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At least 973 records · Page 54Linked to original sources
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↗
USGS1992

Land cover mapping of the Upper Kuskokwim Resource Management Area, Alaska, using Landsat and a digital data base approach

Digital land cover and terrain data for the Upper Kuskokwim Resource Management Area in south-central Alaska were produced by the U.S. Geological Survey for the U.S. Bureau of Land Management. These and other environmental data were incorporated into a digital data base to assist in the production of a land cover map and to facilitate in the management and planning of the area. The digital data base is registered to a 50 m Universal Transverse Mercator grid and is stored on computer-compatible tape to facilitate transfer between different processing systems. The data base includes land cover classifications, terrain models (elevation, slope, aspect, solar illumination), autumn and winter Landsat multi-spectral scanner data, soils and surficial geology data, and area boundary. The spectral classification of Landsat multi-spectral scanner data resulted in seven major classes and 24 subclasses. Major classes were forest, shrubland, dwarf scrub, herbaceous, barren, water, and other.

Canadian Journal of Remote Sensing↗
USGS2010

Evaluation of aquatic biota in relation to environmental characteristics measured at multiple scales in agricultural streams of the Midwest: 1993-2004

This study evaluated the relations between algal, invertebrate, and fish assemblages and physical environmental characteristics of streams at the reach, segment, and watershed scale in agricultural settings in the Midwest. The 86 stream sites selected for study were in predominantly agricultural watersheds sampled as part of the U.S. Geological Survey's National Water-Quality Assessment Program. Species abundance and over 130 biological metrics were used to determine which aspects of the assemblages were most sensitive to change at the three spatial scales. Digital orthophotograph-based riparian land use/land cover was used for analyses of riparian conditions at the reach and segment scales. The percentage area of different land-use/land-cover types was also determined for each watershed. Out of over 230 environmental characteristics examined, those that best explained variation in the biotic assemblages at each spatial scale include the following: 1) reach: bank vegetative cover, fine silty substrate, and open canopy angle; 2) segment: woody vegetation and cropland in the 250-m riparian buffer, and average length of undisturbed buffer; and 3) watershed: land use/land cover (both total forested and row crop), low-permeability soils, slope, drainage area, and latitude. All three biological assemblages, especially fish, correlated more with land use/land cover and other physical characteristics at the watershed scale than at the reach or segment scales. This study identifies biotic measures that can be used to evaluate potential improvements resulting from agricultural best-management practices and other conservation efforts, as well as evaluate potential impairment from urban development or other disturbances.

Midwest↗
USGS2009

Monitoring urban land cover change by updating the national land cover database impervious surface products

The U.S. Geological Survey (USGS) National Land Cover Database (NLCD) 2001 is widely used as a baseline for national land cover and impervious conditions. To ensure timely and relevant data, it is important to update this base to a more recent time period. A prototype method was developed to update the land cover and impervious surface by individual Landsat path and row. This method updates NLCD 2001 to a nominal date of 2006 by using both Landsat imagery and data from NLCD 2001 as the baseline. Pairs of Landsat scenes in the same season from both 2001 and 2006 were acquired according to satellite paths and rows and normalized to allow calculation of change vectors between the two dates. Conservative thresholds based on Anderson Level I land cover classes were used to segregate the change vectors and determine areas of change and no-change. Once change areas had been identified, impervious surface was estimated for areas of change by sampling from NLCD 2001 in unchanged areas. Methods were developed and tested across five Landsat path/row study sites that contain a variety of metropolitan areas. Results from the five study areas show that the vast majority of impervious surface changes associated with urban developments were accurately captured and updated. The approach optimizes mapping efficiency and can provide users a flexible method to generate updated impervious surface at national and regional scales.

