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At least 1,225 records · Page 68Linked to original sources
USGS2021

System characterization report on Planet’s Dove Classic

Executive Summary This report addresses system characterization of Planet’s Dove Classic satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove Classic has an interior geometric performance in the range of −0.218 (−0.073 pixel) to −0.037 m (−0.012 pixel) in easting and −0.167 (−0.056 pixel) to −0.111 m (−0.037 pixel) in northing in band-to-band registration, an exterior geometric error of −6.841 (−2.280 pixels) in easting and −6.235 m (−2.078 pixels) in northing offset in comparison to Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.057 to −0.010 in offset and 0.963 to 1.298 in slope, and a spatial performance in the range of 2.77 to 3.35 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.003 to 0.010.

Open-File Report↗
USGS2021

System characterization report on Planet’s Dove-R

Executive Summary This report addresses system characterization of Planet’s Dove-R and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove-R has an interior geometric performance in the range of −0.306 (−0.102 pixel) to 0.286 m (0.095 pixel) in easting and 0.090 (0.030 pixel) to 1.084 m (0.361 pixel) in northing in band-to-band registration, an exterior geometric performance of −5.10 m (−0.51 pixel) in easting and 3.30 m (0.33 pixel) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.023 to −0.008 in offset and 0.948 to 1.077 in slope, and a spatial performance in the range of 2.96 to 3.15 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.003.

Open-File Report↗
USGS2022

System characterization report on Planet’s SuperDove

Executive Summary This report addresses system characterization of Planet’s SuperDove and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5.8 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 3-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that SuperDove has a band-to-band geometric performance in the range of −1.701 m (−0.567 pixel) to 1.173 m (0.391 pixel) in easting and −4.950 m (−1.650 pixels) to 6.051 m (2.017 pixels) in northing, an image-to-image geometric performance of −1.17 m (−0.39 pixel) to 23.45 m (7.82 pixels) in easting and −10.61 m (−3.54 pixels) to −4.43 m (−1.48 pixels) in northing offset in comparison to Sentinel-2, a radiometric performance in the range of −0.043 to 0.020 in offset and 0.812 to 1.246 in slope, and a spatial performance in the range of 3.59 to 3.70 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.005 to 0.008.

Open-File Report↗
USGS2024

Polar science results from Mars Reconnaissance Orbiter: Multiwavelength, multiyear insights

Mars Reconnaissance Orbiter (MRO), with its arrival in 2006 and nearly continuous operation since, has provided data for the study of martian polar processes spanning nine Mars years. Mars' polar deposits have long been thought to preserve records of past climates, potentially readable like terrestrial ice cores. However, unraveling millions of years of history in the ice depends on understanding Mars' current and recent-past climate, including the interactions of atmospheric and surface processes . MRO has allowed for revolutionary discoveries, long-term monitoring of ongoing processes, and multiple complementary datasets to address the question of how the polar ice deposits reflect climatological changes. In part, MRO has been able to do this from its variety of instrumentation simultaneously observing interannual changes in geomorphology of the surface in up to ∼25 cm/pixel detail, repeatable processes and changes in the atmosphere, and surface composition, as well as investigating signs of past changes recorded in the icy polar layered deposits. In this paper, we summarize the contribution of MRO to our current understanding of Mars polar science, and in particular how MRO's long-duration mission has improved our understanding of the fundamental volatile cycles on Mars.

Icarus↗
USGS2006

EMMMA: A web-based system for environmental mercury mapping, modeling, and analysis

