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At least 1,027 records · Page 57Linked to original sources
USGS2010

Use of regression‐based models to map sensitivity of aquatic resources to atmospheric deposition in Yosemite National Park, USA

An abundance of exposed bedrock, sparse soil and vegetation, and fast hydrologic flushing rates make aquatic ecosystems in Yosemite National Park susceptible to nutrient enrichment and episodic acidification due to atmospheric deposition of nitrogen (N) and sulfur (S). In this study, multiple linear regression (MLR) models were created to estimate fall‐season nitrate and acid neutralizing capacity (ANC) in surface water in Yosemite wilderness. Input data included estimated winter N deposition, fall‐season surface‐water chemistry measurements at 52 sites, and basin characteristics derived from geographic information system layers of topography, geology, and vegetation. The MLR models accounted for 84% and 70% of the variance in surface‐water nitrate and ANC, respectively. Explanatory variables (and the sign of their coefficients) for nitrate included elevation (positive) and the abundance of neoglacial and talus deposits (positive), unvegetated terrain (positive), alluvium (negative), and riparian (negative) areas in the basins. Explanatory variables for ANC included basin area (positive) and the abundance of metamorphic rocks (positive), unvegetated terrain (negative), water (negative), and winter N deposition (negative) in the basins. The MLR equations were applied to 1407 stream reaches delineated in the National Hydrography Data Set for Yosemite, and maps of predicted surface‐water nitrate and ANC concentrations were created. Predicted surface‐water nitrate concentrations were highest in small, high‐elevation cirques, and concentrations declined downstream. Predicted ANC concentrations showed the opposite pattern, except in high‐elevation areas underlain by metamorphic rocks along the Sierran Crest, which had relatively high predicted ANC (>200 μ eq L −1 ). Maps were created to show where basin characteristics predispose aquatic resources to nutrient enrichment and acidification effects from N and S deposition. The maps can be used to help guide development of water‐quality programs designed to monitor and protect natural resources in national parks.

Yosemite Nataional Park↗
USGS2019

Geographic attribution of soils using probabilistic modeling of GIS data for forensic search efforts

Examinations of soil traces associated with forensic evidence can be used to narrow potential source area(s) by characterizing features of the trace soil assemblage, some of which are limited to specific regions. Soil characteristics may be used to infer the likelihoods of the soil trace being derived from distinct areas within digital maps, including both maps of discrete classes such as formations on geologic maps and land cover, and continuous geospatial data, such as distance from a point source. Seldom do digital maps precisely represent the observable characteristics in a soil trace. Nevertheless, logical assigned likelihoods based on the correspondence between the mapped characteristics and the observed soil particulate assemblage permit creation of a model of the more probable sources of the soil trace. This approach is applied to a 2003 case in which forensic soil samples derived from digging tools were characterized for investigative leads and to narrow the search area of a clandestine grave. This grave site was located in 2005. The suspect traveled approximately 5,000 km before arrest, so narrowing the prioritized search area for law enforcement would be beneficial. Soil examination and case circumstances were used to assign relative likelihoods within digital maps (GIS or Geographic Information Systems data) of geology, soil mineralogy, plant distributions, power plant locations, and proximity to the known travel path. The product of these individual probability maps generates joint probability models to narrow the recommended search area. The digital model output can be easily overlaid on infrastructure maps to aid law enforcement searches.

Geochemistry, Geophysics, Geosystems↗
USGS2009

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Journal of Exposure Science and Environmental Epid↗
USGS2023

Mapping methane reduction potential of tidal wetland restoration in the United States

Coastal wetlands can emit excess methane in cases where they are impounded and artificially freshened by structures that impede tidal exchange. We provide a new assessment of coastal methane reduction opportunities for the contiguous United States by combining multiple publicly available map layers, reassessing greenhouse gas emissions datasets, and applying scenarios informed by geospatial information system and by surveys of coastal managers. Independent accuracy assessment indicates that coastal impoundments are under-mapped at the national level by a factor of one-half. Restorations of freshwater-impounded wetlands to brackish or saline conditions have the greatest potential climate benefit of all mapped conversion opportunities, but were rarer than other potential conversion events. At the national scale we estimate potential emissions reduction for coastal wetlands to be 0.91 Teragrams of carbon dioxide equivalents year −1 , a more conservative assessment compared to previous estimates. We provide a map of 1,796 parcels with the potential for tidal re-connection.

