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At least 1,261 records · Page 70Linked to original sources

USGS1985

Evaluation of surface geophysical methods for collection of hydrogeologic data in the Nebraska Sand Hills region

The practicality of using surface geophysical methods for obtaining geohydrologic data in the Nebraska Sand Hills region was studied during the summer of 1984. Seismic refraction and electrical-resistivity equipment were used, because an evaluation of geohydrologic data indicated that results of surveys made with this equipment probably would yield the most useful data. The study area, which included parts of Garfield, Holt, and Wheeler Counties, was selected because it is geohydrologically representative of the eastern part of the Sand Hills region, and because sufficient geohydrologic data were available for use in evaluating the results of geophysical surveys. Geophysical methods were evaluated for their ability to consistently detect selected geohydrologic horizons. These horizons in descending order, are: the water table, the top of Quaternary silt beds, the top of Quaternary sand and gravel beds , the top of the Tertiary Ogallala Formation, and the top of the Cretaceous Pierre Shale. The top of the Pierre Shale generally is the base of the aquifer, which consists of all of the 500 to 700 ft of overlying deposits. Evaluations of the geophysical data indicate that seismic refraction surveys are best suited for determining the depth to the water table, but are not effective in studying beds below the water table. Vertical electrical soundings provided data on the depth to water table and the top of the silt beds. Available geohydrologic data, however, indicate that with some changes in data collection or interpretation techniques, it may be possible to obtain information on the top of the sand and gravel deposits, the top of the Ogallala Formation, and the top of the Pierre Shale with vertical electrical soundings. Use of either geophysical method could enhance the results of geohydrologic investigations in the Nebraska Sand Hills region. (Author 's abstract)

Water-Resources Investigations Report↗

USGS1990

Comparison of accuracy and completeness of data obtained from three types of automatic water-quality monitors

A comparison of data (specific conductance, dissolved-oxygen concentration, temperature, and pH) collected by the U.S. Geological Survey flowthrough monitor, the U.S. Geological Survey minimonitor, and a self-contained commercial 'packaged-sensor' system indicates that the data obtained by means of the most complete of the three systems. The U.S. Geological Survey flowthrough monitor is powered by 120-volt alternating current and in a heated weather-proof shelter. A pumping system brings water from the stream to sensors clustered in a sample clustered in a sample chamber located in the shelter. This instrument measures output from the senors; data are recorded in binary-coded decimal form on a 16-channel punched-paper tape recorder tape recorder housed in the shelter. The U.S. Geological Survey's minimonitor is powered by an external battery and is housed in a weatherproof shelter. This instrument measures output of instream sensors with extension cables having underwater connectors; data are recorded in binary-coded decimal form on a 16-channel punched-paper tape recorder housed in the shelter. The packaged-sensor system also measures output of senors housed in a packages that is submerged in the stream. It has internal power supply, no moving parts, and does not requires a weatherproof shelter; data are stored are stored in solid-state memory. Minimonitors were installed at four sites in Ohio where U.S. Geological survey flowthrough were in operation. Two package-sensor systems also were assigned to each site and were alternated every two weeks. Detailed records were kept of (1) field measurements, for comparison with monitor-system data from each instrument, and (2) equipment problems that resulted in loss of data. Results of the comparisons shows that the flow-through monitor gave the most accurate and the most complete data.

Water-Resources Investigations Report↗

USGS1998

Preliminary estimates of residence times and apparent ages of ground water in the Chesapeake Bay watershed, and water-quality data from a survey of springs

