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At least 1,351 records · Page 75Linked to original sources
USGS2002

Development of a stream habitat index for the Northern Lakes and Forest Ecoregions

Physical habitat was quantified in 105 randomly selected streams across the Northern Lakes and Forests Ecoregion during 1998 and 1999 to develop a stream habitat index for the region. Physical habitat measures (106) were classified into four groups: substrate, instream cover, riparian zone–land use, and geomorphology–hydrology. Variable reduction procedures yielded seven variables: sinuosity, percent of substrate gravel or larger, percent substrate as detritus or muck, percent of bank with forested cover, amount of bank erosion, number of large logs per 100 m, and mean length of pools. Streams were separated by a gradient value of 3 m/km (low N = 70; high N = 35) and assigned to model and test data sets. For low-gradient streams in the model data set, the seven habitat variables explained 47% of the variation in index of biotic integrity (IBI) scores. To produce the habitat index, the coefficients in the regression were used to weight each of the seven variables. For low-gradient streams in the test data set, the habitat index explained 20% of the variation in IBI scores. A habitat index could not be developed for high-gradient sites, probably due to the low number of sites. Comparison of habitat to IBI scores provides resource managers with a method to evaluate the contribution of habitat quality to the IBI score.

Michigan, Minnesota, Wisconsin↗
USGS2006

The USGS Land Cover Institute

The U.S. Geological Survey (USGS) Land Cover Institute (LCI) is located at the Center for Earth Resources Observation and Science (EROS) in Sioux Falls, South Dakota. It provides a focal point for advancing USGS land cover studies and applications. Satellite images and other remotely sensed data play an important role in this research. Land Cover scientists investigate new ways to use satellite images and other data to map land cover. They assess national and global land cover characteristics and monitor how - and how rapidly - land cover changes. They also study the economic impacts of land cover as well as its effects on water quality, the spread of invasive species, habitats and biodiversity, climate variability, and other environmental factors.

Fact Sheet↗
USGS2008

Seasonal temperature responses to land-use change in the western United States

In the western United States, more than 79 000??km2 has been converted to irrigated agriculture and urban areas. These changes have the potential to alter surface temperature by modifying the energy budget at the land-atmosphere interface. This study reports the seasonally varying temperature responses of four regional climate models (RCMs) - RSM, RegCM3, MM5-CLM3, and DRCM - to conversion of potential natural vegetation to modern land-cover and land-use over a 1-year period. Three of the RCMs supplemented soil moisture, producing large decreases in the August mean (- 1.4 to - 3.1????C) and maximum (- 2.9 to - 6.1????C) 2-m air temperatures where natural vegetation was converted to irrigated agriculture. Conversion to irrigated agriculture also resulted in large increases in relative humidity (9% to 36% absolute change). Modeled changes in the August minimum 2-m air temperature were not as pronounced or consistent across the models. Converting natural vegetation to urban land-cover produced less pronounced temperature effects in all models, with the magnitude of the effect dependent upon the preexisting vegetation type and urban parameterizations. Overall, the RCM results indicate that the temperature impacts of land-use change are most pronounced during the summer months, when surface heating is strongest and differences in surface soil moisture between irrigated land and natural vegetation are largest. ?? 2007 Elsevier B.V. All rights reserved.

Global and Planetary Change↗
USGS2025

Using integrated step-selection analyses to map high-risk electrocution areas for a highly mobile species

