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At least 1,261 records · Page 70Linked to original sources
USGS2023

Toxic algae in inland waters of the conterminous United States—A review and synthesis

Cyanobacteria are the most common toxigenic algae in inland waters. Their toxins can affect the health of aquatic and terrestrial organisms, including humans. Other algal groups, such as haptophytes (e.g., Prymnesium parvum ) and euglenoids (e.g., Euglena sanguinea ), can also form harmful algal blooms (HABs) whose toxins cause injury to aquatic biota but currently have no known effects on human health. Prymnesium parvum , however, is responsible for some of the worst HAB-related ecological disasters recorded in inland waters. Here, we provide an overview of the primary toxigenic algae found in U.S. inland waters: cyanobacteria (planktonic forms), P. parvum , and E. sanguinea with the objective of describing their similarities and differences in the areas of HAB ecology, algal toxins, and the potential for future range expansion of HABs. A detailed account of bloom habitats and their known associations with land cover and use is provided from the perspective of water quality. This review revealed that salinity may have an influence on inland cyanobacterial blooms and cyanotoxins that had not been fully recognized previously.

Water↗
USGS2014

A neural network approach for enhancing information extraction from multispectral image data

A back-propagation artificial neural network (ANN) was applied to classify multispectral remote sensing imagery data. The classification procedure included four steps: (i) noisy training that adds minor random variations to the sampling data to make the data more representative and to reduce the training sample size; (ii) iterative or multi-tier classification that reclassifies the unclassified pixels by making a subset of training samples from the original training set, which means the neural model can focus on fewer classes; (iii) spectral channel selection based on neural network weights that can distinguish the relative importance of each channel in the classification process to simplify the ANN model; and (iv) voting rules that adjust the accuracy of classification and produce outputs of different confidence levels. The Purdue Forest, located west of Purdue University, West Lafayette, Indiana, was chosen as the test site. The 1992 Landsat thematic mapper imagery was used as the input data. High-quality airborne photographs of the same Lime period were used for the ground truth. A total of 11 land use and land cover classes were defined, including water, broadleaved forest, coniferous forest, young forest, urban and road, and six types of cropland-grassland. The experiment, indicated that the back-propagation neural network application was satisfactory in distinguishing different land cover types at US Geological Survey levels II-III. The single-tier classification reached an overall accuracy of 85%. and the multi-tier classification an overall accuracy of 95%. For the whole test, region, the final output of this study reached an overall accuracy of 87%. ?? 2005 CASI.

Canadian Journal of Remote Sensing↗
USGS1984

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Conference Paper↗
USGS1991

Proposed U.S. Geological Survey standard for digital orthophotos

The U.S. Geological Survey has added the new category of digital orthophotos to the National Digital Cartographic Data Base. This differentially rectified digital image product enables users to take advantage of the properties of current photoimagery as a source of geographic information. The product and accompanying standard were implemented in spring 1991. The digital orthophotos will be quadrangle based and cast on the Universal Transverse Mercator projection and will extend beyond the 3.75-minute or 7.5-minute quadrangle area at least 300 meters to form a rectangle. The overedge may be used for mosaicking with adjacent digital orthophotos. To provide maximum information content and utility to the user, metadata (header) records exist at the beginning of the digital orthophoto file. Header information includes the photographic source type, date, instrumentation used to create the digital orthophoto, and information relating to the DEM that was used in the rectification process. Additional header information is included on transformation constants from the 1927 and 1983 North American Datums to the orthophoto internal file coordinates to enable the user to register overlays on either datum. The quadrangle corners in both datums are also imprinted on the image. Flexibility has been built into the digital orthophoto format for future enhancements, such as the provision to include the corresponding digital elevation model elevations used to rectify the orthophoto. The digital orthophoto conforms to National Map Accuracy Standards and provides valuable mapping data that can be used as a tool for timely revision of standard map products, for land use and land cover studies, and as a digital layer in a geographic information system.

