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USGS2011

Geospatial characteristics of Florida's coastal and offshore environments: Distribution of important habitats for coastal and offshore biological resources and offshore sand resources

The Geospatial Characteristics GeoPDF of Florida's Coastal and Offshore Environments is a comprehensive collection of geospatial data describing the political boundaries and natural resources of Florida. This interactive map provides spatial information on bathymetry, sand resources, and locations of important habitats (for example, Essential Fish Habitats (EFH), nesting areas, strandings) for marine invertebrates, fish, reptiles, birds, and marine mammals. The map should be useful to coastal resource managers and others interested in marine habitats and submerged obstructions of Florida's coastal region. In particular, as oil and gas explorations continue to expand, the map can be used to explore information regarding sensitive areas and resources in the State of Florida. Users of this geospatial database will have access to synthesized information in a variety of scientific disciplines concerning Florida's coastal zone. This powerful tool provides a one-stop assembly of data that can be tailored to fit the needs of many natural resource managers. The map was originally developed to assist the Bureau of Ocean Energy Management, Regulation, and Enforcement (BOEMRE) and coastal resources managers with planning beach restoration projects. The BOEMRE uses a systematic approach in planning the development of submerged lands of the Continental Shelf seaward of Florida's territorial waters. Such development could affect the environment. BOEMRE is required to ascertain the existing physical, biological, and socioeconomic conditions of the submerged lands and estimate the impact of developing these lands. Data sources included the National Oceanic and Atmospheric Administration, BOEMRE, Florida Department of Environmental Protection, Florida Geographic Data Library, Florida Fish and Wildlife Conservation Commission, Florida Natural Areas Inventory, and the State of Florida, Bureau of Archeological Research. Federal Geographic Data Committee (FGDC) compliant metadata are provided as attached xml files for all geographic information system (GIS) layers.

Florida↗

USGS2011

Geospatial characteristics of Florida's coastal and offshore environments: Coastal habitats, artificial reefs, wrecks, dumping grounds, harbor obstructions and offshore sand resources

The Geospatial Characteristics GeoPDF of Florida's Coastal and Offshore Environments is a comprehensive collection of geospatial data describing the political boundaries and natural resources of Florida. This interactive map provides spatial information on bathymetry, sand resources, coastal habitats, artificial reefs, shipwrecks, dumping grounds, and harbor obstructions. The map should be useful to coastal resource managers and others interested in marine habitats and submerged obstructions of Florida's coastal region. In particular, as oil and gas explorations continue to expand, the map may be used to explore information regarding sensitive areas and resources in the State of Florida. Users of this geospatial database will have access to synthesized information in a variety of scientific disciplines concerning Florida's coastal zone. This powerful tool provides a one-stop assembly of data that can be tailored to fit the needs of many natural resource managers. The map was originally developed to assist the Bureau of Ocean Energy Management, Regulation, and Enforcement (BOEMRE) and coastal resources managers with planning beach restoration projects. The BOEMRE uses a systematic approach in planning the development of submerged lands of the Continental Shelf seaward of Florida's territorial waters. Such development could affect the environment. BOEMRE is required to ascertain the existing physical, biological, and socioeconomic conditions of the submerged lands and estimate the impact of developing these lands. Data sources included the National Oceanic and Atmospheric Administration, BOEMRE, Florida Department of Environmental Protection, Florida Geographic Data Library, Florida Fish and Wildlife Conservation Commission, Florida Natural Areas Inventory, and the State of Florida, Bureau of Archeological Research. Federal Geographic Data Committee (FGDC) compliant metadata are provided as attached xml files for all geographic information system (GIS) layers.

Florida↗

USGS2017

National Park Service Vegetation Mapping Inventory Program: Appalachian National Scenic Trail vegetation mapping project