Communications Earth & Environment↗
USGS2012

Reducing Vulnerability of Ports and Harbors to Earthquake and Tsunami Hazards

Recent scientific research suggests the Pacific Northwest could experience catastrophic earthquakes in the near future, both from distant and local sources, posing a significant threat to coastal communities. Damage could result from numerous earthquake-related hazards, such as severe ground shaking, soil liquefaction, landslides, land subsidence/uplift, and tsunami inundation. Because of their geographic location, ports and harbors are especially vulnerable to these hazards. Ports and harbors, however, are important components of many coastal communities, supporting numerous activities critical to the local and regional economy and possibly serving as vital post-event, response-recovery transportation links. A collaborative, multi-year initiative is underway to increase the resiliency of Pacific Northwest ports and harbors to earthquake and tsunami hazards, involving Oregon Sea Grant (OSG), Washington Sea Grant (WSG), the National Oceanic and Atmospheric Administration Coastal Services Center (CSC), and the U.S. Geological Survey Center for Science Policy (CSP). Specific products of this research, planning, and outreach initiative include a regional stakeholder issues and needs assessment, a community-based mitigation planning process, a Geographic Information System (GIS) — based vulnerability assessment methodology, an educational web-site and a regional data archive. This paper summarizes these efforts, including results of two pilot port-harbor community projects, one in Yaquina Bay, Oregon and the other in Sinclair Inlet, Washington. Finally, plans are outlined for outreach to other port and harbor communities in the Pacific Northwest and beyond, using "getting started" workshops and a web-based tutorial.

Oregon, Washington↗
USGS2001

Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

International Journal of Wildland Fire↗
USGS2010

A portfolio approach to evaluating natural hazard mitigation policies: An application to lateral-spread ground failure in coastal California

In the past, efforts to prevent catastrophic losses from natural hazards have largely been undertaken by individual property owners based on site—specific evaluations of risks to particular buildings. Public efforts to assess community vulnerability and encourage mitigation have focused on either aggregating site—specific estimates or adopting standards based upon broad assumptions about regional risks. This paper develops an alternative, intermediate—scale approach to regional risk assessment and the evaluation of community mitigation policies. Properties are grouped into types with similar land uses and levels of hazard, and hypothetical community mitigation strategies for protecting these properties are modeled like investment portfolios. The portfolios consist of investments in mitigation against the risk to a community posed by a specific natural hazard. and are defined by a community's mitigation budget and the proportion of the budget invested in locations of each type. The usefulness of this approach is demonstrated through an integrated assessment of earthquake—induced lateral—spread ground failure risk in the Watsonville, California area. Data from the magnitude 6.9 Loma Prieta earthquake of 1989 are used to model lateral—spread ground failure susceptibility. Earth science and economic data are combined and analyzed in a Geographic Information System (CIS). The portfolio model is then used to evaluate the benefits of mitigating the risk in different locations. Two mitigation policies, one that prioritizes mitigation by land use type and the other by hazard zone, are compared with a status quo policy of doing no further mitigation beyond that which already exists. The portfolio representing the hazard zone rule yields a higher expected return than the land use portfolio does; however, the hazard zone portfolio experiences a higher standard deviation. Therefore, neither portfolio is clearly preferred. The two mitigation policies both reduce expected losses and increase overall expected community wealth compared to the status quo policy.

California↗
USGS2010

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

International Journal of Remote Sensing↗
USGS2010

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

International Journal of Remote Sensing↗
USGS2010

The IGBP-DIS global 1 km land cover data set, DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.

