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B. C. Reed

Publications and source records attributed to B. C. Reed.

15 recordsLinked to original sources
USGS2013

The Vegetation Drought Response Index (VegDRI): A new integrated approach for monitoring drought stress in vegetation

The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring.

GIScience and Remote Sensing↗
USGS2011

Implementing a U.S. national phenology network

The passing of seasons, as gauged by annual events or phenophases in organisms' life cycles, is arguably one of the most pervasive environmental variations on Earth. Shifts in seasonal timing, or phenology, are observed in flowering and other stages of plant development, animal migration and reproduction, hibernation, and the seasonal activity of cold-blooded animals [e.g., Schwartz , 2003; Root et al ., 2005]. As an important life history trait, phenology is an object of natural selection; depending on timescales, shifts in phenology can lead to evolutionary change. Thus, phenology is not only an indicator of pattern in environmental science, but also its variation has fitness consequences for individuals, and these can scale up to broader ecological dynamics.

Eos, Transactions, American Geophysical Union↗
USGS2010

Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data

Researchers from the U.S. Geological Survey, University of Nebraska-Lincoln and the European Commission's Joint Research Centre, Ispra, Italy produced a 1 km resolution global land cover characteristics database for use in a wide range of continental-to global-scale environmental studies. This database provides a unique view of the broad patterns of the biogeographical and ecoclimatic diversity of the global land surface, and presents a detailed interpretation of the extent of human development. The project was carried out as an International Geosphere-Biosphere Programme, Data and Information Systems (IGBP-DIS) initiative. The IGBP DISCover global land cover product is an integral component of the global land cover database. DISCover includes 17 general land cover classes defined to meet the needs of IGBP core science projects. A formal accuracy assessment of the DISCover data layer will be completed in 1998. The 1 km global land cover database was developed through a continent-by-continent unsupervised classification of 1 km monthly Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) composites covering 1992-1993. Extensive post-classification stratification was necessary to resolve spectral/temporal confusion between disparate land cover types. The complete global database consists of 961 seasonal land cover regions that capture patterns of land cover, seasonality and relative primary productivity. The seasonal land cover regions were aggregated to produce seven separate land cover data sets used for global environmental modelling and assessment. The data sets include IGBP DISCover, U.S. Geological Survey Anderson System, Simple Biosphere Model, Simple Biosphere Model 2, Biosphere-Atmosphere Transfer Scheme, Olson Ecosystems and Running Global Remote Sensing Land Cover. The database also includes all digital sources that were used in the classification. The complete database can be sourced from the website: http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html.

International Journal of Remote Sensing↗
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

Trend analysis of time-series phenology of North America derived from satellite data

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

GIScience and Remote Sensing↗
USGS2005

Trend analysis of time-series phenology derived from satellite data

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface for the past 15 years. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land surface processes over large areas. Repeat observations from satellite-borne multispectral sensors provide a mechanism to move from plant-specific to regional scale studies of phenology. In addition, we now have sufficient time-series (since 1982 at 8-km resolution covering the globe and since 1989 at 1-km resolution over the conterminous US) to study seasonal and interannual trends from satellite data. Phenology metrics including start of season, end of season, duration of season, and seasonally integrated greenness were derived from 8 km AVHRR data over North America spanning the years 1982-2003. Trend analysis was performed on the annual summaries of the metrics to determine areas with increasing or decreasing trends for the time period under study. Results show only small areas of changing start of season, but the end of season is coming later over well defined areas of New England and SE Canada, principally as a result of land use changes. The total greenness metric is most striking at the shrub/tundra boundary of North America, indicating increasing vegetation vigor or possible vegetation conversion as a result of warming. ?? 2005 IEEE.

Conference Paper↗
USGS2004

Intra-seasonal mapping of CO 2 flux in rangelands of northern Kazakhstan at one-kilometer resolution

Algorithms that establish relationships between variables obtained through remote sensing and geographic information system (GIS) technologies are needed to allow the scaling up of site-specific CO2 flux measurements to regional levels. We obtained Bowen ratio-energy balance (BREB) flux tower measurements during the growing seasons of 1998-2000 above a grassland steppe in Kazakhstan. These BREB data were analyzed using ecosystem light-curve equations to quantify 10-day CO2 fluxes associated with gross primary production (GPP) and total respiration (R). Remotely sensed, temporally smoothed normalized difference vegetation index (NDVIsm) and environmental variables were used to develop multiple regression models for the mapping of 10-day CO2 fluxes for the Kazakh steppe. Ten-day GPP was estimated (R 2 = 0.72) by day of year (DOY) and NDVIsm, and 10-day R was estimated (R2 = 0.48) with the estimated GPP and estimated 10-day photosynthetically active radiation (PAR). Regression tree analysis estimated 10-day PAR from latitude, NDVIsm, DOY, and precipitation (R2 = 0.81). Fivefold cross-validation indicated that these algorithms were reasonably robust. GPP, R, and resulting net ecosystem exchange (NEE) were mapped for the Kazakh steppe grassland every 10 days and summed to produce regional growing season estimates of GPP, R, and NEE. Estimates of 10-day NEE agreed well with BREB observations in 2000, showing a slight underestimation in the late summer. Growing season (May to October) mean NEE for Kazakh steppe grasslands was 1.27 Mg C/ha in 2000. Winter flux data were collected during the winter of 2001-2002 and are being analyzed to close the annual carbon budget for the Kazakh steppe. ?? 2004 Springer-Verlag New York, LLC.