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program classifies, describes, and maps existing vegetation of national park units for the NPS Natural Resource Inventory and Monitoring (I&M) Program. The NPS VMI Program is managed by the NPS I&M Division and provides baseline vegetation information to the NPS Natural Resource I&M Program. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, NPS Northeast Temperate Network, and NPS Appalachian National Scenic Trail (APPA) have completed vegetation classification and mapping of APPA for the NPS VMI Program. Mappers, ecologists, and botanists collaborated to affirm vegetation types within the U.S. National Vegetation Classification (USNVC) of APPA and to determine how best to map the vegetation types by using aerial imagery. Analyses of data from 1,618 vegetation plots were used to describe USNVC associations of APPA. Data from 289 verification sites were collected to test the field key to vegetation associations and the application of vegetation associations to a sample set of map polygons. Data from 269 validation sites were collected to assess vegetation mapping prior to submitting the vegetation map for accuracy assessment (AA). Data from 3,265 AA sites were collected, of which 3,204 were used to test accuracy of the vegetation map layer. The collective of these datasets affirmed 280 USNVC associations for the APPA vegetation mapping project. To map the vegetation and land cover of APPA, 169 map classes were developed. The 169 map classes consist of 150 that represent natural (including ruderal) vegetation types in the USNVC, 11 that represent cultural (agricultural and developed) vegetation types in the USNVC, 5 that represent natural landscapes with catastrophic disturbance or some other modification to natural vegetation preventing accurate classification in the USNVC, and 3 that represent nonvegetated water (non-USNVC). Features were interpreted from viewing 4-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems (GIS). (Digital aerial imagery was collected each fall during 2009–11 to capture leaf-phenology change of hardwood trees across the latitudinal range of APPA.) The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in GIS. Polygon units were mapped to either a 0.5-hectare (ha) or 0.25-ha minimum mapping unit, depending on vegetation type or scenario; however, polygon units were mapped to 0.1 ha for alpine vegetation. A geodatabase containing various feature-class layers and tables provide locations and support data to USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, validation sites, AA sites, project boundary extent and zones, and aerial image centers and flight lines. The feature-class layer and related tables of the vegetation map layer provide 30,395 polygons of detailed attribute data covering 110,919.7 ha, with an average polygon size of 3.6 ha; the vegetation map coincides closely with the administrative boundary for APPA. Summary reports generated from the vegetation map layer of the map classes representing USNVC natural (including ruderal) vegetation types apply to 28,242 polygons (92.9% of polygons) and cover 106,413.0 ha (95.9%) of the map extent for APPA. The map layer indicates APPA to be 92.4% forest and woodland (102,480.8 ha), 1.7% shrubland (1866.3 ha), and 1.8% herbaceous cover (2,065.9 ha). Map classes representing park-special vegetation (undefined in the USNVC) apply to 58 polygons (0.2% of polygons) and cover 404.3 ha (0.4%) of the map extent. Map classes representing USNVC cultural types apply to 1,777 polygons (5.8% of polygons) and cover 2,516.3 ha (2.3%) of the map extent. Map classes representing nonvegetated water (non-USNVC) apply to 332 polygons (1.1% of polygons) and cover 1,586.2 ha (1.4%) of the map extent.

Appalachian National Scenic Trail↗

USGS2007

Remote sensing and GIS technology in the Global Land Ice Measurements from Space (GLIMS) Project

Global Land Ice Measurements from Space (GLIMS) is an international consortium established to acquire satellite images of the world's glaciers, analyze them for glacier extent and changes, and to assess these change data in terms of forcings. The consortium is organized into a system of Regional Centers, each of which is responsible for glaciers in their region of expertise. Specialized needs for mapping glaciers in a distributed analysis environment require considerable work developing software tools: terrain classification emphasizing snow, ice, water, and admixtures of ice with rock debris; change detection and analysis; visualization of images and derived data; interpretation and archival of derived data; and analysis to ensure consistency of results from different Regional Centers. A global glacier database has been designed and implemented at the National Snow and Ice Data Center (Boulder, CO); parameters have been expanded from those of the World Glacier Inventory (WGI), and the database has been structured to be compatible with (and to incorporate) WGI data. The project as a whole was originated, and has been coordinated by, the US Geological Survey (Flagstaff, AZ), which has also led the development of an interactive tool for automated analysis and manual editing of glacier images and derived data (GLIMSView). This article addresses remote sensing and Geographic Information Science techniques developed within the framework of GLIMS in order to fulfill the goals of this distributed project. Sample applications illustrating the developed techniques are also shown. ?? 2006 Elsevier Ltd. All rights reserved.

