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At least 883 records · Page 49Linked to original sources
USGS2002

Geographic techniques and recent applications of remote sensing to landscape-water quality studies

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

Water, Air, & Soil Pollution↗
USGS2017

Simulating the effects of management practices on cropland soil organic carbon changes in the Temperate Prairies Ecoregion of the United States from 1980 to 2012

Understanding the effects of management practices on soil organic carbon (SOC) is important for designing effective policies to mitigate greenhouse gas emissions in agriculture. In the Midwest United States, management practices in the croplands have been improved to increase crop production and reduce SOC loss since the 1980s. Many studies of SOC dynamics in croplands have been performed to understand the effects of management, but the results are still not conclusive. This study quantified SOC dynamics in the Midwest croplands from 1980 to 2012 with the General Ensemble Biogeochemical Modelling System (GEMS) and available management data. Our results showed that the total SOC in the croplands decreased from 1190 Tg C in 1980 to 1107 TgC in 1995, and then increased to 1176 TgC in 2012. Continuous cropping and intensive tillage may have driven SOC loss in the early period. The increase of crop production and adoption of conservation tillage increased the total SOC so that the decrease in the total SOC stock after 32 years was only 1%. The small change in average SOC did not reflect the large spatial variations of SOC change in the region. Major SOC losses occurred in the north and south of the region, where SOC baseline values were high and cropland production was low. The SOC gains took place in the central part of the region where SOC baseline values were moderate and cropland production was higher than the other areas. We simulated multiple land-use land-cover (LULC) change scenarios and analyzed the results. The analysis showed that among all the LULC changes, agricultural technology that increased cropland production had the greatest impact on SOC changes, followed by the tillage practices, changes in crop species, and the conversions of cropland to other land use. Information on management practice induced spatial variation in SOC can be useful for policy makers and farm managers to develop long-term management strategies for increasing SOC sequestration in different areas.

Ecological Modelling↗
USGS2017

Building the vegetation drought response index for Canada (VegDRI-Canada) to monitor agricultural drought: first results

Drought is a natural climatic phenomenon that occurs throughout the world and impacts many sectors of society. To help decision-makers reduce the impacts of drought, it is important to improve monitoring tools that provide relevant and timely information in support of drought mitigation decisions. Given that drought is a complex natural hazard that manifests in different forms, monitoring can be improved by integrating various types of information (e.g., remote sensing and climate) that is timely and region specific to identify where and when droughts are occurring. The Vegetation Drought Response Index for Canada (VegDRI-Canada) is a recently developed drought monitoring tool for Canada. VegDRI-Canada extends the initial VegDRI concept developed for the conterminous United States to a broader transnational coverage across North America. VegDRI-Canada models are similar to those developed for the United States, integrating satellite observations of vegetation status, climate data, and biophysical information on land use and land cover, soil characteristics, and other environmental factors. Collectively, these different types of data are integrated into the hybrid VegDRI-Canada to isolate the effects of drought on vegetation. Twenty-three weekly VegDRI-Canada models were built for the growing season (April–September) through the weekly analysis of these data using a regression tree-based data mining approach. A 15-year time series of VegDRI-Canada results (s to 2014) was produced using these models and the output was validated by randomly selecting 20% of the historical data, as well as holdout year (15% unseen data) across the growing season that the Pearson’s correlation ranged from 0.6 to 0.77. A case study was also conducted to evaluate the VegDRI-Canada results over the prairie region of Canada for two drought years and one non-drought year for three weekly periods of the growing season (i.e., early-, mid-, and late season). The comparison of the VegDRI-Canada map with the Canadian Drought Monitor (CDM), an independent drought indicator, showed that the VegDRI-Canada maps depicted key spatial drought severity patterns during the two targeted drought years consistent with the CDM. In addition, VegDRI-Canada was compared with canola yields in the Prairie Provinces at the regional scale for a period from 2000 to 2014 to evaluate the indices’ applicability for monitoring drought impacts on crop production. The result showed that VegDRI-Canada values had a relatively higher correlation (i.e., r > 0.5) with canola yield for nonirrigated croplands in the Canadian Prairies region in areas where drought is typically a limiting factor on crop growth, but showed a negative relationship in the southeastern Prairie region, where water availability is less of a limiting factor and in some cases a hindrance to crop growth when waterlogging occurs. These initial results demonstrate VegDRI-Canada’s utility for monitoring drought-related vegetation conditions, particularly in drought prone areas. In general, the results indicated that the VegDRI-Canada models showed sensitivity to known agricultural drought events in Canada over the 15-year period mainly for nonirrigated areas.

