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At least 721 records · Page 40Linked to original sources
USGS2025

Sentinel imagery detects the presence of live trees following large wildfires in California

Identifying live tree presence following wildfire is important for burn damage assessments and decision making, as these trees serve as seed sources for recovery. Satellite-based remote sensing offers an efficient means to assess burn severity with products representing vegetation greenness and char/ash presence and their change from pre- to post-fire imagery. While effective at assessing burn severity (e.g. ecosystem change), there remain limitations in identifying fire refugia (surviving trees), due to the difficulty of teasing apart different green vegetation types (e.g. trees, shrubs, grasses). In this paper, we use 10 m Sentinel-2 satellite data to predict live tree presence across three sites impacted by the 2021 California fire season. We used vegetation indices (VIs) from post-fire imagery (normalized difference vegetation index [NDVI], normalized burn ratio [NBR], normalized difference water index [NDWI], visible atmospherically resistant index [VARI], and burn area index [BAI]), differential VIs from pre- and post-fire imagery (dNDVI, dNBR, RdNBR, dNDWI, dVARI), and direct reflectance bands (all bands model; visible, near-infrared, and shortwave infrared; B1–B12) to predict live tree presence via random forest modeling. To calibrate and validate the random forest models, we photointerpreted ∼2300 pixels per fire region using 2022 National Agriculture Imagery Program imagery. We performed additional field-based validation using tree presence/absence data two years post-fire ( n = 296 observations across two sites). At the site level, the all bands model outperformed the vegetation index-based models (80%–85% vs 65%–79% accuracy). Errors were mainly false positives attributed to pixels with green understory vegetation but no live trees. In cross-site inference, which involved pooling two sites for model calibration to test on the third site, the all bands model retained good performance (76%–81% accuracy). Evaluation against field survey data demonstrated a larger range of performance (50%–87% accuracy) that highlights limitations based on tree isolation and crown percent greenness. Relative to differential-based VIs, our results highlight potential advantages of using post-fire Sentinel-2 imagery and random forest modeling for identifying live tree presence and scaling to full fire extents.

California↗
USGS2021

National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project

The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program. Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC. To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type. A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines. Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway. A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha). A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval. The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.

North Carolina, Tennessee↗
USGS2005

Hydrogeologic setting, ground-water flow, and ground-water quality at the Lake Wheeler Road research station, 2001-03 : North Carolina Piedmont and Mountains Resource Evaluation Program

Results of a 2-year field study of the regolith-fractured bedrock ground-water system at the Lake Wheeler Road research station in Wake County, North Carolina, indicate both disconnection and interaction among components of the ground-water system. The three components of the ground-water system include (1) shallow, porous regolith; (2) a transition zone, including partially weathered rock, having both secondary (fractures) and primary porosity; and (3) deeper, fractured bedrock that has little, if any, primary porosity and is dominated by secondary fractures. The research station includes 15 wells (including a well transect from topographic high to low settings) completed in the three major components of the ground-water-flow system and a surface-water gaging station on an unnamed tributary. The Lake Wheeler Road research station is considered representative of a felsic gneiss hydrogeologic unit having steeply dipping foliation and a relatively thick overlying regolith. Bedrock foliation generally strikes N. 10? E. to N. 30? E. and N. 20? W. to N. 40? W. to a depth of about 400 feet and dips between 70? and 80? SE. and NE., respectively. From 400 to 600 feet, the foliation generally strikes N. 70? E. to N. 80? E., dipping 70? to 80? SE. Depth to bedrock locally ranges from about 67 to 77 feet below land surface. Fractures in the bedrock generally occur in two primary sets: low dip angle, stress relief fractures that cross cut foliation, and steeply dipping fractures parallel to foliation. Findings of this study generally support the conceptual models of ground-water flow from high to low topographic settings developed for the Piedmont and Blue Ridge Provinces in previous investigations, but are considered a refinement of the generalized conceptual model based on a detailed local-scale investigation. Ground water flows toward a surface-water boundary, and hydraulic gradients generally are downward in recharge areas and upward in discharge areas; however, local variations in vertical gradients are apparent. Water-quality sampling and monitoring efforts were conducted to characterize the interaction of components of the ground-water system. Elevated nitrate concentrations as high as 22 milligrams per liter were detected in shallow ground water from the regolith at the study site. These elevated nitrate concentrations likely are related to land use, which includes agricultural practices that involve animal feeding operations and crop fertilization. Continuous ground-water-quality data indicate seasonal fluctuations in field water-quality properties, differences with respect to depth, and fluctuations during recharge events. Water-quality properties recorded in the regolith well following rainfall indicate the upwelling of deeper ground water in the discharge area, likely from ground water in the transition-zone fractures. Additionally, interaction with a surface-water boundary appears likely in the ground-water discharge area, as water levels in all three ground-water zones, including the deep bedrock, mimic the surface-water rise during rainfall.