Knowledge of the residence times of the ground-water systems in Chesapeake Bay watershed helps resource managers anticipate potential delays between implementation of land-management practices and any improvements in river and estuary water quality. This report presents preliminary estimates of ground-water residence times and apparent ages of water in the shallow aquifers of the Chesapeake Bay watershed. A simple reservoir model, published data, and analyses of spring water were used to estimate residence times and apparent ages of ground-water discharge. Ranges of aquifer hydraulic characteristics throughout the Bay watershed were derived from published literature and were used to estimate ground-water residence times on the basis of a simple reservoir model. Simple combinations of rock type and physiographic province were used to delineate hydrogeomorphic regions (HGMR's) for the study area. The HGMR's are used to facilitate organization and display of the data and analyses. Illustrations depicting the relation of aquifer characteristics and associated residence times as a continuum for each HGMR were developed. In this way, the natural variation of aquifer characteristics can be seen graphically by use of data from selected representative studies. Water samples collected in September and November 1996, from 46 springs throughout the watershed were analyzed for chlorofluorocarbons (CFC's) to estimate the apparent age of ground water. For comparison purposes, apparent ages of water from springs were calculated assuming piston flow. Additional data are given to estimate apparent ages assuming an exponential distribution of ages in spring discharge. Additionally, results from previous studies of CFC-dating of ground water from other springs and wells in the watershed were compiled. The CFC data, and the data on major ions, nutrients, and nitrogen isotopes in the water collected from the 46 springs are included in this report. The apparent ages of water discharging from 30 of the 46 springs sampled were less than 20 years, including 5 that were "modern" (0-4 years). Four samples had apparent ages of 22 to 34 years, and two others from thermal springs were 40 years or greater. The remaining ten samples were contaminated with local sources of CFC and could not be dated. Nitrate concentrations and nitrate delta 15 nitrogen (d 15 N) values in water from many springs are similar to those in shallow ground water beneath fertilized fields, and some values are high enough to indicate a probable source from animal-waste components. The nitrogen data reported here highlight the significance of the springs sampled during this study as pathways for nutrient transport in the Chesapeake Bay watershed. Ground-water samples were collected from springs during an unusually high flow period and thus may not be representative of low base-flow conditions. Residence times estimated from plausible ranges of aquifer properties and results of previous age-dating analyses generally corroborate the apparent-age analysis made in the current study and suggests that some residence times could be much longer. The shortest residence times tend to be in the Blue Ridge and northern carbonate areas; however, the data are preliminary and not appropriate for statistical tests of significance or variance. Because the age distributions in the aquifer discharging to the springs are not known, and because the apparent ages of water from the springs are based on various combinations of CFC criteria, the apparent ages and calculated residence times are compared for illustrative purposes but are considered preliminary until further work is accomplished.

Maryland, Virginia↗

USGS2022

The role of satellite telemetry data in 21st century conservation of polar bears (Ursus maritimus)

Satellite telemetry (ST) has played a critical role in the management and conservation of polar bears ( Ursus maritimus ) over the last 50 years. ST data provide biological information relevant to subpopulation delineation, movements, habitat use, maternal denning, health, human-bear interactions, and accurate estimates of vital rates and abundance. Given that polar bears are distributed at low densities over vast and remote habitats, much of the information provided by ST data cannot be collected by other means. Obtaining ST data for polar bears requires chemical immobilization and application of a tracking device. Although immobilization has not been found to have negative effects beyond a several-day reduction in activity, over the last few decades opposition to immobilization and deployment of satellite-linked radio collars has resulted in a lack of current ST data in many of the 19 recognized polar bear subpopulations. Here, we review the uses of ST data for polar bears and evaluate its role in addressing 21 st century conservation and management challenges, which include estimation of sustainable harvest rates, understanding the impacts of climate warming, delineating critical habitat, and assessing potential anthropogenic impacts from tourism, resource development and extraction. We found that in subpopulations where ST data have been consistently collected, information was available to estimate vital rates and subpopulation density, document the effects of sea-ice loss, and inform management related to subsistence harvest and regulatory requirements. In contrast, a lack of ST data in some subpopulations resulted in increased bias and uncertainty in ecological and demographic parameters, which has a range of negative consequences. As sea-ice loss due to climate warming continues, there is a greater need to monitor polar bear distribution, habitat use, abundance, and subpopulation connectivity. We conclude that continued collection of ST data will be critically important for polar bear management and conservation in the 21 st century and that the benefits of immobilizing small numbers of individual polar bears in order to deploy ST devices significantly outweigh the risks.

Frontiers in Marine Science↗

USGS2017

A land product characterization system for comparative analysis of satellite data and products

A Land Product Characterization System (LPCS) has been developed to provide land data and products to the community of individuals interested in validating space-based land products by comparing them with similar products available from other sensors or surface-based observations. The LPCS facilitates the application of global multi-satellite and in situ data for characterization and validation of higher-level, satellite-derived, land surface products (e.g., surface reflectance, normalized difference vegetation index, and land surface temperature). The LPCS includes data search, inventory, access, and analysis functions that will permit data to be easily identified, retrieved, co-registered, and compared statistically through a single interface. The system currently includes data and products available from Landsat 4 through 8, Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua, Suomi National Polar-Orbiting Partnership (S-NPP)/Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS), and simulated data for the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). In addition to the future inclusion of in situ data, higher-level land products from the European Space Agency (ESA) Sentinel-2 and -3 series of satellites, and other high and medium resolution spatial sensors, will be included as available. When fully implemented, any of the sensor data or products included in the LPCS would be available for comparative analysis.

