Geology topics

Bradley C. Reed

Publications and source records attributed to Bradley C. Reed.

At least 19 recordsLinked to original sources

USGS2020

Seasonality of biological and physical systems as indicators of climatic variation and change

Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change.

Climatic Change↗

USGS2020

Critical land change information enhances the understanding of carbon balance in the United States

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO 2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Global Change Biology↗

USGS2018

Federal lands greenhouse emissions and sequestration in the United States—Estimates for 2005–14

In January 2016, the Secretary of the U.S. Department of the Interior tasked the U.S. Geological Survey (USGS) with producing a publicly available and annually updated database of estimated greenhouse gas emissions associated with the extraction and use (predominantly some form of combustion) of fossil fuels from Federal lands. In response, the USGS has produced estimates of the greenhouse gas emissions resulting from the extraction and end-use combustion of fossil fuels produced on Federal lands in the United States, as well as estimates of ecosystem carbon emissions and sequestration on those lands. American Indian and Tribal lands were not included in this analysis. The emissions estimates span a 10-year period (2005–14) and are reported for 28 States and two offshore areas. Nationwide emissions from fossil fuels produced on Federal lands in 2014 were 1,279.0 million metric tons of carbon dioxide equivalent (MMT CO 2 Eq.) for carbon dioxide (CO 2 ), 47.6 MMT CO 2 Eq. for methane (CH 4 ), and 5.5 MMT CO 2 Eq. for nitrous oxide (N 2 O). Compared to 2005, the 2014 totals represent decreases in emissions for all three greenhouse gases (decreases of 6.1 percent for CO 2 , 10.5 percent for CH 4 , and 20.3 percent for N 2 O). Emissions from fossil fuels produced on Federal lands represent, on average, 23.7 percent of national emissions for CO 2 , 7.3 percent for CH 4 , and 1.5 percent for N 2 O over the 10 years included in this estimate. In 2005, Federal lands of the conterminous United States stored 82,289 MMT CO 2 Eq. in terrestrial ecosystems. By 2014, carbon storage, or sequestration, was estimated at 83,600 MMT CO 2 Eq., representing an increase of 1.6 percent, or 1,311 MMT CO 2 Eq. Soils stored most of the ecosystem carbon (63 percent), followed by live vegetation (26 percent) and dead organic matter (11 percent). The rate of net carbon uptake in ecosystems ranged from a sink (sequestration) of 475 million metric tons of carbon dioxide per year (MMT CO 2 Eq./yr) to a source (emission) of 51 MMT CO 2 Eq./yr because of annual variability in climate and weather, rates of land-use and land-cover change, and wildfire frequency, among other factors. At the national level, the USGS estimates that terrestrial ecosystems (forests, grasslands, and shrublands) on Federal lands sequestered an average of 195 MMT CO 2 Eq./yr between 2005 and 2014, offsetting approximately 15 percent of the CO 2 emissions resulting from the extraction of fossil fuels on Federal lands and their end-use combustion. The USGS estimates presented in this report represent a first-of-its-kind accounting for the emissions resulting from fossil fuel extraction on Federal lands and the end-use combustion of those fuels, as well as for the sequestration of carbon in terrestrial ecosystems on Federal lands. The net CO 2 emissions estimate, which is the difference between the emitted and sequestered CO 2 , provides an informative combined result describing the emissions (fossil fuel extraction and end-use combustion) associated with a State’s Federal lands and sequestration on those same lands. The estimates included in this report can provide context for future energy decisions, as well as a basis to track change in the future.

Scientific Investigations Report↗

USGS2012

Baseline and projected future carbon storage and greenhouse-gas fluxes in ecosystems of the Western United States

This assessment was conducted to fulfill the requirements of section 712 of the Energy Independence and Security Act (EISA) of 2007 and to improve understanding of carbon and greenhouse gas (GHG) fluxes in ecosystems of the Western United States. The assessment examined carbon storage, carbon fluxes, and other GHG fluxes (methane and nitrous oxide) in all major terrestrial ecosystems (forests, grasslands/shrublands, agricultural lands, and wetlands) and aquatic ecosystems (rivers, streams, lakes, reservoirs, and coastal waters) in two time periods: baseline (generally in the first half of the 2010s) and future (projections from baseline to 2050). The assessment was based on measured and observed data collected by the U.S. Geological Survey (USGS) and many other agencies and organizations and used remote sensing, statistical methods, and simulation models.

