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USGS2026

Monitoring changes in Landsat thermal features in urban and non-urban interfaces from 1986 to 2023 in two international urban centers: Implications for climate and global issues

Rapid urbanization is reshaping thermal environments worldwide, with the strongest impacts occurring at the interface between urban and non-urban areas. Impervious surfaces, as key indicators of urban expansion, are critical for monitoring urban growth and assessing surface urban heat island (SUHI) effects. Land use and land cover change (LULCC) provides an essential link between urban dynamics and their environmental and societal consequences. Here, we integrated the U.S. Geological Survey (USGS) Climate Global Issues (CGI) Land Cover Product with Landsat thermal time-series to investigate SUHI evolution in two contrasting metropolitan regions: Wuhan, China, and Brasília, Brazil. Using data spanning 1986–2023, we analyzed the relationships between land cover, Landsat-based land surface temperature (LST), and SUHI intensity, and identified persistent thermal hotspots. Results demonstrate that the land cover data utilized increases the accuracy of impervious surface mapping along urban–rural gradients. Average SUHI intensities were 3.4 °C in Wuhan and 3.3 °C in Brasília, with statistically significant warming trends of 0.04 °C/year and 0.01 °C/year, respectively. Maximum temperature proved to be a robust indicator of SUHI intensification, capturing long-term upward trends. Our findings highlight the important role of urban land cover dynamics in shaping temporal SUHI variability and hotspot emergence. This prototype framework demonstrates the scientific and policy value of combining long-term land cover monitoring information with satellite thermal monitoring to quantify and track SUHI at city scale, supporting sustainable urban planning and climate adaptation strategies.

Remote Sensing↗

USGS2025

Post-wildfire sediment fluxes and turbidity plumes in a coastal-draining watershed

Coastal watersheds impacted by wildfires experience higher erosion resulting in increased sediment delivery to the ocean that alters limiting factors (i.e., light) for marine organisms. With increasing wildfire magnitude and severity, it is critical to explore changes in riverine discharges to the ocean to assess cascading hazards associated with wildfires. In situ data, remotely sensed turbidity data, and hydrological model (Soil and Water Assessment Tool “SWAT”) simulations have been adapted to capture and investigate fire-related land use change impacts on Malibu Creek, California, USA. Modifying SWAT land cover inputs using burn severity data had minimal impact on simulations, requiring additional parameterization for acceptable model performance. Remotely sensed turbidity, in situ discharge, rating curve sediment loads, and SWAT simulated discharge and sediment loads increased following the Woolsey Fire. When compared to in situ and rating curve data in similar non-fire water years, the 2019 Woolsey Fire water year in situ discharge was 1.8 times higher, SWAT simulated discharges were 1.4–1.7 times higher, and rating curve sediment load was 1.3 times higher. However, the SWAT simulated sediment loads were slightly lower (0.8–0.9 times) than rating curve sediment loads in similar non-fire water years. Mean coastal turbidity increased to 18.2 Formazin Nephelometric Unit (FNU) during the first storm post-fire (mean background value of 4.3 FNU). Synergies between methods demonstrated rapid coastal sediment exports (remote sensing) and ongoing erosion post-fire (SWAT). These data are essential to understanding fire-related marine ecological changes and implementing effective management and conservation initiatives.

California↗

USGS2010

Vegetation classification and distribution mapping report: Canyon de Chelly National Monument

