabilized by accumulated precipitation and consequent soil moisture. Yet, the continuous high-resolution soil-moisture measurements needed to aid the understanding of landslide processes are generally absent in steep terrain. Here, we produce soil-moisture time-series maps for a seasonally active grassland landslide in the northern California coast ranges, USA, using backscattering coefficients from NASA's uninhabited aerial vehicle synthetic aperture radar at 6-m resolution. A physically based radar scattering model is used to retrieve the near-surface (5-cm depth) soil moisture for the landslide. Both forward modeling (backscattering estimation) and the retrieval (soil-moisture validation) show good agreement. The root-mean-square errors (RMSE) for vertical transmit vertical receive (VV) and horizontal transmit horizontal receive (HH) polarizations in forward model comparison are 1.93 dB and 1.88 dB, respectively. The soil-moisture retrieval shows unbiased RMSE of 0.054 m 3 /m 3 . Our successful retrieval benefits from the surface and double-bounce scattering, which is common in grasslands. The retrieved maps show saturated wetness conditions within the active landslide boundaries. We also performed sensitivity tests for incidence angle and found that the retrieval is weakly dependent on the angle, especially while using copolarized HH and VV together. Using the two copolarized inputs, the retrieval is also not sensitive to the change of orientation angles of grass cylinders. The physical model inversion presented here can be generally applied for soil-moisture retrieval in areas with the same vegetation cover types in California.

California↗
USGS2022

Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates

Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS2022

River bathymetry retrieval from Landsat-9 images based on neural networks and comparison to SuperDove and Sentinel-2

The Landsat mission has kept an eye on our planet, including water bodies, for 50 years. With the launch of Landsat-9 and its onboard Operational Land Imager 2 (OLI-2) in September 2021, more subtle variations in brightness (14-bit dynamic range) can be captured than previous sensors in the Landsat series (e.g., 12-bit Landsat-8). The enhanced radiometric resolution of OLI-2 appeals to the aquatic remote sensing community because the instrument might be capable of resolving smaller differences in water-leaving radiance. This study evaluates the potential to map river bathymetry from Landsat-9 imagery. We employ a neural network (NN)-based regression model for bathymetry retrieval and compare the results with optimal band ratio analysis (OBRA). The effect of Landsat-9 pan-sharpening on depth retrieval is also examined. In addition, we perform an intersensor comparison with Sentinel-2 and newly available 8-band SuperDoves from the PlanetScope constellation. Depth retrieval results from the Colorado and Potomac Rivers imply that Landsat-9 provided more accurate bathymetry across a range of depths up to 20 m, particularly when pan-sharpened. Downsampling the SuperDove data improved bathymetry retrieval due to enhanced signal-to-noise ratio, most notably in deep waters (maximum detectable depth increased from ∼15 to ∼20 m). Similarly, the enhanced spectral resolution of 8-band SuperDoves improved depth retrieval relative to 4-band Doves. The NN-based model outperformed OBRA by incorporating more spectral information.

Journal of Selected Topics in Applied Earth Observ↗
USGS2022

New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification

Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400–1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have four multispectral broadbands (MBB) with 3–4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72–100% and 75–100%, respectively, for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56–100% and 61–100%, respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65–100% and 60–94%, respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.

IEEE Journal of Selected Topics in Applied Earth O↗
USGS1997

Analysis of habitat selection studies with multiple patches within cover types

Current statistical methods are inadequate for evaluation of the relation between spatial pattern of the landscape and observed patterns of habitat use by individuals or populations. For example, traditional habitat selection analysis methods do not use information about the size and distribution of the several patches of each cover type that may exist within the study area. Statistical tests are presented for hypotheses about disproportional use of cover types and patches within cover types. These tests require that use of individual patches is recorded, as well as the size of individual patches. Different designs are considered in which there are (1) single or multiple samples of use, and (2) equal or unequal habitat availability. Formulas for calculating Type II statistical errors of the tests are presented and Monte Carlo simulation is used to assess the accuracy of the formulas and to check the Type I error rates of the proposed test statistics. With adequate sample sizes, Type II error formulas can be a useful tool for planning of habitat selection studies. An example analysis is presented of a hypothetical study of habitat selection by ring-necked pheasants ( Phasianus colchicus ) in a Midwestern landscape. The proposed tests also represent a contribution toward bringing together concepts of landscape ecology and wildlife habitat selection.

