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USGS2009

Land Cover Trends in the Southern Florida Coastal Plain

This report presents an assessment of land use and land cover change in the Southern Florida Coastal Plain ecoregion for the period from 1973 to 2000. The ecoregion is one of 84 level III ecoregions defined by the Environmental Protection Agency; ecoregions have been designed to serve as a spatial framework for environmental resource management and denote areas that contain a geographically distinct assemblage of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The Southern Florida Coastal Plain ecoregion covers an area of approximately 22,407 square kilometers [8,651 square miles] across the lower portion of the Florida peninsula, from Lake Okeechobee southward through the Florida Keys. It comprises flat plains with wet soils, marshland and swamp land cover with Everglades and palmetto prairie vegetation types.

Scientific Investigations Report↗

USGS2006

Temporal evolution of carbon budgets of the Appalachian forests in the U.S. from 1972 to 2000

Estimating dynamic terrestrial ecosystem carbon (C) sources and sinks over large areas is difficult. The scaling of C sources and sinks from the field level to the regional level has been challenging due to the variations of climate, soil, vegetation, and disturbances. As part of an effort to estimate the spatial, temporal, and sectional dimensions of the United States C sources and sinks (the U.S. Carbon Trends Project), this study estimated the forest ecosystem C sequestration of the Appalachian region (186,000 km 2 ) for the period of 1972–2000 using the General Ensemble Biogeochemical Modeling System (GEMS) that has a strong capability of assimilating land use and land cover change (LUCC) data. On 82 sampling blocks in the Appalachian region, GEMS used sequential 60 m resolution land cover change maps to capture forest stand-replacing events and used forest inventory data to estimate non-stand-replacing changes. GEMS also used Monte Carlo approaches to deal with spatial scaling issues such as initialization of forest age and soil properties. Ensemble simulations were performed to incorporate the uncertainties of input data. Simulated results show that from 1972 to 2000 the net primary productivity (NPP), net ecosystem productivity (NEP), and net biome productivity (NBP) averaged 6.2 Mg C ha −1 y −1 (±1.1), 2.2 Mg C ha −1 y −1 (±0.6), and 1.8 Mg C ha −1 y −1 (±0.6), respectively. The inter-annual variability was driven mostly by climate. Detailed C budgets for the year 2000 were also calculated. Within a total 148,000 km 2 forested area, average forest ecosystem C density was estimated to be 186 Mg C ha −1 (±20), of which 98 Mg C ha −1 (±12) was in biomass and 88 Mg C ha −1 (±13) was in litter and soil. The total simulated C stock of the Appalachian forests was estimated to be 2751 Tg C (±296), including 1454 Tg C (±178) in living biomass and 1297 Tg C (±192) in litter and soil. The total net C sequestration (i.e. NBP) of the forest ecosystem in 2000 was estimated to be 19.5 Tg C y −1 (±6.8).

Forest Ecology and Management↗

USGS2006

Analysis of urban land use change in the Las Vegas metropolitan area using multi-temporal satellite imagery

Urban development has expanded rapidly in Las Vegas, Nevada, over the last fifty years. To assess urban land use change in the area, a sub-pixel change detection approach has been used to map urban extent and its temporal changes by determining sub-pixel level impervious surface areas from Landsat satellite remote sensing data in conjunction with digital orthophotography. Sub-pixel percentages of imperviousness are mapped from the 1980s to the 2000s for the Las Vegas metropolitan area using a regression tree model. The spatial-temporal distribution of vegetation in the urban area has also been quantified using the same approach. Analysis suggests that spatial and temporal changes in impervious surface are useful indicators of spatial extent, intensity, and potentially, the types of urban land cover and land use change. Results indicate that areas of 40–60% imperviousness have experienced the largest increase in Las Vegas, suggesting that areas of medium to high development density areas represent the major urban land uses in the region. Urban area fractional vegetation cover has the highest coverage in medium-density urban areas of Las Vegas.