Executive Summary: The classification and distribution mapping of the vegetation of Canyon de Chelly National Monument (CACH) and surrounding environment was accomplished through a multi-agency effort between 2003 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center and Fort Collins Science Center, Navajo Natural Heritage Program, Northern Arizona University, and NatureServe. The project team described 48 plant communities for CACH—35 of which were described from quantitative classification based on field-relevé data collected in 2004. Five additional plant communities were based on field relevés collected in a previous study. The team derived four additional plant communities from field observations during the photointerpretation phase of the project, and field documented them during accuracy assessment. The National Vegetation Classification Standard served as a conceptual framework for assigning these plant communities to the alliance and association level. Ten of the 48 plant communities were designated “park specials”, that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing CACH, with three different map-class schemas: base, group, and management map classes. The base map classes represented the finest level of spatial detail. Photointerpreters delineated initial polygons through manual interpretation of 2003/2004 1:12,000-scale true color aerial photography supplemented by occasional computer screen digitizing on a mosaic of digitized aerial photos. These polygons were labeled with base map classes during photointerpretation. Field visits verified interpretation concepts. The vegetation map database includes • ? 53 base map classes, which consist of associations and park specials classified with the quantitative analysis • ? additional associations noted during photointerpretation • ? non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 4,718 polygons in the project area. A field-based accuracy assessment of the base map classes showed the overall accuracy to be 50.8% The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, Version 2 (Federal Geographic Data Committee 2008). Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as a first approximation of the group level. The project team identified 16 group map classes in this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 79.9%. In consultation with park staff, the team developed management map classes that consisted of park-defined groupings of base map classes and were intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 28 management map classes have an overall accuracy of 77.1%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary geographic information system and digital database products were also produced that can be used independently, or to augment the main products. These products include shapefiles of the location of field-collected data and relational databases of field-collected data.

Report↗

USGS2001

Compilation of water-resources data and hydrogeologic setting for Brunswick County, North Carolina, 1933-2000

Water-resources data were compiled for Brunswick County, North Carolina, to describe the hydrologic conditions of the County. Hydrologic data collected by the U.S. Geological Survey as well as data collected by other governmental agencies and reviewed by the U.S. Geological Survey are presented. Data from four weather stations and two surface-water stations are summarized. Data also are presented for land use and land cover, soils, geology, hydrogeology, 12 continuously monitored ground-water wells, 73 periodically measured ground-water wells, and water-quality measurements from 39 ground-water wells. Mean monthly precipitation at the Longwood, Shallotte, Southport, and Wilmington Airport weather stations ranged from 2.19 to 7.94 inches for the periods of record, and mean monthly temperatures at the Longwood, Southport, and Wilmington Airport weather stations ranged from 43.4 to 80.1 degrees Fahrenheit for the periods of record. An evaluation of land-use and land-cover data for Brunswick County indicated that most of the County is either forested land (about 57 percent) or wetlands (about 29 percent). Cross sections are presented to illustrate the general hydrogeology beneath Brunswick County. Water-level data for Brunswick County indicate that water levels ranged from about 110 feet above mean sea level to about 22 feet below mean sea level. Chloride concentrations measured in aquifers in Brunswick County ranged from near 0 to 15,000 milligrams per liter. Chloride levels in the Black Creek and Cape Fear aquifers were measured at well above the potable limit for ground water of 250 milligrams per liter set by the U.S. Environmental Protection Agency for safe drinking water.

North Carolina↗

USGS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO 2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Global Change Biology↗

USGS1993

Digital Elevation Models

The Earth Science Information Center (ESIC) distributes digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. Digital cartographic data files may be grouped into four basic types. The first of these, called a Digital Line Graph (DLG), is the line map information in digital form. These data files include information on base data categories, such as transportation, hypsography, hydrography, and boundaries. The second type, called a Digital Elevation Model (DEM), consists of a sampled array of elevations for a number of ground positions at regularly spaced intervals. The third type is Land Use and Land Cover digital data which provides information on nine major classes of land use such as urban, agricultural, or forest as well as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for all known places, features, and areas in the United States identified by a proper name.

Data Users Guide↗

USGS1989

Digital line graphs from 1:100,000-scale maps

The National Cartographic Information Center (NCIC) distributes digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. Digital cartographic data files may be grouped into four basic types. The first of these, called a Digital Line Graph (DLG), is line map information in digital form. These data files include information on planimetric base categories, such as transportation, hydrography, and boundaries. The second form, called a Digital Elevation Model (OEM), consists of a sampled array of elevations for ground positions that are usually, but not always, at regularly spaced intervals. The third type is Land Use and Land Cover digital data, which provides information on nine major classes of land use such as urban, agricultural, or forest as well as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for known places, features, and areas in the United States identified by a proper name.

