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At least 1,207 records · Page 67Linked to original sources
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↗
USGS1998

Mapping the global land surface using 1 km AVHRR data

The scientific requirements for mapping the global land surface using 1 km advanced very high resolution radiometer (AVHRR) data have been set forth by the U.S. Global Change Research Program; the International Geosphere Biosphere Programme (IGBP); The United Nations; the National Oceanic and Atmospheric Administration (NOAA); the Committee on Earth Observations Satellites; and the National Aeronautics and Space Administration (NASA) mission to planet Earth (MTPE) program. Mapping the global land surface using 1 km AVHRR data is an international effort to acquire, archive, process, and distribute 1 km AVHRR data to meet the needs of the international science community. A network of AVHRR receiving stations, along with data recorded by NOAA, has been acquiring daily global land coverage since April 1, 1992. A data set of over 70,000 AVHRR images is archived and distributed by the United States Geological Survey (USGS) EROS Data Center, and the European Space Agency. Under the guidance of the IGBP, processing standards have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are for the study of surface vegetation condition, mapping land cover, and deriving biophysical characteristics of terrestrial ecosystems. A time-series of 54 10-day global vegetation index composites for the period of April 1, 1992 through September 1993 has been produced. The production of a time-series of 33 10-day global vegetation index composites using NOAA-14 data for the period of February 1, 1995 through December 31, 1995 is underway. The data products are available from the USGS, in cooperation with NASA's MTPE program and other international organizations.

Space Technology↗
USGS2005

Analysis of real-time vibration data

In recent years, a few structures have been instrumented to provide continuous vibration data in real time, recording not only large-amplitude motions generated by extreme loads, but also small-amplitude motions generated by ambient loads. The main objective in continuous recording is to track any changes in structural characteristics, and to detect damage after an extreme event, such as an earthquake or explosion. The Fourier-based spectral analysis methods have been the primary tool to analyze vibration data from structures. In general, such methods do not work well for real-time data, because real-time data are mainly composed of ambient vibrations with very low amplitudes and signal-to-noise ratios. The long duration, linearity, and the stationarity of ambient data, however, allow us to utilize statistical signal processing tools, which can compensate for the adverse effects of low amplitudes and high noise. The analysis of real-time data requires tools and techniques that can be applied in real-time; i.e., data are processed and analyzed while being acquired. This paper presents some of the basic tools and techniques for processing and analyzing real-time vibration data. The topics discussed include utilization of running time windows, tracking mean and mean-square values, filtering, system identification, and damage detection.

Conference Paper↗
USGS2006

Modeling habitat distribution from organism occurrences and environmental data: Case study using anemonefishes and their sea anemone hosts

We demonstrate the KGSMapper (Kansas Geological Survey Mapper), a straightforward, web-based biogeographic tool that uses environmental conditions of places where members of a taxon are known to occur to find other places containing suitable habitat for them. Using occurrence data for anemonefishes or their host sea anemones, and data for environmental parameters, we generated maps of suitable habitat for the organisms. The fact that the fishes are obligate symbionts of the anemones allowed us to validate the KGSMapper output: we were able to compare the inferred occurrence of the organism to that of the actual occurrence of its symbiont. Characterizing suitable habitat for these organisms in the Indo-West Pacific, the region where they naturally occur, can be used to guide conservation efforts, field work, etc.; defining suitable habitat for them in the Atlantic and eastern Pacific is relevant to identifying areas vulnerable to biological invasions. We advocate distinguishing between these 2 sorts of model output, terming the former maps of realized habitat and the latter maps of potential habitat. Creation of a niche model requires adding biotic data to the environmental data used for habitat maps: we included data on fish occurrences to infer anemone distribution and vice versa. Altering the selection of environmental variables allowed us to investigate which variables may exert the most influence on organism distribution. Adding variables does not necessarily improve precision of the model output. KGSMapper output distinguishes areas that fall within 1 standard deviation (SD) of the mean environmental variable values for places where members of the taxon occur, within 2 SD, and within the entire range of values; eliminating outliers or data known to be imprecise or inaccurate improved output precision mainly in the 2 SD range and beyond. Thus, KGSMapper is robust in the face of questionable data, offering the user a way to recognize and clean such data. It also functions well with sparse datasets. These features make it useful for biogeographic meta-analyses with the diverse, distributed datasets that are typical for marine organisms lacking direct commercial value. ?? Inter-Research 2006.

