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At least 829 records · Page 46Linked to original sources

USGS2008

Summary and Evaluation of the Quality of Stormwater in Denver, Colorado, October 2001 to October 2005

Stormwater in the Denver area was sampled by the U.S. Geological Survey, in cooperation with the Urban Drainage and Flood Control District, in a network of five monitoring stations - three on the South Platte River and two on tributary streams, beginning in October 2001 and continuing through October 11, 2005. Composite samples of stormwater were analyzed at the U.S. Geological Survey National Water Quality Laboratory during water years 2003-2005 and the Metro Wastewater Reclamation District Laboratory during water year 2002 for water-quality properties such as pH, specific conductance, hardness, and residue on evaporation at 105 degrees Celsius; and for constituents such as major ions (calcium, chloride, fluoride, magnesium, potassium, sodium, and sulfate) in 2005, organic carbon and nutrients, including ammonia, nitrite plus nitrate, ammonia plus organic nitrogen, phosphorus, and orthophosphate; and for metals, including total and dissolved phases of copper, lead, manganese, and zinc. Samples analyzed for bacteriological indicators such as Escherichia coli and fecal coliform collected during selected storms also were analyzed at the Metro Wastewater Reclamation Laboratory. Discrete samples collected during selected storms were analyzed at the U.S. Geological Survey National Water Quality Laboratory for a suite of water-quality properties and constituents similar to those analyzed in the composite samples but that did not include determinations for total phases of metals. Streamflow characteristics associated with 176 composite stormwater samples indicate that most samples were collected from hydrographs classified as falling or event hydrographs and that only a few samples were collected from rising hydrographs. Results from laboratory analyses of the composite samples indicate spatial patterns in which concentrations for some constituents increase with contributing drainage area in the South Platte River and Sand Creek, but no well-defined relation with the amount of urban land cover was identified using data available from the U.S. Geological Survey National Land Cover data. Results from 22 discrete samples collected during two storms and used to obtain composited results with various weighting methods indicate that correlation coefficients between time-weighted and volume-weighted concentrations were generally at least 0.65, indicating a strong direct correlation between the two weighting methods for the stations involved in this study. In addition, the central tendency for relative percent differences between the time- and volume-weighting methods typically has an absolute value of about 10 or less, indicating good agreement for these weighting methods for data collected as part of this study. Comparison of stormwater results to numeric standards for streams developed by the Colorado Department of Public Health and Environment on the basis of use classifications indicates that, for water-quality properties and constituents other than bacteriological indicators, there were very few exceptions to numeric standards. Bacteriological indicators, however, such as Escherichia coli and fecal coliform consistently exceeded numeric standards in all bacteriological samples. An evaluation of laboratory results from composite samples on the basis of annual means indicates the presence of some simple upward and downward temporal trends in concentrations. In general, for annual means of results for all stations, hardness, ammonia plus organic nitrogen, total phosphorus, most dissolved metals (lead, manganese, and zinc), and all total metals (copper, lead, manganese, and zinc) all indicate annual means that decrease each year, or downward trends. Some trends were indicated only at individual stations in the network rather than at all stations. Ammonia as nitrogen at Union, Denver, and Henderson, orthophosphate at Sand Creek, and nitrite plus nitrate at Denver and Henderson all indicate decreasing annual means, or downward tr

Scientific Investigations Report↗

USGS2016

Prioritizing landscapes for longleaf pine conservation

We developed a spatially explicit model and map, as a decision support tool (DST), to aid conservation agencies creating or maintaining open pine ecosystems. The tool identified areas that are likely to provide the greatest benefit to focal bird populations based on a comprehensive landscape analysis. We used NLCD 2011, SSURGO, and SEGAP data to map the density of desired resources for open pine ecosystems and six focal species of birds and 2 reptiles within the historic range of longleaf pine east of the Mississippi River. Binary rasters were created of sites with desired characteristics such as land form, hydrology, land use and land cover, soils, potential habitat for focal species, and putative source populations of focal species. Each raster was smoothed using a kernel density estimator. Rasters were combined and scaled to map priority locations for the management of each focal species. Species’ rasters were combined and scaled to provide maps of overall priority for birds and for birds and reptiles. The spatial data can be used to identify high priority areas for conservation or to compare areas under consideration for maintenance or creation of open pine ecosystems.