Knowledge of animal-movement patterns is a crucial component in identifying areas with high potential for human–wildlife conflict and in prioritizing associated management actions. Electrical energy infrastructure is a major source of mortality for animals worldwide, with millions of birds colliding with or being electrocuted by power lines and power-pole infrastructure each year. Movement, habitat use, and the spatial distribution of electrocution risk can vary with age, but studies of younger age classes are often hampered because these groups are difficult to observe and lack well-defined home ranges. To identify movement patterns and high-use areas of bald eagles in Arizona, USA, we analyzed global positioning system (GPS) telemetry data collected from 13 immature bald eagles ( Haliaeetus leucocephalus ) across Arizona between 2017 and 2023. We built multi-scale, integrated step-selection functions that evaluated eagle responses to a suite of environmental covariates. We then used these models to simulate eagle movement and predict habitat use within and surrounding Maricopa County, which contains both the Phoenix Metropolitan Area and the plurality of bald eagle breeding areas in Arizona. We provide a use case for how these simulated movements could be used by resource managers to identify high-risk areas for electrocution. Eagles avoided urban areas and selected steeper slopes, more pronounced ridges, and areas with greater water and wetland land cover. Predicted habitat use by bald eagles was greatest near waterbodies and along ridges and steep slopes, and indicated where power infrastructure may pose greater electrocution risk. We show how integrated step-selection analyses and movement path simulation may be used for subadult animals lacking stable home ranges to predict high-use areas and identify locations with greater potential for negative human–wildlife interactions.

Arizona↗
USGS2008

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Ecological Modelling↗
USGS2021

Validation of the U.S. Geological Survey’s Land Change Monitoring, Assessment and Projection (LCMAP) collection 1.0 annual land cover products 1985–2017

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs, and other ancillary data. The reference sample of nearly 25,000 pixels and the accompanying 33-year time series of annual land cover reference labels allowed for a comprehensive assessment of accuracy of the LCMAP land cover and land cover change products. Overall accuracy (± standard error) for the per-pixel assessment across all years for the eight land cover classes was 82.5% (±0.2%). Overall accuracy was consistent year-to-year within a range of 1.5% but varied regionally with lower accuracy in the eastern United States. User’s accuracy (UA) and producer’s accuracy (PA) for CONUS ranged from the higher accuracies of Water (UA=96%, PA=93%) and Tree Cover (UA=90%, PA=83%) to the lower accuracies of Wetland (UA=69%, PA=74%) and Barren (UA=43%, PA=57%). For a binary change / no change classification, UA of change was 13% (±0.5%) and PA was 16% (±0.6%) for CONUS when agreement was defined as a match by the exact year of change. UA and PA improved to 28% and 34% when agreement was defined as the change being detected by the map and reference data within a ±2-year window. Change accuracy was higher in the eastern United States compared to the western US. UA was 49% (±0.3) and PA was 54% (±0.3) for the footprint of change (defined as the area experiencing at least one land cover change from 1985–2017). For class-specific loss and gain when agreement was defined as an exact year match, UA and PA were generally below 30%, with Tree Cover loss being the most accurately mapped change (UA=25%, PA=31%). These accuracy results provide users with information to assess the suitability of LCMAP data and information to guide future research for improving LCMAP products, particularly focusing on the challenges of accurately mapping annual land cover change.

Remote Sensing of Environment↗
USGS2003

Simulation of streamflow and water quality in the Brandywine Creek subbasin of the Christina River basin, Pennsylvania and Delaware, 1994-98

The Christina River Basin drains 565 mi 2 (square miles) in Pennsylvania and Delaware. Water from the basin is used for recreation, drinking-water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, Red Clay Creek, White Clay Creek, and Christina River. The Brandywine Creek is the largest of the subbasins and drains an area of 327 mi 2 . Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on streamwater quality. To assist in nonpoint-source evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in small subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at six sites in the Brandywine Creek subbasin and five sites in the other subbasins. The HSPF model for the Brandywine Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 35 reaches draining areas that ranged from 0.6 to 18 mi 2 . Three of the reaches contain regulated reservoir. Eleven different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the basin are forested, agricultural, residential, and urban. The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data for eight U.S. Geological Survey (USGS) stream-flow-measurement stations for the period of January 1, 1994, through October 29, 1998. Daily precipitation data for three National Oceanic and Atmospheric Administration (NOAA) gages and hourly data for one NOAA gage were used for model input. The difference between observed and simulated streamflow volume ranged from -2.7 to 3.9 percent for the nearly 5-year period at the eight calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 237 mi 2 ), annual differences between observed and simulated streamflow ranged from -14.0 to 18.8 percent and the overall error for the 5-year period was 1.0 percent. Calibration errors for 36 storm periods at the eight calibration sites for total volume, low-flow-recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data. The water-quality component of the model was calibrated using monitoring data collected at six USGS streamflow-measurement stations with variable water quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended-solids data may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for two to six individual storms in 1998 at each of the six monitoring sites indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. Assessment of the water-quality calibration under stormflow conditions is limited by the relatively small amount of available water-quality data in the basin. Duration curves for simulated and reported sediment concentration at Brandywine Creek at Wilmington, Del., are similar, indicating model performance is better when evaluated over longer periods than when evaluated on individual storm events.