Computers & Geosciences↗

USGS2002

A portal for the ocean biogeographic information system

Since its inception in 1999 the Ocean Biogeographic Information System (OBIS) has developed into an international science program as well as a globally distributed network of biogeographic databases. An OBIS portal at Rutgers University provides the links and functional interoperability among member database systems. Protocols and standards have been established to support effective communication between the portal and these functional units. The portal provides distributed data searching, a taxonomy name service, a GIS with access to relevant environmental data, biological modeling, and education modules for mariners, students, environmental managers, and scientists. The portal will integrate Census of Marine Life field projects, national data archives, and other functional modules, and provides for network-wide analyses and modeling tools.

Oceanologica Acta↗

USGS2000

Assessing habitat selection in Spring by male American Woodcock in Maine with a geographic information system

Geographic information system (GIS) technology was used to identify habitats available to and used by male American woodcock (Scolopax minor) equipped with radio transmitters--54 in 1987, 51 in 1988, 46 in 1989 at Moosehorn National Wildlife Refuge, Maine. Woodcock were monitored from time of capture (25 March-15 April) to 15 June each year. To determine habitat selection by male woodcock, the following habitat characteristics were measured: land cover, age and stocking density of the forest overstory, soil drainage and texture, aspect, and percent slope. Habitat selection was examined as affected by the covariates weather and age-class of woodcock, and among years for diurnal and crepuscular periods of the breeding period. Multivariate techniques that compare use and availability of habitats were not available, so a statistical model was developed to rate importance of multiple habitat characteristics selected by woodcock. The most critical period for woodcock in terms of survival was from arrival to: mid-April. Second-year and after-second-year woodcock did not select different (P > 0.05) habitat types, but they did select different types among years and within breeding intervals (P < 0.05). In years when weather was moderate, woodcock selected young, dense stands of speckled alder (Alnus rugosa) and hardwoods, interspersed with forest openings. Suitable habitat can be maintained by creating an uneven-aged forest managed in even-aged blocks composed of several hardwood species. Managers can now quantify suitable woodcock habitat in a GIS and plan large-scale forest-harvesting strategies using data on several habitat characteristics (e.g., land cover, stand age, stocking density, soil drainage and texture, and aspect).

Book chapter↗

USGS2005

ShakeMap manual: technical manual, user's guide, and software guide

ShakeMap (http://earthquake.usgs.gov/shakemap) --rapidly, automatically generated shaking and intensity maps--combines instrumental measurements of shaking with information about local geology and earthquake location and magnitude to estimate shaking variations throughout a geographic area. The results are rapidly available via the Web through a variety of map formats, including Geographic Information System (GIS) coverages. These maps have become a valuable tool for emergency response, public information, loss estimation, earthquake planning, and post-earthquake engineering and scientific analyses. With the adoption of ShakeMap as a standard tool for a wide array of users and uses came an impressive demand for up-to-date technical documentation and more general guidelines for users and software developers. This manual is meant to address this need. ShakeMap, and associated Web and data products, are rapidly evolving as new advances in communications, earthquake science, and user needs drive improvements. As such, this documentation is organic in nature. We will make every effort to keep it current, but undoubtedly necessary changes in operational systems take precedence over producing and making documentation publishable.

Techniques and Methods↗

USGS1987

UTILIZATION OF GEOGRAPHIC INFORMATION SYSTEMS TECHNOLOGY IN THE ASSESSMENT OF REGIONAL GROUND-WATER QUALITY.

The U. S. Geological Survey (USGS) in cooperation with the U. S. Environmental Protection Agency Office of Pesticide Programs and several State agencies in Oregon has prepared a digital spatial database at 1:500,000 scale to be used as a basis for evaluating the potential for ground-water contamination by pesticides and other agricultural chemicals. Geographic information system (GIS) software was used to assemble, analyze, and manage spatial and tabular environmental data in support of this project. Physical processes were interpreted relative to published spatial data and an integrated database to support the appraisal of regional ground-water contamination was constructed. Ground-water sampling results were reviewed relative to the environmental factors present in several agricultural areas to develop an empirical knowledge base which could be used to assist in the selection of future sampling or study areas.