North Carolina↗
USGS2023

The Landscape Data Commons: A system for standardizing, accessing, and applying large environmental datasets for agroecosystem research and management

Understanding where, when, and why agroecosystems are changing requires quality information about ecosystems that span land tenure, ecological processes, and spatial scales. Over the past two decades, land management agencies and research groups have adopted a suite of standardized methods for monitoring rangelands, which have been implemented at over 85,000 monitoring locations globally. However, the ability to use these data to understand agroecosystem dynamics and change across scales and across land ownership has been limited because, until now, these data have not been available in a harmonized, accessible format for analyses, modeling, and decision-support tools. We present the Landscape Data Commons, a cyberinfrastructure platform that harmonizes and aggregates standardized agroecosystem data, enables linkages to models, and facilitates analysis and interpretation of data within decision-support tools. The Landscape Data Commons provides a community platform for users to contribute data and develop next-generation tools to support agroecosystem management through the 21st century.

Agricultural & Environmental Letters↗
USGS2013

U.S. Geological Survey ecosystems science strategy—Advancing discovery and application through collaboration

Executive Summary Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policymakers are faced with countless decisions each year at local, regional, and national levels on issues as diverse as renewable and nonrenewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decisionmaking is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and wellbeing of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decisionmakers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that U.S. Geological Survey (USGS) scientists and partners can play in building scientific understanding and providing timely information to decisionmakers. The strategy underscores the connection between scientific discoveries and the application of new knowledge, and it integrates ecosystem science and decisionmaking, producing new scientific outcomes to assist resource managers and providing public benefits. We envision the USGS as a leader in integrating scientific information into decisionmaking processes that affect the Nation’s natural resources and human well-being. The USGS is uniquely positioned to play a pivotal role in ecosystem science. With its wide range of expertise, the Bureau can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the Department of the Interior (DOI) and other partners in the natural sciences, in both conducting science and applying the results. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected components that reinforce our vision of the USGS providing science that is at the forefront of decisionmaking: Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. Develop tools, technologies, and capacities to inform decisionmaking about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. Support decisionmaking. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the four strategic approaches are a set of proposed actions that represent a sampling of specific USGS activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role in guiding sound decisionmaking during the next decade by advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Circular↗
USGS2017