Mercury in our environment - in our air, water, soil, and especially our food - poses significant hazards to human health, particularly for developing fetuses and young children. Because of the importance of this issue and the length of time it has been studied, large and complex data sets of mercury concentrations in various media and associated ancillary data have been generated by many Federal, State, Tribal, and local agencies. To facilitate efficient and effective use of these data in managing and mitigating human and wildlife exposure to mercury, the U.S. Geological Survey (USGS) and the National Institute of Environmental Health Sciences have developed a website for visualizing and studying the distribution of mercury in our environment. The Environmental Mercury Mapping, Modeling, and Analysis (EMMMA) website (http://emmma.usgs.gov) provides health and environmental researchers, managers, and other decision-makers the ability to: 1) Interactively view and access a nationwide collection of environmental mercury data (fish tissue, atmospheric emissions and deposition, stream sediments, soils, and coal) and mercuryrelated data (mine locations); 2) Interactively view and access predictions of the National Descriptive Model of Mercury in Fish (NDMMF) at 4,976 sites and 6,829 sampling events (events are unique combinations of site and sampling date) across the United States; and 3) Use interactive mapping and graphing capabilities to visualize spatial and temporal trends and study relationships between mercury and other variables.

Open-File Report↗
USGS2021

Managing water resources on Long Island, New York, with integrated, multidisciplinary science

Nutrients, harmful algal blooms, and synthetic chemicals like per- and polyfluoroalkyl substances (PFAS) and 1,4-dioxane threaten Long Island’s water resources by affecting the quality of drinking water and ecologically sensitive habitats that support the diverse wildlife throughout the island. Understanding the occurrence, fate, and transport of these potentially harmful chemicals is critical to protect these vital resources. The U.S. Geological Survey (USGS) is collecting and analyzing data to support informed water-resource management decisions. This fact sheet introduces ongoing efforts and future areas of study aimed to help water professionals develop a comprehensive science strategy to address contamination of the Long Island aquifer system, the sole source of drinking water for nearly 3 million people. These studies include surface and groundwater collection and groundwater flow modeling. Funding for the data collection has been provided by the USGS, New York State Department of Environmental Conservation, New York City Department of Environmental Protection, Suffolk County Water Authority, Nassau County Department of Public Works, State and local agencies, and Tribal and Federal partners. Without the foresight and long-term commitment of these funding partners, evaluating sustainability and planning for future water needs would not be possible.

New York↗
USGS2019

Nearshore survey and cleanup of benthic marine debris using citizen science divers along the Mediterranean coast of Israel

Information on marine debris along the Mediterranean coast of Israel, especially on the seafloor, is limited. Many recreational divers are enthusiasts of marine conservation and can thus contribute to data collection which does not require highly specialized training. The Society for the Protection of Nature in Israel together with The Israeli Diving Federation established the diver volunteer program “Sea Guard” (“Mishmar Hayam” in Hebrew), which supports marine conservation through citizen science. The divers were trained in marine ecology and survey methods to conduct independent surveys and lead underwater cleanups. For the first time, we have described the patterns of benthic debris density and composition in the nearshore environment of the southeastern part of the Mediterranean Sea. We found that benthic marine debris in the nearshore along the Israeli Mediterranean coast is primarily plastic, likely originating from the use of local beaches. Fishing, boating and domestic activities also play an important role as sources for marine debris. The currents' regime prevented the debris from accumulating on the seafloor in the nearshore environment, with the exception of several “debris traps”. Our findings will be useful for the development of programs to improve coastal waste management.

Ocean and Coastal Management↗
USGS2015

Archive of sediment data from vibracores collected in 2010 offshore of the Mississippi barrier islands

In 2010, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center collected sediment cores from coastal waters offshore of the Mississippi barrier islands. With funding support from the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project, 65 subaqueous sediment cores were collected over an area of 480 square kilometers (km 2 ), from Ship Island to Petit Bois Island Pass, Mississippi, within the boundary of Gulf Islands National Seashore. This represents only a fraction of the total area encompassed by the NGOM project, which extends from Sabine Lake, Louisiana, to Perdido Bay, Alabama. The primary objectives of the NGOM project are to understand the evolution of coastal ecosystems on the northern gulf coast, the impact of human activities on these ecosystems, and the vulnerability of ecosystems and human communities to more frequent and intense hurricanes in the future. Selection of the core site locations was based on geophysical surveys conducted around the islands from 2008 to 2010. The surveys, using acoustic systems to image and interpret the nearsurface stratigraphy, were conducted to investigate the geologic controls on island evolution. This data series serves as an archive of sediment data collected from August to September 2010, offshore of the Mississippi barrier islands. Data products, including descriptive core logs, core photographs, results of sediment grain-size analyses, sample location maps, and geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FDGC) metadata can be downloaded from the data products and downloads page.