Conference Paper↗

USGS2005

Upper Auglaize watershed AGNPS modeling project

The Upper Auglaize Watershed agricultural non-point source modeling project was an interagency effort to use a Geographic Information System (GIS)-based modeling approach for assessing and reducing pollution from agricultural runoff and other non-point sources. This project applied the U.S. Department of Agriculture (USDA), Agricultural Research Service’s AGricultural Non-Point Source (AGNPS) suite of models to the Upper Auglaize River Watershed, a major watershed within the Maumee River Basin. This modeling project was conducted by an interagency team consisting of a partnership between the: (1) USDA, Agricultural Research Service (ARS); (2) USDA, Natural Resources Conservation Service (NRCS); (3) U.S. Army Corps of Engineers (USACE); (4) U.S. Geological Survey (USGS); (5) Ohio State University; (6) University of Toledo (UT); (7) Heidelberg College; (8) Ohio Department of Natural Resources (ODNR), Division of Soil and Water Conservation; (9) Ohio Environmental Protection Agency (OEPA); and (10) Allen, Auglaize, Van Wert, and Putnam Soil and Water Conservation Districts. The partnership was the first step in a process to eventually apply the model in a portioned subset of watersheds for the Maumee Basin, and then to link them to form a comprehensive basin-wide model This work was performed under the authority of Section 516(e) of the Water Resources Development Act (WRDA) of 1996, as amended, for the purpose of assisting State and local watershed managers with their evaluation, prioritization and implementation of alternatives for soil conservation, sediment trapping and non-point source pollution prevention in the Upper Auglaize River watershed. The project team, working in a cooperative effort, used the models to determine sediment sources, contributing locations, and the effect of application of best management practices (BMPs) on rates of sediment delivery to the mouth of the watershed. The results will be used to guide conservation incentive and land treatment programs. The team relied heavily on Geographic Information System (GIS)-based applications to expedite the application of the model. The results of the analysis demonstrated that the application of BMPs would have a positive effect on reducing the loadings of sediment leaving the mouth of the Upper Auglaize Watershed. An application of 17 percent new no-till acres and eight percent new grassland acres, when randomly applied to the watershed, reduced loadings at the mouth to 82 percent of the simulated existing condition loadings. No-till, conversion of cropland to grassland, other uses including grass buffers, and reforestation of parts of the watershed, were all shown by the model to have a measurable effect on reducing sediment loads. Conversion of all of the cropland in the watershed to no-till would reduce the average unit load (tons of sediment per acre) leaving the mouth of the watershed to a level that is 42 percent of the simulated existing condition load. Ephemeral gullies were found to be the primary source of erosion (72 percent), sediment yield (73 percent), and sediment loading (73 percent). Controlling sediment load means controlling gully erosion and possibly trapping sediment yield before it reaches the stream system. Most BMPs (e.g., no-till, conversion of cropland, etc.) that reduce sheet and rill erosion and its sediment yield will also reduce gully erosion and its sediment yield. However, grassed waterways, which have no effect on sheet and rill erosion, are frequently an effective BMP to prevent ephemeral gullies. And, of course, riparian vegetation and sediment traps would reduce the delivery ratios of all types of landscape erosion. New techniques were developed by the team to quantify the ephemeral gully erosion within the model. When calibrated to available stream gage data the model suggests that more (73% in the existing condition simulation) of the sediment load originates from ephemeral gully erosion than from traditional sheet and rill erosion. The model quantified the value of tile drainage in reducing the sediment load from the watershed. Loadings under drained conditions were always less than loadings under undrained conditions for otherwise identical land uses. The average sediment load of all alternatives for drained loadings was 89.2 percent of the load for the corresponding undrained loadings. The model established that while many conservation incentive programs treat tile drainage as a production practice, significant erosion and sediment control benefits are provided by the practice in comparison to cultivation in an undrained state.

Ohio↗

USGS2015

StreamStats in Georgia: A water-resources web application

Summary Part of the mission of the U.S. Geological Survey (USGS) is to provide information on streamflow in the Nation's streams to help understand the Nation's water resources. Streamflow statistics are used by water managers, engineers, scientists, and others to protect people and property during floods and droughts, and to manage, protect, and enhance water resources. StreamStats is a Web-based Geographic Information System (GIS) application that was created by the USGS, in cooperation with the Environmental Systems Research Institute, Inc., that allows users to easily obtain streamflow statistics, basin characteristics, and descriptive information for USGS streamgages and user-selected ungaged locations on streams (Ries and others, 2008). StreamStats is being implemented on a State-by-State basis to allow for customization of the data development and underlying datasets to address their specific needs, issues, and objectives. The USGS, in cooperation with the Georgia Environmental Protection Division and Georgia Department of Transportation, has implemented StreamStats for Georgia. The Georgia StreamStats Web site is available through the national StreamStats Web-page portal at http://streamstats.usgs.gov. Links are provided on this Web page for individual State applications, instructions for using StreamStats, definitions of basin characteristics and streamflow statistics, and other supporting information.