Water-Resources Investigations Report↗
USGS2003

Simulation of streamflow and water quality in the White Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98

The Christina River Basin drains 565 square miles (mi 2 ) in Pennsylvania, Maryland, and Delaware. Water from the basin is used for recreation, drinking water supply, and to support aquatic life. The Christina River Basin includes the major subbasins of Brandywine Creek, White Clay Creek, and Red Clay Creek. The White Clay Creek is the second largest of the subbasins and drains an area of 108 mi 2 . Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the streams. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in non point-source evaluation, four independent models, one for each of the three major subbasins and for the Christina River, were developed and calibrated using the model code Hydrological Simulation Program—Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base- flow samples were collected during 1998 at two sites in the White Clay Creek subbasin and at nine sites in the other subbasins. The HSPF model for the White Clay Creek Basin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into 17 reaches draining areas that ranged from 1.37 to 13 mi 2 . Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the White Clay Creek Basin are agricultural, forested, residential, and urban. The hydrologic component of the model was run at an hourly time step and primarily calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Additional calibration was done using data from two other USGS streamflow-measurement stations with periods of record shorter than the calibration period. Daily precipitation data from two National Oceanic and Atmospheric Administration (NOAA) gages and hourly precipitation and other meteorological data for one NOAA gage were used for model input. The difference between simulated and observed streamflow volume ranged from -0.9 to 1.8 percent for the 4-year period at the two calibration sites with 4-year records. Annual differences between observed and simulated streamflow generally were greater than the overall error. For example, at a site near the bottom of the basin (drainage area of 89.1 mi 2 ), annual differences between observed and simulated streamflow ranged from -5.8 to 14.4 percent and the overall error for the 4-year period was -0.9 percent. Calibration errors for 36 storm periods at the two calibration sites for total volume, low-flowrecession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the hourly rainfall data. The water-quality component of the model was calibrated using data collected by the USGS and state agencies at three USGS streamflow-measurement stations with variable water-quality monitoring periods ending October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for up to five storms in 1998 at each of the two nonpoint-source monitoring sites in the White Clay Creek Basin indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual events. The accuracy of the water-quality calibration under stormflow conditions is limited by the relatively small amount of water-quality data available for the White Clay Creek Basin. Users of the White Clay Creek HSPF model should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow and water quality for individual storm events may not be well simulated, but the model performance is reasonable when evaluated over longer periods of time; the observed flow-duration curve for the simulation period is similar to the long-term flow-duration curve at White Clay Creek near Newark, Del., indicating that the calibration period is representative of all but highest 0.1 percent and lowest 0.1 percent of flows at that site; relative errors in streamflow and water-quality simulations are greater for smaller drainage areas than for larger areas; and calibration for water-quality was based on sparse data.

Water-Resources Investigations Report↗
USGS2003

Simulation of streamflow and water quality in the Red Clay Creek subbasin of the Christina River Basin, Pennsylvania and Delaware, 1994-98