Cooperator Science Series↗

USGS2012

History of land cover mapping

The historical roots of land-cover mapping reside in the early history of aerial photography and applications spanning forestry, agriculture, urban planning, and water-resources management. Considering this long span of mapping, any attempt to provide an exhaustive treatment of the full history of land-cover mapping will necessarily be incomplete. For that reason, this chapter on the history of land-cover mapping emphasizes the “modern” era of land-cover mapping, which has been arbitrarily defined to begin in the early 1970s. This was when civil space-based remote sensing came of age, and intellectual efforts focused on strategies for using new observations in understanding the characteristics of, and the changes in, land use and land cover.

Book chapter↗

USGS2017

Future scenarios of land change based on empirical data and demographic trends

Changes in land use and land cover (LULC) have important and fundamental interactions with the global climate system. Top-down global scale projections of land use change have been an important component of climate change research; however, their utility at local to regional scales is often limited. The goal of this study was to develop an approach for projecting changes in LULC based on land use histories and demographic trends. We developed a set of stochastic, empirical-based projections of LULC change for the state of California, for the period 2001–2100. Land use histories and demographic trends were used to project a “business-as-usual” (BAU) scenario and three population growth scenarios. For the BAU scenario, we projected developed lands would more than double by 2100. When combined with cultivated areas, we projected a 28% increase in anthropogenic land use by 2100. As a result, natural lands were projected to decline at a rate of 139 km 2 yr −1 ; grasslands experienced the largest net decline, followed by shrublands and forests. The amount of cultivated land was projected to decline by approximately 10%; however, the relatively modest change masked large shifts between annual and perennial crop types. Under the three population scenarios, developed lands were projected to increase 40–90% by 2100. Our results suggest that when compared to the BAU projection, scenarios based on demographic trends may underestimate future changes in LULC. Furthermore, regardless of scenario, the spatial pattern of LULC change was likely to have the greatest negative impacts on rangeland ecosystems.

California↗

USGS2009

Water-Quality Characterization of Surface Water in the Onondaga Lake Basin, Onondaga County, New York, 2005-08

Water-resources managers in Onondaga County, N.Y., have been faced with the challenge of improving the water-quality of Onondaga Lake. To assist in this endeavor, the U.S. Geological Survey undertook a 3-year basinwide study to assess the water quality of surface water in the Onondaga Lake Basin. The study quantified the relative contributions of nonpoint sources associated with the major land uses in the basin and also focused on known sources (streams with large sediment loads) and presumed sinks (Onondaga Reservoir and Otisco Lake) of sediment and nutrient loads, which previously had not been evaluated. Water samples were collected and analyzed for nutrients and suspended sediment at 26 surface-water sites and 4 springs in the 285-square-mile Onondaga Lake Basin from October 2005 through December 2008. More than 1,060 base-flow, stormflow, snowmelt, spring-water, and quality-assurance samples collected during the study were analyzed for ammonia, nitrite, nitrate-plus-nitrite, ammonia-plus-organic nitrogen, orthophosphate, phosphorus, and suspended sediment. The concentration of total suspended solids was measured in selected samples. Ninety-one additional samples were collected, including 80 samples from 4 county-operated sites, which were analyzed for suspended sediment or total suspended solids, and 8 precipitation and 3 snowpack samples, which were analyzed for nutrients. Specific conductance, salinity, dissolved oxygen, and water temperature were periodically measured in the field. The mean concentrations of selected constituents in base-flow, stormflow, and snowmelt samples were related to the land use or land cover that either dominated the basin or had a substantial effect on the water quality of the basin. Almost 40 percent of the Onondaga Lake Basin is forested, 30 percent is in agricultural uses, and almost 21 percent, including the city of Syracuse, is in developed uses. The data indicated expected relative differences among the land types for concentrations of nitrate, ammonia-plus-organic nitrogen, and orthophosphate. The data departed from the expected relations for concentrations of phosphorus and suspended sediment, and plausible explanations for these departures were posited. Snowmelt concentrations of dissolved constituents generally were greater and those of particulate constituents were less than concentrations of these constituents in storm runoff. Presumably, the snowpack acted as a short-term sink for dissolved constituents that had accumulated from atmospheric deposition, and streambed erosion and resuspension of previously deposited material, rather than land-surface erosion, were the primary sources of particulate constituents in snowmelt flows. Longitudinal assessments documented the changes in the median concentrations of constituents in base flows and event flows (combined stormflow and snowmelt) from upstream to downstream monitoring sites along the two major tributaries to Onondaga Lake - Onondaga Creek and Ninemile Creek. Median base-flow concentrations of ammonia and phosphorus and event concentrations of ammonia increased in the downstream direction in both streams. Whereas median event concentrations of other constituents in Onondaga Creek displayed no consistent trends, concentrations of ammonia-plus-organic nitrogen, orthophosphate, phosphorus, and suspended sediment in Ninemile Creek decreased from upstream to downstream sites. Springs discharging from the Onondaga and Bertie Limestone had measureable effects on water temperatures in the receiving streams and increased salinity and values of specific conductance in base flows. Loads of selected nutrients and suspended sediment transported in three tributaries of Otisco Lake were compared with loads from 1981-83. Loads of ammonia-plus-organic nitrogen and orthophosphate decreased from 1981-83 to 2005-08, but those of nitrate-plus-nitrite, phosphorus, and suspended sediment increased. The largest load increase was for suspende