International Journal of Remote Sensing↗
USGS2007

United States‐Mexican border watershed assessment: Modeling nonpoint source pollution in Ambos Nogales

Ecological considerations need to be interwoven with economic policy and planning along the United States‐Mexican border. Non‐point source pollution can have significant implications for the availability of potable water and the continued health of borderland ecosystems in arid lands. However, environmental assessments in this region present a host of unique issues and problems. A common obstacle to the solution of these problems is the integration of data with different resolutions, naming conventions, and quality to create a consistent database across the binational study area. This report presents a simple modeling approach to predict nonpoint source pollution that can be used for border watersheds. The modeling approach links a hillslopescale erosion‐prediction model and a spatially derived sediment‐delivery model within a geographic information system to estimate erosion, sediment yield, and sediment deposition across the Ambos Nogales watershed in Sonora, Mexico, and Arizona. This paper discusses the procedures used for creating a watershed database to apply the models and presents an example of the modeling approach applied to a conservation‐planning problem.

Arizona, Sonoma↗
USGS2003

A temporal study of urban development for the municipality of Anchorage, Alaska

A land use/land cover database was produced for a portion of the Municipality of Anchorage, Alaska to document the temporal and spatial extent of urbanization to assist in the analysis of changes in impervious cover and water quality. Data were derived from black and white and color infrared aerial photography, and satellite imagery from the early 1970's to 2000 in roughly ten‐year increments. Aerial photographs and satellite data were manually interpreted to identify and map land use/land cover classes which were then entered into a geographic information system, attributed, and georeferenced to a U.S. Geological Survey topographic map base. The spatial extent of the study was 31,117 hectares. In the early 1970's, approximately 7,356 hectares (24%) of the study area were mapped as urban developed. During the 30‐year analysis period, the largest increase in urban development occurred between the late 1970's and early 1980's when urban area increased to 12,263 hectares (39%). Between 1980 and 1990, and 1990 and 2000, urban area increased to 12,762 hectares (41%), and 13,980 hectares (45%) respectively. Most development occurred in forested or tall shrub areas, although some also occurred in wetlands. Between 1970 and 2000, close to 1,300 hectares of wetlands were lost due to development. Contrary to this, the amount of lake and pond area increased slightly from 261 hectares in 1973 to 334 hectares in 1980, and reduced to 310 hectares by 2000. The increase was primarily due to the filling in of gravel pits with spring melt water.

Alasak↗
USGS2014

Geospatial considerations for a multiorganizational, landscape-scale program

Geospatial data play an increasingly important role in natural resources management, conservation, and science-based projects. The management and effective use of spatial data becomes significantly more complex when the efforts involve a myriad of landscape-scale projects combined with a multiorganizational collaboration. There is sparse literature to guide users on this daunting subject; therefore, we present a framework of considerations for working with geospatial data that will provide direction to data stewards, scientists, collaborators, and managers for developing geospatial management plans. The concepts we present apply to a variety of geospatial programs or projects, which we describe as a “scalable framework” of processes for integrating geospatial efforts with management, science, and conservation initiatives. Our framework includes five tenets of geospatial data management: (1) the importance of investing in data management and standardization, (2) the scalability of content/efforts addressed in geospatial management plans, (3) the lifecycle of a geospatial effort, (4) a framework for the integration of geographic information systems (GIS) in a landscape-scale conservation or management program, and (5) the major geospatial considerations prior to data acquisition. We conclude with a discussion of future considerations and challenges.

Journal of Map & Geography Libraries↗
USGS2019

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.

GIScience and Remote Sensing↗
USGS2025

GIScience in the era of Artificial Intelligence: A research agenda towards Autonomous GIS

The advent of generative AI exemplified by large language models (LLMs) opens new ways to represent and compute geographic information and transcends the process of geographic knowledge production, driving geographic information systems (GIS) towards autonomous GIS. Leveraging LLMs as the decision core, autonomous GIS can independently generate and execute geoprocessing workflows to perform spatial analysis. In this vision paper, we further elaborate on the concept of autonomous GIS and present a conceptual framework that defines its five autonomous goals, five levels of autonomy, five core functions, and three operational scales. We demonstrate how autonomous GIS could perform geospatial data retrieval, spatial analysis, and map making with four proof-of-concept GIS agents. We conclude by identifying critical challenges and future research directions, including fine-tuning and self-growing decision-cores, autonomous modelling, and examining the societal and practical implications of autonomous GIS. By establishing the groundwork for a paradigm shift in GIScience, this paper envisions a future where GIS moves beyond traditional workflows to autonomously reason, derive, innovate, and advance geospatial solutions to pressing global challenges. Meanwhile, we emphasize that as we design and deploy increasingly intelligent geospatial systems, we carry a responsibility to ensure they are developed in socially responsible ways, serve the public good, and support the continued value of human geographic insight in an AI-augmented future.