Arizona;California;Colorado;Idaho;Montana;Nevada;N↗

USGS2011

The contribution of C3 and C4 grasses to interannual variability in time-integrated NDVI over the U.S. Great Plains

Time integrated normalized difference vegetation index (ΣNDVI) derived from NOAA AVHRR multitemporal imagery over a 10-yr period (1989-1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern U.S. Great Plains. First, the contribution of C 3 and C 4 species abundances to the major grassland ecosystems of the U.S. Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxi for interannual climate variability is analyzed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C 3 - and C 4 -dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C4 grasslands), whereas the C 3 -dominated classes in the northern and eastern portion of the U.S. Great Plains generally show lower CV ΣNDVI values.

central and northern Great Plains↗

USGS2010

Surface phenology and satellite sensor-derived onset of greenness: An initial comparison

The objective of this work was to document the utility of phenological data derived from satellite sensors by comparing them with modelled phenology. Surface phenological model outputs (first leaf and first bloom dates) were correlated positively with satellite sensor-derived start of season (SOS) dates for 1991-1995 across the eastern United States. The correlation was highest for forest (r 0.62 for deciduous trees and 0.64 for mixed woodland) and tall grass (r 0.46) and lowest for short grass (r 0.37). The average correlation over all land cover types was 0.61. Average SOS dates were consistently earlier than Spring Index dates across all land cover types. This finding and limited native tree phenology data suggest that the SOS technique detects understorey green-up in the forest rather than overstorey species. The biweekly temporal resolution of the satellite sensor data placed an upper limit on prediction accuracy; thus, year-to-year variations at individual sites were typically small. Nevertheless, the correct biweek SOS could be identified from the surface models 61% of the time, and 1 biweek 96% of the time. Further temporal refinement of the satellite sensor measurements is necessary in order to connect them with surface phenology adequately and to develop links among 'green wave' components in selected biomes.

International Journal of Remote Sensing↗

USGS2010

The role of C 3 and C 4 grasses to interannual variability in remotely sensed ecosystem performance over the US Great Plains

Time integrated normalized difference vegetation index (ΣNDVI) derived from National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) multi-temporal imagery over a 10-year period (1989–1998) was used as a surrogate for primary production to investigate the impact of interannual climate variability on grassland performance for central and northern US Great Plains. First, the contribution of C 3 and C 4 species abundance to the major grassland ecosystems of the US Great Plains is described. Next, the relation between mean ΣNDVI and the ΣNDVI coefficient of variation (CV ΣNDVI) used as a proxy for interannual climate variability is analysed. Results suggest that the differences in the long-term climatic control over ecosystem performance approximately coincide with changes between C 3 - and C 4 -dominant grassland classes. Variation in remotely sensed net primary production over time is higher for the southern and western plains grasslands (primarily C 4 grasslands), whereas the C 3 -dominated classes in the northern and eastern portion of the US Great Plains, generally show lower CV ΣNDVI values.

International Journal of Remote Sensing↗

USGS2010

Trends in night-time city lights and vegetation indices associated with urbanization within the conterminous USA

Two datasets that depict the night-time light emitted from the conterminous USA during 1992/1993 and 2000 were compared for changes in light emission. The locations of observed differences in night-time light during this interval were examined for differences observed in a time-integrated vegetation index associated with net primary production. Just over 13% of the land area within the study region exhibited greater night-time light emitted in 2000 compared to 1992/1993. The locations of greater emitted light were found to have decreased values of the time-integrated vegetation index compared to locations that did not exhibit significant increases in emitted light. The observed decrease in the time-integrated vegetation index within the regions of greater emitted light is likely to be due to the change in land cover (increased urbanization) during this interval. The results suggest that the emitted light data were more useful for assessment of urban growth than the integrated vegetation index data.