Georgia↗

USGS2015

Regulating services as measures of ecological resilience on DoD lands

Knowledge of the capacity and flow of ecosystem services can help DoD land managers make decisions that enhance cost-effectiveness, minimize environmental damage, and maximize resources available for military missions. We demonstrated a methodology to quantify and map selected regulating services (RS), which helps land managers envision tradeoffs. Our objectives were to 1) estimate current capacity of and demand for selected RS within DoD lands, 2) examine the effects of future DoD land management and climate changes on the capacity and flow of these RS, and 3) project how land-use and climate changes in nearby lands affect future demand for RS. Our approach incorporates widely accepted models and equations, remote sensing, GIS analysis, and stakeholder involvement. Required data include land cover/use, soil type, precipitation, and air temperature. We integrated data into the a) Surface Curve Number Method and b) Revised Universal Soil Loss Equation to estimate capacity of sediment, nitrogen (N) and surface-water regulation. Capacities and flows of RS vary greatly across landscapes and are likely to vary as climate changes or development occurs. Analyses of RS capacity and flow can help managers and planners prioritize actions in the context of best management practices and compatible use buffers. Staff surveys indicated that our approach was informative and easy to use. Implementation may be most limited by on-installation personnel time.

Report↗

USGS2003

Historic range of variability in landscape structure in subalpine forests of the Greater Yellowstone Area, USA

A measure of the historic range of variability (HRV) in landscape structure is essential for evaluating current landscape patterns of Rocky Mountain coniferous forests that have been subjected to intensive timber harvest. We used a geographic information system (GIS) and FRAGSTATS to calculate key landscape metrics on two ???130,000-ha landscapes in the Greater Yellowstone Area, USA: one in Yellowstone National Park (YNP), which has been primarily shaped by natural fires, and a second in the adjacent Targhee National Forest (TNF), which has undergone intensive clearcutting for nearly 30 years. Digital maps of the current and historical landscape in YNP were developed from earlier stand age maps developed by Romme and Despain. Maps of the TNF landscape were adapted from United States Forest Service Resource Information System (RIS) data. Key landscape metrics were calculated at 20-yr intervals for YNP for the period from 1705-1995. These metrics were used to first evaluate the relative effects of small vs. large fire events on landscape structure and were then compared to similar metrics calculated for both pre- and post-harvest landscapes of the TNF. Large fires, such as those that burned in 1988, produced a structurally different landscape than did previous, smaller fires (1705-1985). The total number of patches of all types was higher after 1988 (694 vs. 340-404 before 1988), and mean patch size was reduced by almost half (186 ha vs. 319-379 ha). The amount of unburned forest was less following the 1988 fires (63% vs. 72-90% prior to 1988), yet the number of unburned patches increased by nearly an order of magnitude (230 vs. a maximum of 41 prior to 1988). Total core area and mean core area per patch decreased after 1988 relative to smaller fires (???73,700 ha vs. 87,000-110,000 ha, and 320 ha vs. 2,123 ha, respectively). Notably, only edge density was similar (17 m ha-1 after 1988) to earlier landscapes (9.8-14.2 m ha-1). Three decades of timber harvesting dramatically altered landscape structure in the TNF. Total number of patches increased threefold (1,481 after harvest vs. 437 before harvest), and mean patch size decreased by ???70% (91.3 ha vs. 309 ha). None of the post-harvest landscape metrics calculated for the TNF fell within the HRV as defined in YNP, even when the post-1988 landscape was considered. In contrast, pre-harvest TNF landscape metrics were all within, or very nearly within, the HRV for YNP While reference conditions such as those identified by this study are useful for local and regional landscape evaluation and planning, additional research is necessary to understand the consequences of changes in landscape structure for population, community, ecosystem, and landscape function.