GIScience and Remote Sensing↗
USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗
USGS2019

Seasonal fluxes of dissolved nutrients in streams of catchments dominated by swidden agriculture in the Maya Forest of Belize, Central America

The biogeochemistry of nitrogen (N) and phosphorus (P) in tropical streams and rivers is strongly regulated by the pronounced seasonality of rainfall and associated changes in hydrology. Land use and land cover change (LULCC) can also be a dominant driver of changes in stream biogeochemistry yet responses are not fully understood and vary across different LULCC scenarios. We measured dissolved and total nitrogen (N) and phosphorus (P) concentrations in four tributary streams of the Temash River watershed in southern Belize, Central America. The dominant land use practice in each of the four study catchments was swidden agriculture. We documented a strong seasonal control on the export of nutrients from these study systems with daily N fluxes increasing approximately 10-fold during the onset of the rainy season. P fluxes increased almost 4-fold during the same time period. Comparisons with nutrient export coefficients from other tropical streams suggest that nutrient export in streams of the Temash River watershed is similar or slightly lower. Establishing improved understanding of the terrestrial and hydrologic controls of N and P transport across the terrestrial-aquatic boundary and developing a comprehensive nutrient budget that includes inputs and outputs associated with crop production is warranted in future work.

Water↗
USGS2017

The effects of forest cover on base flow of streams in the mountainous interior of Puerto Rico, 2010

The U.S. Geological Survey, in cooperation with the Puerto Rico Department of Natural and Environmental Resources, completed a study to determine whether a relation exists between the extent of forest cover and the magnitude of base flow at two sets of paired drainage basins in the highlands of the municipalities of Adjuntas and Utuado within the mountainous interior of Puerto Rico. One set of paired basins includes the Río Guaónica and Río Tanamá, both tributaries of the Río Grande de Arecibo. The other set includes two smaller basins in the drainage basin of the Río Coabey, which is a tributary of the Río Tanamá. The paired basins in each set have similar rainfall patterns, geologic substrate, and aspect; the principal difference identified in the study is the extent of forest cover and related land uses such as the cultivation of shade and sun coffee. Data describing the hydrology, hydrogeology, and streamflow were used in the analysis. The principal objective of the study was to compare base flow per unit area among basins having different areal extents of forest cover and land uses such as shade coffee and sun coffee cultivation. Within the mountainous interior of Puerto Rico, a substantial amount of the annual rainfall (45 to 39 percent in the Rio Guaónica and Rio Tanamá, respectively) can migrate to the subsurface and later emerge as base flow in streams. The magnitude of base flow within the two sets of paired basins varies seasonally. Minimum base flows occur during the annual dry season (generally from January to March), and maximum base flows occur during the wet season (generally from August to October). During the dry season or periods of below-normal rainfall, base flow is either the primary or the sole component of streamflow. Daily mean base flow ranged from 3.2 to 20.5 cubic feet per second (ft3 /s) at the Rio Guaónica Basin, and from 4.2 to 23.0 ft3 /s at the Rio Tanamá Basin. The daily mean base flows during 2010 ranged from 0.28 to 0.98 ft3 /s at Tributary 1 and from 0.22 to 0.58 ft3 /s at Tributary 2 of the Rio Coabey. The normalized daily base flow at the Río Guaónica and Río Tanamá Basin during 2010 ranged from 1.3 to 8.1 cubic feet per second per square mile (ft3 /s)/mi2 and from 1.1 to 6.1 (ft3 /s)/mi2 , respectively. The normalized daily base flow for the basins of Tributary 1 and Tributary 2 of Río Coabey during 2010 ranged from 1.0 to 3.6 (ft3 /s)/mi2 and from 1.5 to 3.9 (ft3 /s)/mi2 , respectively. The normalized mean annual base flow is similar within the larger paired basins of Río Tanamá (2.74 [ft3 /s]/mi2 ) and Río Guaónica (3.15 [ft3 /s]/mi2 ). The mean annual base flow per unit area for both of these basins is about 79 percent of the mean annual streamflow. In the large paired basins, the proportion of Type I land use (forest patches, shade and mixed shade/sun coffee with associated cash crops) is substantially higher in Rio Guaónica Basin (81 percent) than in the Rio Tanamá Basin (59 percent), and the base flow per unit area is also higher. In the small paired basins of Rio Coabey, the proportion of Type I land use is much higher at Tributary 1 (52 percent) than at Tributary 2 (15 percent), but, in contrast to the large basins, the mean annual base flow per unit area is lower (2.22 and 2.62 [ft3 /s]/mi2 , respectively). There is no consistent relation between land use and normalized base flow between the two sets of paired basins in the study.