Nevada↗

USGS2013

Estimating the extent of impervious surfaces and turf grass across large regions

The ability of researchers to accurately assess the extent of impervious and pervious developed surfaces, e.g., turf grass, using land-cover data derived from Landsat satellite imagery in the Chesapeake Bay watershed is limited due to the resolution of the data and systematic discrepancies between developed land-cover classes, surface mines, forests, and farmlands. Estimates of impervious surface and turf grass area in the Mid-Atlantic, United States that were based on 2006 Landsat-derived land-cover data were substantially lower than estimates based on more authoritative and independent sources. New estimates of impervious surfaces and turf grass area derived using land-cover data combined with ancillary information on roads, housing units, surface mines, and sampled estimates of road width and residential impervious area were up to 57 and 45% higher than estimates based strictly on land-cover data. These new estimates closely approximate estimates derived from authoritative and independent sources in developed counties.

Delaware;Maryl;New York;Pennsylvania↗

USGS1998

Pesticides and nitrate in surficial sand and gravel aquifers as related to modeled contamination susceptibility in part of the Upper Mississippi River Basin

The occurrence of pesticides and nitrate (nitrite plus nitrate as nitrogen) in surficial sand and gravel aquifers in parts of Minnesota and Wisconsin was summarized as part of an analysis of historical water-quality data for the Upper Mississippi River Basin study unit of the U.S. Geological Survey's National Water-Quality Assessment (NAWQA) Program. Water samples were collected by State and Federal agencies for pesticides (366) and nitrate (410) between 1971 and 1994 from wells completed in surficial sand and gravel aquifers. State agencies in Minnesota and Wisconsin have developed models to determine areas where ground water is susceptible to contamination based on geologic and hydrologic conditions (Schmidt, 1987, and Porcher, 1989). Water-quality data is evaluated with respect to the ground-water susceptibility models. The results also are evaluated with respect to overlying land use and land cover. Samples from wells with detectable levels of one or more pesticides or nitrate concentrations exceeding the U.S. Environmental Protection Agency's (USEPA) Maximum Contaminant Level (MCL) of 10 milligrams per liter (mg/L) generally coincided with areas of high contamination susceptibility. Furthermore, samples from wells located in areas of high contamination susceptibility had pesticide detection frequencies and nitrate concentrations that correlated to overlying land use and land cover. Samples from wells located in high susceptibility areas that were surrounded by cropland had greater pesticide detection frequencies and greater nitrate concentrations than wells located in similar susceptibility areas but surrounded by different land use and land cover types such as forest, urban, and wetlands.

Minnesota, Wisconsin↗

USGS2008

Development and Evaluation of a Riparian Buffer Mapping Tool

Land use and land cover within riparian areas greatly affect the conditions of adjacent water features. In particular, riparian forests provide many environmental benefits, including nutrient uptake, bank stabilization, steam shading, sediment trapping, aquatic and terrestrial habitat, and stream organic matter. In contrast, residential and commercial development and associated transportation infrastructure increase pollutant and nutrient loading and change the hydrologic characteristics of the landscape, thereby affecting both water quality and habitat. Restoring riparian areas is a popular and cost effective restoration technique to improve and protect water quality. Recognizing this, the Chesapeake Executive Council committed to restoring 10,000 miles of riparian forest buffers throughout the Chesapeake Bay watershed by the year 2010. In 2006, the Chesapeake Executive Council further committed to 'using the best available...tools to identify areas where retention and expansion of forests is most needed to protect water quality'. The Chesapeake Bay watershed encompasses 64,000 square miles, including portions of six States and Washington, D.C. Therefore, the interpretation of remotely sensed imagery provides the only effective technique for comprehensively evaluating riparian forest protection and restoration opportunities throughout the watershed. Although 30-meter-resolution land use and land cover data have proved useful on a regional scale, they have not been equally successful at providing the detail required for local-scale assessment of riparian area characteristics. Use of high-resolution imagery (HRI) provides sufficient detail for local-scale assessments, although at greater cost owing to the cost of the imagery and the skill and time required to process the data. To facilitate the use of HRI for monitoring the extent of riparian forest buffers, the U.S. Forest Service and the U.S. Geological Survey Eastern Geographic Science Center funded the development of a prototype semiautomated image classification tool, RBMapper, that is designed for use by technicians with limited image processing training. This document provides an overview of the RBMapper tool, includes instructions on how to obtain the RBMapper tool and tutorial datasets, and contains a summary evaluation of the tool

Open-File Report↗

USGS1997

Land cover characterization and land surface parameterization research

The understanding of land surface processes and their parameterization in atmospheric, hydrologic, and ecosystem models has been a dominant research theme over the past decade. For example, many studies have demonstrated the key role of land cover characteristics as controlling factors in determining land surface processes, such as the exchange of water, energy, carbon, and trace gases between the land surface and the lower atmosphere. The requirements for multiresolution land cover characteristics data to support coupled-systems modeling have also been well documented, including the need for data on land cover type, land use, and many seasonally variable land cover characteristics, such as albedo, leaf area index, canopy conductance, surface roughness, and net primary productivity. Recently, the developers of land data have worked more closely with the land surface process modelers in these efforts.