The Christina River Basin drains 565 square miles (mi 2 ) in Pennsylvania and Delaware and includes the major subbasins of Red Clay Creek, White Clay Creek, Brandywine Creek, and Christina River. The Red Clay Creek is the smallest of the subbasins and drains an area of 54 mi 2 . Streams in the Christina River Basin are used for recreation, drinking-water supply, and to support aquatic life. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the stream. A multi-agency, waterquality management strategy included a modeling component to evaluate the effects of point and nonpointsource contributions of nutrients and suspended sediment on stream water quality. To assist in nonpointsource evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program?Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in smaller subbasins predominantly covered by one land use following a nonpoint-source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at 1 site in the Red Clay Creek subbasin and at 10 sites elsewhere in the Christina River Basin. The HSPF model for the Red Clay Creek subbasin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into nine reaches draining areas that ranged from 1.7 to 10 mi 2 . One of the reaches contains a regulated reservoir. Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the Red Clay Creek subbasin are agricultural, forested, residential, and urban. The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data from three U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Daily precipitation data from one National Oceanic and Atmospheric Administration (NOAA) gage and hourly data from one NOAA gage were used for model input. The difference between observed and simulated stream- flow volume ranged from -0.8 to 2.1 percent for the 4-year period at the three calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error for the 4-year period. For example, at a site near Stanton, Del., near the bottom of the basin (drainage area of 50.2 mi 2 ), annual differences between observed and simulated streamflow ranged from -5.8 to 6.0 percent and the overall error for the 4-year period was -0.8 percent. Calibration errors for 36 storm periods at the three calibration sites for total volume, low-flow-recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data. The water-quality component of the model was calibrated using nonpoint-source monitoring data collected in 1998 at one USGS streamflowmeasurement station and other water-quality monitoring data collected at three USGS streamflowmeasurement stations. The period of record for waterquality monitoring was variable at the stations, with an end date of October 1998 but the start date ranging from October 1994 to January 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspendedsediment concentrations, although suspended solids may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulation. Comparison of observed to simulated loads for five storms in 1998 at the one nonpoint-source monitoring site at Wooddale, Del., indicates that simulation error commonly is as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved utrients than particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria, with much larger errors possible for individual storm events. Assessment of the accuracy of the water-quality calibration under stormflow conditions is limited by the sparsity of available water-quality data in the basin. Users of the Red Clay Creek HSPF model should be aware of model limitations and consider the following when predictive scenarios are desired: streamflow-duration curves indicate the model simulates stream-flow reasonably well when evaluated over a broad range of conditions and time, although streamflow and the corresponding water quality for individual storm events may not be well simulated; streamflow-duration curves for the simulation period compare well with duration curves for the 57.5-year period ending in 2001 at Wooddale, Del., and include all but the extreme high-flow and low-flow events; calibration for water quality was based on sparse data, with the result of increasing uncertainty in the water-quality simulation.

Water-Resources Investigations Report↗
USGS2003

Simulation of streamflow and water quality in the Christina River subbasin and overview of simulations in other subbasins of the Christina River Basin, Pennsylvania, Maryland, and Delaware, 1994-98

