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At least 55 records · Page 3Linked to original sources
USGS2016

Land–atmosphere feedbacks amplify aridity increase over land under global warming

The response of the terrestrial water cycle to global warming is central to issues including water resources, agriculture and ecosystem health. Recent studies indicate that aridity, defined in terms of atmospheric supply (precipitation, P) and demand (potential evapotranspiration, Ep) of water at the land surface, will increase globally in a warmer world. Recently proposed mechanisms for this response emphasize the driving role of oceanic warming and associated atmospheric processes. Here we show that the aridity response is substantially amplified by land–atmosphere feedbacks associated with the land surface’s response to climate and CO2 change. Using simulations from the Global Land Atmosphere Coupling Experiment (GLACE)-CMIP5 experiment, we show that global aridity is enhanced by the feedbacks of projected soil moisture decrease on land surface temperature, relative humidity and precipitation. The physiological impact of increasing atmospheric CO2 on vegetation exerts a qualitatively similar control on aridity. We reconcile these findings with previously proposed mechanisms by showing that the moist enthalpy change over land is unaffected by the land hydrological response. Thus, although oceanic warming constrains the combined moisture and temperature changes over land, land hydrology modulates the partitioning of this enthalpy increase towards increased aridity.

Nature Climate Change↗
USGS2017

West Africa land use and land cover time series

Started in 1999, the West Africa Land Use Dynamics project represents an effort to map land use and land cover, characterize the trends in time and space, and understand their effects on the environment across West Africa. The outcome of the West Africa Land Use Dynamics project is the production of a three-time period (1975, 2000, and 2013) land use and land cover dataset for the Sub-Saharan region of West Africa, including the Cabo Verde archipelago. The West Africa Land Use Land Cover Time Series dataset offers a unique basis for characterizing and analyzing land changes across the region, systematically and at an unprecedented level of detail.

West Africa↗
USGS1980

Land use and land cover, 1972-77, Culpeper Basin, Virginia-Maryland

Land use refers to man's activities that relate directly to the land. Lover cover describes the land surface- vegetation, water, naturally- occurring soil and rock, and artificial constructions. In showing land use and land cover in the Culpeper Basin, this map features a consistent level of detail and standardization of categories. The use of the 1:125,000 compilation scale is appropriate, because this type of data is used frequently for different purposes by people representing several disciplines- land use planners, land managers, resource managemnet planners, and others. For example, maps and data similar to this publication have been used for river basin planning, for analysis of land use and land cover changes relative to recreation, for river quality assessment, for preparation of environmental impact statements, and for studies on urbanization. These efforts have been made at the multicounty regional, State, and Federal levels.

Maryland↗
USGS2017

Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in Southern Westchester County, New York

In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment at the Hillview Reservoir in southern Westchester County, New York. Monthly site inspections at the reservoir indicated an approximately 90-square-foot depression in the land surface directly upslope from a seep that has episodically flowed since 2007. In July 2008, the U.S. Geological Survey surveyed the topography of land surface in this depression area by collecting high-accuracy (resolution less than 1 inch) measurements. A point of origin was established for the topographic survey by using differentially corrected positional data collected by a global navigation satellite system. Eleven points were surveyed along the edge of the depression area and at arbitrary locations within the depression area by using robotic land-surveying techniques. The points were surveyed again in March 2012 to evaluate temporal changes in land-surface altitude. Survey measurements of the depression area indicated that the land-surface altitude at 8 of the 11 points decreased beyond the accepted measurement uncertainty during the 44 months from July 2008 to March 2012. Two additional control points were established at stable locations along Hillview Avenue, which runs parallel to the embankment. These points were measured during the July 2008 survey and measured again during the March 2012 survey to evaluate the relative accuracy of the altitude measurements. The relative horizontal and vertical (altitude) accuracies of the 11 topographic measurements collected in March 2012 were ±0.098 and ±0.060 feet (ft), respectively. Changes in topography at 8 of the 11 points ranged from 0.09 to 0.63 ft and topography remained constant, or within the measurement uncertainty, for 3 of the 11 points. Two cross sections were constructed through the depression area by using land-surface altitude data that were interpolated from positional data collected during the two topographic surveys. Cross section A–A′ was approximately 8.5 ft long and consisted of three surveyed points that trended north to south across the depression. Land-surface altitude change decreased along the entire north-south trending cross section during the 44 months, and ranged from 0.2 to more than 0.6 ft. In general, greater land-surface altitude change was measured north of the midpoint as compared to south of the midpoint of the cross section. Cross section B–B′ was 18 ft long and consisted of six surveyed points that trended east to west across the depression. Land-surface altitude change generally decreased or remained constant along the east-west trending cross section during the 44 months and ranged from 0.0 to 0.3 ft. Volume change of the depression area was calculated by using a three-dimensional geographic information system utility that subtracts interpolated surfaces. The results indicated a net volume loss of approximately 38 ±5 cubic feet of material from the depression area during the 44 months.