Data Users Guide↗

USGS1990

Digital line graphs from 1:24,000-scale maps

The Earth Science Information Centers (ESIC) distribute digital cartographic/geographic data files produced by the U.S. Geological Survey (USGS) as part of the National Mapping Program. Digital cartographic data flles are grouped into four basic types. The first of these, called a Digital Line . Graph (DLG), is line map information in digital form. These data files include information on planimetric base categories, such as transportation, hydrography, and boundaries. The second type, called a Digital Elevation Model (DEM), consists of a sampled array of elevations for a number of ground positions that are usually at regularly spaced intervals. The third type is Land Use and Land Cover digital data, which provides information on nine major classes of land use such as urban, agricultural, or forest as wen as associated map data such as political units and Federal land ownership. The fourth type, the Geographic Names Information System, provides primary information for all known places, features, and areas in the United States identified by a proper name.

Data Users Guide↗

USGS1984

GIRAS TO MOSS INTERFACE.

An analysis of land use change for an area in Boulder County, Colorado, was conducted using digital cartographic data. The authors selected data in the Geographic Information Retrieval and Analysis System (GIRAS) format which is digitized from the 1:250,000-scale land use and land cover map series. The Map Overlay and Statistical System (MOSS) was used as an analytical tool for the study. The authors describe the methodology used in converting the GIRAS file into a MOSS format and the activities associated with the conversion.

Conference Paper↗

USGS1977

Urban and regional land use change detected by using Landsat data

The Atlanta Regional Commission and the Earth Resources Observation Systems Data Center participated in a demonstration of the use of Landsat digital data to detect land use change in the Atlanta, Ga., area. Temporal overlays combining Landsat band-5 data from October 1972 and 1974 were made by using the General Electric Image 100 system. The 1972 data were divided by the 1974 data, and low ratios indicate areas where land use and land cover had changed. These low ratios were classified into a land-use-change theme. A classification based on the use of the four bands of the multispectral scanner data, taken in 1974, yielded six land use categories: (1) Commercial-industrial-multifamily, (2) single-family residential, (3) cleared land, (4) open space, (5) forested land, and (6) water. These six 1974 categories and the change theme were combined to determine changes between 1972 and 1974.

Georgia↗

USGS2010

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Open-File Report↗

USGS2015

A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30 m continuous field tree cover data

The methodology for selection, creation, and application of a global remote sensing validation dataset using high resolution commercial satellite data is presented. High resolution data are obtained for a stratified random sample of 500 primary sampling units (5 km × 5 km sample blocks), where the stratification based on Köppen climate classes is used to distribute the sample globally among biomes. The high resolution data are classified to categorical land cover maps using an analyst mediated classification workflow. Our initial application of these data is to evaluate a global 30 m Landsat-derived, continuous field tree cover product. For this application, the categorical reference classification produced at 2 m resolution is converted to percent tree cover per 30 m pixel (secondary sampling unit)for comparison to Landsat-derived estimates of tree cover. We provide example results (based on a subsample of 25 sample blocks in South America) illustrating basic analyses of agreement that can be produced from these reference data. Commercial high resolution data availability and data quality are shown to provide a viable means of validating continuous field tree cover. When completed, the reference classifications for the full sample of 500 blocks will be released for public use.

Remote Sensing of Environment↗

USGS2007

Development of an Impervious-Surface Database for the Little Blackwater River Watershed, Dorchester County, Maryland

Many agricultural and forested areas in proximity to National Wildlife Refuges (NWR) are under increasing economic pressure for commercial or residential development. The upper portion of the Little Blackwater River watershed - a 27 square mile area within largely low-lying Dorchester County, Maryland, on the eastern shore of the Chesapeake Bay - is important to the U.S. Fish and Wildlife Service (USFWS) because it flows toward the Blackwater National Wildlife Refuge (BNWR), and developmental impacts of areas upstream from the BNWR are unknown. One of the primary concerns for the Refuge is how storm-water runoff may affect living resources downstream. The Egypt Road project (fig. 1), for which approximately 600 residential units have been approved, has the potential to markedly change the land use and land cover on the west bank of the Little Blackwater River. In an effort to limit anticipated impacts, the Maryland Department of Natural Resources (Maryland DNR) recently decided to purchase some of the lands previously slated for development. Local topography, a high water table (typically 1 foot or less below the land surface), and hydric soils present a challenge for the best management of storm-water flow from developed surfaces. A spatial data coordination group was formed by the Dorchester County Soil and Conservation District to collect data to aid decisionmakers in watershed management and on the possible impacts of development on this watershed. Determination of streamflow combined with land cover and impervious-surface baselines will allow linking of hydrologic and geologic factors that influence the land surface. This baseline information will help planners, refuge managers, and developers discuss issues and formulate best management practices to mitigate development impacts on the refuge. In consultation with the Eastern Region Geospatial Information Office, the dataset selected to be that baseline land cover source was the June-July 2005 National Agricultural Imagery Program (NAIP) 1-meter resolution orthoimagery of Maryland. This publicly available, statewide dataset provided imagery corresponding to the closest in time to the installation of a U.S. Geological Survey (USGS) Water Resources Discipline gaging station on the Little Blackwater River. It also captures land cover status just before major residential development occurs. This document describes the process used to create a database of impervious surfaces for the Little Blackwater watershed.