The Christina River Basin drains 565 square miles (mi 2 ) in Pennsylvania and Delaware and includes the major subbasins of Brandywine Creek, Red Clay Creek, White Clay Creek, and Christina River. The Christina River subbasin (exclusive of the Brandywine, Red Clay, and White Clay Creek subbasins) drains an area of 76 mi 2 . Streams in the Christina River Basin are used for recreation, drinking water supply, and support of aquatic life. Water quality in some parts of the Christina River Basin is impaired and does not support designated uses of the stream. A multi-agency water-quality management strategy included a modeling component to evaluate the effects of point- and nonpoint-source contributions of nutrients and suspended sediment on stream water quality. To assist in nonpoint-source evaluation, four independent models, one for each of the four main subbasins of the Christina River Basin, were developed and calibrated using the model code Hydrological Simulation Program–Fortran (HSPF). Water-quality data for model calibration were collected in each of the four main subbasins and in small subbasins predominantly covered by one land use following a nonpoint- source monitoring plan. Under this plan, stormflow and base-flow samples were collected during 1998 at two sites in the Christina River subbasin and nine sites elsewhere in the Christina River Basin. The HSPF model for the Christina River subbasin simulates streamflow, suspended sediment, and the nutrients, nitrogen and phosphorus. In addition, the model simulates water temperature, dissolved oxygen, biochemical oxygen demand, and plankton as secondary objectives needed to support the sediment and nutrient simulations. For the model, the basin was subdivided into nine reaches draining areas that ranged from 3.8 to 21.9 mi 2 . Ten different pervious land uses and two impervious land uses were selected for simulation. Land-use areas were determined from 1995 land-use data. The predominant land uses in the Christina River subbasin are residential, urban, forested, agricultural, and open. The hydrologic component of the model was run at an hourly time step and calibrated using streamflow data from two U.S. Geological Survey (USGS) streamflow-measurement stations for the period of October 1, 1994, through October 29, 1998. Daily precipitation data from one National Oceanic and Atmospheric Administration (NOAA) meteorologic station and hourly data from one NOAA meteorologic station were used for model input. The difference between observed and simulated streamflow volume ranged from -2.3 to 5.3 percent for a 10-month portion of the calibration period at the two calibration sites. Annual differences between observed and simulated streamflow generally were greater than the overall error for the 4-year period. For example, at Christina River at Coochs Bridge, near the bottom of the free-flowing part of the subbasin (drainage area of 21 mi 2 ), annual differences between observed and simulated streamflow ranged from -6.9 to 6.5 percent and the overall error for the 4-year period was -1.1 percent. Calibration errors for 36 storm periods at the three calibration sites for total volume, low-flow recession rate, 50-percent lowest flows, 10-percent highest flows, and storm peaks were within the recommended criteria of 20 percent or less. Much of the error in simulating storm events on an hourly time step can be attributed to uncertainty in the rainfall data. The water-quality component of the model was calibrated using nonpoint-source monitoring data collected at two USGS streamflow-measurement stations and other water-quality monitoring data. The period of record for water-quality monitoring was variable at the stations, with a start date ranging from October 1994 to January 1998 and an end date of October 1998. Because of availability, monitoring data for suspended-solids concentrations were used as surrogates for suspended-sediment concentrations, although suspended-solids data may underestimate suspended sediment and affect apparent accuracy of the suspended-sediment simulaion. Comparison of observed to simulated loads for up to six storms in 1998 at the two nonpoint-source monitoring sites (Little Mill Creek near Newport and Christina River at Coochs Bridge, Del.) indicate that simulation error is commonly as large as an order of magnitude for suspended sediment and nutrients. The simulation error tends to be smaller for dissolved nutrients than for particulate nutrients. Errors of 40 percent or less for monthly or annual values indicate a fair to good water-quality calibration according to recommended criteria; much larger errors are possible for individual events. Assessment of the water-quality calibration under stormflow conditions is limited by the relatively small amount of available water-quality data in the subbasin. Users of the Christina River subbasin HSPF model and HSPF models for other subbasins in the Christina River Basin should be aware of model limitations and consider the following if the model is used for predictive purposes: streamflow-duration curves suggest the model simulates streamflow reasonably well when measured over a broad range of conditions and time although streamflow and the corresponding water quality for individual storm events may not be well simulated; streamflow-duration curves for the simulation period compare well with duration curves for the 8-year period ending in 2001 at Christina River at Coochs Bridge, Del., and include all but the extreme high-flow and low-flow events; and calibration for water quality was based on limited data, with the result of increasing uncertainty in the water-quality simulation.

Water-Resources Investigations Report↗
USGS2003

Statistical sampling to characterize recent United States land-cover change

The U.S. Geological Survey, in conjunction with the U.S. Environmental Protection Agency, is conducting a study focused on developing methods for estimating changes in land-cover and landscape pattern for the conterminous United States from 1973 to 2000. Eleven land-cover and land-use classes are interpreted from Landsat imagery for five sampling dates. Because of the high cost and potential effect of classification error associated with developing change estimates from wall-to-wall land-cover maps, a probability sampling approach is employed. The basic sampling unit is a 20 x 20 km area, and land cover is obtained for each 60 x 60 m pixel within the sampling unit. The sampling design is stratified based on ecoregions, and land-cover change estimates are constructed for each stratum. The sampling design and analyses are documented, and estimates of change accompanied by standard errors are presented to demonstrate the methodology. Analyses of the completed strata suggest that the sampling unit should be reduced to a 10 x 10 km block, and poststratified estimation and regression estimation are viable options to improve precision of estimated change. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS1981

Analysis of variance of thematic mapping experiment data.