A pesticide paradox: Fungicides indirectly increase fungal infections

There are many examples where the use of chemicals have had profound unintended consequences, such as fertilizers reducing crop yields (paradox of enrichment) and insecticides increasing insect pests (by reducing natural biocontrol). Recently, the application of agrochemicals, such as agricultural disinfectants and fungicides, has been explored as an approach to curb the pathogenic fungus, Batrachochytrium dendrobatidis ( Bd ), which is associated with worldwide amphibian declines. However, the long-term, net effects of early-life exposure to these chemicals on amphibian disease risk have not been thoroughly investigated. Using a combination of laboratory experiments and analysis of data from the literature, we explored the effects of fungicide exposure on Bd infections in two frog species. Extremely low concentrations of the fungicides azoxystrobin, chlorothalonil, and mancozeb were directly toxic to Bd in culture. However, estimated environmental concentrations of the fungicides did not reduce Bd on Cuban tree frog ( Osteopilus septentrionalis ) tadpoles exposed simultaneously to any of these fungicides and Bd , and fungicide exposure actually increased Bd -induced mortality. Additionally, exposure to any of these fungicides as tadpoles resulted in higher Bd abundance and greater Bd -induced mortality when challenged with Bd post-metamorphosis, an average of 71 d after their last fungicide exposure. Analysis of data from the literature revealed that previous exposure to the fungicide itraconazole, which is commonly used to clear Bd infections, made the critically endangered booroolong frog ( Litoria booroolongensis ) more susceptible to Bd . Finally, a field survey revealed that Bd prevalence was positively associated with concentrations of fungicides in ponds. Although fungicides show promise for controlling Bd , these results suggest that, if fungicides do not completely eliminate Bd or if Bd recolonizes, exposure to fungicides has the potential to do more harm than good. To ensure that fungicide applications have the intended consequence of curbing amphibian declines, researchers must identify which fungicides do not compromise the pathogen resistance mechanisms of amphibians.

Ecological Applications↗
USGS2001

Estimation of population size using open capture-recapture models

One of the most important needs for wildlife managers is an accurate estimate of population size. Yet, for many species, including most marine species and large mammals, accurate and precise estimation of numbers is one of the most difficult of all research challenges. Open-population capture-recapture models have proven useful in many situations to estimate survival probabilities but typically have not been used to estimate population size. We show that open-population models can be used to estimate population size by developing a Horvitz-Thompson-type estimate of population size and an estimator of its variance. Our population size estimate keys on the probability of capture at each trap occasion and therefore is quite general and can be made a function of external covariates measured during the study. Here we define the estimator and investigate its bias, variance, and variance estimator via computer simulation. Computer simulations make extensive use of real data taken from a study of polar bears (Ursus maritimus) in the Beaufort Sea. The population size estimator is shown to be useful because it was negligibly biased in all situations studied. The variance estimator is shown to be useful in all situations, but caution is warranted in cases of extreme capture heterogeneity.

Journal of Agricultural, Biological, and Environme↗
USGS2004

Trace elements and organic compounds in sediment and fish tissue from the Great Salt Lake basins, Utah, Idaho, and Wyoming, 1998-99