Remote Sensing↗

USGS2020

Gap fill of Land surface temperature and reflectance products in Analysis Ready Data

The recently released Landsat Analysis Ready Data (ARD) over the United States provides the opportunity to investigate landscape dynamics using dense time series observations at 30-m resolution. However, the dataset often contains data gaps (or missing data) because of cloud contamination or data acquisition strategy. We present a new algorithm that focuses on data gap filling using clear observations from orbit overlap regions. Multiple linear regression models were established for each pixel time series to estimate stable predictions and uncertainties. The model's training data came from stratified random samples based on the time series similarity between the pixel and data from the overlap regions. The algorithm was evaluated using four tiles (5,000 × 5,000 30-m pixels for each tile) from 2018 land surface temperature data (LST) in Atlanta, Georgia. The accuracy was assessed using 1,000 randomly masked pixels and daily air temperature from eight ground stations. Both assessments showed the r2 value above 0.75, except two stations with mixed Landsat pixels. We also compared our results with the eMODIS LST product in terms of annual mean temperature. The two maps showed a similar spatial pattern at the region level, but our results showed more spatial detail in the urban area that matched the pattern of impervious surface. We also applied the method on ARD surface reflectance bands at Fairbanks, Alaska, to illustrate its improvements in surface reflectance products and in land change modeling. This approach can also be applied to other datasets, vegetation indexes, or spectral reflectance bands of other sensors.

Remote Sensing↗

USGS2024

SSEBop evapotranspiration estimates using synthetically derived Landsat data from the continuous change detection and classification algorithm

The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.

Remote Sensing↗

USGS2024

Application of normalized radar backscatter and hyperspectral data to augment rangeland vegetation fractional classification

Rangeland ecosystems in the western United States are vulnerable to climate change, fire, and anthropogenic disturbances, yet classification of rangeland areas remains difficult due to frequently sparse vegetation canopies that increase the influence of soils and senesced vegetation, the overall abundance of senesced vegetation, heterogeneity of life forms, and limited ground-based data. The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project provides fractional vegetation cover maps across western North America using Landsat imagery and artificial intelligence from 1985 to 2023 at yearly time-steps. The objectives of this case study are to apply hyperspectral data from several new data streams, including Sentinel Synthetic Aperture Radar (SAR) and Earth Surface Mineral Dust Source Investigation (EMIT), to the RCMAP model . We run a series of five tests (Landsat-base model, base + SAR, base + EMIT, base + SAR + EMIT, and base + Landsat NEXT [LNEXT] synthesized from EMIT) over a difficult-to-classify region centered in southwest Montana, USA. Our testing results indicate a clear accuracy benefit of adding SAR and EMIT data to the RCMAP model, with a 7.5% and 29% relative increase in independent accuracy ( R 2 ), respectively. The ability of SAR data to observe vegetation height allows for more accurate classification of vegetation types, whereas EMIT’s continuous characterization of the spectral response boosts discriminatory power relative to multispectral data. Our spectral profile analysis reveals the enhanced classification power with EMIT is related to both the improved spectral resolution and representation of the entire domain as compared to legacy Landsat. One key finding is that legacy Landsat bands largely miss portions of the electromagnetic spectrum where separation among important rangeland targets exists, namely in the 900–1250 nm and 1500–1780 nm range. Synthesized LNEXT data include these gaps, but the reduced spectral resolution compared to EMIT results in an intermediate 18% increase in accuracy relative to the base run. Here, we show the promise of enhanced classification accuracy using EMIT data, and to a smaller extent, SAR.

Idaho, Montana, Wyoming↗

USGS2011

Mapping irrigated areas of Ghana using fusion of 30 m and 250 m resolution remote-sensing data