As an example of the methodology, data from an experiment using three scales of land-use and land-cover mapping have been analyzed. The binomial proportions of correct interpretations have been analyzed untransformed and transformed by both the arcsine and the logit transformations. A weighted analysis of variance adjustment has been used. There is evidence of a significant difference among the three scales of mapping (1:24 000, 1:100 000 and 1:250 000) using the transformed data. Multiple range tests showed that all three scales are different for the arcsine transformed data. - from Author

Photogrammetric Engineering and Remote Sensing↗
USGS2001

Thematic accuracy of MRLC land cover for the eastern United States

One objective of the MultiResolution Land Characteristics (MRLC) consortium is to map general land-cover categories for the conterminous United States using Landsat Thematic Mapper (TM) data. Land-cover mapping and classification accuracy assessment are complete for the eastern United States. The accuracy assessment was based on photo-interpreted reference data obtained from a stratified probability sample of pixels. Agreement was defined as a match between primary or alternate reference land-cover labels assigned to each sample pixel and the mode (most common class) of the map's land-cover labels within a 3×3-pixel neighborhood surrounding the sampled point. At 30-m resolution, overall accuracy was 59.7% at an Anderson Level II thematic detail, and 80.5% at Anderson Level I.

Remote Sensing of Environment↗
USGS2005

Mapping and improving frequency, accuracy, and interpretation of land cover change: Classifying coastal Louisiana with 1990, 1993, 1996, and 1999 Landsat Thematic Mapper image data

Landsat Thematic Mapper images and collateral data sources were used to classify the land cover of the Mermentau River Basin within the chenier coastal plain and the adjacent uplands of Louisiana, USA. Landcover classes followed that of the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; however, classification methods needed to be developed to meet these national standards. Our first classification was limited to the Mermentau River Basin (MRB) in southcentral Louisiana, and the years of 1990, 1993, and 1996. To overcome problems due to class spectral inseparable, spatial and spectra continuums, mixed landcovers, and abnormal transitions, we separated the coastal area into regions of commonality and applying masks to specific land mixtures. Over the three years and 14 landcover classes (aggregating the cultivated land and grassland, and water and floating vegetation classes), overall accuracies ranged from 82% to 90%. To enhance landcover change interpretation, three indicators were introduced as Location Stability, Residence stability, and Turnover. Implementing methods substantiated in the multiple date MRB classification, we spatially extended the classification to the entire Louisiana coast and temporally extended the original 1990, 1993, 1996 classifications to 1999 (Figure 1). We also advanced the operational functionality of the classification and increased the credibility of change detection results. Increased operational functionality that resulted in diminished user input was for the most part gained by implementing a classification logic based on forbidden transitions. The logic detected and corrected misclassifications and mostly alleviated the necessity of subregion separation prior to the classification. The new methods provided an improved ability for more timely detection and response to landcover impact. ?? 2005 IEEE.

Conference Paper↗
USGS2015

Median nitrate concentrations in groundwater in the New Jersey Highlands Region estimated using regression models and land-surface characteristics