Conference Paper↗
USGS2013

The changing southwest

This chapter describes important geographical and socio-economic characteristics and trends in the Southwest—such as population and economic growth and changes in land ownership, land use, and land cover—that provide the context for how climate change will likely affect the Southwest. The chapter also describes key laws and institutions relevant to adaptive management of resources.

Southwest United States↗
USGS2000

Use of Argon, Corona, and Landsat imagery to assess 30 years of land resource changes in west-central Senegal

Over the past 35 years, an agricultural area of west-central Senegal has experienced rapid population growth, fast expansion of agricultural lands, a decline in rainfall, and degradation of vegetative and soil resources. Although such changes have not escaped the attention of Senegal's people, its government, and the scientific community the ability to monitor and quantify land resource trends of recent decades has been difficult. Recently available high-resolution satellite photographs from the American Argon and Corona Programs provide coverage of Senegal back to 1963. The photographs make it possible to study and map land resources at the beginning of the Space Age. In this study, we characterize the changes that have occurred in the region from the early 1960s to the mid-1 990s. Early Argon and Corona photographs are used to reconstruct the historical land use and land cover; comparisons are made with assessments from recent Landsat images. Field studies and aerial surveys provide additional insight. The forces of change, driven primarily by population growth and unsustainable agricultural practices, are examined

Photogrammetric Engineering and Remote Sensing↗
USGS2006

Coping with climate change

What have we learned so far about how climate change is affecting our global environment? Studies show that it adversely affects human and natural systems by • reducing biodiversity • altering hydrological systems • impairing biological and chemical cycles • making it more difficult to restore degraded ecosystems Climate is not the only factor in the deterioration of natural systems.We are making big changes to the landscape, altering land use and land cover in major ways. These changes combined present a challenge to environmental management. Adaptive management is a scientific approach to managing the adverse impacts of climate and landscape change.

ActionBioscience↗
USGS2015

Golden Eagle predation of an adult turkey vulture

The Golden Eagle (Aquila chrysaetos) is a large, apex predator that occurs at low densities, has a long life span, experiences delayed maturity, has low reproductive rates, and has no natural predators (Watson 1997, Kochert et al. 2002). Golden Eagles are sensitive to anthropogenic driven landscape changes in land cover and land use (Hunt 2002, Kochert and Steenhof 2002). Landscape level alterations, such as encroachment of woody vegetation in eagle foraging areas, may result in decreased abundance of suitable prey or interfere with the eagle’s ability to see or capture prey. Currently, there is substantial concern for Golden Eagle conservation due to widespread anthropogenic changes to landscapes across much of the species distribution (U.S. Fish and Wildlife Service 2013). In particular, the rapid expansion of wind energy development has led to heightened concerns for Golden Eagle conservation, as the species is susceptible to mortality through collision with turbines (Hunt 2002, U.S. Fish and Wildlife Service 2013). This has resulted in a recent increase in research to develop a better understanding of the species’ ecology.

Bulletin of the Texas Ornithological Society↗
USGS1978

An evaluation of errors in mapping land use changes for the Central Atlantic Regional Ecological Test Site

Land use change maps prepared from high-resolution, high-altitude aerial photographs for the period 1970 to 1972 of the CARETS (Central Atlantic Regional Ecological Test Site) project were analyzed for accuracy. To test for errors of commission, all outlined areas (polygons) of land use change on five l:100000-scale maps were examined for accuracy. An estimate of the errors of omission was obtained by comparing the land use change polygons mapped for the Norfolk SMSA (Norfolk and Virginia Beach Standard Metropolitan Statistical Area) with the changes mapped in a more detailed study of the same area. The results of these studies showed that the largest number of errors was caused by misregistration of the land use change map to the 1970 land use map; however, only a very small percentage of the total land area of the land use change was affected by misregistration. In the Norfolk SMSA, which is an area of rapid urban growth, many of the land use changes had not been mapped. The conclusion is that quality control checks are necessary both during and after map compilation. The experience of the CARETS and other land use mapping experiments conducted in the early 1970's has resulted in the development of carefully designed quality-control procedures implemented in the nationwide mapping of land use and land cover currently being carried out by the U.S. Geological Survey.