Open-File Report↗

USGS2011

Land-cover change research at the U.S. Geological Survey-assessing our nation's dynamic land surface

The U.S. Geological Survey (USGS) recently completed an unprecedented, 27-year assessment of land-use and land-cover change for the conterminous United States. For the period 1973 to 2000, scientists generated estimates of change in major types of land use and land cover, such as development, mining, agriculture, forest, grasslands, and wetlands. To help provide the insight that our Nation will need to make land-use decisions in coming decades, the historical trends data is now being used by the USGS to help model potential future land use/land cover under different scenarios, including climate, environmental, economic, population, public policy, and technological change.

Fact Sheet↗

USGS2026

Aligning legacy NLCD land cover maps based on Landsat Collection 1 to Collection 2

The transition from Landsat Collection 1 to Collection 2 introduced significant improvements in radiometric and geometric accuracy. However, the improvements cause location misalignment between the existing Landsat-derived land cover products and the new collection. The legacy National Land Cover Database (NLCD) has been used as a cornerstone land cover source for a variety of research. Therefore, a method aligning the legacy NLCD product to Collection 2 is required to ensure its continuity and consistency of service. We developed a strategy to not only align legacy NLCD to match new Collection 2 geometric locations but also improve land cover labeling in the region that was affected by the geometric shifts. The method identifies boundary pixels of homogeneous land cover patches as potential problem areas that are likely impacted by geometric shifts and generates candidate labels from 3 × 3 window with the target pixel at the center and segmentation-derived majority label. Standard phenology patterns of each candidate land cover type are established based on the random samples except boundary pixels within a 1000-pixels × 1000-pixels processing window region. The phenological distance to each standard land cover type pattern is calculated through a penalty dynamic time warping (DTW) method for each target pixel in the boundary region. Finally, the method determines the most suitable label based on the phenological distance from the candidate labels. Both visual and accuracy assessment results demonstrate that the alignment preserves the overall land cover patterns in the original legacy NLCD product while reducing the spatial discrepancies between the Landsat Collection 2 and land cover. In addition, it enhances the accuracy of land cover labeling of boundary pixels. The overall accuracy (OA) was increased by 7% in the land cover boundary regions after alignment. The quality and confusion matrix comparison between the alignment results and the original legacy NLCD confirm the reliability of the method. Our alignment method has the potential to serve as a framework for aligning other Landsat-derived land cover products to future collections.

conterminous United States↗

USGS2008

National Land Cover Database 2001 (NLCD01) Tile 2, Northeast United States: NLCD01_2

This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg). 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). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

Data Series↗

USGS2008

National Land Cover Database 2001 (NLCD01) Tile 1, Northwest United States: NLCD01_1

Data Series↗

USGS2008

National Land Cover Database 2001 (NLCD01) Tile 3, Southwest United States: NLCD01_3

This 30-meter data set represents land use and land cover for the conterminous United States for the 2001 time period. The data have been arranged into four tiles to facilitate timely display and manipulation within a Geographic Information System (see http://water.usgs.gov/GIS/browse/nlcd01-partition.jpg).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). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer and others (2004), (see: http://www.mrlc.gov/mrlc2k.asp). The NLCD 2001 was created by partitioning the United States into mapping zones. A total of 68 mapping zones (see http://water.usgs.gov/GIS/browse/nlcd01-mappingzones.jpg), were delineated within the conterminous United States based on ecoregion and geographical characteristics, edge-matching features, and the size requirement of Landsat mosaics. Mapping zones encompass the whole or parts of several states. Questions about the NLCD mapping zones can be directed to the NLCD 2001 Land Cover Mapping Team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

Data Series↗