Mississippi↗
USGS2018

Nearshore coastal bathymetry data collected in 2016 from West Ship Island to Horn Island, Gulf Islands National Seashore, Mississippi

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center, in cooperation with the U.S. Army Corps of Engineers, Mobile District, conducted bathymetric surveys of the nearshore waters surrounding Ship and Horn Islands, Gulf Islands National Seashore, Mississippi. The objective of this study was to establish base-level elevation conditions around West Ship, East Ship, and Horn Islands and their associated active littoral system prior to restoration activities. These activities include the closure of Camille Cut and the placement of sediment in the littoral zone of East Ship Island. These surveys can be compared with future surveys to monitor sediment migration patterns post-restoration and can also be measured against historic bathymetric datasets to further our understanding of island evolution. The USGS collected 667 line-kilometers (km) of single-beam bathymetry data and 844 line-km of interferometric swath bathymetry data in July 2016 under Field Activity Number 2016-347-FA. Data are provided in three datums: (1) the International Terrestrial Reference Frame of 2000 (ellipsoid height); (2) the North American Datum of 1983 (NAD83) CORS96 realization and the North American Vertical Datum of 1988 with respect to the GEOID12B model (orthometric height); and (3) NAD83 (CORS96) and Mean Lower Low Water (tidal datum). Data products, including x,y,z point datasets, trackline shapefiles, digital and handwritten Field Activity Collection Systems logs, 50-meter digital elevation model, and formal Federal Geographic Data Committee metadata, are available for download.

Mississippi↗
USGS2025

Absolute radiometric calibration evaluation of Uncrewed Aerial System (UAS) Headwall and MicaSense sensors and improving data quality using the Empirical Line Method

The use of Uncrewed Aerial Systems (UASs) for remote sensing applications has increased significantly in recent years due to their low cost, operational flexibility, and rapid advancements in sensor technologies. In many cases, UAS platforms are considered viable alternatives to conventional satellite and crewed airborne platforms, offering very high spatial, spectral, and temporal resolution data. However, the radiometric quality of UAS-acquired data has not received equivalent attention, particularly with respect to absolute calibration. In this study, we (1) evaluate the absolute radiometric performance of two commonly used UAS sensors: the Headwall Nano-Hyperspec hyperspectral sensor and the MicaSense RedEdge-MX Dual Camera multispectral system; (2) assess the effectiveness of the Empirical Line Method (ELM) in improving the radiometric accuracy of reflectance products generated by these sensors; and (3) investigate the influence of calibration target characteristics—including size, material type, reflectance intensity, and quantity—on the performance of ELM for UAS data. A field campaign was conducted jointly by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the USGS National Uncrewed Systems Office (NUSO) from 15 to 18 July 2023, at the USGS EROS Ground Validation Radiometer (GVR) site in Sioux Falls, South Dakota, USA, over a 160 m × 160 m vegetated area. Absolute calibration accuracy was evaluated by comparing UAS sensor-derived reflectance to in situ measurements of the site. Results indicate that the Headwall Nano-Hyperspec and MicaSense sensors underestimated reflectance by approximately 0.05 and 0.015 reflectance units, respectively. While the MicaSense sensor demonstrated better inherent radiometric accuracy, it exhibited saturation over bright targets due to limitations in its automatic gain and exposure settings. Application of the ELM using just two calibration targets reduced discrepancies to within 0.005 reflectance units. Reflectance products generated using various target materials—such as felt, melamine, or commercially available validation targets—showed comparable agreement with in situ measurements when used with the Nano-Hyperspec sensor. Furthermore, increasing the number of calibration targets beyond two did not yield measurable improvements in calibration accuracy. At a flight altitude of 200 ft above ground level (AGL), a target size of 0.6 m × 0.6 m or larger was sufficient to provide pure pixels for ELM implementation, whereas smaller targets (e.g., 0.3 m × 0.3 m) posed challenges in isolating pure pixels. Overall, the standard manufacturer-recommended calibration procedures were insufficient for achieving high radiometric accuracy with the tested sensors, which may restrict their applicability in scenarios requiring greater accuracy and precision. The use of the ELM significantly improved data quality, enhancing the reliability and applicability of UAS-based remote sensing in contexts requiring high precision and accuracy.

