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Results for “Transactions in GIS”

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11 recordsLinked to original sources
USGS2017

Modeling global Hammond landform regions from 250-m elevation data

In 1964, E.H. Hammond proposed criteria for classifying and mapping physiographic regions of the United States. Hammond produced a map entitled “Classes of Land Surface Form in the Forty-Eight States, USA”, which is regarded as a pioneering and rigorous treatment of regional physiography. Several researchers automated Hammond?s model in GIS. However, these were local or regional in application, and resulted in inadequate characterization of tablelands. We used a global 250 m DEM to produce a new characterization of global Hammond landform regions. The improved algorithm we developed for the regional landform modeling: (1) incorporated a profile parameter for the delineation of tablelands; (2) accommodated negative elevation data values; (3) allowed neighborhood analysis window (NAW) size to vary between parameters; (4) more accurately bounded plains regions; and (5) mapped landform regions as opposed to discrete landform features. The new global Hammond landform regions product builds on an existing global Hammond landform features product developed by the U.S. Geological Survey, which, while globally comprehensive, did not include tablelands, used a fixed NAW size, and essentially classified pixels rather than regions. Our algorithm also permits the disaggregation of “mixed” Hammond types (e.g. plains with high mountains) into their component parts.

Transactions in GIS↗
USGS2012

Advancing global marine biogeography research with open-source GIS software and cloud-computing

Across many scientific domains, the ability to aggregate disparate datasets enables more meaningful global analyses. Within marine biology, the Census of Marine Life served as the catalyst for such a global data aggregation effort. Under the Census framework, the Ocean Biogeographic Information System was established to coordinate an unprecedented aggregation of global marine biogeography data. The OBIS data system now contains 31.3 million observations, freely accessible through a geospatial portal. The challenges of storing, querying, disseminating, and mapping a global data collection of this complexity and magnitude are significant. In the face of declining performance and expanding feature requests, a redevelopment of the OBIS data system was undertaken. Following an Open Source philosophy, the OBIS technology stack was rebuilt using PostgreSQL, PostGIS, GeoServer and OpenLayers. This approach has markedly improved the performance and online user experience while maintaining a standards-compliant and interoperable framework. Due to the distributed nature of the project and increasing needs for storage, scalability and deployment flexibility, the entire hardware and software stack was built on a Cloud Computing environment. The flexibility of the platform, combined with the power of the application stack, enabled rapid re-development of the OBIS infrastructure, and ensured complete standards-compliance.

Transactions in GIS↗
USGS2017

Partial polygon pruning of hydrographic features in automated generalization

This paper demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are known to be sensitive to scale change; and thus multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features—such as long and sinuous reservoir arms, stream channels that are too narrow at the target scale, and islands that begin to coalesce—entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the U.S. Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This paper describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the United States.

Transactions in GIS↗
USGS2020

Improving geospatial query performance of an interoperable geographic situation-awareness system (IGSAS) for disaster response

Disaster response operations require fast and coordinated actions based on the real-time disaster situation information. Although Volunteered Geographic Information (VGI) or crowdsourced geospatial data applications have demonstrated to be valuable tools for gathering real-time disaster situation information, they only provide limited utility for disaster response coordination because of the lack of compatibility and interoperability. VGI based on Geospatial Semantic Web (GSW) technologies has the potential to overcome the incompatibility and heterogeneity problems. However, GSW-based VGI often has poor performance due to complex geometric computation. The objective of this research is to explore how to use optimization techniques to improve performance of an interoperable geographic situation-awareness system (IGSAS) based on GSW technologies for disaster response. We conducted experiments to evaluate various client-side optimization techniques for improving performance of an IGSAS prototype for flooding disaster response in New Haven, Connecticut. Our experimental results show that the developed prototype can greatly reduce the runtime costs of geospatial semantic queries through on-the-fly spatial indexing, tile-based rendering, efficient algorithms for spatial join, and caching, especially for those spatial-join geospatial queries that involve a large number of spatial features and heavy geometric computation.

Transactions in GIS↗
USGS2020

GeoNat v1.0: A dataset for natural feature mapping with artificial intelligence and supervised learning

Machine learning allows “the machine” to deduce the complex and sometimes unrecognized rules governing spatial systems, particularly topographic mapping, by exposing it to the end product. Often, the obstacle to this approach is the acquisition of many good and labeled training examples of the desired result. Such is the case with most types of natural features. To address such limitations, this research introduces GeoNat v1.0, a natural feature dataset, used to support artificial intelligence‐based mapping and automated detection of natural features under a supervised learning paradigm. The dataset was created by randomly selecting points from the U.S. Geological Survey’s Geographic Names Information System and includes approximately 200 examples each of 10 classes of natural features. Resulting data were tested in an object‐detection problem using a region‐based convolutional neural network. The object‐detection tests resulted in a 62% mean average precision as baseline results. Major challenges in developing training data in the geospatial domain, such as scale and geographical representativeness, are addressed in this article. We hope that the resulting dataset will be useful for a variety of applications and shed light on training data collection and labeling in the geospatial artificial intelligence domain.

Transactions in GIS↗
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↗
USGS2006

Applications of GIS and database technologies to manage a Karst Feature Database

This paper describes the management of a Karst Feature Database (KFD) in Minnesota. Two sets of applications in both GIS and Database Management System (DBMS) have been developed for the KFD of Minnesota. These applications were used to manage and to enhance the usability of the KFD. Structured Query Language (SQL) was used to manipulate transactions of the database and to facilitate the functionality of the user interfaces. The Database Administrator (DBA) authorized users with different access permissions to enhance the security of the database. Database consistency and recovery are accomplished by creating data logs and maintaining backups on a regular basis. The working database provides guidelines and management tools for future studies of karst features in Minnesota. The methodology of designing this DBMS is applicable to develop GIS-based databases to analyze and manage geomorphic and hydrologic datasets at both regional and local scales. The short-term goal of this research is to develop a regional KFD for the Upper Mississippi Valley Karst and the long-term goal is to expand this database to manage and study karst features at national and global scales.