Journal of Wildlife Management↗
USGS2011

Multimodel inference and adaptive management

Ecology is an inherently complex science coping with correlated variables, nonlinear interactions and multiple scales of pattern and process, making it difficult for experiments to result in clear, strong inference. Natural resource managers, policy makers, and stakeholders rely on science to provide timely and accurate management recommendations. However, the time necessary to untangle the complexities of interactions within ecosystems is often far greater than the time available to make management decisions. One method of coping with this problem is multimodel inference. Multimodel inference assesses uncertainty by calculating likelihoods among multiple competing hypotheses, but multimodel inference results are often equivocal. Despite this, there may be pressure for ecologists to provide management recommendations regardless of the strength of their study’s inference. We reviewed papers in the Journal of Wildlife Management (JWM) and the journal Conservation Biology (CB) to quantify the prevalence of multimodel inference approaches, the resulting inference (weak versus strong), and how authors dealt with the uncertainty. Thirty-eight percent and 14%, respectively, of articles in the JWM and CB used multimodel inference approaches. Strong inference was rarely observed, with only 7% of JWM and 20% of CB articles resulting in strong inference. We found the majority of weak inference papers in both journals (59%) gave specific management recommendations. Model selection uncertainty was ignored in most recommendations for management. We suggest that adaptive management is an ideal method to resolve uncertainty when research results in weak inference.

Journal of Environmental Management↗
USGS2023

60 years and beyond of Reviews of Geophysics

Reviews of Geophysics is an AGU journal, first established in February 1963. It is a hybrid open access invitation-only journal that publishes comprehensive review articles across various disciplines within the Earth and Space Sciences. The selection criteria are rigorous and many submissions are declined without review. The journal is the highest ranked in the fields of Geochemistry and Geophysics, with a high Journal Impact Factor (JIF 2021 = 24.9), which is indicative of its high visibility and influence within the scientific community. The journal's published review papers, beyond a mere summary of literature, provide crucial context for current work, and establish the framework for comprehensive understanding of research progress, challenges, and interconnections between different communities, so that research may be appreciated by a broad audience. We emphasize the importance of publishing studies that provide a comprehensive overview and synthesis of the current state of knowledge in a field, especially in the case of geophysics, where knowledge is rapidly developing, increasing and becoming more specialized.

Reviews of Geophysics↗
USGS2015

A guide to Bayesian model selection for ecologists

The steady upward trend in the use of model selection and Bayesian methods in ecological research has made it clear that both approaches to inference are important for modern analysis of models and data. However, in teaching Bayesian methods and in working with our research colleagues, we have noticed a general dissatisfaction with the available literature on Bayesian model selection and multimodel inference. Students and researchers new to Bayesian methods quickly find that the published advice on model selection is often preferential in its treatment of options for analysis, frequently advocating one particular method above others. The recent appearance of many articles and textbooks on Bayesian modeling has provided welcome background on relevant approaches to model selection in the Bayesian framework, but most of these are either very narrowly focused in scope or inaccessible to ecologists. Moreover, the methodological details of Bayesian model selection approaches are spread thinly throughout the literature, appearing in journals from many different fields. Our aim with this guide is to condense the large body of literature on Bayesian approaches to model selection and multimodel inference and present it specifically for quantitative ecologists as neutrally as possible. We also bring to light a few important and fundamental concepts relating directly to model selection that seem to have gone unnoticed in the ecological literature. Throughout, we provide only a minimal discussion of philosophy, preferring instead to examine the breadth of approaches as well as their practical advantages and disadvantages. This guide serves as a reference for ecologists using Bayesian methods, so that they can better understand their options and can make an informed choice that is best aligned with their goals for inference.

Ecological Monographs↗
USGS1977

Land use and land cover changes: A framework for monitoring

Information on the rate and kind of change in the use of land resources is essential to-the proper planning, management, and regulation of the use of such resources. Starting in 1975, the U.S. Geological Survey has been engaged in mapping and inventory of land use and land cover at a scale of 1:250000 with plans to change to new 1:100000 base maps as they become available. Such scales are-appropriate for acquiring benchmark or baseline data on land use and land cover on a nationwide basis with a reasonable degree of standardization and accuracy, within a practical time frame of 6 to 7 years, and at a reasonable level of detail in categorization. Changes in land use and land cover occur at highly variable rates from place to place. Thus variations, in the intervals between updates of land use and land cover data should be carefully evaluated in order to provide an appropriate frame of reference for the systematic updating of such data. A methodological framework is needed for monitoring changes in land use and land cover that will be timely, relatively inexpensive, and appropriate for widely varying needs at national, interstate-regional, state, multicounty, county, and city levels. In this article a partial framework for monitoring land use and land cover changes is provided with opportunity to extend its use to greater levels of detail in categorization and shorter intervals of frequency when needed for selected areas.

