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At least 613 records · Page 34Linked to original sources
USGS2016

Estimating spatially and temporally varying recharge and runoff from precipitation and urban irrigation in the Los Angeles Basin, California

A daily precipitation-runoff model, referred to as the Los Angeles Basin watershed model (LABWM), was used to estimate recharge and runoff for a 5,047 square kilometer study area that included the greater Los Angeles area and all surface-water drainages potentially contributing recharge to a 1,450 square kilometer groundwater-study area underlying the greater Los Angeles area, referred to as the Los Angeles groundwater-study area. The recharge estimates for the Los Angeles groundwater-study area included spatially distributed recharge in response to the infiltration of precipitation, runoff, and urban irrigation, as well as mountain-front recharge from surface-water drainages bordering the groundwater-study area. The recharge and runoff estimates incorporated a new method for estimating urban irrigation, consisting of residential and commercial landscape watering, based on land use and the percentage of pervious land area. The LABWM used a 201.17-meter gridded discretization of the study area to represent spatially distributed climate and watershed characteristics affecting the surface and shallow sub-surface hydrology for the Los Angeles groundwater study area. Climate data from a local network of 201 monitoring sites and published maps of 30-year-average monthly precipitation and maximum and minimum air temperature were used to develop the climate inputs for the LABWM. Published maps of land use, land cover, soils, vegetation, and surficial geology were used to represent the physical characteristics of the LABWM area. The LABWM was calibrated to available streamflow records at six streamflow-gaging stations. Model results for a 100-year target-simulation period, from water years 1915 through 2014, were used to quantify and evaluate the spatial and temporal variability of water-budget components, including evapotranspiration (ET), recharge, and runoff. The largest outflow of water from the LABWM was ET; the 100-year average ET rate of 362 millimeters per year (mm/yr) accounted for 66 percent of the combined water inflow of 551 mm/yr, including 488 mm/yr from precipitation and 63 mm/yr from urban irrigation. The simulated ET rate varied from a minimum of 0 mm/yr for impervious areas to high values of more than 1,000 mm/yr for many areas, including the south-facing slopes of the San Gabriel Mountains, stream channels underlain by permeable soils and thick root zones, and pervious locations receiving inflows both from urban irrigation and surface water. Runoff was the next largest outflow, averaging 145 mm/yr for the 100-year period, or 26 percent of the combined precipitation and urban-irrigation inflow. Recharge averaged 45 mm/yr, or about 8 percent of the combined inflow from precipitation and urban irrigation. Simulation results indicated that recharge in response to urban irrigation was an important component of spatially distributed recharge, contributing an average of 56 percent of the total recharge to the eight LABWM subdomains containing the Los Angeles groundwater study area. The 100‑year average recharge rate for the eight subdomains was 41 mm/yr, or 8,473 hectare-meters per year (ha-m/yr), with urban irrigation included in the simulation compared to a recharge rate of 18 mm/yr, or 3,741 ha-m/yr, with urban irrigation excluded. In contrast to recharge, the effect of urban irrigation on runoff was slight; runoff was 72,667 ha-m/yr with urban irrigation included compared to 72,618 ha-m/yr with urban irrigation excluded, an increase of only 48 ha-m/yr (about 0.1 percent). Simulation results also indicated that potential recharge from hilly drainages outside of, but bordering and tributary to, the lower-lying area of the Los Angeles groundwater study area, in this study referred to as mountain-front recharge, could provide an important contribution to the total recharge for the groundwater basins. The time-averaged recharge rate was similar to the combined direct and mountain-front recharge components estimated in a previous study and used as input for a calibrated groundwater model. The annual (water year) recharge estimates simulated in this study, however, indicated much greater year-to-year variability, which was dependent on year-to-year variability in the magnitude and distribution of daily precipitation, compared to the previous estimates.