A study to determine the occurrence and distribution of trace elements, organochlorine pesticides, polychlorinated biphenyls (PCBs), and semivolatile organic compounds in sediment and in fish tissue was conducted in the Great Salt Lake Basins study unit of the National Water-Quality Assessment (NAWQA) program during 1998-99. Streambed-sediment and fish-tissue samples were collected concurrently at 11 sites and analyzed for trace-element concentration. An additional four sites were sampled for streambed sediment only and one site for fish tissue only. Organic compounds were analyzed from streambed-sediment and fish-tissue samples at 15 sites concurrently. Bed-sediment cores from lakes, reservoirs, and Farmington Bay collected by the NAWQA program in 1998 and by other researchers in 1982 were used to examine historical trends in trace-element concentration and to determine anthropogenic sources of contaminants. Cores collected in 1982 from Mirror Lake, a high-mountain reference location, showed an enrichment of arsenic, cadmium, copper, lead, tin, and zinc in the surface sediments relative to the deeper sediments, indicating that enrichment likely began after about 1900. This enrichment was attributed to atmospheric deposition during the period of metal-ore mining and smelting. A core from Echo Reservoir, in the Weber River Basin, however, showed a different pattern of trace-element concentration that was attributed to a local source. This site is located downstream from the Park City mining district, which is the most likely historical source of trace elements. Cores collected in 1998 from Farmington Bay show that the concentration of lead began to increase after 1842 and peaked during the mid-1980s and has been in decline since. Recent sediments deposited during 1996-98 indicate a 41- to 62-percent reduction since the peak in the mid-1980s. The concentration of trace elements in streambed sediment was greatest at sites that have been affected by historic mining, including sites on Little Cottonwood Creek in the Jordan River basin, Silver Creek in the Weber River basin, and the Weber River below the confluence with Silver Creek. There was significant correlation of lead concentrations in streambed sediment and fish tissue, but other trace elements did not correlate well. Streambed sediment and fish tissue collected from sites in the Bear River basin, which is predominantly rangeland and agriculture, generally had low concentrations of most elements. Sediment-quality guidelines were used to assess the relative toxicity of streambed-sediment sites to aquatic communities. Sites affected by mining exceeded the Probable Effect Concentration (PEC), the concentration at which it is likely there will be a negative effect on the aquatic community, for arsenic, cadmium, copper, lead, silver, mercury, and zinc. Sites that were not affected by mining did not exceed these criteria. Concentrations of trace elements in samples collected from the Great Salt Lake Basins study unit (GRSL) are high compared to those of samples collected nationally with the NAWQA program. Nine of 15 streambed-sediment samples and 11 of 14 fish-tissue samples had concentrations of at least one trace element greater than the concentration of 90 percent of the samples collected nationally during 1993-2000. Organic compounds that were examined in streambed sediment and fish-tissue samples also were examined in bed-sediment cores. A bed-sediment core from Farmington Bay of Great Salt Lake showed an increase in total polycyclic aromatic hydrocarbon (PAH) concentrations coincident with the increase in population in Salt Lake Valley, which drains into this bay. Analysis of streambed-sediment samples showed that the highest concentrations of PAHs were detected at urban sites, including two sites in the lower Jordan River (the Jordan River flows into Farmington Bay), the Weber River at Ogden Bay, and the Provo River near Provo. Other organic compounds detected in streambed sediment in the lower Jordan River were PCBs, DDT compounds, and chlordane compounds. Organic compounds were detected more frequently in fish tissue than in streambed sediment. Chlordane compounds and PCBs were detected more frequently at urban sites. DDT compounds were detected at 13 of 15 sites including urban and agricultural sites. Concentrations of total DDT in fish tissue exceeded the guideline for protection of fish-eating wildlife at two urban sites. The concentration of organic compounds in the GRSL study unit is low compared with that of samples collected nationally.

Idaho, Utah, Wyoming↗
USGS2024

Beyond the wedge: Impact of tidal streams on salinization of groundwater in a coastal aquifer stressed by pumping and sea-level rise

Saltwater intrusion (SWI) is a well-studied phenomenon that threatens the freshwater supplies of coastal communities around the world. The development and advancement of numerical models has led to improved assessment of the risk of salinization. However, these studies often fail to include the impact of surface waters as potential sources of aquifer salinity and how they may impact SWI. Based on field-collected data, we developed a regional, variable-density groundwater model using SEAWAT for east Dover, Delaware. In this location, major users of groundwater from the surficial aquifer are the City of Dover and irrigation for agriculture. Our model includes salinized marshland and tidal streams, along with irrigation and municipal pumping wells. Model scenarios were run for 100 years and included changes in pumping rates and sea-level rise (SLR). We examined how these drivers of SWI affect the extent and location of salinization in the surficial aquifer by evaluating differences in chloride concentration near surface waters and the subsurface freshwater-saltwater interface. We found the presence of the marsh inverts the typical freshwater-saltwater wedge interface and that the edge of the interface did not migrate farther inland. Additionally, we found that tidal streams are the dominant pathways of SWI at our site with salinization from streams being exacerbated by SLR. Our results also show that spatial distribution of pumping affects both the magnitude and extent of salinization, with an increase in concentrated pumping leading to more intensive salinization than a more widely distributed increase of the same total pumping volume.