Scientific Investigations Report↗
USGS2024

Seasonal resource selection of a grassland bird in a dynamic landscape: Importance of a heterogeneous landscape

Habitat loss and fragmentation are a growing threat to wildlife, and a better understanding of these landscape processes is needed to mitigate their effects on species populations. Grassland biomes are among the most imperiled ecosystems in the world, and grassland birds are experiencing significant population declines in North America. Understanding how species respond to differences in resource availabilities across spatiotemporal extents is critical to determining animals' distributions. Here, we investigated the relationship of landscape attributes to spatiotemporal distribution of a grassland bird, the ring-necked pheasant ( Phasianus colchicus ), which has experienced population declines in the Midwest, USA. Pheasant declines have been attributed to two anthropogenic stressors, land use change and climate change. In this study, we evaluated the effect of landscape attributes (composition and configuration) on the home-range size and resource selection of pheasants. We used a 95% fixed kernel estimator to estimate home-range size and identified scales at which landscape features influenced home-range sizes. We quantified landscape features within radii of 250, 500, and 1000 m (i.e., local to broader spatial scales) from the home-range center. We also used resource selection functions to predict the home-range placement (second order) and resource selection within home ranges (third order) of pheasants during winter, pre-nesting, and nesting seasons. We developed multi-scale predictions of pheasant resource selection and identified wetlands, grasslands, Conservation Reserve Program (CRP) grasslands, and small grains as land cover types used by pheasants to fulfill their life requirements. Our results indicated home ranges were more likely to be in a landscape with more CRP, wetlands, and grasslands; more connected grasslands; and a greater number of grassland patches. Pheasants also selected heterogeneous landscape and avoided row crops at both orders of selection. Maintaining habitat heterogeneity, by managing landscapes composed of a high proportion of grasslands and CRP surrounded by small grains and wetlands, could enhance the benefits of local management practices for pheasants. Collectively, insights obtained from our study can advance habitat conservation efforts for similar grassland birds and consequently are of broad utility to biologists and wildlife managers.

South Dakota↗
USGS2000

Stream chemistry modeling of two watersheds in the Front Range, Colorado

We investigated the hydrologic, geochemical, and biogeochemical controls on stream chemical composition on the Green Lakes Valley and Andrews Creek watersheds using the alpine hydrochemical model (AHM). Both sites had comparable data sets from 1994 and 1996, including high‐resolution spatial data and high‐frequency time series of hydrology, geochemistry, and meteorology. The model of each watershed consisted of three terrestrial subunits (soil, talus, and rock), with the routing between the subunits determined by spatial land cover data. Using 1994 data for model calibration and 1996 data for evaluation, AHM captured the dominant processes and successfully simulated daily stream chemical composition on both watersheds. These results confirm our procedure of using spatial and site‐specific field and laboratory data to generate an initial catchment model and then calibrating the model to calculate effective parameters for unmeasured processes. A net source of nitrogen was identified in the Andrews Creek watershed during the spring snowmelt period, whereas nitrogen was immobilized in the Green Lakes Valley. This difference was most likely due to the larger and more dominant area of talus in the Andrews Creek watershed. Our results also indicate that routing of snowmelt through either soil or talus material is sufficient for retention of H + and release of base cations but that N retention is more important on areas mapped as soil. Owing to the larger ionic pulse and larger fraction of surface runoff the Green Lakes Valley was more sensitive to a doubling of wet deposition chemistry than the Andrews Creek watershed.