California↗
USGS2022

Influence of anthropogenic subsidies on movements of common ravens

Anthropogenic subsidies can benefit populations of generalist predators such as common ravens (ravens; Corvus corax ), which in turn may depress populations of many types of species at lower-trophic levels, including desert tortoises (Gopherus agassizii ) or greater sage-grouse (Centrocercus urophasianus). Management of subsidized ravens often has targeted local breeding populations that are presumed to affect species of concern and ignored “urban” populations of ravens. However, little is known about how ravens move, especially in response to the presence of anthropogenic subsidies. Therefore, subsidized ravens from distant populations that are not managed may influence local prey. To better understand this issue, we deployed global positioning system – global system for mobile communications transmitters to track movements of 19 ravens from September to December 2020 relative to 2 land cover types that provide subsidies: developed areas and cultivated crops. On average, ravens moved 41.5 km (±30.5) per day, although daily movement distances ranged from 0.13– 206.1 km. Raven movement among cover types during the non-breeding season varied widely, with 100% of individuals each using land cover types that provide subsidy and other types at least once in the season. On 100% of days ravens used areas that did not provide subsidy, on 86.7% of days they used developed areas, and on 20.5% of days they used cultivated crops. Although on some days a raven would stay exclusively in areas that did not provide subsidy, there were no days in which a single raven ever stayed exclusively in developed or cultivated crops. Ravens moved shorter distances on days when they used subsidies more frequently. Further, time spent in developed areas and cultivated crops increased when ravens roosted closer to them, although this effect was greater for developed areas than for cultivated crops. Individual ravens were not associated exclusively with either of the subsidy-providing landscapes we considered, but instead all birds used both subsidized and other landscapes. Our research suggests that management of ravens during the non-breeding season and possibly during the breeding season, intended to reduce risk of predation on desert tortoises, will be most effective if conducted on a broad scale because of distances the birds travel and the lack of separation between putative “urban” and “natural” populations of ravens.

California↗
USGS2007

Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey

Historical land-use and land-cover data, available from the U.S. Geological Survey (USGS) for the conterminous United States and Hawaii, have been enhanced for use in geographic information systems (GIS) applications. The original digital data sets were created by the USGS in the late 1970s and early 1980s and were later converted by USGS and the U.S. Environmental Protection Agency (USEPA) to a geographic information system (GIS) format in the early 1990s. These data were made available on USEPA's Web site since the early 1990s and have been used for many national applications, despite minor coding and topological errors. During the 1990s, a group of USGS researchers made modifications to the data set for use in the National Water-Quality Assessment Program. These edited files have been further modified to create a more accurate, topologically clean, and seamless national data set. Several different methods, including custom editing software and several batch processes, were applied to create this enhanced version of the national data set. The data sets are included in this report in the commonly used shapefile and Tagged Image Format File (TIFF) formats. In addition, this report includes two polygon data sets (in shapefile format) representing (1) land-use and land-cover source documentation extracted from the previously published USGS data files, and (2) the extent of each polygon data file.

Data Series↗
USGS2012

Role of remote sensing for land-use and land-cover change modeling

As the impacts of land-use and land-cover (LULC) change on carbon dynamics, climate change, hydrology, and biodiversity have been recognized, modeling of this transformational force has become increasingly important. Given the wide variety of applications that rely on the availability of LULC projections, modeling approaches have originated from a variety of disciplines, including geography, landscape ecology, economics, biology, and others. Initial modeling was often isolated within each discipline, but multidisciplinary modeling frameworks were developed as LULC modelers began to integrate the socioeconomic and biophysical components of LULC change. The empirical and theoretical basis for this work falls within land-use science, and this ‡eld documents both land-use and land-cover change, explains the coupled human-environment dynamics that produce the changes, and provides tools for producing spatially explicit LULC models (Mertens and Lambin, 1999; Rindfuss et al., 2004).

Book chapter↗
USGS2021

Developing a set of indicators to identify, monitor, and track impacts and change in forests of the United States

United States forestland is an important ecosystem type, land cover, land use, and economic resource that is facing several drivers of change including climatic. Because of its significance, forestland was identified through the National Climate Assessment (NCA) as a key sector and system of concern to be included in a system of climate indicators as part of a sustained assessment effort. Here, we describe 11 informative core indicators of forests and climate change impacts with metrics available or nearly available for use in the NCA efforts. The recommended indicators are based on a comprehensive conceptual model which recognizes forests as a land use, an ecosystem, and an economic sector. The indicators cover major forest attributes such as extent, structural components such as biomass, functions such as growth and productivity, and ecosystem services such as biodiversity and outdoor recreation. Interactions between humans and forests are represented through indicators focused on the wildland-urban interface, cost to mitigate wildfire risk, and energy produced from forest-based biomass. Selected indicators also include drought and disturbance from both wildfires and biotic agents. The forest indicators presented are an initial set that will need further refinement in coordination with other NCA indicator teams. Our effort ideally will initiate the collection of critical measurements and observations and lead to additional research on forest-climate indicators.