Scientific Investigations Report↗

USGS2013

An integrated multi-criteria scenario evaluation web tool for participatory land-use planning in urbanized areas: The Ecosystem Portfolio Model

Land-use land-cover change is one of the most important and direct drivers of changes in ecosystem functions and services. Given the complexity of the decision-making, there is a need for Internet-based decision support systems with scenario evaluation capabilities to help planners, resource managers and communities visualize, compare and consider trade-offs among the many values at stake in land use planning. This article presents details on an Ecosystem Portfolio Model (EPM) prototype that integrates ecological, socio-economic information and associated values of relevance to decision-makers and stakeholders. The EPM uses a multi-criteria scenario evaluation framework, Geographic Information Systems (GIS) analysis and spatially-explicit land-use/land-cover change-sensitive models to characterize changes in important land-cover related ecosystem values related to ecosystem services and functions, land parcel prices, and community quality-of-life (QoL) metrics. Parameters in the underlying models can be modified through the interface, allowing users in a facilitated group setting to explore simultaneously issues of scientific uncertainty and divergence in the preferences of stakeholders. One application of the South Florida EPM prototype reported in this article shows the modeled changes (which are significant) in aggregate ecological value, landscape patterns and fragmentation, biodiversity potential and ecological restoration potential for current land uses compared to the 2050 land-use scenario. Ongoing refinements to EPM, and future work especially in regard to modifiable sea level rise scenarios are also discussed.

Environmental Modelling and Software↗

USGS2009

Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year-round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79-91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org). ?? 2009 Taylor & Francis.

International Journal of Remote Sensing↗

USGS2020

Migratory status determines resource selection by American Woodcock at an important fall stopover, Cape May, New Jersey

Migration is a period of high activity and exposure during which risks and energetic demand on individuals may be greater than during nonmigratory periods. Stopover locations can help mitigate these threats by providing supplemental energy en route to the animal’s end destination. Effective conservation of migratory species therefore requires an understanding of use of space that provides resources to migratory animals at stopover sites. We conducted a radio-telemetry study of a short-distance migrant, the American Woodcock ( Scolopax minor ), at an important stopover site, the Cape May Peninsula, New Jersey. Our objectives were to describe land-cover types used by American Woodcock and evaluate home range habitat selection for individuals that stopover during fall migration and those that choose to overwinter. We radio-marked 271 individuals and collected 1,949 locations from these birds (0–21 points individual –1 ) over 4 yr (2010 to 2013) to inform resource selection functions of land-cover types and other landscape characteristics by this species. We evaluated these relationships at multiple spatial extents for (1) birds known to have ultimately left the peninsula (presumed migrants), and (2) birds known to have remained on the peninsula into the winter (presumed winter residents). We found that migrants selected deciduous wetland forest, agriculture, mixed shrub, coniferous wetland forest, and coniferous shrub, while wintering residents selected deciduous wetland forest, coniferous shrub, and deciduous shrub. We used these results to develop predictive models of potential habitat: 7.80% of the peninsula was predicted to be potential stopover habitat for American Woodcock (95% classification accuracy) and 4.96% of the peninsula was predicted to be potential wintering habitat (85% classification accuracy). Our study is the first to report habitat relationships for migratory American Woodcock in the coastal U.S. and provides important spatial tools for local and regional managers to support migratory and winter resident woodcock populations into the future.