Landscape Ecology↗

USGS1993

Multivariate model of female black bear habitat use for a Geographic Information System

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear ( Ursus americanus ) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

Arkansas↗

USGS2026

USGS Geochron—A database of geochronological and thermochronological dates and data—Technical documentation

Geochronological and thermochronological data are essential for constraining the timing and rates of geological processes, supporting geologic mapping, natural hazard assessment, and resource exploration. The U.S. Geological Survey (USGS) Geochron Database is a centralized, relational database that integrates USGS and State geological survey data in accordance with the National Geologic Mapping Act of 1992 and its reauthorizations. This report documents the structure and development of the database, including its standardized schema, controlled vocabularies, and protocols for compiling legacy and newly published data. The database is designed to adhere to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and currently (2026) includes data from a wide range of geochronological and thermochronological methods. Public access is provided through the USGS Geochron Database Explorer, a browser-based geographic information system (GIS) interface, and through versioned data releases available on ScienceBase, which provide sample-level summary data and detailed analytical information. The USGS Geochron Database is structured to be extensible, supporting the inclusion of additional methods and data types as they are compiled. This format ensures long-term utility and aligns with the Mapping Act’s directive to create a national archive of geochronological information that adds interpretive value to geologic map data. Its design reflects a commitment to data transparency, scientific reproducibility, and national geoscience priorities.

Data Report↗

USGS2006

Scientific developments ISFD3

Highlights, trends, and consensus from the 63 papers submitted to the Scientific Developments theme of the Third International Symposium on Flood Defence (ISFD) are presented. Realizing that absolute protection against flooding can never be guaranteed, trends in flood management have shifted: (1) from flood protection to flood-risk management, (2) from reinforcing structural protection to lowering flood levels, and (3) to sustainable management through integrated problem solving. Improved understanding of watershed responses, climate changes, applications of GIS and remote-sensing technologies, and advanced analytical tools appeared to be the driving forces for renewing flood-risk management strategies. Technical competence in integrating analytical tools to form the basin wide management systems are demonstrated by several large, transnation models. However, analyses from social-economic-environmental points of view are found lag in general. ?? 2006 Taylor & Francis Group.

Conference Paper↗

USGS2007

EAARL first return topography— Fire Island National Seashore

This Web site contains 31 LIDAR-derived first return topography maps and GIS files for Fire Island National Seashore. These lidar-derived topographic maps were produced as a collaborative effort between the U.S. Geological Survey (USGS) Coastal and Marine Geology Program, the National Park Service (NPS), Northeast Coastal and Barrier Network, Inventory and Monitoring Program, and the National Aeronautics and Space Administration (NASA) Wallops Flight Facility. The aims of the partnership that created this product are to develop advanced survey techniques for mapping barrier island geomorphology and habitats, and to enable the monitoring of ecological and geological change within National Seashores. This product is based on data from an innovative airborne lidar instrument under development at the NASA Wallops Flight Facility, the NASA Experimental Advanced Airborne Research Lidar (EAARL).

New York↗

USGS2010

Developing an ecosystem services online decision support tool to assess the impacts of climate change and urban growth in the Santa Cruz watershed: Where we live, work, and play

Using respective strengths of the biological, physical, and social sciences, we are developing an online decision support tool, the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), to help promote the use of information relevant to water allocation and land management in a binational watershed along the U.S.-Mexico border. The SCWEPM will include an ES valuation system within a suite of linked regional driver-response models and will use a multicriteria scenario-evaluation framework that builds on GIS analysis and spatially-explicit models that characterize important ecological, economic, and societal endpoints and consequences that are sensitive to climate patterns, regional water budgets, and regional LULC change in the SCW.

Arizona↗

USGS2021

GeoAI in the US Geological Survey for topographic mapping

Geospatial artificial intelligence (GeoAI) can be defined broadly as the application of artificial intelligence methods and techniques to geospatial data, processes, models, and applications. The application of these methods to topographic data and phenomena is a focus of research in the US Geological Survey (USGS). Specifically, the USGS has researched and developed applications in terrain feature extraction, hydrographic network extraction, and semantic modeling. This article is a documentation of the recent work and current state of research and development. The article helps define the accomplishments and directions of research and applications in fields of GeoAI for topographic mapping within the USGS and more broadly.

Transactions in GIS↗