Geology topics

Bradley C. Reed

Publications and source records attributed to Bradley C. Reed.

28 records · Page 2Linked to original sources

USGS2002

Assessing satellite-derived start-of-season measures in the conterminous USA

National Oceanic and Atmospheric Administration (NOAA)-series satellites, carrying advanced very high-resolution radiometer (AVHRR) sensors, have allowed moderate resolution (1 km) measurements of the normalized difference vegetation index (NDVI) to be collected from the Earth's land surfaces for over 20 years. Across the conterminous USA, a readily accessible and decade-long data set is now available to study many aspects of vegetation activity in this region. One feature, the onset of deciduous plant growth at the start of the spring season (SOS) is of special interest, as it appears to be crucial for accurate computation of several important biospheric processes, and a sensitive measure of the impacts of global change. In this study, satellite-derived SOS dates produced by the delayed moving average (DMA) and seasonal midpoint NDVI (SMN) methods, and modelled surface phenology (spring indices, SI) were compared at widespread deciduous forest and mixed woodland sites during 1990–93 and 1995–99, and these three measures were also matched to native species bud-break data collected at the Harvard Forest (Massachusetts) over the same time period. The results show that both SOS methods are doing a modestly accurate job of tracking the general pattern of surface phenology, but highlight the temporal limitations of biweekly satellite data. Specifically, at deciduous forest sites: (1) SMN SOS dates are close in time to SI first bloom dates (average bias of +0.74 days), whereas DMA SOS dates are considerably earlier (average bias of −41.24 days) and also systematically earlier in late spring than in early spring; (2) SMN SOS tracks overall yearly trends in deciduous forests somewhat better than DMA SOS, but with larger average error (MAEs 8.64 days and 7.37 days respectively); and (3) error in both SOS techniques varies considerably by year. Copyright © 2002 Royal Meteorological Society.

International Journal of Climatology↗

USGS2002

A prototype drought monitoring system integrating climate and satellite data

Droughts are natural hazards with varying patterns in space, time, and intensity. Their dynamic character challenges our ability in planning, predicting, monitoring, and providing relief to affected areas. Because of the spatial and temporal variability and multiple impacts of droughts, we need to improve the tools and data available for mapping and monitoring this phenomenon on all scales. A team of researchers from the US Geological Survey’s EROS Data Center, the National Drought Mitigation Center, and the High Plains Regional Climate Center are developing methods for regional-scale mapping and monitoring drought conditions for the conterminous U.S. Currently in its first year, the project is focusing on developing a prototype model for the central U.S. The ultimate goal of the project is to deliver timely geo-referenced information (in the form of maps and data) about areas where the vegetation is impacted by drought, using the Internet as the primary delivery mechanism. Data collected from the Advanced Very High Resolution Radiometer (AVHRR) sensor provide synoptic, near real time measurements of surface conditions. Previous studies have established significant relationships between climate variables and satellite-derived vegetation indices over non-irrigated croplands and grasslands. We are researching methods for integrating information provided by satellite-derived metrics on seasonal vegetation performance and climate-based drought indicators to produce a timely and spatially-detailed drought monitoring product. Eventually, this information, coupled with map products of key drought indicators, will be available to many end users for making critical and timely decisions, from farm to regional scale.

Conference Paper↗

USGS2002

Integrating satellite and climate data for U.S. drought mapping and monitoring: First steps

Although droughts are normal, recurring climate phenomena, they challenge our current ability to plan, predict, monitor, and provide relief to drought stricken areas. Because of the spatial and temporal variability of droughts, we need to improve the tools available to map and monitor them on many scales from local to national. A team of researchers from the US Geological Survey’s EROS Data Center, the National Drought Mitigation Center, and the High Plains Regional Climate Center are developing a prototype system for regional-scale drought monitoring for the conterminous US. This project is in its first year of development. The ultimate goal is to deliver near real-time geo-referenced information (in the form of maps and data) about drought-impacted areas in the US, using the Internet as a primary delivery mechanism. For the pilot study, the project team is developing methods to integrate satellite data and traditional climate data over the central US. Although, these two information sources reflect different spatial resolutions they should prove complementary for the mapping goals of the project. During the summer of 2002, much of the Great Plains and the Southwest U.S. experienced drought conditions. We initiated a case study in South Dakota and Nebraska to develop and test methods.