New Jersey↗

USGS2011

Improvement in precipitation-runoff model simulations by recalibration with basin-specific data, and subsequent model applications, Onondaga Lake Basin, Onondaga County, New York

Water-resource managers in Onondaga County, New York, are faced with the challenge of improving the water quality of Onondaga Lake, which has the distinction of being one of the most contaminated lakes in the United States. To assist in this endeavor, during 2003-07 the U.S. Geological Survey (USGS), in cooperation with the Onondaga Lake Partnership, developed a precipitation-runoff model of the 285-square-mile Onondaga Lake Basin with the computer program Hydrological Simulation Program-Fortran (HSPF). The model was intended to provide a tool whereby the processes responsible for the generation of loads of sediment and nutrients that are transported to Onondaga Lake could be better understood. This objective was only partly attained because data for calibration of the model were available from monitoring sites only at or near the mouths of the major tributaries to Onondaga Lake; no calibration data from headwater subbasins, where the loads originated, were available. To address this limitation and thereby decrease the uncertainty in the simulated results that were associated with headwater processes, the USGS conducted a 3-year (2005-08) basinwide study to assess the quality of surface water in the Onondaga Lake Basin. The study quantified the relative contributions of nonpoint sources associated with the major land uses and land covers in the basin and also monitored known sources and presumed sinks of sediment and nutrient loads, which previously had not been evaluated. The use of the newly acquired data to recalibrate the HSPF model resulted in improvements in the simulation of processes in the headwater subbasins, including suspended-sediment, orthophosphate, and phosphorus generation and transport. Simulation of streamflows in small subbasins was improved by adjusting model parameter values to match base flows, storm peaks, and storm recessions more precisely than had been done with the original model. Simulated recessional and low flows were either increased or decreased as appropriate for a given stream, and simulated peak flows generally were lowered in the revised model. The use of suspended-sediment concentrations rather than concentrations of the surrogate constituent, total suspended solids, resulted in increases in the simulated low-flow sediment concentrations and, in most cases, decreases in the simulated peak-flow sediment concentrations. Simulated orthophosphate concentrations in base flows generally increased but decreased for peak flows in selected headwater subbasins in the revised model. Compared with the original model, phosphorus concentrations simulated by the revised model were comparable in forested subbasins, generally decreased in developed and wetland-dominated subbasins, and increased in agricultural subbasins. A final revision to the model was made by the addition of the simulation of chloride (salt) concentrations in the Onondaga Creek Basin to help water-resource managers better understand the relative contributions of salt from multiple sources in this particular tributary. The calibrated revised model was used to (1) compute loading rates for the various land types that were simulated in the model, (2) conduct a watershed-management analysis that estimated the portion of the total load that was likely to be transported to Onondaga Lake from each of the modeled subbasins, (3) compute and assess chloride loads to Onondaga Lake from the Onondaga Creek Basin, and (4) simulate precolonization (forested) conditions in the basin to estimate the probable minimum phosphorus loads to the lake.

Onondaga, New York↗

USGS2017

Land change monitoring, assessment, and projection (LCMAP) revolutionizes land cover and land change research

When nature and humanity change Earth’s landscapes - through flood or fire, public policy, natural resources management, or economic development - the results are often dramatic and lasting. Wildfires can reshape ecosystems. Hurricanes with names like Sandy or Katrina will howl for days while altering the landscape for years. One growing season in the evolution of drought-resistant genetics can transform semiarid landscapes into farm fields. In the past, valuable land cover maps created for understanding the effects of those events - whether changes in wildlife habitat, water-quality impacts, or the role land use and land cover play in affecting weather and climate - came out at best every 5 to 7 years. Those high quality, high resolution maps were good, but users always craved more: even higher quality data, additional land cover and land change variables, more detailed legends, and most importantly, more frequent land change information. Now a bold new initiative called Land Change Monitoring, Assessment, and Projection (LCMAP) promises to fulfill that demand. Developed at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center in Sioux Falls, South Dakota, LCMAP provides definitive, timely information on how, why, and where the planet is changing. LCMAP’s continuous monitoring process can detect changes as they happen every day that Landsat satellites acquire clear observations. The result will be to place near real-time information in the hands of land and resource managers who need to understand the effects these changes have on landscapes.