Environmental Management↗
USGS2002

Issues in characterizing phenology from satellite observations

Over the past decade, many investigators have published techniques for deriving phenological parameters, especially the start of the growing season (SOS), from time-series satellite imagery. The principal satellite sensor for these studies is the advanced very high resolution radiometer (AVHRR). This study investigates the characteristics of four of the primary methods for identifying SOS; maximum curvature, delayed moving average, time of greatest NDVI increase and time of half-maximum NDVI. Comparisons between the methods illustrate biases for earlier or later SOS in the various methods and a relatively low statistical relationship among all four methods (r 2 =.10 to .35). Our analysis indicates that each of the methods is detecting fundamentally different stages of the start of season. These stages include snow melt, initial growth of herbaceous plants and understory, and first leaf; each of which is important in determining differences in land/atmosphere interactions. Users of these types of datasets need to be aware of the different environmental conditions being measured by the various approaches and use careful judgment in selecting the proper method.

Conference Paper↗
USGS2001

MODIS land data at the EROS data center DAAC

The US Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center (EDC) in Sioux Falls, SD, USA, is the primary national archive for land processes data and one of the National Aeronautics and Space Administration's (NASA) Distributed Active Archive Centers (DAAC) for the Earth Observing System (EOS). One of EDC's functions as a DAAC is the archival and distribution of Moderate Resolution Spectroradiometer (MODIS) Land Data collected from the Earth Observing System (EOS) satellite Terra. More than 500,000 publicly available MODIS land data granules totaling 25 Terabytes (Tb) are currently stored in the EDC archive. This collection is managed, archived, and distributed by EOS Data and Information System (EOSDIS) Core System (ECS) at EDC. EDC User Services support the use of MODIS Land data, which include land surface reflectance/albedo, temperature/emissivity, vegetation characteristics, and land cover, by responding to user inquiries, constructing user information sites on the EDC web page, and presenting MODIS materials worldwide.

Conference Paper↗
USGS2001

Interactive visualization of vegetation dynamics

Satellite imagery provides a mechanism for observing seasonal dynamics of the landscape that have implications for near real-time monitoring of agriculture, forest, and range resources. This study illustrates a technique for visualizing timely information on key events during the growing season (e.g., onset, peak, duration, and end of growing season), as well as the status of the current growing season with respect to the recent historical average. Using time-series analysis of normalized difference vegetation index (NDVI) data from the advanced very high resolution radiometer (AVHRR) satellite sensor, seasonal dynamics can be derived. We have developed a set of Java-based visualization and analysis tools to make comparisons between the seasonal dynamics of the current year with those from the past twelve years. In addition, the visualization tools allow the user to query underlying databases such as land cover or administrative boundaries to analyze the seasonal dynamics of areas of their own interest. The Java-based tools (data exploration and visualization analysis or DEVA) use a Web-based client-server model for processing the data. The resulting visualization and analysis, available via the Internet, is of value to those responsible for land management decisions, resource allocation, and at-risk population targeting.

Conference Paper↗
USGS1996

An approach for using AVHRR data to monitor U.S. great plains grasslands

Environmental monitoring requires regular observations regarding the status of the landscape- The concept behind most monitoring efforts using satellite data involve deriving normalized difference vegetation index (NDVI) values or accumulating the NDVI over a specified time period. These efforts attempt to estimate the continuous growth of green biomass by using continuous additions of NDVI as a surrogate measure. To build upon this concept, this study proposes three refinements; 1) use an objective definition of the current growing season to adjust the time window during which the NDVI is accumulated, 2) accumulate only the NDVI values which are affected by green vegetation, and 3) base monitoring units upon land cover type. These refinements improve the sensitivity of detecting interannual vegetation variability, reduce the need for extensive and detailed knowledge of ground conditions and crop calendars, provide a framework in which several types of monitoring can take place over diverse land cover types, and provide an objective time frame during which monitoring takes place.

Geocarto International↗
USGS1994

Designing global land cover databases to maximize utility—the US prototype

One of the most pressing problems in global climate and ecosystem studies is a lack of adequate land cover data. Staff from the United States Geological Survey's EROS Data Center and the University of Nebraska have developed a United States prototype for a proposed global land cover characteristics database derived from 1-km Advanced Very High Resolution Radiometer satellite data. A total of 159 seasonally distinct spectral I temporal land cover classes were labelled according to their constituent vegetation types rather than forcing the vegetation complexes into a predefined classification scheme. The database contains attributes that characterize each land cover class, including elevation, climate attributes and biophysical parameters derived from the normalized difference vegetation index (ND VI). The database design permits convenient translation to a variety of land cover classification schemes commonly used in global scale models. This approach allows scientists to continue using classification schemes with which they are comfortable, eliminates duplicating land cover database development, and provides a degree of uniformity in the development of parallel but distinct databases.

Book chapter↗