Nebraska, South Dakota↗

USGS2001

Monthly fractional green vegetation cover associated with land cover classes of the conterminous USA

The land cover classes developed under the coordination of the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) have been analyzed for a study area that includes the Conterminous United States and portions of Mexico and Canada. The 1-km resolution data have been analyzed to produce a gridded data set that includes within each 20-km grid cell: 1) the three most dominant land cover classes, 2) the fractional area associated with each of the three dominant classes, and 3) the fractional area covered by water. Additionally, the monthly fraction of green vegetation cover (fgreen) associated with each of the three dominant land cover classes per grid cell was derived from a 5-year climatology of 1-km resolution NOAA-AVHRR data. The variables derived in this study provide a potential improvement over the use of monthly fgreen linked to a single land cover class per model grid cell.

Geophysical Research Letters↗

USGS2001

A Landsat 7 scene selection strategy for a national land cover database

A strategy for selecting Landsat 7 ETM+ imagery for development of a new generation national land cover database of the United States has been developed. This strategy is formulated to target Landsat 7 ETM+ scenes based on land cover and land use, vegetation phenology and image quality (cloudiness, haze). Criteria based on phenology and scene quality provide a national baseline for acquiring Landsat 7 data. Optimal time periods for discriminating land cover types were identified for each Landsat 7 path-row footprint and each proposed land cover mapping zone (mosaic of several path-rows based on landscape and ecoregion), from which three Landsat scenes were selected. This database of selected scenes is used to guide Landsat 7 data purchasing. This methodology provides a consistent framework for populating Landsat 7 imagery to be used for a new national land cover characterization initiative.

Conference Paper↗

USGS1999

A weighted least-squares approach to temporal NDVI smoothing

Satellite imagery provides a unique vantage point for observing seasonal dynamics of the landscape that have implications for global change issues. An objective evaluation of surface conditions may be performed using the normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration advanced very high resolution radiometer data. NDVI data are typically very noisy, affected by a number of phenomena including cloud contamination, atmospheric perturbations, and variable illumination and viewing geometry, each of which usually reduces the NDVI. This work describes a weighted least-squares linear regression approach to temporal NDVI smoothing to more efficiently reduce contamination in the NDVI signal. This approach uses a moving window operating on temporal NDVI to calculate a regression line. The window is moved one period at a time, resulting in a family of regression lines associated with each point; this family of lines is then averaged at each point and interpolated between points to provide a continuous temporal NDVI signal. Also, since the factors that cause contamination usually serve to reduce NDVI values, the system applies a weighting factor that favors peak points over sloping or valley points. A final operation assures that all peak NDVI values are retained. The resulting relationship between the smoothed curve and the original data is statistically based. The smoothed data may be used to improve applications involving time-series NDVI data, such as land cover classification, seasonal vegetation characterization, and vegetation monitoring

Conference Paper↗

USGS1998

An analysis of relationships among climate forcing and time-integrated NDVI of grasslands over the U.S. northern and central Great Plains

Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989–1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negative correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R 2 ) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models . The photosynthetic pathway is an important determinant of grassland performance with northern mixed prairie (mixture of C 3 and C 4 grassland) TI NDVI affected by both thermal and moisture conditions during the growing season while southern plains grasslands (primarily C 4 grassland) were predominantly influenced by spring and summer precipitation. Grassland land cover classes associated with sandy soils also demonstrated a strong relationship between TI NDVI and growing season rainfall. Significant impact of interannual climate variability on the TI NDVI–climate relationship was also observed. The study suggests an integrated approach involving numerical models, satellite remote sensing, and field observations to monitor grassland ecosystem dynamics on a regional scale.

Remote Sensing of Environment↗

USGS1997

Seasonal vegetation characteristics of the United States

The U.S. Geological Survey's EROS Data Center has created a prototype 1‐km resolution data base of vegetation seasonal characteristics. The characteristics are derived from time‐series NDVI data collected by the AVHRR satellite sensor. Information covering the 5 years 1989–1993 is included in the data base. Although quantitative validation of the seasonal characteristics cannot be made until several evaluation efforts are completed, general observations are possible by viewing images of the seasonal parameters. Figures 2 through 8 show several examples of the seasonal characteristics data base.