Maps of irrigated areas are essential for Ghana's agricultural development. The goal of this research was to map irrigated agricultural areas and explain methods and protocols using remote sensing. Landsat Enhanced Thematic Mapper (ETM+) data and time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data were used to map irrigated agricultural areas as well as other land use/land cover (LULC) classes, for Ghana. Temporal variations in the normalized difference vegetation index (NDVI) pattern obtained in the LULC class were used to identify irrigated and non-irrigated areas. First, the temporal variations in NDVI pattern were found to be more consistent in long-duration irrigated crops than with short-duration rainfed crops due to more assured water supply for irrigated areas. Second, surface water availability for irrigated areas is dependent on shallow dug-wells (on river banks) and dug-outs (in river bottoms) that affect the timing of crop sowing and growth stages, which was in turn reflected in the seasonal NDVI pattern. A decision tree approach using Landsat 30 m one time data fusion with MODIS 250 m time-series data was adopted to classify, group, and label classes. Finally, classes were tested and verified using ground truth data and national statistics. Fuzzy classification accuracy assessment for the irrigated classes varied between 67 and 93%. An irrigated area derived from remote sensing (32,421 ha) was 20-57% higher than irrigated areas reported by Ghana's Irrigation Development Authority (GIDA). This was because of the uncertainties involved in factors such as: (a) absence of shallow irrigated area statistics in GIDA statistics, (b) non-clarity in the irrigated areas in its use, under-development, and potential for development in GIDA statistics, (c) errors of omissions and commissions in the remote sensing approach, and (d) comparison involving widely varying data types, methods, and approaches used in determining irrigated area statistics using GIDA and remote sensing. Extensive field campaigns to help in better classification and validation of irrigated areas using high (30 m ) to very high (<5 m) resolution remote sensing data that are fused with multi temporal data like MODIS are the way forward. This is especially true in accounting for small yet contiguous patches of irrigated areas from dug-wells and dug-outs. ?? 2011 by the authors.

Remote Sensing↗

USGS2012

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (k hat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (k hat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms . The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/ ). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.

Remote Sensing↗

USGS2021

Knowledge inventory of foundational data products in planetary science

Some of the key components of any Planetary Spatial Data Infrastructure (PDSI) are the data products that end-users wish to discover, access, and interrogate. One precursor to the implementation of a PSDI is a knowledge inventory that catalogs what products are available, from which data producers, and at what initially understood data qualities. We present a knowledge inventory of foundational PSDI data products: geodetic coordinate reference frames, elevation or topography, and orthoimages or orthomosaics. Additionally, we catalog the available gravity models that serve as critical data for the assessment of spatial location, spatial accuracy, and ultimately spatial efficacy. We strengthen our previously published definitions of foundational data products to assist in solidifying a common vocabulary that will improve communication about these essential data products.

The Planetary Science Journal↗

USGS2018

Geospatial data for developing nutrient SPARROW models for the Midcontinental region of Canada and the United States

Through the International Watersheds Initiative of the International Joint Commission (IJC), the SpatiallyReferenced Regressions on Watershed attributes (SPARROW) model developed by the U.S. Geological Survey (USGS) is being applied to the Great Lakes, Rainy River – Lake of the Woods and Red-Assiniboine basins. The objective of this binational application of the SPARROW model is to better understand and quantify the sources of phosphorus (P) and nitrogen (N) that contribute to regional water-quality issues like algal blooms and eutrophication in Lake Erie and other parts of the Great Lakes, as well as Lake of the Woods. Led by the IJC, a team of researchers from the National Research Council of Canada – Ocean, Coastal and River Engineering Research Centre, USGS, and IJC are extending the SPARROW modelling work previously completed for the Red-Assiniboine basin and the U.S. portions of the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy river basins to cover all of the Great Lakes, Rainy River – Lake of the Woods and RedAssiniboine basins. The current effort is termed the Midcontinent SPARROW modelling study. This report describes the data used to develop the Midcontinent SPARROW models, specifically the sources of original data, assembling the data, and the processing and harmonization required between the U.S. and Canada data needed to produce these models. Details provided include the: development of a digital stream network and related catchments – most significantly in the Canadian regions of the Great Lakes and Rainy River – Lake of the Woods basins where these data were not available to create a seamless binational network across the model domain; calculation of variables to aid in the determination of in-stream and in-reservoir decay of P and N; quantification of Canadian diversions within the Midcontinental region (i.e., Lake St. Joseph, Long Lake and the Ogoki Reservoir); development of binational input nutrient sources considered for model development (i.e., land cover, inorganic farm fertilizer, manure, atmospheric deposition, point-source pollution from wastewatertreatment plants and contribution from non-modelled watersheds); and development of delivery variables considered to be most predominant (i.e., temperature, precipitation and ensuing runoff, soil permeability and clay content, slope of the catchments, and tile drainage). The majority of the geospatial data collection and processing was required for Canadian datasets because many of the U.S. datasets were already assembled for previous SPARROW model applications in the U.S. The task of harmonizing data between the U.S. and Canada was important to ensure consistency of the datasets used in the models. The harmonized digital stream network, delineated catchments and input data for each catchment (i.e., source and delivery variables), created for the Midcontinent SPARROW models, are available for download at url: https://doi.org/10.4224/300.0001.