Delaware↗
USGS2023

Mapping vegetation index-derived actual evapotranspiration across croplands using the Google Earth Engine platform

Precise knowledge of crop water consumption is essential to better manage agricultural water use, particularly in regions where most countries struggle with increasing water and food insecurity. Approaches such as cloud computing and remote sensing (RS) have facilitated access, process, and visualization of big geospatial data to map and monitor crop water requirements. To find the most reliable Vegetation Index (VI)-based evapotranspiration (ETa) for croplands in drylands, we modeled and mapped ETa using empirical RS methods across the Zayandehrud river basin in Iran for two decades (2000–2019) on the Google Earth Engine platform using the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index 2 (EVI2). Developed ET-VI products in this study comprise three NDVI-based ETa (ET-NDVI*, ET-NDVI* scaled , and ET-NDVI Kc ) and an EVI2-based ETa (ET-EVI2). We (a) applied, for the first time, the ET-NDVI* method to croplands as a crop-independent index and then compared its performance with the ET-EVI2 and crop ET, and (b) assessed the ease and feasibility of the transferability of these methods to other regions. Comparing four ET-VI products showed that annual ET-EVI2 and ET-NDVI* scaled estimations were close. ET-NDVI Kc consistently overestimated ETa. Our findings indicate that ET-EVI2 and ET-NDVI Kc were easy to parametrize and adopt to other regions, while ET-NDVI* and ET-NDVI* scaled are site-dependent and sensitive to image acquisition time. ET-EVI2 performed robustly in arid and semi-arid regions making it a better tool. Future research should further develop and confirm these findings by characterizing the accuracy of VI-based ETa over croplands in drylands by comparing them with available ETa products and examining their performance using crop-specific comparisons.

Remote Sensing↗
USGS1976

Improving estimates of streamflow characteristics by using Landsat-1 imagery

Imagery from the first Earth Resources Technology Satellite (renamed Landsat-1) was used to discriminate physical features of drainage basins in an effort to improve equations used to estimate streamflow characteristics at gaged and ungaged sites. Records of 20 gaged basins in the Delmarva Peninsula of Maryland, Delaware, and Virginia were analyzed for 40 statistical streamflow characteristics. Equations relating these characteristics to basin characteristics were obtained by a technique of multiple linear regression. A control group of equations contains basin characteristics derived from maps. An experimental group of equations contains basin characteristics derived from maps and imagery. Characteristics from imagery were forest, riparian (streambank) vegetation, water, and combined agricultural and urban land use. These basin characteristics were isolated photographically by techniques of film-density discrimination. The area of each characteristic in each basin was measured photometrically. Comparison of equations in the control group with corresponding equations in the experimental group reveals that for 12 out of 40 equations the standard error of estimate was reduced by more than 10 percent. As an example, the standard error of estimate of the equation for the 5-year recurrence-interval flood peak was reduced from 46 to 32 percent. Similarly, the standard error of the equation for the mean monthly flow for September was reduced from 32 to 24 percent, the standard error for the 7-day, 2-year recurrence low flow was reduced from 136 to 102 percent, and the standard error for the 3-day, 2-year flood volume was reduced from 30 to 12 percent. It is concluded that data from Landsat imagery can substantially improve the accuracy of estimates of some streamflow characteristics at sites in the Delmarva Peninsula.

Maryland, Delaware, Virginia↗
USGS2010

An initial SPARROW model of land use and in-stream controls on total organic carbon in streams of the conterminous United States