Geocarto International↗

USGS1997

NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes

The distributions of C 3 and C 4 grasses were used to interpret the distribution, seasonal performance, and potential production of grasslands in the Great Plains of North America. Thirteen major grassland seasonal land cover classes were studied with data from three distinct sources. Normalized Difference Vegetation Index (NDVI) data derived from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensor were collected for each pixel over a 5-yr period (1989–1993), analyzed for quantitative attributes and seasonal relationships, and then aggregated by land cover class. Data from the State Soil Geographic (STATSGO) database were used to identify dominant plant species contributing to the potential production in each map unit. These species were identified as C 3 or C 4 , and contributions to production were aggregated to provide estimates of the percentage of C 3 and C 4 production for each intersection of the STATSGO map units and the seasonal land cover classes. Carbon isotope values were obtained at specific sites from the soil organic matter of the upper horizon of soil cores and were related to STATSGO estimates of potential production. The grassland classes were distributed with broad northwest-to-southeast orientations. Some classes had large variations in C 3 and C 4 composition with high proportions of C 4 species in the south and low proportions in the north. This diversity of photosynthetic types within land cover classes that cross regions of different temperature and precipitation results in similar seasonal patterns and magnitudes of NDVI. The easternmost class, 65, containing tallgrass prairie components, bluestem, Indiangrass, and switchgrass, possessed the highest maximum NDVI and time-integrated NDVI values each year. Grassland classes varied over 5 yr from a high integrated NDVI mean of 4.9 in class 65 in the east to a low of 1.2 in class 76 (sand sage, blue grama, wheatgrass, and buffalograss) in the southwest. Although environmental conditions varied widely during the 5 yr, the rankings of class performance were consistent across years for these NDVI metrics. Land cover classes were less consistent in time of onset, which was often earlier in areas in the north dominated by C 3 grasses than in areas to the south dominated by C 4 grasses. At the level of seasonal land cover classes, no significant relationship was found between the proportions of C 3 and C 4 species and estimates of potential production derived from the STATSGO database or inferred from the seasonal patterns of NDVI. The isotopic data from specific sites and the potential production data from STATSGO suggest similar patterns of high proportional production by C 4 species throughout the south and a decline in proportional production north of the central Great Plains. The land cover classes integrate ecosystem units that encompass a wide diversity of species and C 3 and C 4 proportions and provide a classification that consistently captures significant ecosystem parameters for the Great Plains.

Colorado, Kansas, Montana, Nebraska, New Mexico, N↗

USGS1993

Integration of environmental simulation models with satellite remote sensing and geographic information systems technologies: case studies

Environmental modelers are testing and evaluating a prototype land cover characteristics database for the conterminous United States developed by the EROS Data Center of the U.S. Geological Survey and the University of Nebraska Center for Advanced Land Management Information Technologies. This database was developed from multi temporal, 1-kilometer advanced very high resolution radiometer (AVHRR) data for 1990 and various ancillary data sets such as elevation, ecological regions, and selected climatic normals. Several case studies using this database were analyzed to illustrate the integration of satellite remote sensing and geographic information systems technologies with land-atmosphere interactions models at a variety of spatial and temporal scales. The case studies are representative of contemporary environmental simulation modeling at local to regional levels in global change research, land and water resource management, and environmental simulation modeling at local to regional levels in global change research, land and water resource management and environmental risk assessment. The case studies feature land surface parameterizations for atmospheric mesoscale and global climate models; biogenic-hydrocarbons emissions models; distributed parameter watershed and other hydrological models; and various ecological models such as ecosystem, dynamics, biogeochemical cycles, ecotone variability, and equilibrium vegetation models. The case studies demonstrate the important of multi temporal AVHRR data to develop to develop and maintain a flexible, near-realtime land cover characteristics database. Moreover, such a flexible database is needed to derive various vegetation classification schemes, to aggregate data for nested models, to develop remote sensing algorithms, and to provide data on dynamic landscape characteristics. The case studies illustrate how such a database supports research on spatial heterogeneity, land use, sensitivity analysis, and scaling issues involving regional extrapolations and parameterizations of dynamic land processes within simulation models.

Pecora 12 Symposium↗