Marine Ecology Progress Series↗
USGS1967

Hydrogeologic data for the Shetucket River basin, Connecticut

This report presents hydrologic and geologic data collected by the U.S. Geological Survey during an investigation of water resources in the Shetucket River basin of Connecticut in cooperation with the Connecticut Water Resources Commission. The Shetucket River basin occupies about 507 square miles in the eastern part of the State, including the towns of Andover, Ashford, Chaplin, Coventry, Mansfield, Scotland, Sprague, Windham, and Willington, and parts of Bolton, Canterbury, Columbia, Eastford, Ellington, Franklin, Lebanon, Lisbon, Hampton, Hebron, Norwich, Pomfret, Stafford, Tolland, Union, Vernon, and Woodstock. A companion interpretive report, Connecticut Water Resources Bulletin ll, (Thomas, and others, 1967), evaluates the water resources of the basin. The data on the following pages serve to document and supplement that report and should be especially useful in planning the development of water resources at specific localities. Data were collected as part of this investigation during the period July 1962 through December 1964. Streamflow records from continuous-record gaging stations in the basin for this period have been published annually along with data from other parts of the State in a series of U.S. Geological Survey reports entitled "Surface Water Records of Connecticut." Water-level measurements in wells throughout the State from 1960 through 1964, including those made as part of this investigation, are published in Connecticut Water Resources Bulletin 7 (Meikle and Baker, 1965). Most other data collected during this investigation are tabulated on the following pages. Included are some well records and chemical analyses of water samples collected prior to July 1962 and not previously published. The locations of sites at which data were collected are shown on plate A in the pocket at the back of the report. Plate A includes the locations of 98 sites where 2 to 6 miscellaneous measurements of streamflow were made during 1963 and 13 other sites where continuous records are maintained. Data for these sites have already been published in "Surface Water Records of Connecticut" and are not repeated here. Data presented~ unless otherwise noted, were collected by U.S. Geological Survey personnel.

Connecticut↗
USGS2013

Towards integration of GLAS data into a national fuels mapping program

Comprehensive canopy structure and fuel data are critical for understanding and modeling wildland fire. The LANDFIRE project produces such data nationwide based on a collection of field observations, Landsat imagery, and other geospatial data. Where field data are not available, alternate strategies are being investigated. In this study, vegetation structure data available from GLAS were used to fill this data gap for the Yukon Flats Ecoregion of interior Alaska. The GLAS-derived structure and fuel layers and the original LANDFIRE layers were subsequently used as inputs into a fire behavior model to determine what effect the revised inputs would have on the model outputs. The outputs showed that inclusion of the GLAS data enabled better landscape-level characterization of vegetation structure and therefore enabled a broader wildland fire modeling capability. The results of this work underscore how GLAS data can be incorporated into LANDFIRE canopy structure and fuel mapping.

Alaska↗
USGS1984

Evaluation of the ground-water resources of coastal Georgia: preliminary report of the data available as of July 1983

A compilation of ground-water data that have been collected for nearly 100 years in the coastal area of Georgia is presented in this report. The compilation of pertinent data indicates what information is available for use in the evaluation of the ground-water resources of the 13 counties of coastal Georgia. Also included in this report is a fairly complete discussion of previous and ongoing investigations and monitoring networks, and an extensive list of references. Maps at 1:24,000 and 1:1,000,000 scales contain well locations and identifiers for all wells in the Ground Water Site Inventory (GWSI) data base of the National Water Data Storage and retrieval System (WATSTORE). Tabular summaries of selected site information from GWSI, including well identifiers and names, latitude-longitude location, depth of well, altitude of land surface, and use of water are presented. Water-use data from the National Water Use Data System, and water use for irrigation from the University of Georgia, Department of Agriculture survey, also are tabulated. Also included are pertinent information on geophysical surveys and data obtained, and proposed project activities, particularly test-monitor well drilling. The data in this report were collected and compiled as part of the cooperative activities between the U.S. Geological Survey and other agencies.

Georgia↗