New York↗
USGS2022

Limited land base and competing land uses force societal tradeoffs when siting energy development

As human populations grow, decisions regarding use of the world's finite land base become increasingly complex. We adopted a land use–conflict scenario involving renewable energy to illustrate one potential cause of these conflicts and resulting tradeoff decisions. Renewable energy industries wishing to expand operations in the United States are limited by multijurisdictional regulations in finding developable land. Interest groups entreat industries to avoid land for various reasons, including avoidance of prime wildlife habitat in accordance with an “avoidance-first” mitigation strategy. By applying a uniform set of rules for renewable energy facilities to the Prairie Pothole Region and portions of the Northern Great Plains, we evaluated the effects of regulations and avoidance of prime wildlife habitat on the amount of land available for development. In our scenario, existing regulations excluded 39% of the project area from potential development, with human infrastructure accounting for 30% (10–66% among states), whereas federally protected species accounted for < 1% at project area and state levels. Unregulated lands accounted for 61% of the project area, with conservation areas predicted as high-quality sites for breeding grassland birds and waterfowl and for migrating whooping cranes Grus americana accounting for 19% within the project area (6–27% among states). This model demonstrated a limited land base available for new development when accounting for regulations and concerns of a subset of societal interest groups. Additional interest groups likely will have different and competing concerns, further emphasizing the complexity of future land-use decisions as the available land base for development diminishes.

Journal of Fish and Wildlife Management↗
USGS2012

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.

Global Environmental Change↗
USGS2011

Thematic accuracy of the National Land Cover Database (NLCD) 2001 land cover for Alaska

The National Land Cover Database (NLCD) 2001 Alaska land cover classification is the first 30-m resolution land cover product available covering the entire state of Alaska. The accuracy assessment of the NLCD 2001 Alaska land cover classification employed a geographically stratified three-stage sampling design to select the reference sample of pixels. Reference land cover class labels were determined via fixed wing aircraft , as the high resolution imagery used for determining the reference land cover classification in the conterminous U.S. was not available for most of Alaska. Overall thematic accuracy for the Alaska NLCD was 76.2% (s.e. 2.8%) at Level II (12 classes evaluated) and 83.9% (s.e. 2.1%) at Level I (6 classes evaluated) when agreement was defined as a match between the map class and either the primary or alternate reference class label. When agreement was defined as a match between the map class and primary reference label only, overall accuracy was 59.4% at Level II and 69.3% at Level I. The majority of classification errors occurred at Level I of the classification hierarchy (i.e., misclassifications were generally to a different Level I class, not to a Level II class within the same Level I class). Classification accuracy was higher for more abundant land cover classes and for pixels located in the interior of homogeneous land cover patches.

Alaska↗
USGS2000

The land cover trends project: A strategy for monitoring land cover change at a national scale

Policy-makers and scientists often require comprehensive data on the types and rates of land use and land cover change at a variety of scales. However, there is generally a lack of local, regional, and national land use and land cover data of sufficient reliability and temporal and geographic detail for providing accurate estimates of landscape change. The U.S. Geological Survey's EROS Data Center and the Landscape Ecology Branch of the U.S. Environmental Protection Agency are collaborating on a four-year research project to document the types, distributions, rates, drivers, and consequences of land cover change for the conterminous United States over the past 30 years. The project is using an ecoregion framework as a geographic stratifier. Both regional and national characteristics of change are determined by sampling land cover change for each of 84 ecoregions using five selected epochs of data from the nearly 30-year record of Landsat 1-7 data. Three types of land cover variables are being mapped or derived: (1) general land cover type; (2) landscape biophysical properties; and (3) landscape pattern. Assessments of the drivers and consequences of change within each ecoregion are then prepared. A pilot phase focusing on the analysis of five selected ecoregions is well underway and will be completed by the end of 2000. Methodologies established during the pilot phase will then be applied to the remaining ecoregions.