General Information Product↗

USGS2017

Integrating future scenario‐based crop expansion and crop conditions to map switchgrass biofuel potential in eastern Nebraska, USA

Switchgrass ( Panicum virgatum ) has been evaluated as one potential source for cellulosic biofuel feedstocks. Planting switchgrass in marginal croplands and waterway buffers can reduce soil erosion, improve water quality, and improve regional ecosystem services (i.e. it serves as a potential carbon sink). In previous studies, we mapped high risk marginal croplands and highly erodible cropland buffers that are potentially suitable for switchgrass development, which would improve ecosystem services and minimally impact food production. In this study, we advance our previous study results and integrate future crop expansion information to develop a switchgrass biofuel potential ensemble map for current and future croplands in eastern Nebraska. The switchgrass biomass productivity and carbon benefits (i.e. NEP: net ecosystem production) for the identified biofuel potential ensemble areas were quantified. The future scenario‐based (‘A1B’) land use and land cover map for 2050, the US Geological Survey crop type and Compound Topographic Index (CTI) maps, and long‐term (1981–2010) averaged annual precipitation data were used to identify future crop expansion regions that are suitable for switchgrass development. Results show that 2528 km 2 of future crop expansion regions (~3.6% of the study area) are potentially suitable for switchgrass development. The total estimated biofuel potential ensemble area (including cropland buffers, marginal croplands, and future crop expansion regions) is 4232 km 2 (~6% of the study area), potentially producing 3.52 million metric tons of switchgrass biomass per year. Converting biofuel ensemble regions to switchgrass leads to potential carbon sinks (the total NEP for biofuel potential areas is 0.45 million metric tons C) and is environmentally sustainable. Results from this study improve our understanding of environmental conditions and ecosystem services of current and future cropland systems in eastern Nebraska and provide useful information to land managers to make land use decisions regarding switchgrass development.

Nebraska↗

USGS2023

Toward consistent change detection across irregular remote sensing time series observations

The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.

Remote Sensing of Environment↗

USGS2010

What you should know about land-cover data

Wildlife biologists are using land-characteristics data sets for a variety of applications. Many kinds of landscape variables have been characterized and the resultant data sets or maps are readily accessible. Often, too little consideration is given to the accuracy or traits of these data sets, most likely because biologists do not know how such data are compiled and rendered, or the potential pitfalls that can be encountered when applying these data. To increase understanding of the nature of land-characteristics data sets, I introduce aspects of source information and data-handling methodology that include the following: ambiguity of land characteristics; temporal considerations and the dynamic nature of the landscape; type of source data versus landscape features of interest; data resolution, scale, and geographic extent; data entry and positional problems; rare landscape features; and interpreter variation. I also include guidance for determining the quality of land-characteristics data sets through metadata or published documentation, visual clues, and independent information. The quality or suitability of the data sets for wildlife applications may be improved with thematic or spatial generalization, avoidance of transitional areas on maps, and merging of multiple data sources. Knowledge of the underlying challenges in compiling such data sets will help wildlife biologists to better assess the strengths and limitations and determine how best to use these data.

Journal of Wildlife Management↗

USGS2016

Differential heating in the Indian Ocean differentially modulates precipitation in the Ganges and Brahmaputra basins

Indo-Pacific sea surface temperature dynamics play a prominent role in Asian summer monsoon variability. Two interactive climate modes of the Indo-Pacific—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean dipole mode—modulate the amount of precipitation over India, in addition to precipitation over Africa, Indonesia, and Australia. However, this modulation is not spatially uniform. The precipitation in southern India is strongly forced by the Indian Ocean dipole mode and ENSO. In contrast, across northern India, encompassing the Ganges and Brahmaputra basins, the climate mode influence on precipitation is much less. Understanding the forcing of precipitation in these river basins is vital for food security and ecosystem services for over half a billion people. Using 28 years of remote sensing observations, we demonstrate that (i) the tropical west-east differential heating in the Indian Ocean influences the Ganges precipitation and (ii) the north-south differential heating in the Indian Ocean influences the Brahmaputra precipitation. The El Niño phase induces warming in the warm pool of the Indian Ocean and exerts more influence on Ganges precipitation than Brahmaputra precipitation. The analyses indicate that both the magnitude and position of the sea surface temperature anomalies in the Indian Ocean are important drivers for precipitation dynamics that can be effectively summarized using two new indices, one tuned for each basin. These new indices have the potential to aid forecasting of drought and flooding, to contextualize land cover and land use change, and to assess the regional impacts of climate change.