Nitrate-concentration data are used in conjunction with land-use and land-cover data to estimate median nitrate concentrations in groundwater underlying the New Jersey (NJ) Highlands Region. Sources of data on nitrate in 19,670 groundwater samples are from the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the NJ Private Well Testing Act (PWTA). In a study conducted by the USGS, in cooperation with the New Jersey Department of Environmental Protection, logistic regression was used to relate measured nitrate concentrations to five explanatory variables (percent urban and agricultural land use, septic-system density, total length of streams, and number of known contaminated sites) quantified in 610-meter-square grid cells. A method for calculating the median concentrations of nitrate from a series of logistic regression models was developed. Two calibration and two validation procedures showed that the logistic-regression-based method can estimate groundwater-nitrate concentrations in the Highlands Region accurately to within 0.1 milligram per liter as nitrogen (mg/L as N). Limitations of the logistic-regression-based method include the inability to select a logistic model with exactly 0.5 probability of exceeding the threshold value and lack of an algorithm to directly calculate the median value. Quantile regression was evaluated as a suitable alternative and was slightly less accurate than the logistic-regression method in estimating median groundwater nitrate concentrations in the Highlands Region. Multiple-linear regression with log-transformed nitrate-concentration data and the same five explanatory values was less accurate than either logistic or quantile regression in estimating median nitrate concentrations. On the basis of 4,516 2000 x 2000 foot grid cells that contain wells with data stored in NWIS and the PWTA database, the estimated median nitrate concentration for the entire Highlands Region is about 1.25 mg/L as N, and estimated median concentrations range from about 1.05 to 1.78 mg/L as N among 11 smaller administratively defined areas within the Highlands Region that vary in percentages of urban land use, agricultural land use, and septic-system density. The Kaplan-Meier method of estimating summary statistics from left-censored data was applied in order to include nondetects (left-censored data) in median nitrate-concentration calculations. Median concentrations also were determined using three alternative methods of handling nondetects. Treatment of the 23 percent of samples that were nondetects had little effect on estimated median nitrate concentrations because method detection limits were mostly less than median values.

New Jersey↗
USGS2009

Landsat mapping of local landscape change: The satellite-era context

To set the stage for a vulnerability analysis, investigators must describe and understand the geographic context, including physical characteristics of the landscape and the political and socioeconomic milieu of the population (Jianchu et al . 2005). Vulnerability studies focus on a particular place, at a specific time through its three dimensions, exposure, sensitivity, and adaptive capacity; therefore, understanding place is essential to analyzing vulnerability. Land-use studies are essential to understanding place because they generalize human activities on the physical landscape. Essentially, land use indicates past human decisions and actions, environmental constraints, and, in some cases, gives insight into subsequent change. Like vulnerability, land use is particular to a place at a certain time, and the analysis of that land use can be used as a baseline for future change and its implications. Vulnerability and land use are linked by the concept of place and are fundamental to contemporary research on human–environment interactions. Although the literature on land use, land-use change, and climate change is extensive, the land-use component of vulnerability is usually conceptualized as a feedback mechanism to climate change: forest cutting releases carbon dioxide, which increases atmospheric carbon dioxide concentrations, which increases radiative forcing, which changes climate, and which ultimately changes land cover and subsequent land use (e.g. DeFries and Bounoua 2004; Jianchu et al . 2005; Salinger et al . 2005; Watson 2005). Moreover, land use is rarely specifically identified as a component of vulnerability.

Book chapter↗
USGS2014

The Multi-Resolution Land Characteristics (MRLC) Consortium: 20 years of development and integration of USA national land cover data

The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Project (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

Remote Sensing↗
USGS2008

Temporal change in fragmentation of continental US forests

Changes in forest ecosystem function and condition arise from changes in forest fragmentation. Previous studies estimated forest fragmentation for the continental United States (US). In this study, new temporal land-cover data from the National Land Cover Database (NLCD) were used to estimate changes in forest fragmentation at multiple scales for the continental US. Early and late dates for the land-cover change data were ca. 1992 and ca. 2001. Forest density was used as a multi-scale index of fragmentation by measuring the proportion of forest in neighborhoods ranging in size from 2.25 to 5314.41 ha. The multi-scale forest density maps were classified using thresholds of 40% (patch), 60% (dominant), and 90% (interior) to analyze temporal change of fragmentation. The loss of dominant and interior forest showed distinct scale effects, whereas loss of patch forest was much less scale-dependent. Dominant forest loss doubled from the smallest to the largest spatial scale, while interior forest loss increased by approximately 80% from the smallest to the second largest spatial scale, then decreased somewhat. At the largest spatial scale, losses of dominant and interior forest were 5 and 10%, respectively, of their ca. 1992 amounts. In contrast, patch forest loss increased by only 25% from the smallest to largest spatial scale. These results indicate that continental US forests were sensitive to forest loss because of their already fragmented state. Forest loss would have had to occur in an unlikely spatial pattern in order to avoid the proportionately greater impact on dominant and interior forest at larger spatial scales.

Landscape Ecology↗