Watersheds play many important roles in the carbon cycle: (1) they are a site for both terrestrial and aquatic carbon dioxide (CO2) removal through photosynthesis; (2) they transport living and decomposing organic carbon in streams and groundwater; and (3) they store organic carbon for widely varying lengths of time as a function of many biogeochemical factors. Using the U.S. Geological Survey (USGS) Spatially Referenced Regression on Watershed Attributes (SPARROW) model, along with long-term monitoring data on total organic carbon (TOC), this research quantitatively estimates the sources, transport, and fate of the long-term mean annual load of TOC in streams of the conterminous United States. The model simulations use surrogate measures of the major terrestrial and aquatic sources of organic carbon to estimate the long-term mean annual load of TOC in streams. The estimated carbon sources in the model are associated with four land uses (urban, cultivated, forest, and wetlands) and autochthonous fixation of carbon (stream photosynthesis). Stream photosynthesis is determined by reach-level application of an empirical model of stream chlorophyll based on total phosphorus concentration, and a mechanistic model of photosynthetic rate based on chlorophyll, average daily solar irradiance, water column light attenuation, and reach dimensions. It was found that the estimate of in-stream photosynthesis is a major contributor to the mean annual TOC load per unit of drainage area (that is, yield) in large streams, with a median share of about 60 percent of the total mean annual carbon load in streams with mean flows above 500 cubic feet per second. The interquartile range of the model predictions of TOC from in-stream photosynthesis is from 0.1 to 0.4 grams (g) carbon (C) per square meter (m-2) per day (day-1) for the approximately 62,000 stream reaches in the continental United States, which compares favorably with the reported literature range for net carbon fixation by phytoplankton in lakes and streams. The largest contributors per unit of drainage area to the mean annual stream TOC load among the terrestrial sources are, in descending order: wetlands, urban lands, mixed forests, agricultural lands, evergreen forests, and deciduous forests . It was found that the SPARROW model estimates of TOC contributions to streams associated with these land uses are also consistent with literature estimates. SPARROW model calibration results are used to simulate the delivery of TOC loads to the coastal areas of seven major regional drainages. It was found that stream photosynthesis is the largest source of the TOC yields ( about 50 percent) delivered to the coastal waters in two of the seven regional drainages (the Pacific Northwest and Mississippi-Atchafalaya-Red River basins ), whereas terrestrial sources are dominant (greater than 60 percent) in all other regions (North Atlantic, South Atlantic-Gulf, California, Texas-Gulf, and Great Lakes).

Conterminous United States↗
USGS2025

Source and longevity of streambed sediment and phosphorus retention in a lake-plain tributary of the Maumee River

We described abundance and source of soft, fine-grained, streambed sediment and associated phosphorus (sed-P) during summer low flow in Little Flatrock Creek (LFR), a channelized tributary of the Maumee River and western Lake Erie. Reach-level assessments compared streambed-sediment storage to streambank erosion. Streambed sediment was fingerprinted and analyzed for sed-P and the potential for P de/sorption between the water column and streambed sediment. The ratio of two fallout radionuclides apportioned “new sediment” in streambed storage. Basin-wide streambed-sediment storage exceeded both annual streambank erosion and the annual suspended-sediment load. Streambed sediment was generally a mix of streambank and cropland sources and each equaled or exceeded abundance of new streambed sediment, indicating accumulation of sediment from both sources during the current agricultural cycle. The implication is that this mix of new and old sediment, and legacy P, takes multiple events and seasons to be transported downstream. Streambed sediment had the potential to adsorb dissolved P (DP) from the water column, with sed-P stored in the silt + clay fraction similar to the annual particulate-P (total-dissolved) load transported with suspended sediment, but with lower concentrations than cropland- and streambank-sourced sediment. This indicates supplementation of water-column DP as sediment settles to the bottom and a lag between land and channel management and in-channel P availability. Storage of fine-grained sediment and sed-P in this lake-plain/bed basin is distinct from another Maumee headwater tributary with glacial-moraine controlled geomorphology. The implication is that streambank erosion, in-channel sediment accumulation, and the resultant total-dissolved-sediment P spiral differ based on geomorphic setting and drainage history.

Indiana, Michigan, Ohio↗
USGS2019

Detecting signals of large‐scale climate phenomena in discharge and nutrient loads in the Mississippi‐Atchafalaya River Basin

Agricultural runoff from the Mississippi‐Atchafalaya River Basin delivers nitrogen (N) and phosphorus (P) to the Gulf of Mexico, causing hypoxia, and climate drives interannual variation in nutrient loads. Climate phenomena such as El Niño–Southern Oscillation may influence nutrient export through effects on river flow, nutrient uptake, or biogeochemical transformation, but landscape variation at smaller spatial scales can mask climate signals in load or discharge time series within large river networks. We used multivariate autoregressive state‐space modeling to investigate climate signals in the long‐term record (1979–2014) of discharge, N, P, and SiO 2 loads at three nested spatial scales within the Mississippi‐Atchafalaya River Basin. We detected significant signals of El Niño–Southern Oscillation and land‐surface temperature anomalies in N loads but not discharge, SiO 2 , or P, suggesting that large‐scale climate phenomena contribute to interannual variation in nutrient loads through biogeochemical mechanisms beyond simple discharge‐load relationships.