Annals of GIS↗
USGS2023

Named landforms of the World: A geomorphological and physiographic compilation

Prior to the current era of digital geomorphological mapping, global and regional-scale land surface characterization was advanced by qualitative interpretations that relied on human visualization aided by disciplinary knowledge of geophysical processes combined with extensive field study. In the early twentieth century, Fenneman proposed to devise systematic physiographic divisions of the United States and in 1916 produced what is still regarded as an authoritative map of these divisions. His physiographic regions were developed to provide context when describing land surface characteristics of smaller areas using well-known regional characteristics and descriptors. In 1968, geographer Richard E. Murphy published a large-format map of the “Landforms of the World” to fill a gap in the suite of standard classroom maps. In 1990, the British geomorphologist E. M. Bridges published World Geomorphology , providing the first global treatment and description of divisions, provinces, and sections—the same hierarchical land partitioning concepts that Fenneman used decades earlier. In the twenty-first century, geographic information systems (GIS) technologies are nearly ubiquitous, yet neither Murphy’s nor Bridges’s work existed as GIS data. To further illuminate their pioneering work, we (1) recompiled Murphy’s landforms as a spatial combination of modern existing data layers, and (2) used the recompiled Murphy’s landforms as a basis for the boundaries of the divisions, provinces, and sections described by Bridges. Our aggregation yields a new resource, Named Landforms of the World, version 2.0, which provides a reference-level, basemap-quality data layer that can significantly facilitate mapping, assessing, and understanding Earth surface features.

Annals of the AAG↗
USGS2017

Multistressor predictive models of invertebrate condition in the Corn Belt, USA

Understanding the complex relations between multiple environmental stressors and ecological conditions in streams can help guide resource-management decisions. During 14 weeks in spring/summer 2013, personnel from the US Geological Survey and the US Environmental Protection Agency sampled 98 wadeable streams across the Midwest Corn Belt region of the USA for water and sediment quality, physical and habitat characteristics, and ecological communities. We used these data to develop independent predictive disturbance models for 3 macroinvertebrate metrics and a multimetric index. We developed the models based on boosted regression trees (BRT) for 3 stressor categories, land use/land cover (geographic information system [GIS]), all in-stream stressors combined (nutrients, habitat, and contaminants), and for GIS plus in-stream stressors. The GIS plus in-stream stressor models had the best overall performance with an average cross-validation R 2 across all models of 0.41. The models were generally consistent in the explanatory variables selected within each stressor group across the 4 invertebrate metrics modeled. Variables related to riparian condition, substrate size or embeddedness, velocity and channel shape, nutrients (primarily NH 3 ), and contaminants (pyrethroid degradates) were important descriptors of the invertebrate metrics. Models based on all measured in-stream stressors performed comparably to models based on GIS landscape variables, suggesting that the in-stream stressor characterization reasonably represents the dominant factors affecting invertebrate communities and that GIS variables are acting as surrogates for in-stream stressors that directly affect in-stream biota.

Illinois, Indiana, Iowa, Kansas, Kentucky, Minneso↗
USGS2011

Bounding species distribution models

Species distribution models are increasing in popularity for mapping suitable habitat for species of management concern. Many investigators now recognize that extrapolations of these models with geographic information systems (GIS) might be sensitive to the environmental bounds of the data used in their development, yet there is no recommended best practice for “clamping” model extrapolations. We relied on two commonly used modeling approaches: classification and regression tree (CART) and maximum entropy (Maxent) models, and we tested a simple alteration of the model extrapolations, bounding extrapolations to the maximum and minimum values of primary environmental predictors, to provide a more realistic map of suitable habitat of hybridized Africanized honey bees in the southwestern United States. Findings suggest that multiple models of bounding, and the most conservative bounding of species distribution models, like those presented here, should probably replace the unbounded or loosely bounded techniques currently used.

Current Zoology↗