Climatic Change↗
USGS1987

BIA interpretation techniques for vegetation mapping using thematic mapper false color composites (interim report for San Carlos Reservation)

The Bureau of Indian Affairs (BIA) is responsible for the natural resource management of approximately 52 million acres of Trust lands in the contiguous United States. The lands are distributed in a "patchwork" fashion throughout the country. Management responsibilities on these areas include: minerals, range, timber, fish and wildlife, agricultural, cultural, and archaeological resources. In an age of decreasing natural resources and increasing natural resource values, effective multiple resource management is critical. BIA has adopted a "systems approach" to natural resource management which utilizes Geographic Information System (GIS) technology. The GIS encompasses a continuum of spatial and relational data elements, and included functional capabilities such as: data collection, data entry, data base development, data analysis, data base management, display, and report generalization. In support of database development activities, BIA and BLM/TGS conducted a cooperative effort to investigate the potential of 1:100,000 scale Thematic Mapper (TM) False Color Composites (FCCs) for providing vegetation information suitable for input to the GIS and to later be incorporated as a generalized Bureau wide land cover map. Land cover information is critical as the majority of reservations currently have no land cover information in either map or digital form. This poster outlines an approach which includes the manual interpretation of land cover using TM FCCs, the digitizing of interpreted polygons, and the editing of digital data, used upon ground truthing exercises. An efficient and cost-effective methodology for generating large area land cover information is illustrated for the Mineral Strip area on the San Carlos Indian Reservation in Arizona. Techniques which capitalize on the knowledge of the local natural resources professionals, while minimizing machine processing requirements, are suggested.

Arizona↗
USGS2013

A comprehensive change detection method for updating the National Land Cover Database to circa 2011

The importance of characterizing, quantifying, and monitoring land cover, land use, and their changes has been widely recognized by global and environmental change studies. Since the early 1990s, three U.S. National Land Cover Database (NLCD) products (circa 1992, 2001, and 2006) have been released as free downloads for users. The NLCD 2006 also provides land cover change products between 2001 and 2006. To continue providing updated national land cover and change datasets, a new initiative in developing NLCD 2011 is currently underway. We present a new Comprehensive Change Detection Method (CCDM) designed as a key component for the development of NLCD 2011 and the research results from two exemplar studies. The CCDM integrates spectral-based change detection algorithms including a Multi-Index Integrated Change Analysis (MIICA) model and a novel change model called Zone, which extracts change information from two Landsat image pairs. The MIICA model is the core module of the change detection strategy and uses four spectral indices (CV, RCVMAX, dNBR, and dNDVI) to obtain the changes that occurred between two image dates. The CCDM also includes a knowledge-based system, which uses critical information on historical and current land cover conditions and trends and the likelihood of land cover change, to combine the changes from MIICA and Zone. For NLCD 2011, the improved and enhanced change products obtained from the CCDM provide critical information on location, magnitude, and direction of potential change areas and serve as a basis for further characterizing land cover changes for the nation. An accuracy assessment from the two study areas show 100% agreement between CCDM mapped no-change class with reference dataset, and 18% and 82% disagreement for the change class for WRS path/row p22r39 and p33r33, respectively. The strength of the CCDM is that the method is simple, easy to operate, widely applicable, and capable of capturing a variety of natural and anthropogenic disturbances potentially associated with land cover changes on different landscapes.