Technical Report↗

USGS2009

Missing data and the accuracy of magnetic-observatory hour means

Analysis is made of the accuracy of magnetic-observatory hourly means constructed from definitive minute data having missing values (gaps). Bootstrap sampling from different data-gap distributions is used to estimate average errors on hourly means as a function of the number of missing data. Absolute and relative error results are calculated for horizontal-intensity, declination, and vertical-component data collected at high, medium, and low magnetic latitudes. For 90% complete coverage (10% missing data), average (RMS) absolute errors on hourly means are generally less than errors permitted by Intermagnet for minute data. As a rule of thumb, the average relative error for hourly means with 10% missing minute data is approximately equal to 10% of the hourly standard deviation of the source minute data.

Annales Geophysicae↗

USGS2011

Using multi-source satellite data for lake level modelling in ungauged basins: A case study for Lake Turkana, East Africa

Managing limited surface water resources is a great challenge in areas where ground-based data are either limited or unavailable. Direct or indirect measurements of surface water resources through remote sensing offer several advantages of monitoring in ungauged basins. A physical based hydrologic technique to monitor lake water levels in ungauged basins using multi-source satellite data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, a digital elevation model, and other data is presented. This approach is applied to model Lake Turkana water levels from 1998 to 2009. Modelling results showed that the model can reasonably capture all the patterns and seasonal variations of the lake water level fluctuations. A composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data is used for model calibration (1998-2000) and model validation (2001-2009). Validation results showed that model-based lake levels are in good agreement with observed satellite altimetry data. Compared to satellite altimetry data, the Pearson's correlation coefficient was found to be 0.81 during the validation period. The model efficiency estimated using NSCE is found to be 0.93, 0.55 and 0.66 for calibration, validation and combined periods, respectively. Further, the model-based estimates showed a root mean square error of 0.62 m and mean absolute error of 0.46 m with a positive mean bias error of 0.36 m for the validation period (2001-2009). These error estimates were found to be less than 15 % of the natural variability of the lake, thus giving high confidence on the modelled lake level estimates. The approach presented in this paper can be used to (a) simulate patterns of lake water level variations in data scarce regions, (b) operationally monitor lake water levels in ungauged basins, (c) derive historical lake level information using satellite rainfall and evapotranspiration data, and (d) augment the information provided by the satellite altimetry systems on changes in lake water levels. ?? Author(s) 2011.

Hydrology and Earth System Sciences Discussions↗

USGS2020

Porphyry copper potential of the U.S. Southern Basin and Range using ASTER data integrated with geochemical and geologic datasets to assess potential near-surface deposits in well-explored permissive tracts

ArcGIS was used to spatially assess and rank potential porphyry copper deposits using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data together with geochemical and geologic datasets in order to estimate undiscovered deposits in the southern Basin and Range Province in the southwestern United States. The assessment was done using a traditional expert opinion three-part method and a prospectivity model developed using weights of evidence and logistic regression techniques to determine if ASTER data integrated with other geologic datasets can be used to find additional areas of prospectivity in well-explored permissive tracts. ASTER hydrothermal alteration data were expressed as 457 alteration polygons defined from a low-pass filtered alteration density map of combined argillic, phyllic, and propylitic rock units. Sediment stream samples were plotted as map grid data and used as spatial information in ASTER polygons. Gravity and magnetic data were also used to define basins greater than 1 km in depth. Each ASTER alteration polygon was ranked for porphyry copper potential using alteration types, spatial amounts of alteration, stream sediment geochemistry, lithology, polygon shape, proximity to other alteration polygons, and deposit and prospects data. Permissive tracts defined for the assessment in the southern Basin and Range Province include the Laramide Northwest, Laramide Southeast, Jurassic, and Tertiary tracts. Expert opinion estimates using the three-part assessment method resulted in a mean estimate of 17 undiscovered porphyry copper deposits, whereas the prospectivity modeling predicted a mean estimate of nine undiscovered deposits. In the well-explored Laramide Southeast tract, which contains the most deposits and has been explored for over 100 years, an average of 4.3 undiscovered deposits was estimated using ASTER alteration polygon data versus 2.8 undiscovered deposits without ASTER data. The Tertiary tract, which contains the largest number of ASTER alteration polygons not associated with known Tertiary deposits, was predicted to contain the most undiscovered resources in the southern Basin and Range Province.