Maryland, Virginia↗
USGS1995

Validation of national land-cover characteristics data for regional water-quality assessment

Land-cover information is used routinely to support the interpretation of water-quality data. The Prototype 1990 Conterminous US Land Cover Characteristics Data Set, developed primarily from Advanced Very High Resolution Radiometer (AVHRR) data, was made available to the US Geological Survey's National Water-Quality Assessment (NAWQA) Program. The study described in this paper explored the utility of the 1990 national data set for developing quantitative estimates of the areal extent of principal land-cover types within large areal units. Land-cover data were collected in 1993 at 210 sites in the Central Nebraska Basins, one of the NAWQA study units. Median percentage-corn estimates for each sampling stratum wre used to produce areally weighted estimates of the percentage-corn cover for hydrologic units. Comparison of those areal estimates with an independent source of 1992 land-cover data showed good agreement. -Authors

Geocarto International↗
USGS1999

An analysis of IGBP global land-cover characterization process

The international Geosphere Biosphere Programme (IGBP) has called for the development of improved global land-cover data for use in increasingly sophisticated global environmental models. To meet this need, the staff of the U.S. Geological Survey and the University of Nebraska-Lincoln developed and applied a global land-cover characterization methodology using 1992-1993 1-km resolution Advanced Very High Resolution Radiometer (AVHRR) and other spatial data. The methodology, based on unsupervised classification with extensive postclassification refinement, yielded a multi-layer database consisting of eight land-cover data sets, descriptive attributes, and source data. An independent IGBP accuracy assessment reports a global accuracy of 73.5 percent, and continental results vary from 63 percent to 83 percent. Although data quality, methodology, interpreter performance, and logistics affected the results, significant problems were associated with the relationship between AVHRR data and fine-scale, spectrally similar land-cover patterns in complex natural or disturbed landscapes.

Photogrammetric Engineering and Remote Sensing↗
USGS2010

A method for assessing carbon stocks, carbon sequestration, and greenhouse-gas fluxes in ecosystems of the United States under present conditions and future scenarios

he Energy Independence and Security Act of 2007 (EISA), Section 712, mandates the U.S. Department of the Interior to develop a methodology and conduct an assessment of the Nation’s ecosystems, focusing on carbon stocks, carbon sequestration, and emissions of three greenhouse gases (GHGs): carbon dioxide, methane, and nitrous oxide. The major requirements include (1) an assessment of all ecosystems (terrestrial systems, such as forests, croplands, wetlands, grasslands/shrublands; and aquatic ecosystems, such as rivers, lakes, and estuaries); (2) an estimate of the annual potential capacities of ecosystems to increase carbon sequestration and reduce net GHG emissions in the context of mitigation strategies (including management and restoration activities); and (3) an evaluation of the effects of controlling processes, such as climate change, land-use and land-cover change, and disturbances such as wildfires. The concepts of ecosystems, carbon pools, and GHG fluxes follow conventional definitions in use by major national and international assessment or inventory efforts. In order to estimate current ecosystem carbon stocks and GHG fluxes and to understand the potential capacity and effects of mitigation strategies, the method will use two time periods for the assessment: 2001 through 2010, which establishes a current ecosystem carbon and GHG baseline and will be used to validate the models; and 2011 through 2050, which will be used to assess potential capacities based on a set of scenarios. The scenario framework will be constructed using storylines of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES), along with both reference and enhanced land-use and land-cover (LULC) and land-management parameters. Additional LULC and land-management mitigation scenarios will be constructed for each storyline to increase carbon sequestration and reduce GHG fluxes in ecosystems. Input from regional experts and stakeholders will be solicited to construct these scenarios. The methods for mapping the current LULC and ecosystem disturbances will require the extensive use of both remote-sensing data and field-survey data (for example, forest inventories) to capture and characterize landscape-changing events. For potential LULC changes and ecosystem disturbances, key drivers such as socioeconomic and climate changes will be used in addition to the biophysical data. The result of these analyses will be a series of maps for each future year for each scenario. These annual maps will form the basis for estimating carbon storage and GHG emissions. For terrestrial ecosystems, carbon storage, carbon-sequestration capacities, and GHG emissions under the present conditions and future scenarios will be assessed using the LULC-change and ecosystem-disturbance estimates in map format with a spatially explicit biogeochemical ensemble modeling system that incorporates properties of management activities (such as tillage or harvesting) and properties of individual ecosystems (such as energy exchange, vegetation characteristics, hydrological cycling, and soil attributes). For aquatic ecosystems, carbon burial in sediments and fluxes of GHG are functions of the present and future potential stream flow and sediment transport and will be assessed using empirical hydrological modeling methods. Validation and uncertainty analysis methods described in the methodology will follow established guidelines to assess the quality of the assessment results. The U.S. Environmental Protection Agency’s Level II ecoregions map will be the practical instrument for developing and delivering assessment results. Consequently, the ecoregion (there are 22 modified ecoregions) will be the reporting unit of the assessment because the scenarios, assessment results, validation, and uncertainty analysis will be produced at that scale. The implementation of these methods will require collaborations among various Federal agencies, State agencies, nongovernmental organizations, and the science community. Using the method described in this document, the assessment can be completed in approximately 3 to 4 years. The primary deliverables will be assessment reports containing tables, charts, and maps that will present the estimated GHG parameters annually for 2001 through 2050 by ecosystem, pool, and scenario. The results will permit the evaluation of a range of policies, mitigation options, and research topics, such as the demographic, LULC-change, or climate-change effects on carbon stocks, carbon sequestration, and GHG fluxes in ecosystems.