Remote Sensing↗
USGS2006

usSEABED: Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) offshore surficial sediment data release

Over the past 50 years there has been an explosion in scientific interest, research effort and information gathered on the geologic sedimentary character of the United States continental margin. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the shelf surface but rarely have those data been combined and integrated. This publication is the first release of the Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) coastal and offshore data from the usSEABED database. The report contains a compilation of published and previously unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to bring assorted data together in a unified database. The dbSEABED system is used to process the data. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the USGS Marine Aggregate Resources and Processes Assessment and Benthic Habitats projects, and will be useful to the marine science community for other studies of the Gulf of Mexico and Caribbean continental margins. This publication is divided into ten sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and those to pages outside this report will open in a new browser window.

Caribbean Sea, Gulf of Mexico↗
USGS2006

usSEABED: Pacific Coast (California, Oregon, Washington) Offshore Surficial-Sediment Data Release, version 1

Over the past 50 years there has been an explosion in scientific interest, research effort, and information gathered on the geologic sedimentary character of the continental margin of the United States. Data and information from thousands of publications have greatly increased our scientific understanding of the geologic origins of the margin surface but rarely have those data been combined and integrated. This publication is the first release of the Pacific coast data from the usSEABED database. The report contains a compilation of published and unpublished sediment texture and other geologic data about the sea floor from diverse sources. usSEABED is an innovative database system developed to unify assorted data, with the data processed by the dbSEABED system. Examples of maps displaying attributes such as grain size and sediment color are included. This database contains information that is a scientific foundation for the U.S. Geological Survey (USGS) Sea floor Mapping and Benthic Habitats project and the Marine Aggregate Resources and Processes assessment project, and will be useful to the marine science community for other studies of the Pacific coast continental margin. The publication is divided into 10 sections: Home, Introduction, Content, usSEABED (data), dbSEABED (processing), Data Catalog, References, Contacts, Acknowledgments, and Frequently Asked Questions. Use the navigation bar on the left to navigate to specific sections of this report. Underlined topics throughout the publication are links to more information. Links to specific and detailed information on processing and to those to pages outside this report will open in a new browser window.

California, Oregon, Washington↗
USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗
USGS2017

Smartphone technologies and Bayesian networks to assess shorebird habitat selection

Understanding patterns of habitat selection across a species’ geographic distribution can be critical for adequately managing populations and planning for habitat loss and related threats. However, studies of habitat selection can be time consuming and expensive over broad spatial scales, and a lack of standardized monitoring targets or methods can impede the generalization of site-based studies. Our objective was to collaborate with natural resource managers to define available nesting habitat for piping plovers ( Charadrius melodus ) throughout their U.S. Atlantic coast distribution from Maine to North Carolina, with a goal of providing science that could inform habitat management in response to sea-level rise. We characterized a data collection and analysis approach as being effective if it provided low-cost collection of standardized habitat-selection data across the species’ breeding range within 1–2 nesting seasons and accurate nesting location predictions. In the method developed, >30 managers and conservation practitioners from government agencies and private organizations used a smartphone application, “iPlover,” to collect data on landcover characteristics at piping plover nest locations and random points on 83 beaches and barrier islands in 2014 and 2015. We analyzed these data with a Bayesian network that predicted the probability a specific combination of landcover variables would be associated with a nesting site. Although we focused on a shorebird, our approach can be modified for other taxa. Results showed that the Bayesian network performed well in predicting habitat availability and confirmed predicted habitat preferences across the Atlantic coast breeding range of the piping plover. We used the Bayesian network to map areas with a high probability of containing nesting habitat on the Rockaway Peninsula in New York, USA, as an example application. Our approach facilitated the collation of evidence-based information on habitat selection from many locations and sources, which can be used in management and decision-making applications.