Remote Sensing of Environment↗
USGS2018

Landsat classification of surface-water presence during multiple years to assess response of playa wetlands to climatic variability across the Great Plains Landscape Conservation Cooperative region

To improve understanding of the distribution of ecologically important, ephemeral wetland habitats across the Great Plains, the occurrence and distribution of surface water in playa wetland complexes were documented for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. This information is important because it informs land and wildlife managers about the timing and location of habitat availability. Data with an accurate timestamp that indicate the presence of water, the percent of the area inundated with water, and the spatial distribution of playa wetlands with water are needed for a host of resource inventory, monitoring, and research applications. For example, the distribution of inundated wetlands forms the spatial pattern of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface waters that recharge the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Documentation of these spatially and temporally intricate processes, here, provides data required to assess connections between inundation and multiple environmental drivers, such as climate, land use, soil, and topography. Climate drivers are understood to interact with land cover, land use and soil attributes in determining the amount of water that flows overland into playa wetlands. Results indicated significant spatial variability represented by differences in the percent of playas inundated among States within the GPLCC. Further, analysis-of-variance comparison of differences in inundation between years showed significant differences in all cases. Although some connections with seasonal moisture patterns may be observed, the complex spatial-temporal gradients of precipitation, temperature, soils, and land use need to be combined as covariates in multivariate models to effectively account for these patterns. We demonstrate the feasibility of using classification of Landsat satellite imagery to describe playa-wetland inundation across years and seasons. Evaluating classifications representing only 4 years of imagery, we found significant year-to-year and state-to-state differences in inundation rates.

Great Plains Landscape Conservation Cooperative↗
USGS2024

The interplay of future solar energy, land cover change, and their projected impacts on natural lands and croplands in the US

Projections for deep decarbonization require large amounts of solar energy, which may compete with other land uses such as agriculture, urbanization, and conservation of natural lands. Existing capacity expansion models do not integrate land use land cover change (LULC) dynamics into projections. We explored the interaction between projected LULC, solar photovoltaic (PV) deployment, and solar impacts on natural lands and croplands by integrating projections of LULC with a model that can project future deployment of solar PV with high spatial resolution for the conterminous United States. We used scenarios of LULC projections from the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios from 2010 to 2050 and two electricity grid scenarios to model future PV deployment and compared those results against a baseline that held 2010 land cover constant through 2050. Though solar PV's overall technical potential was minimally impacted by LULC scenarios, deployed PV varied by −16.5 to 11.6 % in 2050 from the baseline scenario. Total land requirements for projected PV were similar to other studies, but measures of PV impacts on natural systems depended on the underlying land change dynamics occurring in a scenario. The solar PV deployed through 2050 resulted in 1.1 %–2.4 % of croplands and 0.3 %–0.7 % of natural lands being converted to PV. However, the deepest understanding of PV impacts and interactions with land cover emerged when the complete net gains and losses from all land cover change dynamics, including PV, were integrated. For example, one of the four LULC projections allows for high solar development and a net gain in natural lands, even though PV drives a larger percentage of natural land conversion. This paper shows that integrating land cover change dynamics with energy expansion models generates new insights into trade offs between decarbonization, impacts of renewables, and ongoing land cover change.

conterminous United States↗
USGS2014

Land cover mapping of Greater Mesoamerica using MODIS data

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: ( i ) qualitative assessment provided good insight in identifying and correcting gross errors; ( ii ) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest ( r 2  = 0.88), shrubland ( r 2  = 0.75), and cropland ( r 2  = 0.97) but weak positive association for grassland ( r 2  = 0.26); and ( iii ) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Canadian Journal of Remote Sensing↗
USGS2024

Classifying plant communities in the North American Coastal Plain with PRISMA spaceborne hyperspectral imagery and the spectral mixture residual

The effort to map terrestrial biodiversity, in recent years limited mostly to the use of broadband multispectral remote sensing at decameter scales, can be greatly enhanced by harnessing hyperspectral imagery. Interpretation of hyperspectral imagery may be aided by the Mixture Residual (MR) spectral preprocessing transformation. MR integrates the benefits of spectral mixture analysis with the absorption peak-enhancing characteristics of continuum removal. MR characterizes each pixel as a linear combination of generic end-members estimating the spectral continuum, from which the residual of each wavelength is computed and treated as a source of additional information. Using Hyperspectral Precursor of the Application Mission (PRISMA) imagery, we tested the ability of MR-transformed reflectance as compared to untransformed surface reflectance (SR) to map plant associations and land cover using ground truthing and random forest classifications across four landscapes within the North American Coastal Plain. We used a forward stepwise selection algorithm to choose bands for each classification and subsequently compared these between SR and MR. Our MR classifications distinguished land cover with 5% greater balanced accuracy on average than the SR-based classifications across all four landscapes. The MR-based classification that integrated data from all landscapes into a unified model encompassing all 21 land cover types achieved a 76% average balanced accuracy over three iterations. Generally, MR utilized the near-infrared region to a greater degree than SR while deemphasizing the green peak. Based on our results, MR improves the accuracy of mapping terrestrial biodiversity, likely extending to other current and planned satellite hyperspectral missions.