Scientific Investigations Report↗
USGS2014

Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States

Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on 5 Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the Forecasting Scenarios of Land-use Change (FORE-SCE) model. Four spatially explicit datasets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of 10 anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting have relatively lower mean stand ages compared to those with less 15 forest cutting. Stand ages differ substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, 20 biodiversity, climate and weather variability, hydrologic change, and other ecological processes.

Ecological Applications↗
USGS2019

Where’s the rock: Using convolutional neural networks to improve land cover classification

While machine learning techniques have been increasingly applied to land cover classification problems, these techniques have not focused on separating exposed bare rock from soil covered areas. Therefore, we built a convolutional neural network (CNN) to differentiate exposed bare rock ( rock ) from soil cover ( other ). We made a training dataset by mapping exposed rock at eight test sites across the Sierra Nevada Mountains (California, USA) using USDA’s 0.6 m National Aerial Inventory Program (NAIP) orthoimagery. These areas were then used to train and test the CNN. The resulting machine learning approach classifies bare rock in NAIP orthoimagery with a 0.95 F 1 "> F 1 score. Comparatively, the classical OBIA approach gives only a 0.84 F 1 "> F 1 score. This is an improvement over existing land cover maps, which underestimate rock by almost 90%. The resulting CNN approach is likely scalable but dependent on high-quality imagery and high-performance algorithms using representative training sets informed by expert mapping. As image quality and quantity continue to increase globally, machine learning models that incorporate high-quality training data informed by geologic, topographic, or other topical maps may be applied to more effectively identify exposed rock in large image collections.

California↗
USGS2020

Causes of land change in the U.S. Interior Highlands, 2001–2011

The causes of land change from 2001 through 2011 for the Interior Highlands region of the south-central United States were assessed using satellite imagery, historical land-use and land-cover data, and digital orthophotos. The study was designed to develop improved regional land-use and land-cover change information, including identification of the proximate causes of change. The four leading causes of land change involved various stages of forest change: harvest (376,497 hectares), reforestation (105,150 hectares), stand loss to fire (98,875 hectares), and thinning (54,029 hectares). The study provides baseline spatial data for understanding human and ecological dynamics in the region. The spatial data, including metadata, are available in the data release associated with this report at https://doi.org/10.5066/P9W4SF05.