U.S Atlantic Coast↗
USGS2005

Mapping stream habitats with a global positioning system: Accuracy, precision, and comparison with traditional methods

We tested the precision and accuracy of the Trimble GeoXT??? global positioning system (GPS) handheld receiver on point and area features and compared estimates of stream habitat dimensions (e.g., lengths and areas of riffles and pools) that were made in three different Oklahoma streams using the GPS receiver and a tape measure. The precision of differentially corrected GPS (DGPS) points was not affected by the number of GPS position fixes (i.e., geographic location estimates) averaged per DGPS point. Horizontal error of points ranged from 0.03 to 2.77 m and did not differ with the number of position fixes per point. The error of area measurements ranged from 0.1% to 110.1% but decreased as the area increased. Again, error was independent of the number of position fixes averaged per polygon corner. The estimates of habitat lengths, widths, and areas did not differ when measured using two methods of data collection (GPS and a tape measure), nor did the differences among methods change at three stream sites with contrasting morphologies. Measuring features with a GPS receiver was up to 3.3 times faster on average than using a tape measure, although signal interference from high streambanks or overhanging vegetation occasionally limited satellite signal availability and prolonged measurements with a GPS receiver. There were also no differences in precision of habitat dimensions when mapped using a continuous versus a position fix average GPS data collection method. Despite there being some disadvantages to using the GPS in stream habitat studies, measuring stream habitats with a GPS resulted in spatially referenced data that allowed the assessment of relative habitat position and changes in habitats over time, and was often faster than using a tape measure. For most spatial scales of interest, the precision and accuracy of DGPS data are adequate and have logistical advantages when compared to traditional methods of measurement. ?? 2006 Springer Science+Business Media, Inc.

Environmental Management↗
USGS2021

Identifying policy-relevant indicators for assessing landscape vegetation patterns to inform planning and management on multiple use public lands

Understanding the structure and composition of landscapes can empower agencies to effectively manage public lands for multiple uses while sustaining land health. Many landscape metrics exist, but they are not often used in public land decision-making. Our objectives were to (1) develop and (2) apply a process for identifying a core set of indicators that public land managers can use to understand landscape-level resource patterns on and around public lands. We first developed a process for identifying indicators that are grounded in policy, feasible to quantify using existing data and resources, and useful for managers. We surveyed landscape monitoring efforts by other agencies, gathered science and agency input on monitoring goals, and quantified the prevalence of potential indicators in agency land health standards to identify five landscape indicators: amount, distribution, patch size, structural connectivity, and diversity of vegetation types. We then conducted pilot applications in four bureau of land management (BLM) field offices in Arizona, California, and Colorado to refine procedures for quantifying the indicators and assess the utility of the indicators for managers. Results highlighted the dominance of upland and the limited extent of riparian/wetland vegetation communities, moderate connectivity of priority vegetation patches, and lower diversity of native vegetation types on BLM compared to non-BLM lands. Agency staff can use the indicators to inform the development of quantitative resource management objectives in land use plans, evaluate progress in meeting those objectives, quantify potential impacts of proposed actions, and as a foundation for an all-lands approach to landscape-level management across public lands.

Environmental Management↗
USGS2002

A new mathematical solution for predicting char activation reactions

The differential conservation equations that describe typical gas-solid reactions, such as activation of coal chars, yield a set of coupled second-order partial differential equations. The solution of these coupled equations by exact analytical methods is impossible. In addition, an approximate or exact solution only provides predictions for either reaction- or diffusion-controlling cases. A new mathematical solution, the quantize method (QM), was applied to predict the gasification rates of coal char when both chemical reaction and diffusion through the porous char are present. Carbon conversion rates predicted by the QM were in closer agreement with the experimental data than those predicted by the random pore model and the simple particle model. ?? 2002 Elsevier Science Ltd. All rights reserved.

Carbon↗
USGS2000

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

Computers & Geosciences↗