Colorado↗
USGS2017

Understanding recurrent land use processes and long-term transitions in the dynamic south-central United States, c. 1800 to 2006

Forests have historically been under significant land use pressures that cause periods of degradation, clearance, and recovery. To understand these changes, studies are needed that place trends in a historical landscape context and also examine recent dynamics. Here, we use historical investigation (c. 1800) and an examination of land use and land cover change between 1973 and 2006 to establish a baseline trajectory of the forested system of the south-central United States (US) plains. The study culminates in a highly detailed accounting of the processes and causes of land change between 2001 and 2006. In the study region, the forest transitioned from early low-intensity use, to clearance for farming and timber, to widespread recovery from degradation beginning in the 1930s. By 1970, the region was transitioning from recovered woodlands to an intensive regime of recurrent timber harvest and replanting. The recurring cycle inherent in intensive silviculture has been the main cause of land change for the past several decades, accounting for more than 95% of the total extent of change between 2001 and 2006. The transition to forest recovery in the south-central US was an important historical occurrence. However, the dynamic post-transition landscape needs to be better understood.

Arkansas, Louisiana, Oklahoma, Texas↗
USGS2017

Changes in aquatic vegetation and floodplain land cover in the Upper Mississippi and Illinois rivers (1989–2000–2010)

Quantifying changes in the cover of river-floodplain systems can provide important insights into the processes that structure these landscapes as well as the potential consequences to the ecosystem services they provide. We examined net changes in 13 different aquatic and floodplain land cover classes using photo interpreted maps of the navigable portions of the Upper Mississippi River (UMR, above the confluence with the Ohio River) and Illinois River from 1989 to 2000 and from 2000 to 2010. We detected net decreases in vegetated aquatic area in nearly all river reaches from 1989 to 2000. The only river reaches that experienced a subsequent recovery of vegetated aquatic area from 2000 to 2010 were located in the northern portion of the UMR (above navigation pool 14) and two reaches in the Illinois River. Changes on the floodplain were dominated by urban development, which increased in nearly every river reach studied from 1989 to 2000. Agricultural lands declined in most river reaches from 2000 to 2010. The loss of agricultural land cover in the northern UMR was accompanied by increases in forest cover, whereas in the lower UMR and Illinois River, declines in agriculture were accompanied by increases in forest and shallow marsh communities. The changes in aquatic vegetation occupied between 5 and 20% of the total aquatic area and are likely associated with previously reported regional improvements in water clarity, while smaller (1–15% of the total floodplain area) changes in anthropogenic land cover types on the floodplain are likely driven by broad-scale socio-economic conditions.

Illinois River, Upper Mississippi River↗
USGS1988

Land cover mapping of the upper Kuskokwim Resource Managment Area using LANDSAT and a digital data base approach

Digital land cover and terrain data for the Upper Kuskokwim Resource Hanagement Area (UKRMA) were produced by the U.S. Geological Survey, Earth Resources Observation Systems Field Office, Anchorage, Alaska for the Bureau of Land Management. These and other environmental data, were incorporated into a digital data base to assist in the management and planning of the UKRMA. The digital data base includes land cover classifications, elevation, slope, and aspect data centering on the UKRMA boundaries. The data are stored on computer compatible tapes at a 50-m pixel size. Additional digital data in the data base include: (a) summer and winter Landsat multispectral scanner (MSS) data registered to a 50-m Universal Transverse Mercator grid; (b) elevation, slope, aspect, and solar illumination data; (c) soils and surficial geology; and (e) study area boundary. The classification of Landsat MSS data resulted in seven major classes and 24 subclasses. Major classes include: forest, shrubland, dwarf scrub, herbaceous, barren, water, and other. The final data base will be used by resource personnel for management and planning within the UKRMA.