Journal of Research of the U.S. Geological Survey↗
USGS2009

UZIG USGS research: Advances through interdisciplinary interaction

B Because vadose zone research relates to diverse disciplines, applications, and modes of research, collaboration across traditional operational and topical divisions is especially likely to yield major advances in understanding. The Unsaturated Zone Interest Group (UZIG) is an informal organization sponsored by the USGS to encourage and support interdisciplinary collaboration in vadose or unsaturated zone hydrologic research across organizational boundaries. It includes both USGS and non-USGS scientists. Formed in 1987, the UZIG operates to promote communication, especially through periodic meetings with presentations, discussions, and field trips. The 10th meeting of the UZIG at Los Alamos, NM, in August 2007 was jointly sponsored by the USGS and Los Alamos National Laboratory. Presentations at this meeting served as the initial basis for selecting papers for this special section of Vadose Zone Journal , the purpose of which is to present noteworthy cutting-edge unsaturated zone research promoted by, facilitated by, or presented in connection with the UZIG.

Vadose Zone Journal↗
USGS1976

Prehnite in plutonic and metamorphic rocks of the northern Santa Lucia Range, Salinian block, California

Prehnite is abundant as monomineralic veins, as lenses in biotite, and as discrete patches and apparent primary crystals in both plutonic and metamorphic rocks along the west edge of the Salinian block in the northern Santa Lucia Range. The prehnite appears to be concentrated near the Sur fault zone but is very rare in Franciscan rocks west of the fault zone. The prehnite does not appear to have been derived by local alteration of minerals in the country rocks (either plutonic or metamorphic). I suggest that solutions, derived from a "substratum" (possibly Franciscan graywacke) beneath the tectonically thinned west margin of the Salinian block, migrated through the fractured rocks of the Salinian block near the Sur fault zone and that the chemical and physical nature of these rocks favored selective prehnite deposition.

California↗
USGS1970

Annotated bibliography on artificial recharge of ground water, 1955-67

Artificial ground-water recharge has become more important as water use by agriculture, industry, and municipalities increases. Water management agencies are increasingly interested in potential use of recharge for pollution abatement, waste-water disposal, and re-use and reclamation of locally available supplies. Research projects and theoretical analyses of operational recharge systems show increased scientific emphasis on the practice. Overall ground-water basin management systems generally now contain considerations of artificial recharge, whether by direct or indirect methods. Artificial ground-water recharge is a means of conserving surface runoff for future use in places where it would otherwise be lost, of protecting ground-water basins from salt-water encroachment along coastal areas, and of storing and distributing imported water. The biblio-graphy emphasizes technology; however, annotations of articles on waste-water reclamation, ground-water management and ground-water basin management are included. Subjects closely related to artificial recharge, including colloidal flow through porous media, field or laboratory instrumentation, and waste disposal by deep well injection are included where they specifically relate to potential recharge problems. Where almost the same material has been published in several journals, all references are included on the assumption that some publications may be more readily available to interested persons than others. Other publications, especially those of foreign literature, provided abstracts that were used freely as time limitations precluded obtaining and annotating all materials. Abstracts taken from published sources are noted. These are: "Abstracts of North American Geology," U.S. Department of the Interior, Geological Survey; "Abstracts of Recent Published Material on Foil and Water Conservation," ARS-41 series, Agricultural F.esearch Service, U.S. Department of Agriculture; "Water and1 Water Engineering," published by Fuel and Metallurgical Journals, Ltd., London, England; "Journal of Geophysical Research," American Geophysical Union, Washington, D.C.; "American Society of Civil Engineers Transactions," New York; "Selected Bibliography of Hydrology, United Kingdom, for the Years 1955-59," International Association of Scientific Hydrology; "Water Wells, an Annotated Bibliography," California University Water Resources Center Archives Report 13; "Re-use of Effluent in the Future With an Annotated Bibliography," by G. A. Whetstone, Texas Water Development Board Report 8, Austin, Tex.; "Journal of Water Pollution Control Federation," Washington, D.C.; and "A List of Selected Technical References on Artificial Recharge of Ground-Water Reservoirs," compiled by Roy W. Graves, Tulsa University, Information Services Department, Tulsa, Okla. Other notations are self-explanatory, and initials are those of the authors (DCS, DJG, WK). An unpublished compilation of recharge references by Arnon Arad sponsored by the United Nations Educational, Scientific, and Cultural Organization during a training period with the U.S. Geological Survey was also used. The bibliography is arranged alphabetically by author. Where an author has more than one publication, the arrangement is chronological; where an author has more than one publication in a given year, a, b, c, . . . are added. The indexing is by subject and geographic location. Each article was assigned the key words or phrases to best characterize its contents. Units of measure are as they were in the original article; abbreviations retained are generally those in common use such as mg/1 (milligrams per liter), ppm (parts per million), gpm (gallons per minute), km (kilometers), m (meters), cu m per hr (cubic meters p^r hour), cfs (cubic feet per second), me/1 (milliequivalents per liter), psi (pounds per square inch), BOD (biochemical oxygen demand), sq m (square meters), gpd (gallons per day), and mgd (million gallons per day). The bibliography was prepared because of the worldwide interest in the field of artificial recharge and the need for a single source of references to the literature published since 1954. The work is a sequel to the "Annotated Bibliography on Artificial Recharge of Ground Water Through 1954," by D. K. Todd, U.S. Geological Survey Water-Supply Paper 1477, published in 1959.