Conference Paper↗
USGS2021

Simulated atmospheric response to four projected land-use land-cover change scenarios for 2050 in the north-central United States

Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg −1 ). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.

Nebraska, South Dakota↗
USGS1982

Land Use and land cover and associated maps

The Geological Survey is compiling land use and land cover and associated maps for the entire United States. Land use refers to man's activities which are directly related to the land. Land cover describes the vegetation, water, natural surface, and artificial constructions at the land surface. These maps will help satisfy a longstanding need for a consistent level of detail, standardization of categories, and appropriate use of scales of compilation for a type of data frequently used by land use planners, land managers, resource management planners, and others.

General Information Product↗
USGS2010

Attributes for NHDPlus Catchments (Version 1.1) for the Conterminous United States: NLCD 2001 Land Use and Land Cover

This data set represents the estimated area of land use and land cover from the National Land Cover Dataset 2001 (LaMotte, 2008), compiled for every catchment of NHDPlus for the conterminous United States. The source data set represents land use and land cover for the conterminous United States for 2001. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The NHDPlus Version 1.1 is an integrated suite of application-ready geospatial datasets that incorporates many of the best features of the National Hydrography Dataset (NHD) and the National Elevation Dataset (NED). The NHDPlus includes a stream network (based on the 1:100,00-scale NHD), improved networking, naming, and value-added attributes (VAAs). NHDPlus also includes elevation-derived catchments (drainage areas) produced using a drainage enforcement technique first widely used in New England, and thus referred to as "the New England Method." This technique involves "burning in" the 1:100,000-scale NHD and when available building "walls" using the National Watershed Boundary Dataset (WBD). The resulting modified digital elevation model (HydroDEM) is used to produce hydrologic derivatives that agree with the NHD and WBD. Over the past two years, an interdisciplinary team from the U.S. Geological Survey (USGS), and the U.S. Environmental Protection Agency (USEPA), and contractors, found that this method produces the best quality NHD catchments using an automated process (USEPA, 2007). The NHDPlus dataset is organized by 18 Production Units that cover the conterminous United States. The NHDPlus version 1.1 data are grouped by the U.S. Geologic Survey's Major River Basins (MRBs, Crawford and others, 2006). MRB1, covering the New England and Mid-Atlantic River basins, contains NHDPlus Production Units 1 and 2. MRB2, covering the South Atlantic-Gulf and Tennessee River basins, contains NHDPlus Production Units 3 and 6. MRB3, covering the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy River basins, contains NHDPlus Production Units 4, 5, 7 and 9. MRB4, covering the Missouri River basins, contains NHDPlus Production Units 10-lower and 10-upper. MRB5, covering the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf River basins, contains NHDPlus Production Units 8, 11 and 12. MRB6, covering the Rio Grande, Colorado and Great Basin River basins, contains NHDPlus Production Units 13, 14, 15 and 16. MRB7, covering the Pacific Northwest River basins, contains NHDPlus Production Unit 17. MRB8, covering California River basins, contains NHDPlus Production Unit 18.

Data Series↗
USGS2010

Attributes for MRB_E2RF1 Catchments by Major River Basins in the Conterminous United States: NLCD 2001 Land Use and Land Cover

This tabular data set represents the estimated area of land use and land cover from the National Land Cover Dataset 2001 (LaMotte, 2008), compiled for every MRB_E2RF1 catchment of the Major River Basins (MRBs, Crawford and others, 2006). The source data set represents land use and land cover for the conterminous United States for 2001. The National Land Cover Data Set for 2001 was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of Federal agencies (http://www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Environmental Protection Agency (USEPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (USFWS), the Bureau of Land Management (BLM), and the USDA Natural Resources Conservation Service (NRCS). The MRB_E2RF1 catchments are based on a modified version of the U.S. Environmental Protection Agency's (USEPA) ERF1_2 and include enhancements to support national and regional-scale surface-water quality modeling (Nolan and others, 2002; Brakebill and others, 2011). Data were compiled for every MRB_E2RF1 catchment for the conterminous United States covering the South Atlantic-Gulf and Tennessee (MRB2), the Great Lakes, Ohio, Upper Mississippi, and Souris-Red-Rainy (MRB3), the Missouri (MRB4), the Lower Mississippi, Arkansas-White-Red, and Texas-Gulf (MRB5) and the Pacific Northwest (MRB7) river basins.