JGR Biogeosciences↗
USGS2007

Chapter B. Physical, Chemical, and Biological Responses of Streams to Increasing Watershed Urbanization in the Piedmont Ecoregion of Georgia and Alabama, 2003

As part of the U.S. Geological Survey National Water-Quality Assessment Program?s effort to assess the physical, chemical, and biological responses of streams to urbanization, 30 wadable streams were sampled near Atlanta, Ga., during 2002?2003. Watersheds were selected to minimize natural factors such as geology, altitude, and climate while representing a range of urban development. A multimetric urban intensity index was calculated using watershed land use, land cover, infrastructure, and socioeconomic variables that are highly correlated with population density. The index was used to select sites along a gradient from low to high urban intensity. Response variables measured include stream hydrology and water temperature, instream habitat, field properties (pH, conductivity, dissolved oxygen, turbidity), nutrients, pesticides, suspended sediment, sulfate, chloride, Escherichia coli (E. coli) concentrations, and characterization of algal, invertebrate and fish communities. In addition, semipermeablemembrane devices (SPMDs)?passive samplers that concentrate hydrophobic organic contaminants such as polycyclicaromatic hydrocarbons (PAHs)?were used to evaluate water-quality conditions during the 4 weeks prior to biological sampling. Changes in physical, chemical, and biological conditions were evaluated using both nonparametric correlation analysis and nonmetric multidimensional scaling (MDS) ordinations and associated comparisons of dataset similarity matrices. Many of the commonly reported effects of watershed urbanization on streams were observed in this study, such as altered hydrology and increases in some chemical constituent levels. Analysis of water-chemistry data showed that specific conductance, chloride, sulfate, and pesticides increased as urbanization increased. Nutrient concentrations were not directly correlated to increases in development, but were inversely correlated to percent forest in the watershed. Analyses of SPMD-derived data showed that bioassays and certain chemical constituents such as pyrene and benzophenanthrene, both PAHs found in coal tar, were strongly correlated with measures of watershed urbanization. Hydrologic variability metrics indicated that as urban development increased, streams became flashier, with characteristic high flows having shorter duration. The hydrologic effects associated with urbanization were greatest during the fall and least apparent during the winter. No correlations were observed between increasing urbanization and stream temperature or changes in stream habitat. Algal, invertebrate, and fish communities exhibited statistically significant changes as watersheds became increasingly urban, with the strongest responses observed in the invertebrate community followed by fishes, then algal diatom communities. Invertebrate communities were the most responsive to increasing urbanization with Ephemeroptera, Plecoptera, and Tricoptera taxa, especially Plecoptera (stoneflies) responding negatively and most strongly to increasing urbanization. Invertebrate communities were influenced more significantly by water quality, although significant responses to altered hydrology also were noted. In terms of the fish community, the percentage of cyprinids present in the stream was the only Index of Biotic Integrity metric that responded negatively to increases in watershed urbanization. Fish community response to urbanization was intermediate relative to algae and invertebrates with respect to significant metric responses as well as the overall community response to increasing urbanization. Measures of hydrologic variability were the most influential environmental variables affecting the algal community. Although sites were originally chosen to represent a gradient of increasing urbanization, a cluster analysis performed on the component metrics of the urban index categorized sites into four distinct groups. Multivariate analysis based on nonmetric MDS and related analyses of data ma

Alabama, Georgia↗
USGS2012

Land-use and land-cover scenarios and spatial modeling at the regional scale

Land-use and land-cover (LULC) change has altered a large part of the earth's surface. Scenarios of potential future LULC change are required in order to better manage potential impacts on biodiversity, carbon fluxes, climate change, hydrology, and many other ecological processes. The U.S. Geological Survey is analyzing potential future LULC change in the United States, using an approach based on scenario construction and spatially explicit modeling. Similar modeling techniques are being used to produce historical LULC maps from 1940 to present. With the combination of backcast and forecast LULC data, the USGS is providing consistent LULC data for historical, current, and future time frames to support a variety of research applications.