Ecological Applications↗

USGS1997

Environmental setting and implications for water quality in the Western Lake Michigan Drainages

In 1991, the U.S. Geological Survey began to implement its National Water-Quality Assessment (NAWQA) program. The Western Lake Michigan Drainages was one of 20 study units selected for investigation to begin in 1991. The study-unit investigation will include an assessment of surface- and ground-water quality. The quality of water in a study unit is intrinsically related to the natural and anthropogenic features of the study unit. The natural features include geology, weather and climate, vegetation, and hydrology. The anthropogenic features of the basin include population distribution, land use and land cover, agricultural practices, and water use. This report describes the natural and anthropogenic features that constitute the environmental setting of the Western Lake Michigan Drainages as well as the implications of those features on the water quality.

Wisconsin↗

USGS2006

Quantifying the Components of Impervious Surfaces

This study's objectives were to (1) determine the relative contribution of impervious surface individual components by collecting digital information from high-resolution imagery, 1-meter or better; and to (2) determine which of the more advanced techniques, such as spectral unmixing or the application of coefficients to land use or land cover data, was the most suitable method that could be used by State and local governments as well as Federal agencies to efficiently measure the imperviousness in any given watershed or area of interest. The components of impervious surfaces, combined from all the watersheds and time periods from objective one were the following: buildings 29.2-percent, roads 28.3-percent, parking lots 24.6-percent; with the remaining three totaling 14-percent - driveways, sidewalks, and other, where other were any other features that were not contained within the first five. Results from objective two were spectral unmixing techniques will ultimately be the most efficient method of determining imperviousness, but are not yet accurate enough as it is critical to achieve accuracy better than 10-percent of the truth, of which the method is not consistently accomplishing as observed in this study. Of the three techniques in coefficient application tested, land use coefficient application was not practical, while if the last two methods, coefficients applied to land cover data, were merged, their end results could be to within 5-percent or better, of the truth. Until the spectral unmixing technique has been further refined, land cover coefficients should be used, which offer quick results, but not current as they were developed for the 1992 National Land Characteristics Data.

Open-File Report↗

USGS2012

Modeling habitat dynamics accounting for possible misclassification

Land cover data are widely used in ecology as land cover change is a major component of changes affecting ecological systems. Landscape change estimates are characterized by classification errors. Researchers have used error matrices to adjust estimates of areal extent, but estimation of land cover change is more difficult and more challenging, with error in classification being confused with change. We modeled land cover dynamics for a discrete set of habitat states. The approach accounts for state uncertainty to produce unbiased estimates of habitat transition probabilities using ground information to inform error rates. We consider the case when true and observed habitat states are available for the same geographic unit (pixel) and when true and observed states are obtained at one level of resolution, but transition probabilities estimated at a different level of resolution (aggregations of pixels). Simulation results showed a strong bias when estimating transition probabilities if misclassification was not accounted for. Scaling-up does not necessarily decrease the bias and can even increase it. Analyses of land cover data in the Southeast region of the USA showed that land change patterns appeared distorted if misclassification was not accounted for: rate of habitat turnover was artificially increased and habitat composition appeared more homogeneous. Not properly accounting for land cover misclassification can produce misleading inferences about habitat state and dynamics and also misleading predictions about species distributions based on habitat. Our models that explicitly account for state uncertainty should be useful in obtaining more accurate inferences about change from data that include errors.