Mississippi‐Atchafalaya River Basin↗
USGS2014

Habitat prioritization across large landscapes, multiple seasons, and novel areas: an example using greater sage-grouse in Wyoming

Animal habitat selection is an important and expansive area of research in ecology. In particular, the study of habitat selection is critical in habitat prioritization efforts for species of conservation concern. Landscape planning for species is happening at ever-increasing extents because of the appreciation for the role of landscape-scale patterns in species persistence coupled to improved datasets for species and habitats, and the expanding and intensifying footprint of human land uses on the landscape. We present a large-scale collaborative effort to develop habitat selection models across large landscapes and multiple seasons for prioritizing habitat for a species of conservation concern. Greater sage-grouse ( Centrocercus urophasianus , hereafter sage-grouse) occur in western semi-arid landscapes in North America. Range-wide population declines of this species have been documented, and it is currently considered as “warranted but precluded” from listing under the United States Endangered Species Act. Wyoming is predicted to remain a stronghold for sage-grouse populations and contains approximately 37% of remaining birds. We compiled location data from 14 unique radiotelemetry studies (data collected 1994–2010) and habitat data from high-quality, biologically relevant, geographic information system (GIS) layers across Wyoming. We developed habitat selection models for greater sage-grouse across Wyoming for 3 distinct life stages: 1) nesting, 2) summer, and 3) winter. We developed patch and landscape models across 4 extents, producing statewide and regional (southwest, central, northeast) models for Wyoming. Habitat selection varied among regions and seasons, yet preferred habitat attributes generally matched the extensive literature on sage-grouse seasonal habitat requirements. Across seasons and regions, birds preferred areas with greater percentage sagebrush cover and avoided paved roads, agriculture, and forested areas. Birds consistently preferred areas with higher precipitation in the summer and avoided rugged terrain in the winter. Selection for sagebrush cover varied regionally with stronger selection in the Northeast region, likely because of limited availability, whereas avoidance of paved roads was fairly consistent across regions. We chose resource selection function (RSF) thresholds for each model set (seasonal × regional combination) that delineated important seasonal habitats for sage-grouse. Each model set showed good validation and discriminatory capabilities within study-site boundaries. We applied the nesting-season models to a novel area not included in model development. The percentage of independent nest locations that fell directly within identified important habitat was not overly impressive in the novel area (49%); however, including a 500-m buffer around important habitat captured 98% of independent nest locations within the novel area. We also used leks and associated peak male counts as a proxy for nesting habitat outside of the study sites used to develop the models. A 1.5-km buffer around the important nesting habitat boundaries included 77% of males counted at leks in Wyoming outside of the study sites. Data were not available to quantitatively test the performance of the summer and winter models outside our study sites. The collection of models presented here represents large-scale resource-management planning tools that are a significant advancement to previous tools in terms of spatial and temporal resolution.

Wyoming↗
USGS2024

Causal inference approaches reveal both positive and negative unintended effects of agricultural and urban management practices on instream biological condition