Journal of Cave and Karst Studies↗
USGS2012

KINEROS2/AGWA: Model use, calibration and validation

KINEROS (KINematic runoff and EROSion) originated in the 1960s as a distributed event-based model that conceptualizes a watershed as a cascade of overland flow model elements that flow into trapezoidal channel model elements. KINEROS was one of the first widely available watershed models that interactively coupled a finite difference approximation of the kinematic overland flow equations to a physically based infiltration model. Development and improvement of KINEROS continued from the 1960s on a variety of projects for a range of purposes, which has resulted in a suite of KINEROS-based modeling tools. This article focuses on KINEROS2 (K2), a spatially distributed, event-based watershed rainfall-runoff and erosion model, and the companion ArcGIS-based Automated Geospatial Watershed Assessment (AGWA) tool. AGWA automates the time-consuming tasks of watershed delineation into distributed model elements and initial parameterization of these elements using commonly available, national GIS data layers. A variety of approaches have been used to calibrate and validate K2 successfully across a relatively broad range of applications (e.g., urbanization, pre- and post-fire, hillslope erosion, erosion from roads, runoff and recharge, and manure transport). The case studies presented in this article (1) compare lumped to stepwise calibration and validation of runoff and sediment at plot, hillslope, and small watershed scales; and (2) demonstrate an uncalibrated application to address relative change in watershed response to wildfire.

Transactions of the ASABE↗
USGS2020

Evaluation of genetic structuring within GIS‐derived Brook Trout management units

Delineation of management units across broad spatial scales can help to visualize population structuring and identify conservation opportunities. Geographical information system (GIS) approaches can be useful for developing broad‐scale management units, especially when paired with field data that can validate the GIS‐based delineations. Genetic data can be useful for evaluating whether management units accurately represent population structuring. The Eastern Brook Trout Joint Venture, a regionwide collaborative group, delineated patch‐based management units for Brook Trout Salvelinus fontinalis by using GIS approaches to inform conservation strategies across the eastern United States. The objectives of this research were to (1) evaluate how well the patches predicted Brook Trout genetic structuring in Connecticut, USA; (2) modify the patches as needed to represent contemporary genetic structuring; and (3) identify catchment‐ and patch‐scale riverscape characteristics that predict genetic diversity. Patches with dams and high levels of upstream impervious surfaces (>3%) had increased intrapatch genetic structuring, which we incorporated into our revised patch delineation algorithm. Patch area and catchment area were the best predictors of genetic diversity, suggesting the importance of maintaining connectivity and incorporating patch‐scale processes into conservation actions. The modified patch layer could be used as the basis for Brook Trout management units to help predict population structuring in the absence of watershed‐scale genetic data, allowing opportunities for Brook Trout conservation to be identified.

Connecticut↗
USGS2012

A remote-sensing, GIS-based approach to identify, characterize, and model spawning habitat for fall-run chum salmon in a sub-arctic, glacially fed river

At northern limits of a species’ distribution, fish habitat requirements are often linked to thermal preferences, and the presence of overwintering habitat. However, logistical challenges and hydrologic processes typical of glacial systems could compromize the identification of these habitats, particularly in large river environments. Our goal was to identify and characterize spawning habitat for fall-run chum salmon Oncorhynchus keta and model habitat selection from spatial distributions of tagged individuals in the Tanana River, Alaska using an approach that combined ground surveys with remote sensing. Models included braiding, sinuosity, ice-free water surface area (indicating groundwater influence), and persistent ice-free water (i.e., consistent presence of ice-free water for a 12-year period according to satellite imagery). Candidate models containing persistent ice-free water were selected as most likely, highlighting the utility of remote sensing for monitoring and identifying salmon habitat in remote areas. A combination of ground and remote surveys revealed spatial and temporal thermal characteristics of these habitats that could have strong biological implications. Persistent ice-free sites identified using synthetic aperture radar appear to serve as core areas for spawning fall chum salmon, and the importance of stability through time suggests a legacy of successful reproductive effort for this homing species. These features would not be captured with a one-visit traditional survey but rather required remote-sensing monitoring of the sites through time.

Transactions of the American Fisheries Society↗
USGS2013

Temporal changes and sexual differences in spatial distribution of Burbot in Lake Erie

We used GIS mapping techniques to examine capture data for Burbot Lota lota from annual gill-net surveys in Canadian waters of Lake Erie during late August and September 1994–2011. Adult males were captured over a larger area (3–17% for ≥20% maximum yearly catch [MYC]) than adult females. More males than females were caught in the gill nets in 14 of the 15 study years. Collectively, these results support a hypothesis of greater activity by adult males during summer, when Burbot are actively feeding. The area of capture contracted by more than 60% (for ≥20% MYC) for both sexes during the time period, which is consistent with the documented decrease of the Burbot population in the lake. The sex ratio (females: males) varied over the time series but declined steadily from 0.97 in 2001 to 0.59 in 2011. The overlap in the capture areas of adult males and females was scale dependent. The depth distribution at which adult Burbot were caught did not change over the time series, and there was no difference in the median depths (about 30 m) at which adult male and female Burbot were caught. The last results are consistent with the Burbot's reliance on coldwater habitats. Additional research is recommended, including telemetry to describe daily and seasonal movements and assessment of gender bias in active and passive capture gear.

Lake Erie↗