Landscape Ecology↗

USGS2007

Factors affecting herbicide yields in the Chesapeake Bay watershed, June 1994

Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 199094 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.Median concentrations and instantaneous yields of alachlor, metolachlor, atrazine, cyanazine, and simazine were generally highest at sites in the Lower Susquehanna River Basin and in agricultural subbasins. Instantaneous herbicide yields are related to land use, hydrogeologic setting, streamflow yield, and agricultural row cropping practices. The significance of these relations may be affected by the interdependence of the factors. The percentage of basin area planted in corn is the most influential factor in the prediction of herbicide yield. Instantaneous yields of all five herbicides measured in June 1994 related poorly to averaged 1990-94 herbicide use. Annually averaged herbicide-use data are too general to use as a predictor for short-term herbicide yields. An evaluation of factors affecting herbicide yields could be refined with more-current land use and land cover information and a more accurate estimate of the percentage of basin area planted in corn. Factors related to herbicide yields can be used to predict herbicide yields in other basins within the Chesapeake Bay watershed and to develop an estimate of herbicide loads to Chesapeake Bay.

Water Resources Bulletin↗

USGS1997

Classification and Mapping of Agricultural Land for National Water-Quality Assessment

Agricultural land use is one of the most important influences on water quality at national and regional scales. Although there is great diversity in the character of agricultural land, variations follow regional patterns that are influenced by environmental setting and economics. These regional patterns can be characterized by the distribution of crops. A new approach to classifying and mapping agricultural land use for national water-quality assessment was developed by combining information on general land-use distribution with information on crop patterns from agricultural census data. Separate classification systems were developed for row crops and for orchards, vineyards, and nurseries. These two general categories of agricultural land are distinguished from each other in the land-use classification system used in the U.S. Geological Survey national Land Use and Land Cover database. Classification of cropland was based on the areal extent of crops harvested. The acreage of each crop in each county was divided by total row-crop area or total orchard, vineyard, and nursery area, as appropriate, thus normalizing the crop data and making the classification independent of total cropland area. The classification system was developed using simple percentage criteria to define combinations of 1 to 3 crops that account for 50 percent or more or harvested acreage in a county. The classification system consists of 21 level I categories and 46 level II subcategories for row crops, and 26 level I categories and 19 level II subcategories for orchards, vineyards, and nurseries. All counties in the United States with reported harvested acreage are classified in these categories. The distribution of agricultural land within each county, however, must be evaluated on the basis of general land-use data. This can be done at the national scale using 'Major Land Uses of the United States,' at the regional scale using data from the national Land Use and Land Cover database, or at smaller scales using locally available data.

Circular↗

USGS2017

The landscapes of West Africa—40 years of change

What has driven changes in land use and land cover in West Africa over the past 40 years? What trends or patterns can be discerned in those changes? To answer these questions, the U.S. Geological Survey West Africa Land Use Dynamics project partnered with the Permanent Interstate Committee for Drought Control in the Sahel and the U.S. Agency for International Development/West Africa to map land use and land cover across the region for three time periods (years): 1975, 2000, and 2013. This cooperative effort has resulted in the publication of a 219-page atlas, “Landscapes of West Africa: A Window on a Changing World.” The atlas uses satellite imagery, maps, and pictures to tell a complex story of landscape change at regional and national scales. It includes a collection of focused studies, some of which raise cause for concern, and others that provide considerable hope.

Fact Sheet↗

USGS1994

Identification of tundra land cover near Teschekpuk Lake, Alaska using SPOT satellite data

Tundra vegetation in the Teshekpuk Lake area of the Alaskan Arctic Coastal Plain was mapped to assess distribution and abundance of waterfowl habitats. Three SPOT satellite scenes were acquired and registered to a 20 m Universal Transverse Mercator grid. Two clustering techniques were used to develop statistical parameters by which the SPOT data were spectrally classified. A maximum likelihood algorithm that correlated spectral classes with land cover types was applied to the SPOT data. Field data were used to assist in spectral class labeling and vegetation descriptions. Twelve cover classes were mapped. The most common type was moist sedge meadow tundra (13.5%); the least common was moss/peat shoreline (0.2%). The moss/peat shoreline type, important to moulting geese and other waterfowl, was spectrally identified using supervised clustering techniques. All other land cover types were identified using unsupervised clustering techniques. Cover classes were described, and a tundra landscape profile produced.