Economic Geology↗

USGS2014

Characterization of intra-annual reflectance properties of land cover classes in southeastern South Dakota using Landsat TM and ETM+ data

Landsat-7 and Landsat-5 have orbits that are offset from each other by 8 days. During the time that the sensors on both satellites are operational, there is an opportunity for conducting analyses that incorporate multiple intra-annual high spatial resolution data sets for characterizing the Earth's land surface. In the current study, nine Landsat thematic mapper (TM) and enhanced thematic mapper plus (ETM+) data sets, covering the same path and row on different dates, were acquired during a 1-year time interval for a region in southeastern South Dakota and analyzed. Scenes were normalized using pseudoinvariant objects, and digital data from a series of test sites were extracted from the imagery and converted to surface reflectance. Sunphotometer data acquired on site were used to atmospherically correct the data. Ground observations that were made throughout the growing season by a large group of volunteers were used to help interpret spectroradiometric patterns and trends. Normalized images were found to be very effective in portraying the seasonal patterns of reflectance change that occurred throughout the region. Many of the radiometric patterns related to plant growth and development, but some also related to different background properties. The different kinds of land cover in the region were spectrally and radiometrically characterized and were found to have different seasonal patterns of reflectance. The degree to which the land cover classes could be separated spectrally and radiometrically, however, depended on the time of year during which the data sets were acquired, and no single data set appeared to be adequate for separating all types of land cover. This has practical implications for classification studies because known patterns of seasonal reflectance properties for the different types of land cover within a region will facilitate selection of the most appropriate data sets for producing land cover classifications.

Canadian Journal of Remote Sensing↗

USGS2021

Data management and interactive visualizations for the evolving marine biodiversity observation network

Assessing the current state of and predicting change in the ocean’s biological and ecosystem resources requires observations and research to safeguard these valuable public assets. The Marine Biodiversity Observation Network (MBON) partnered with the Global Ocean Observing System Biology and Ecosystems Panel and the Ocean Biodiversity Information System to address these needs through collaboration, data standardization, and data sharing. Here, we describe the generalized MBON data processing flow, which includes several steps to ensure that data are findable, accessible, interoperable, and reusable. By following this flow, data collected and managed by MBON have contributed to our understanding of the Global Ocean Observing System Essential Ocean Variables and demonstrated the value of web-based, interactive tools to explore and better understand environmental change. Although the MBON’s generalized data processing flow is already in practice, work remains in building ontologies for biological concepts, improving processing scripts for data standardization, and speeding up the data collection-to-sharing timeframe.

Oceanography↗

USGS1991

Louisiana coastal GIS network: Graphical user interface for access to spatial data

Louisiana's coastal wetlands support a large percentage of the nation's seafood and fur industries, vast deposits of oil and natural gas, habitat for thousands of species of plants and animals, winter nesting grounds and migratory paths for numerous waterfowl, and many recreational resources enjoyed by residents and tourists. Louisiana's wetlands also have the highest rates of coastal erosion and wetland loss in the nation. While numerous studies across many disciplines have been conducted on both local and regional scales, no complete inventory exists for this information. The Louisiana Coastal Geographic Information System Network (LCGISN) is currently being developed to facilitate access to existing data for coastal zone planners, managers, and researchers. The Louisiana Geological Survey (LGS), in cooperation with the LSU Department of Geography and Anthropology, the Computer Aided Design and Geographic Information Systems Research Laboratory (CADGIS), and others, is pursuing this project under the terms of a cooperative agreement with the U.S. Geological Survey. LCGISN is an automated system for searching and retrieving geographic, cartographic, and bibliographic data. By linking original programming with an existing GIS software package and an industry standard relational database management system, LCGISN will provide the capability for users to search for data references by interactively defining the area of interest on a displayed map/image reference background. Several agencies will be networked to provide easy access to a wide variety of information. LCGISN, with its headquarters at LGS, will serve as the central node on the network, providing data format conversions, projection and datum transformations, and storage of several of the most commonly used data sets. Thematic mapper data, USGS 7.5-minute quadrangle map boundaries, political and legal boundaries, major transportation routes, and other digital data will provide a base map to aid the user in selecting the exact area of interest. Then, the user will set search criteria by proceeding through a series of menu-driven options. The system will then return any or all of the following: a list of digital maps or imagery that can be displayed immediately and visually overlayed, a list of maps/remotely sensed data and information on their availability, and a list of bibliographic references concerning the area and subject defined.

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