Fact Sheet↗
USGS2025

Annual NLCD (National Land Cover Database)—The next generation of land cover mapping

Introduction The widely used National Land Cover Database (NLCD) has long been the foundational land cover source for scientists, resource managers, and decision makers across the United States. In 2024, a reinvention as Annual NLCD added the key improvement of annual time steps to show decades of change at a higher frequency than the intervals of 2–3 years used in the legacy NLCD. Annual NLCD was derived primarily from the long Landsat satellite data record, and it includes data from other sources. The first release in 2024 of Annual NLCD provides Collection 1.0 of products encompassing land cover and land change from 1985 through 2023 for the conterminous United States (CONUS). The Annual NLCD Collection 1.0 consists of six operational products that map the unique characteristics of land cover. A map created from Annual NLCD shows 16 land cover classes for the CONUS in 2023.

conterminous United States↗
USGS1999

Digital data used to relate nutrient inputs to water quality in the Chesapeake Bay watershed

Digital data sets were compiled by the U. S. Geological Survey (USGS) and used as input for a collection of Spatially Referenced Regressions On Watershed attributes for the Chesapeake Bay region. These regressions relate streamwater loads to nutrient sources and the factors that affect the transport of these nutrients throughout the watershed. A digital segmented network based on watershed boundaries serves as the primary foundation for spatially referencing total nitrogen and total phosphorus source and land-surface characteristic data sets within a Geographic Information System. Digital data sets of atmospheric wet deposition of nitrate, point-source discharge locations, land cover, and agricultural sources such as fertilizer and manure were created and compiled from numerous sources and represent nitrogen and phosphorus inputs. Some land-surface characteristics representing factors that affect the transport of nutrients include land use, land cover, average annual precipitation and temperature, slope, and soil permeability. Nutrient input and land-surface characteristic data sets merged with the segmented watershed network provide the spatial detail by watershed segment required by the models. Nutrient stream loads were estimated for total nitrogen, total phosphorus, nitrate/nitrite, amonium, phosphate, and total suspended soilds at as many as 109 sites within the Chesapeake Bay watershed. The total nitrogen and total phosphorus load estimates are the dependent variables for the regressions and were used for model calibration. Other nutrient-load estimates may be used for calibration in future applications of the models.

Open-File Report↗
USGS2021

Time marches on, but do the causal pathways driving instream habitat and biology remain consistent?

Stream ecosystems are complex networks of interacting terrestrial and aquatic drivers. To untangle these ecological networks, efforts evaluating the direct and indirect effects of landscape, climate, and instream predictors on biological condition through time are needed. We used structural equation modeling and leveraged a stream survey program to identify and compare important predictors driving condition of benthic macroinvertebrate and fish assemblages. We used data resampled 14 years apart at 252 locations across Maryland, USA. Sample locations covered a wide range of conditions that varied spatiotemporally. Overall, the relationship directions were consistent between sample periods, but their relative strength varied temporally. For benthic macroinvertebrates, we found that the total effect of natural landscape ( e.g ., elevation, longitude, latitude, geology) and land use ( i.e ., forest, development, agriculture) predictors was 1.4 and 1.5 times greater in the late 2010s compared to the 2000s. Moreover, the total effect of water quality ( e.g ., total nitrogen and conductivity) and habitat ( e.g ., embeddedness, riffle quality) was 1.2 and 4.8 times lower in the 2010s, respectively. For fish assemblage condition, the total effect of land use-land cover predictors was 2.3 times greater in the 2010s compared to the 2000s, while the total effect of local habitat was 1.4 times lower in the 2010s, respectively. As expected, we found biological assemblages in catchments with more agriculture and urban development were generally comprised of tolerant, generalist species, while assemblages in catchments with greater forest cover had more-specialized, less-tolerant species ( e.g., Ephemeroptera, Plecoptera, and Trichoptera taxa, clingers, benthic and lithophilic spawning fishes). Changes in the relative importance of landscape and land-use predictors suggest other correlated, yet unmeasured, proximal factors became more important over time. By untangling these ecological networks, stakeholders can gain a better understanding of the spatiotemporal relationships driving biological condition to implement management practices aimed at improving stream condition.