Conference Paper↗
USGS2009

Land Cover Change in the Boston Mountains, 1973-2000

The U.S. Geological Survey (USGS) Land Cover Trends project is focused on understanding the rates, trends, causes, and consequences of contemporary U.S. land-cover change. The objectives of the study are to: (1) to develop a comprehensive methodology for using sampling and change analysis techniques and Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data to measure regional land-cover change across the United States; (2) to characterize the types, rates, and temporal variability of change for a 30-year period; (3) to document regional driving forces and consequences of change; and (4) to prepare a national synthesis of land-cover change (Loveland and others, 1999). The 1999 Environmental Protection Agency (EPA) Level III ecoregions derived from Omernik (1987) provide the geographic framework for the geospatial data collected between 1973 and 2000. The 27-year study period was divided into five temporal periods: 1973-1980, 1980-1986, 1986-1992, 1992-2000, and 1973-2000, and the data are evaluated using a modified Anderson Land Use Land Cover Classification System (Anderson and others, 1976) for image interpretation. The rates of land-cover change are estimated using a stratified, random sampling of 10-kilometer (km) by 10-km blocks allocated within each ecoregion. For each sample block, satellite images are used to interpret land-cover change for the five time periods previously mentioned. Additionally, historic aerial photographs from similar time frames and other ancillary data, such as census statistics and published literature, are used. The sample block data are then incorporated into statistical analyses to generate an overall change matrix for the ecoregion. Field data of the sample blocks include direct measurements of land cover, particularly ground-survey data collected for training and validation of image classifications (Loveland and others, 2002). The field experience allows for additional observations of the character and condition of the landscape, assistance in sample block interpretation, ground truthing of Landsat imagery, and determination of the driving forces of change identified in an ecoregion.

Open-File Report↗
USGS2014

Characterization of intra-annual reflectance properties of land cover classes in southeastern South Dakota using Landsat TM and ETM+ data

Landsat-7 and Landsat-5 have orbits that are offset from each other by 8 days. During the time that the sensors on both satellites are operational, there is an opportunity for conducting analyses that incorporate multiple intra-annual high spatial resolution data sets for characterizing the Earth's land surface. In the current study, nine Landsat thematic mapper (TM) and enhanced thematic mapper plus (ETM+) data sets, covering the same path and row on different dates, were acquired during a 1-year time interval for a region in southeastern South Dakota and analyzed. Scenes were normalized using pseudoinvariant objects, and digital data from a series of test sites were extracted from the imagery and converted to surface reflectance. Sunphotometer data acquired on site were used to atmospherically correct the data. Ground observations that were made throughout the growing season by a large group of volunteers were used to help interpret spectroradiometric patterns and trends. Normalized images were found to be very effective in portraying the seasonal patterns of reflectance change that occurred throughout the region. Many of the radiometric patterns related to plant growth and development, but some also related to different background properties. The different kinds of land cover in the region were spectrally and radiometrically characterized and were found to have different seasonal patterns of reflectance. The degree to which the land cover classes could be separated spectrally and radiometrically, however, depended on the time of year during which the data sets were acquired, and no single data set appeared to be adequate for separating all types of land cover. This has practical implications for classification studies because known patterns of seasonal reflectance properties for the different types of land cover within a region will facilitate selection of the most appropriate data sets for producing land cover classifications.

Canadian Journal of Remote Sensing↗
USGS2025

Factors influencing spatial and temporal patterns of Lanius ludovicianus (Loggerhead Shrike) occupancy at a grassland-sagebrush ecotone

Lanius ludovicianus (Loggerhead Shrike) is a predatory songbird that has experienced a severe population decline throughout its range since the 1940s, likely driven by a combination of factors, including habitat loss and fragmentation. Occupying larger territories compared to many other passerines, L. ludovicianus requires open habitat with interspersed trees and shrubs for nesting and perching and heterogeneous ground cover that provides hunting opportunities. Despite the general knowledge of L. ludovicianus habitat during breeding and nonbreeding seasons in many parts of its range, factors influencing long-term occupancy dynamics remain poorly understood. We addressed these knowledge gaps by analyzing 10 yr of L. ludovicianus detection data collected from breeding bird point-count surveys at a grassland-sagebrush ecotone in northeastern Wyoming, USA. We quantified L. ludovicianus detection and occupancy probabilities using a dynamic (multi-season) occupancy modeling framework and evaluated how initial occupancy and dynamic parameters (i.e., colonization and local extinction) were influenced by land-cover composition and change using remotely sensed land-cover data. Additionally, we used observer survey data to assess the influence of survey date and cloud cover on detection probability. We found that detection probability was influenced by survey date, while initial occupancy probability was positively associated with fences and presence of trees. Colonization probability increased with increased litter cover, whereas extinction probability was not influenced by land-cover and remained low throughout our study period. These findings highlight the importance of habitat features, such as fences and tree presence in shaping L. ludovicianus occupancy patterns. Further research is needed to understand the relationships between L. ludovicianus occupancy dynamics and demographic processes such as reproduction and survival. Identifying factors that influence occupancy dynamics and associated demographic processes can guide efforts to conserve and sustain L. ludovicianus populations.

Ornithological Applications↗