Remote Sensing↗

USGS2011

Chapter 4: A sampling and analytical approach to develop spatial distribution models for sagebrush-associated species

Understanding multi-scale floral and faunal responses to human land use is crucial for informing natural resource management and conservation planning. However, our knowledge on how land use influences sagebrush (Artemisia spp.) ecosystems is limited primarily to site-specific studies. To fill this void, studies across large regions are needed that address how species are distributed relative to type, extent, and intensity of land use. We present a study design for the Wyoming Basin Ecoregional Assessment (WBEA) to sample sagebrush-associated flora and fauna along a land cover-human land use gradient. To minimize field costs, we sampled various taxonomic groups simultaneously on transects (ungulates and lagomorphs), point counts (song birds), and area-searches of 7.29-ha survey blocks (pellet counts, burrow counts, reptile surveys, medium-sized mammals, ant mounds, rodent trapping, and vegetation sampling of native and exotic plants). We then present an exploratory approach to develop species occurrence and abundance models when a priori model building is not an option. Our study design has broad applications for large-scale evaluations of arid ecosystems.

Wyoming↗

USGS1990

Catalog of US GeoData

The development of geographic information systems (GIS) is a rapidly growing industry that supports natural resources, studies, land management, environmental analysis, and urban and transporation planning. The increasing use of computers for storing and analyzing earth science information has greatly expanded the demand for digital cartographic and geographic data. Digital cartography involves the collection, storage, processing, analysis, and display of map data with the aid of computers. The U.S. Geological Survey (USGS), the Nation's largest earth science research agency, through its National Mapping Program, has expanded digital cartography operations to include the collection of elevation, planimetric, land use and land cover, and geographic names information in digital form. This digital information is available on 9-track magnetic tapes and, in the case of 1:2,000,000-scale planimetric digital line graph data, in Compact Disc Read Only Memory (CD-ROM) format. Digital information can be used with all types of geographic and land information systems.

General Information Product↗

USGS2014

Integrating land cover modeling and adaptive management to conserve endangered species and reduce catastrophic fire risk

Land cover modeling is used to inform land management, but most often via a two-step process, where science informs how management alternatives can influence resources, and then, decision makers can use this information to make decisions. A more efficient process is to directly integrate science and decision-making, where science allows us to learn in order to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by the specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuel monitoring with decision-making focused on the dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy; other conditions require tradeoffs between objectives. Knowledge about system responses to actions can be informed by developing hypotheses based on ideas about fire behavior and then applying competing management actions to different land units in the same system state. Monitoring and management integration is important to optimize state-specific management decisions and to increase knowledge about system responses. We believe this approach has broad utility and identifies a clear role for land cover modeling programs intended to inform decision-making.

Land↗

USGS2004

Contemporary carbon dynamics in terrestrial ecosystems in the Southeastern Plains of the United States

Quantifying carbon dynamics over large areas is frequently hindered by the lack of consistent, high-quality, spatially explicit land use and land cover change databases and appropriate modeling techniques. In this paper, we present a generic approach to address some of these challenges. Land cover change information in the Southeastern Plains ecoregion was derived from Landsat data acquired in 1973, 1980, 1986, 1992, and 2000 within 11 randomly located 20-km × 20-km sample blocks. Carbon dynamics within each of the sample blocks was simulated using the General Ensemble Biogeochemical Modeling System (GEMS), capable of assimilating the variances and covariance of major input variables into simulations using an ensemble approach. Results indicate that urban and forest areas have been increasing, whereas agricultural land has been decreasing since 1973. Forest clear-cutting activity has intensified, more than doubling from 1973 to 2000. The Southeastern Plains has been acting as a carbon sink since 1973, with an average rate of 0.89 Mg C/ha/yr. Biomass, soil organic carbon (SOC), and harvested materials account for 56%, 34%, and 10% of the sink, respectively. However, the sink has declined continuously during the same period owing to forest aging in the northern part of the ecoregion and increased forest clear-cutting activities in the south. The relative contributions to the sink from SOC and harvested materials have increased, implying that these components deserve more study in the future. The methods developed here can be used to quantify the impacts of human management activities on the carbon cycle at landscape to global scales.

Environmental Management↗