Maryland↗
USGS2023

Above- and belowground biomass carbon stock and net primary productivity maps for tidal herbaceous marshes of the United States

Accurate assessments of greenhouse gas emissions and carbon sequestration in natural ecosystems are necessary to develop climate mitigation strategies. Regional and national-level assessments of carbon sequestration require high-resolution data to be available for large areas, increasing the need for remote sensing products that quantify carbon stocks and fluxes. The Intergovernmental Panel on Climate Change (IPCC) provides guidelines on how to quantify carbon flux using land cover land change and biomass carbon stock information. Net primary productivity (NPP), carbon uptake, and storage in vegetation, can also be used to model net carbon sequestration and net carbon export from an ecosystem (net ecosystem carbon balance). While biomass and NPP map products for terrestrial ecosystems are available, there are currently no conterminous United States (CONUS) biomass carbon stock or NPP maps for tidal herbaceous marshes. In this study, we used peak soil adjusted vegetation index (SAVI) values, derived from Landsat 8 composites, and five other vegetation indices, plus a categorical variable for the CONUS region (Pacific Northwest, California, Northeast, Mid-Atlantic, South Atlantic-Gulf, or Everglades), to model spatially explicit aboveground peak biomass stocks in tidal marshes (i.e., tidal palustrine and estuarine herbaceous marshes) for the first time. Tidal marsh carbon conversion factors, root-to-shoot ratios, and vegetation turnover rates, were compiled from the literature and used to convert peak aboveground biomass to peak total (above- and belowground) biomass and NPP. An extensive literature search for aboveground turnover rates produced sparse and variable values; therefore, we used an informed assumption of a turnover rate of one crop per year for all CONUS tidal marshes. Due to the lack of turnover rate data, the NPP map is identical to the peak biomass carbon stock map. In reality, it is probable that turnover rate varies by region, given seasonal length differences; however, the NPP map provides the best available information on spatially explicit CONUS tidal marsh NPP. This study identifies gaps in the scientific knowledge, to support future studies in addressing this lack of turnover data. Across CONUS, average total peak biomass carbon stock in tidal marshes was 848 g C m −2 (871 g C m −2 in palustrine and 838 g C m −2 in estuarine marshes), and based on a median biomass turnover rate of 1, it is expected that the mean NPP annual flux for tidal marshes is similar (e.g., 848 g C m −2 y −1 ). Peak biomass carbon stocks in tidal marshes were lowest in the Florida Everglades region and highest in the California regions. These are the first fine-scale national maps of biomass carbon and NPP for tidal wetlands, spanning all of CONUS. These estimates of CONUS total peak biomass carbon stocks and NPP rates for tidal marshes can support regional- and national-scale assessments of greenhouse gas emissions, as well as natural resource management of coastal wetlands, as part of nature-based climate solution efforts.

Remote Sensing↗
USGS2012

Revolutionary land use change in the 21st century: Is (rangeland) science relevant?

Rapidly increasing demand for food, fiber, and fuel together with new technologies and the mobility of global capital are driving revolutionary changes in land use throughout the world. Efforts to increase land productivity include conversion of millions of hectares of rangelands to crop production, including many marginal lands with low resistance and resilience to degradation. Sustaining the productivity of these lands requires careful land use planning and innovative management systems. Historically, this responsibility has been left to agronomists and others with expertise in crop production. In this article, we argue that the revolutionary land use changes necessary to support national and global food security potentially make rangeland science more relevant now than ever. Maintaining and increasing relevance will require a revolutionary change in range science from a discipline that focuses on a particular land use or land cover to one that addresses the challenge of managing all lands that, at one time, were considered to be marginal for crop production. We propose four strategies to increase the relevance of rangeland science to global land management: 1) expand our awareness and understanding of local to global economic, social, and technological trends in order to anticipate and identify drivers and patterns of conversion; 2) emphasize empirical studies and modeling that anticipate the biophysical (ecosystem services) and societal consequences of large-scale changes in land cover and use; 3) significantly increase communication and collaboration with the disciplines and sectors of society currently responsible for managing the new land uses; and 4) develop and adopt a dynamic and flexible resilience-based land classification system and data-supported conceptual models (e.g., state-and-transition models) that represent all lands, regardless of use and the consequences of land conversion to various uses instead of changes in state or condition that are focused on a single land use.

Rangeland Ecology and Management↗