Alaska↗

USGS2013

Assessment of the NASA-USGS Global Land Survey (GLS) Datasets

The Global Land Survey (GLS) datasets are a collection of orthorectified, cloud-minimized Landsat-type satellite images, providing near complete coverage of the global land area decadally since the early 1970s. The global mosaics are centered on 1975, 1990, 2000, 2005, and 2010, and consist of data acquired from four sensors: Enhanced Thematic Mapper Plus, Thematic Mapper, Multispectral Scanner, and Advanced Land Imager. The GLS datasets have been widely used in land-cover and land-use change studies at local, regional, and global scales. This study evaluates the GLS datasets with respect to their spatial coverage, temporal consistency, geodetic accuracy, radiometric calibration consistency, image completeness, extent of cloud contamination, and residual gaps. In general, the three latest GLS datasets are of a better quality than the GLS-1990 and GLS-1975 datasets, with most of the imagery (85%) having cloud cover of less than 10%, the acquisition years clustered much more tightly around their target years, better co-registration relative to GLS-2000, and better radiometric absolute calibration. Probably, the most significant impediment to scientific use of the datasets is the variability of image phenology (i.e., acquisition day of year). This paper provides end-users with an assessment of the quality of the GLS datasets for specific applications, and where possible, suggestions for mitigating their deficiencies.

Remote Sensing of Environment↗

USGS2005

Dynamic modeling of Tampa Bay urban development using parallel computing

Urban land use and land cover has changed significantly in the environs of Tampa Bay, Florida, over the past 50 years. Extensive urbanization has created substantial change to the region's landscape and ecosystems. This paper uses a dynamic urban-growth model, SLEUTH, which applies six geospatial data themes (slope, land use, exclusion, urban extent, transportation, hillside), to study the process of urbanization and associated land use and land cover change in the Tampa Bay area. To reduce processing time and complete the modeling process within an acceptable period, the model is recoded and ported to a Beowulf cluster. The parallel-processing computer system accomplishes the massive amount of computation the modeling simulation requires. SLEUTH calibration process for the Tampa Bay urban growth simulation spends only 10 h CPU time. The model predicts future land use/cover change trends for Tampa Bay from 1992 to 2025. Urban extent is predicted to double in the Tampa Bay watershed between 1992 and 2025. Results show an upward trend of urbanization at the expense of a decline of 58% and 80% in agriculture and forested lands, respectively.

Computers & Geosciences↗

USGS2013

Stochastic empirical loading and dilution model (SELDM) version 1.0.0

The Stochastic Empirical Loading and Dilution Model (SELDM) is designed to transform complex scientific data into meaningful information about the risk of adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. The U.S. Geological Survey developed SELDM in cooperation with the Federal Highway Administration to help develop planning-level estimates of event mean concentrations, flows, and loads in stormwater from a site of interest and from an upstream basin. Planning-level estimates are defined as the results of analyses used to evaluate alternative management measures; planning-level estimates are recognized to include substantial uncertainties (commonly orders of magnitude). SELDM uses information about a highway site, the associated receiving-water basin, precipitation events, stormflow, water quality, and the performance of mitigation measures to produce a stochastic population of runoff-quality variables. SELDM provides input statistics for precipitation, prestorm flow, runoff coefficients, and concentrations of selected water-quality constituents from National datasets. Input statistics may be selected on the basis of the latitude, longitude, and physical characteristics of the site of interest and the upstream basin. The user also may derive and input statistics for each variable that are specific to a given site of interest or a given area. SELDM is a stochastic model because it uses Monte Carlo methods to produce the random combinations of input variable values needed to generate the stochastic population of values for each component variable. SELDM calculates the dilution of runoff in the receiving waters and the resulting downstream event mean concentrations and annual average lake concentrations. Results are ranked, and plotting positions are calculated, to indicate the level of risk of adverse effects caused by runoff concentrations, flows, and loads on receiving waters by storm and by year. Unlike deterministic hydrologic models, SELDM is not calibrated by changing values of input variables to match a historical record of values. Instead, input values for SELDM are based on site characteristics and representative statistics for each hydrologic variable. Thus, SELDM is an empirical model based on data and statistics rather than theoretical physiochemical equations. SELDM is a lumped parameter model because the highway site, the upstream basin, and the lake basin each are represented as a single homogeneous unit. Each of these source areas is represented by average basin properties, and results from SELDM are calculated as point estimates for the site of interest. Use of the lumped parameter approach facilitates rapid specification of model parameters to develop planning-level estimates with available data. The approach allows for parsimony in the required inputs to and outputs from the model and flexibility in the use of the model. For example, SELDM can be used to model runoff from various land covers or land uses by using the highway-site definition as long as representative water quality and impervious-fraction data are available.

Techniques and Methods↗

USGS2014

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Remote Sensing↗