International Journal of Remote Sensing↗

USGS2010

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Remote Sensing↗

USGS2009

Integration of MODIS-derived metrics to assess interannual variability in snowpack, lake ice, and NDVI in southwest Alaska

Impacts of global climate change are expected to result in greater variation in the seasonality of snowpack, lake ice, and vegetation dynamics in southwest Alaska. All have wide-reaching physical and biological ecosystem effects in the region. We used Moderate Resolution Imaging Spectroradiometer (MODIS) calibrated radiance, snow cover extent, and vegetation index products for interpreting interannual variation in the duration and extent of snowpack, lake ice, and vegetation dynamics for southwest Alaska. The approach integrates multiple seasonal metrics across large ecological regions. Throughout the observation period (2001-2007), snow cover duration was stable within ecoregions, with variable start and end dates. The start of the lake ice season lagged the snow season by 2 to 3??months. Within a given lake, freeze-up dates varied in timing and duration, while break-up dates were more consistent. Vegetation phenology varied less than snow and ice metrics, with start-of-season dates comparatively consistent across years. The start of growing season and snow melt were related to one another as they are both temperature dependent. Higher than average temperatures during the El Ni??o winter of 2002-2003 were expressed in anomalous ice and snow season patterns. We are developing a consistent, MODIS-based dataset that will be used to monitor temporal trends of each of these seasonal metrics and to map areas of change for the study area.

Remote Sensing of Environment↗

USGS2007

Evolving plans for the USA National Phenology Network

Phenology is the study of periodic plant and animal life cycle events, how these are influenced by seasonal and interannual variations in climate, and how they modulate the abundance, diversity, and interactions of organisms. The USA National Phenology Network (USA-NPN) is currently being organized to engage federal agencies, environmental networks and field stations, educational institutions, and citizen scientists. The first USA-NPN planning workshop was held August 2005, in Tucson, Ariz. (Betancourt et al. [2005]; http://www.uwm.edu/Dept/Geography/npn/; by 1 June 2007, also see http://www.usanpn.org). With sponsorship from the U.S. National Science Foundation, the U.S. Geological Survey (USGS), the U.S. Fish and Wildlife Service, and NASA, the second USA-NPN planning workshop was held at the University of Wisconsin-Milwaukee on 10–12 October 2006 to (1) develop lists of target species and observation protocols; (2) identify existing networks that could comprise the backbone of nationwide observations by 2008; (3) develop opportunities for education, citizen science, and outreach beginning in spring 2007; (4) design strategies for implementing the remote sensing component of USA-NPN; and (5) draft a data management and cyberinfrastructure plan.

Eos, Transactions, American Geophysical Union↗

USGS2006

Vegetation greenness impacts on maximum and minimum temperatures in northeast Colorado

The impact of vegetation on the microclimate has not been adequately considered in the analysis of temperature forecasting and modelling. To fill part of this gap, the following study was undertaken. A daily 850–700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalised Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989–98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (r 2 value) of surface maximum and minimum temperature by only the 850–700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850–700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the r 2 values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March–October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.

Meteorological Applications↗

USGS2006

Range condition as input to water quality monitoring in the northern Plains

Federal Clean Water Act requires that states develop Total Maximum Daily Loads (TMDLs) for water bodies. Once the state has developed an inventory of TMDLs, it is required to provide public notice of the report and have it approved by the Environmental Protection Agency. The South Dakota Department of Environment and Natural Resources (DENR) is using the USDA’s annualized Agricultural Non-Point Source Pollution Model to determine what land use changes are required to meet TMDL goals (South Dakota DENR, 2006). Of the approximately 450 parameters required for running the model, several are related to the condition of range and pasture sites and their respective management practices. Range condition is highly correlated with the nature of runoff occurring in a site. In addition to assisting the DENR, USGS is interested in improving its overall ability to monitor Northern Plains range condition with particular emphasis on the seven state study area (Colorado, Kansas, Montana, Nebraska, North Dakota, South Dakota, and Wyoming) being addressed by the Drought Monitoring, Carbon Cycle Research, Phenological Trends and other projects. It is understood than no one project can develop tools that adequately characterize the dynamics of the region’s rangelands, but by developing a suite of tools brought together from a number of projects there exists the opportunity to provide state, regional, and tribal land managers with the ability to address their particular needs.