Water Supply Paper↗
USGS2006

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a Bayesian hierarchical discrete-choice model for resource selection can provide managers with 2 components of population-level inference: average population selection and variability of selection. Both components are necessary to make sound management decisions based on animal selection.

Journal of Wildlife Management↗
USGS1992

Selected papers in the hydrologic sciences, 1988-92

Selected Papers in the Hydrologic Sciences , a U.S. Geological Survey-managed journal-type publication in the Water-Supply Paper series, is aimed at meeting widespread public and professional needs for results of state-of-the-art broad-based hydrologic studies. The results of these studies are derived from integrated water-resource projects of the Federal research program, Federal-State cooperative program, and, to some extent, work done on behalf of other Federal agencies. This is the sixth issue of the series and consists of 18 topical papers that address a broad array of topics, including ground- and surface-water hydrology, hydrogeochemis-try, land use, and selected hydrologic, geologic, and climatologic techniques for Water-Resources studies. Selected Papers is intended to serve as a forum that encourages dialog between readers and authors. Participation in such dialog among hydroscientists within and outside the Federal sector would be beneficial. Discussion papers by all members of the hydroscience community are desired. A discussion section for readers' comments and authors' replies will be included in each issue. Such discussion papers for this issue will be open until December 1994,

Water Supply Paper↗
USGS2000

Density and foraging habitat selection of waterbirds breeding in the San Luis Valley of Colorado

We determined density and foraging habitat selection of American avocet ( Recurvirostra americana ), killdeer ( Charadrius vociferus ), Wilson's phalarope ( Phalaropus tricolor ), cinnamon teal ( Anas cyanoptera ), gadwall ( A. strepera ), mallard ( A. platyrhynchos ), redhead ( Aythya americana ), and white-faced ibis ( Plegadis chihi ) during prenesting and nesting on a wetland complex in the San Luis Valley, Colorado from 15 April to 4 July 1995 and 1996. Selection patterns differed among species and between the prenesting and nesting periods for cinnamon teal and redheads. Most species preferred seasonally flooded basins with no emergent vegetation and wetlands with short (<40 cm) emergent vegetation compared to other available habitats. The results suggest that, for each of these species, biologically meaningful differences occur among foraging habitats at the spatial resolution we measured. Because we used water depth to control for differences in habitat availability for individual species, we speculate that differences in food abundance, vegetation structure and composition, and behavioral strategies during the breeding season collectively influence selection of foraging habitats.

Journal of Wildlife Management↗
USGS1980

Summaries of data on and lists of references to metallic and selected nonmetallic mineral deposits in the McCarthy Quadrangle, Alaska

These summaries of data on metallic and selected nonmetallic mineral occurrences and lists of selected references to .them in Geological Survey, U.S. Bureau of Mines, and State of Alaska Division of Geological and Geophysical Surveys (and predecessor State and Territorial agencies) reports and maps and a very.few journal articles are designed to aid in library research on the mineral resources of the McCarthy quadrangle, Alaska. The references listed are selected in the sense that mainly statistical reports such as the annual Minerals Yearbook of the U.S. Bureau of Mines and many annual and biennial reports of the Alaska Division of Geological and Geophysical Surveys and its predecessor agencies are not included. Also not included are data on claims about which little more than their locations is known (for example, localities 126- 130 in MacKevett and Holloway, 1977 (OF 77-169A). These omissions should not be interpreted as a judgement that the claims are not on valid mineral occurrences, but only chat there are insufficient data to describe any mineral deposits that might be present. Geochemical anomalies determined by analyses of rock and stream-sediment samples in which no metallic mineral was indentified are also omitted.

Alaska↗