Data Series↗
USGS2017

Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual

The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).

Open-File Report↗
USGS1987

Reconnaissance of hydrology, land use, ground-water chemistry, and effects of land use on ground-water chemistry in the Albuquerque-Belen basin, New Mexico

In 1984, the U.S. Geological Survey began regional assessments of groundwater contamination in 14 areas, one of which was the Albuquerque-Belen basin. Groundwater recharge occurs along the basin margins. Groundwater discharge occurs as evapotranspiration in the Rio Grande valley, pumpage, and groundwater flow to the Socorro basin. Open-space land use, which primarily is used for grazing livestock, occupies the majority of the basin. In the Rio Grande valley, agricultural and residential land uses are predominant; in the area near Albuquerque, the land also is used for commercial, institutional , and industrial purposes. The Albuquerque-Belen basin was divided into seven zones on the basis of water chemistry. These water-chemistry zones indicate that large variations in water chemistry exist in the basin as the result of natural processes. Groundwater in the majority of the Albuquerque-Belen basin has a relatively low susceptibility to contamination because the depth to water is > 100 ft and there is virtually no natural mechanism for recharge to the groundwater system. Groundwater in the Rio Grande valley has a relatively high susceptibility to contamination because the depth to water is generally < 30 ft and there are many types of recharge to the groundwater system. Changes in land use may cause changes in the chemical composition of recharge to the groundwater system. The relatively large concentrations of dissolved iron in the Rio Grande valley near Albuquerque may result from the change from agricultural land use to residential land use. Recharge associated with agricultural land use is relatively oxidized because the water is in equilibrium with the atmosphere, whereas recharge associated with residential land use (onsite waste-disposal effluent) is relatively reduced and has larger concentrations of organic carbon, biological oxygen demand, and chemical oxygen demand. The constituents in the onsite waste-disposal effluent could cause reducing conditions in the aquifer and the subsequent dissolution of iron and manganese oxides. Trace metals adsorbed to these iron and manganese oxides could be remobilized in groundwater after dissolution of the oxides.

New Mexico↗
USGS1981

The role of change data in a land use and land cover map updating program

An assessment of current land use and a process for identifying and measuring change are needed to evaluate trends and problems associated with the use of our Nation's land resources. The U. S. Geological Survey is designing a program to maintain the currency of its land use and land cover maps and digital data base and to provide data on changes in our Nation's land use and land cover. Ways to produce and use change data in a map updating program are being evaluated. A dual role for change data is suggested. For users whose applications require specific polygon data on land use change, showing the locations of all individual category changes and detailed statistical data on these changes can be provided as byproducts of the map-revision process. Such products can be produced quickly and inexpensively either by conventional mapmaking methods or as specialized output from a computerized geographic information system. Secondly, spatial data on land use change are used directly for updating existing maps and statistical data. By incorporating only selected change data, maps and digital data can be updated in an efficient and timely manner without the need for complete and costly detailed remapping and redigitization of polygon data.

Pecora VII Symposium↗
USGS2012

A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Changes in land use, land cover, disturbance regimes, and land management have considerable influence on carbon and greenhouse gas (GHG) fluxes within ecosystems. Through targeted land-use and land-management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of other GHGs. National-scale, comprehensive analyses of carbon sequestration potential by ecosystem are needed, with a consistent, nationally applicable land-use and land-cover (LULC) modeling framework a key component of such analyses. The U.S. Geological Survey has initiated a project to analyze current and projected future GHG fluxes by ecosystem and quantify potential mitigation strategies. We have developed a unique LULC modeling framework to support this work. Downscaled scenarios consistent with IPCC Special Report on Emissions Scenarios (SRES) were constructed for U.S. ecoregions, and the FORE-SCE model was used to spatially map the scenarios. Results for a prototype demonstrate our ability to model LULC change and inform a biogeochemical modeling framework for analysis of subsequent GHG fluxes. The methodology was then successfully used to model LULC change for four IPCC SRES scenarios for an ecoregion in the Great Plains. The scenario-based LULC projections are now being used to analyze potential GHG impacts of LULC change across the U.S.

Applied Geography↗
USGS2005

A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.

Remote Sensing of Environment↗
USGS2020

Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).

Remote Sensing of Environment↗