Colorado, Kansas, Montana, Nebraska, North Dakota,↗

USGS2005

Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems.

Remote Sensing of Environment↗

USGS2005

Characteristics of seasonal vegetation cover in the conterminous USA

A data set of the fractional green vegetation cover (FGREEN) for the Conterminous USA was evaluated for regional and seasonal variation. The value of FGREEN was derived monthly for the three most dominant land cover classes per 20 km by 20 km grid cell within the study area. At this grid cell resolution (comprised of 400 1-km pixels), 97 percent of the grid cells included three or fewer land cover classes. FGREEN was found to vary regionally due to local land cover and climate variations. FGREEN was found significantly different between one or more of the land cover classes, for one or more months, in 58 percent of the grid cells included in the study. Monthly FGREEN values for the land cover classes vary sufficiently between the land cover classes to warrant monthly FGREEN data for each of the one to three most dominant land cover classes per grid cell .

Photogrammetric Engineering and Remote Sensing↗

USGS2004

Comparison of MODIS and AVHRR 16-day normalized difference vegetation index composite data

Normalized difference vegetation index (NDVI) data derived from visible and near-infrared data acquired by the MODIS and AVHRR sensors were compared over the same time periods and a variety of land cover classes within the conterminous USA. The relationship between the AVHRR derived NDVI values and those of future sensors is critical to continued long term monitoring of land surface properties. The results indicate that the 16-day composite values are quite similar over the 23 intervals of 2001 that were analyzed, and a linear relationship exists between the NDVI values from the two sensors. The composite AVHRR NDVI data were associated with over 90% of the variation in the MODIS NDVI values. Copyright 2004 by the American Geophysical Union.

Geophysical Research Letters↗

USGS2003

Monitoring vegetation phenology using MODIS

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

Remote Sensing of Environment↗

USGS2003

Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem

The net ecosystem exchange (NEE) of carbon flux can be partitioned into gross primary productivity (GPP) and respiration ( R ). The contribution of remote sensing and modeling holds the potential to predict these components and map them spatially and temporally. This has obvious utility to quantify carbon sink and source relationships and to identify improved land management strategies for optimizing carbon sequestration. The objective of our study was to evaluate prediction of 14-day average daytime CO 2 fluxes ( F day ) and nighttime CO 2 fluxes ( R n ) using remote sensing and other data. F day and R n were measured with a Bowen ratio–energy balance (BREB) technique in a sagebrush ( Artemisia spp.)–steppe ecosystem in northeast Idaho, USA, during 1996–1999. Micrometeorological variables aggregated across 14-day periods and time-integrated Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (iNDVI) were determined during four growing seasons (1996–1999) and used to predict F day and R n . We found that iNDVI was a strong predictor of F day ( R 2 =0.79, n =66, P <0.0001). Inclusion of evapotranspiration in the predictive equation led to improved predictions of F day ( R 2 =0.82, n =66, P <0.0001). Crossvalidation indicated that regression tree predictions of F day were prone to overfitting and that linear regression models were more robust. Multiple regression and regression tree models predicted R n quite well ( R 2 =0.75–0.77, n =66) with the regression tree model being slightly more robust in crossvalidation. Temporal mapping of F day and R n is possible with these techniques and would allow the assessment of NEE in sagebrush–steppe ecosystems. Simulations of periodic F day measurements, as might be provided by a mobile flux tower, indicated that such measurements could be used in combination with iNDVI to accurately predict F day . These periodic measurements could maximize the utility of expensive flux towers for evaluating various carbon management strategies, carbon certification, and validation and calibration of carbon flux models.

Idaho↗