Agricultural and urban management practices (MPs) are primarily designed and implemented to reduce nutrient and sediment concentrations in streams. However, there is growing interest in determining if MPs produce any unintended positive effects, or co-benefits, to instream biological and habitat conditions. Identifying co-benefits is challenging though because of confounding variables (i.e., those that affect both where MPs are applied and stream biota), which can be accounted for in novel causal inference approaches. Here, we used two causal inference approaches, propensity score matching (PSM) and Bayesian network learning (BNL), to identify potential MP co-benefits in the Chesapeake Bay watershed portion of Maryland, USA. Specifically, we examined how MPs may modify instream conditions that impact fish and macroinvertebrate indices of biotic integrity (IBI) and functional and taxonomic endpoints. We found evidence of positive unintended effects of MPs for both benthic macroinvertebrates and fish indicated by higher IBI scores and specific endpoints like the number of scraper macroinvertebrate taxa and lithophilic spawning fish taxa in a subset of regions. However, our results also suggest MPs have negative unintended effects, especially on sensitive benthic macroinvertebrate taxa and key instream habitat and water quality metrics like specific conductivity. Overall, our results suggest MPs offer co-benefits in some regions and catchments with largely degraded conditions but can have negative unintended effects in some regions, especially in catchments with good biological conditions. We suggest the number and types of MPs drove these mixed results and highlight carefully designed MP implementation that incorporates instream biological data at the catchment scale could facilitate co-benefits to instream biological conditions. Our study underscores the need for more research on identifying effects of individual MP types on instream biological and habitat conditions.

Maryland↗
USGS2013

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

Journal of Agricultural, Biological, and Environme↗
USGS2012

Weathering, landscape equilibrium, and carbon in four watersheds in eastern Puerto Rico: Chapter H in Water quality and landscape processes of four watersheds in eastern Puerto Rico

The U.S. Geological Survey's Water, Energy, and Biogeochemical Budgets (WEBB) program research in eastern Puerto Rico involves a double pair-wise comparison of four montane river basins, two on granitic bedrock and two on fine-grained volcaniclastic bedrock; for each rock type, one is forested and the other is developed. A confounding factor in this comparison is that the developed watersheds are substantially drier than the forested (runoff of 900–1,600 millimeters per year compared with 2,800–3,700 millimeters per year). To reduce the effects of contrasting runoff, the relation between annual runoff and annual constituent yield were used to estimate mean-annual yields at a common, intermediate mean-annual runoff of 1,860 millimeters per year. Upon projection to this intermediate runoff, the ranges of mean-annual yields among all watersheds became more compact or did not substantially change for dissolved bedrock, sodium, silica, chloride, dissolved organic carbon, and calcium. These constituents are the primary indicators of chemical weathering, biological activity on the landscape, and atmospheric inputs; the narrow ranges indicate little preferential influence by either geology or land cover. The projected yields of biologically active constituents (potassium, nitrate, ammonium ion, phosphate), and particulate constituents (suspended bedrock and particulate organic carbon) were considerably greater for developed landscapes compared with forested watersheds, consistent with the known effects of land clearing and human waste inputs. Equilibrium rates of combined chemical and physical weathering were estimated by using a method based on concentrations of silicon and sodium in bedrock, river-borne solids, and river-borne solutes. The observed rates of landscape denudation greatly exceed rates expected for a dynamic equilibrium, except possibly for the forested watershed on volcaniclastic rock. Deforestation and agriculture can explain the accelerated physical erosion in the two developed watersheds. Because there has been no appreciable deforestation, something else, possibly climate or forest-quality change, must explain the accelerated erosion in the forested watersheds on granitic rocks. Particulate organic carbon yields are closely linked to sediment yields. This relation implies that much of the particulate organic carbon transport in the four rivers is being caused by this enhanced erosion aided by landslides and fast carbon recovery. The increase in particulate organic carbon yields over equilibrium is estimated to range from 300 kilomoles per square kilometer per year (6 metric tons carbon per square kilometer per year) to 1,700 kilomoles per square kilometer per year (22 metric tons carbon per square kilometer per year) and is consistent with human-accelerated particulate-organic-carbon erosion and burial observed globally. There is no strong evidence of human perturbation of silicate weathering in the four study watersheds, and differences in dissolved inorganic carbon are consistent with watershed geology. Although dissolved organic carbon is slightly elevated in the developed watersheds, that elevation is not enough to unambiguously demonstrate human causes; more work is needed. Accordingly, the dissolved organic carbon and dissolved inorganic carbon yields of tropical rivers, although large, are of secondary importance in the study of the anthropgenically perturbed carbon cycle.

Professional Paper↗