Alaska↗
USGS2012

Estimating the benefits of land imagery in environmental applications: a case study in nonpoint source pollution of groundwater

Moderate-resolution land imagery (MRLI) is crucial to a more complete assessment of the cumulative, landscape-level effect of agricultural land use and land cover on environmental quality. If this improved assessment yields a net social benefit, then that benefit reflects the value of information (VOI) from MRLI. Environmental quality and the capacity to provide ecosystem services evolve because of human actions, changing natural conditions, and their interaction with natural physical processes. The human actions, in turn, are constrained and redirected by many institutions and regulations such as agricultural, energy, and environmental policies. We present a general framework for bringing together sociologic, biologic, physical, hydrologic, and geologic processes at meaningful scales to interpret environmental implications of MRLI applications. We set out a specific application using MRLI observations to identify crop planting patterns and thus estimate surface management activities that influence groundwater resources over a regional landscape. We tailor the application to the characteristics of nonpoint source groundwater pollution hazards in Iowa to illustrate a general framework in a land use-hydrologic-economic system. In the example, MRLI VOI derives from reducing the risk of both losses to agricultural production and damage to human health and other consequences of contaminated groundwater.

Book chapter↗
USGS2022

Conterminous United States land-cover change (1985-2016): New insights from annual time series

Sample-based estimates augmented by complete coverage land-cover maps were used to estimate area and describe patterns of annual land-cover change across the conterminous United States (CONUS) between 1985 and 2016. Most of the CONUS land cover remained stable in terms of net class change over this time, but a substantial gross change dynamic was captured by the annual and cumulative time intervals. The dominant types of changes can be grouped into natural resource cycles, increases in urbanization, and surface-water dynamics. The annual estimates over the 30-year time series showed a reduction in the rate of urban expansion after 2006, new growth in cropland after 2007, but a net overall decline in cropland since 1985, and two eras of net tree cover loss, the first one early in the time series and the second starting in 2012. Our study provides a holistic assessment of the CONUS land-cover conversion (class) change and can serve as a new benchmark for future research.

Land↗
USGS2010

Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project

Vegetation Continuous Field (VCF) layers of 30 m percent tree cover, bare ground, other vegetation and probability of water were derived for the conterminous United States (CONUS) using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data sets from the Web-Enabled Landsat Data (WELD) project. Turnkey approaches to land cover characterization were enabled due to the systematic WELD Landsat processing, including conversion of digital numbers to calibrated top of atmosphere reflectance and brightness temperature, cloud masking, reprojection into a continental map projection and temporal compositing. Annual, seasonal and monthly WELD composites for 2008 were used as spectral inputs to a bagged regression and classification tree procedure using a large training data set derived from very high spatial resolution imagery and available ancillary data. The results illustrate the ability to perform Landsat land cover characterizations at continental scales that are internally consistent while retaining local spatial and thematic detail.

Remote Sensing Letters↗
USGS2009

Monitoring land use on military installations

The US Geological Survey's Land Cover Trends is a research projects aimed to understand the rates, trends, causes, and consequences of contemporary US land use and land-cover change. The project is using the EPA Level III eco-regions as a geographic framework to process geospatial data collected between 1973 and 2000 to characterize ecosystem responses to land-use changes. The results are expected to be used for collaborative environmental change consequences research with various partners including the National Science Foundation, the National Oceanic and Atmospheric Administration, and the US Fish and Wildlife Service. The Land Cover project can provide geographic understanding of the state of the nation's ecosystems. The project is scheduled to be completed by 2010 and expected to provide an unbiased, national synthesis of land-cover changes.

Military Engineer↗
USGS1995

Watershed characterization for precipitation-runoff modeling system, north fork, American River and east fork, Carson River watersheds, California