Missouri, Arkansas, Oklahoma, Kansas↗
USGS2021

Modeling moose habitat use by age, sex, and season in Vermont, USA using high-resolution lidar and national land cover data

Moose ( Alces alces ) populations have experienced unprecedented declines along the southern periphery of their range, including Vermont, USA. Habitat management may be used to improve the status of the population and health of individuals. To date, however, Vermont wildlife managers have been challenged to effectively use this important tool due to the lack of fine-scale information on moose space use and habitat characteristics. To assess habitat use, we combined more than 40,000 moose locations collected from radio-collared individuals (n = 74), recent land cover data, and high resolution, 3-dimensional lidar ( light detection and ranging ) data to develop Resource Utilization Functions (RUF) by age (mature and young adult), season (dormant and growth), and sex. Each RUF linked home range use to average habitat conditions within 400 m or 1 km of each 30 m 2 pixel within the home range. Across analyses, the top RUF models included both composition (as measured through the National Land Cover Database) and structure (as measured through lidar) variables, and significantly outperformed models that excluded lidar variables. These findings support the notion that lidar is an effective tool for improving the ability of models to estimate patterns of habitat use, especially for larger bodied mammals. Generally speaking, female moose actively used areas with proportionally more regenerating forest (i.e., forage < 3.0 m) and more mature forest (i.e., canopy structure > 6.0 m), while males actively used more high elevation, mixed forest types. Further, moose exhibited important seasonal differences in habitat use that likely reflect temporal changes in energetic and nutritional requirements and behavior across the year. Moose used areas with proportionally more regenerating forest (i.e., forage < 3.0 m) during the growth period and female moose had strong positive associations with lidar-derived canopy structure during the growth (but not the dormant) period. Ultimately, the resultant maps of habitat use provide a means of informing management activities (e.g., the restoration or alteration of habitats to benefit moose) and policies around land use that may contribute to population recovery.

Vermont↗
USGS2023

Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau

Livestock removal is increasingly used as a management option to mitigate the negative impacts of grazing-related disturbances on rangelands. Removal generally increases plant cover, but it is unclear when, where, and by how much plant and soil cover changes can be expected. On the Colorado Plateau, complex geology, topography, soils, and climate all interact to mediate the relationship between land cover, climate, and disturbance. In this study we used new developments in land cover mapping and analysis to assess landscape plant and bare soil cover up to 30 years after livestock removal from two grazing allotments in Capitol Reef National Park, Utah, USA. Results indicate that livestock removal increases plant cover 0.17-0.32% per year and reduces bare soil cover 0.34-0.41% per year, although these rates may be suppressed by warming temperatures. Soils, assessed through Soil Geomorphic Units, played a strong but complex role in mediating land cover changes through time. These results suggest that livestock removal is an effective strategy for increasing plant cover and reducing bare soil on the Colorado Plateau, but including soil information in decision making will enhance efficiency by improving manager's ability to prioritize management actions effectively across space and through time.

Environmental Research Letters↗
USGS1987

Producing Alaska interim land cover maps from Landsat digital and ancillary data

In 1985, the U.S. Geological Survey initiated a research program to produce 1:250,000-scale land cover maps of Alaska using digital Landsat multispectral scanner data and ancillary data and to evaluate the potential of establishing a statewide land cover mapping program using this approach. The geometrically corrected and resampled Landsat pixel data are registered to a Universal Transverse Mercator (UTM) projection, along with arc-second digital elevation model data used as an aid in the final computer classification. Areas summaries of the land cover classes are extracted by merging the Landsat digital classification files with the U.S. Bureau of Land Management's Public Land Survey digital file. Registration of the digital land cover data is verified and control points are identified so that a laser plotter can products screened film separate for printing the classification data at map scale directly from the digital file. The final land cover classification is retained both as a color map at 1:250,000 scale registered to the U.S. Geological Survey base map, with area summaries by township and range on the reverse, and as a digital file where it may be used as a category in a geographic information system.

Alaska↗