As part of its Global Change Hydrology Program, the U.S. Geological Survey (USGS) is investigating the potential effects of climate change on the water resources of several river basins in the United States. The American River Basin in California represents the windward slope of the north-central Sierra Nevada, and the California part of the Carson River Basin, most of which is in Nevada, represents the leeward slope. Parts of the American River and Carson River Basins—the North Fork American River and East Fork Carson River watersheds, both in California—were studied to determine the sensitivity of water resources to potential climate change. The water resources of both basins are derived primarily from snowmelt. A geographic information system (GIS) data base has been created to facilitate paired-basin analysis. The GIS data base incorporates (1) land-surface data, which include elevation, land use and land cover, soil type, and geology; (2) hydrologic data, such as stream networks and streamflow-gaging stations; and (3) climatic data, such as snow-course, snow-telemetry, radiosonde, and meteorological data. Precipitation-runoff models were developed and calibrated for the North Fork watershed within the American River Basin and for the East Fork watershed within the Carson River Basin. (These watersheds were selected to represent the climatic and physiographic variability of the two larger basins.) Synthesized climate scenarios then were used in the model to predict potential effects of climate change.

Hydrologic Atlas↗
USGS2001

Use of the USDA Forest Service Geographic Information System for determining cover type use by white-tailed deer

Cover type use by white-tailed deer (Odocoileus virginianus dacotensis) in the central Black Hills of South Dakota was compared to the United States Department of Agriculture (USDA) Forest Service digital data using a Geographic Information System (GIS). Cover types were determined from observations of radiocollared deer and random locations and from corresponding point locations in the Forest Service digital data. Cover type information was collected at 3,145 white-tailed deer locations and 1,044 random locations. On winter range, cover types determined from observations of radiocollared deer included pine (Pinus ponderosa), pine-deciduous, aspen (Populus tremuloides), aspen-coniferous, burned pine, and meadows; cover types determined from Forest Service data included pine, aspen, grasslands, and private land. On summer range, cover types determined from observations of radiocollared deer included pine, pine-deciduous, aspen, aspen-coniferous, white spruce (Picea glauca), white spruce-deciduous, and meadows; cover types determined from Forest Service digital map data included pine, aspen, grasslands, and private land. Cover types used by white-tailed deer compared to the Forest Service data resulted in 42% agreement on summer range and 62% agreement on winter range. On winter and summer range, Forest Service data tended to overestimate ponderosa pine and aspen habitats used by white-tailed deer, while failing to account for mixed (secondary) cover types. To improve the accuracy of habitat management decisions relative to white-tailed deer, the Forest Service GIS would be strengthened if mixed (secondary) cover type classifications were included in the database.

South Dakota, Wyoming↗
USGS2023

Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013

Salinity is known to affect drinking-water supplies and damage irrigated agricultural lands. Selenium in high concentrations is harmful to fish and other wildlife. Land managers, water providers, and agricultural producers in the lower Gunnison River Basin in western Colorado expend resources mitigating the effects of these constituents. The U.S. Geological Survey revised existing salinity (total dissolved solids) and selenium models for the lower Gunnison River Basin in an attempt to better identify areas of greatest salinity and selenium yield. This effort developed maps of yields predicted from multiple linear regression (MLR) models for the lower Gunnison River Basin. The models included data for irrigation and nonirrigation seasons and two periods, 1992–2004 and 2005–13. Concentrations of salinity and selenium and discharge measurements made at the time of sampling were used to compute loads for subbasins (component drainages of the larger lower Gunnison River Basin study area), which were adjusted for inflows and outflows of canal loads. Load regression equations were determined from explanatory basin characteristics that included physical properties, precipitation, land use and cover, surficial deposits (soil and unconsolidated geologic materials), and bedrock geology. Loads of salinity and selenium were converted to yields by using the subbasin drainage areas, and an empirical Bayesian kriging procedure was used to produce robust grids of yields for salinity and selenium. Salinity yields ranged from 0.00667 to 6.564 tons per year per acre. The highest salinity yields, greater than about 5.0 tons per year per acre, are predicted on the western side of the Uncompahgre River upstream from Delta, Colorado, an area with a high density of irrigated land. The selenium yield map shows a similar pattern, but the highest yields are somewhat more confined to the eastern side of the lower Uncompahgre River and south of the Gunnison River near the confluence with the Uncompahgre River at Delta, Colorado. Selenium yields ranged from 2.6888 x 10 -10 to 0.000445 pounds per day per acre. The highest predicted selenium yields, greater than 0.0003 pounds per day per acre, were in the area downstream from Montrose, Colorado, on the eastern side of the Uncompahgre River.

Colorado↗