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B.K. Wylie

Publications and source records attributed to B.K. Wylie.

10 recordsLinked to original sources
USGS2015

NLEAP/GIS approach for identifying and mitigating regional nitrate-nitrogen leaching

Improved simulation-based methodology is needed to help identify broad geographical areas where potential NO 3 -N leaching may be occurring from agriculture and suggest management alternatives that minimize the problem. The Nitrate Leaching and Economic Analysis Package (NLEAP) model was applied to estimate regional NO 3 -N leaching in eastern Colorado. Results show that a combined NLEAP/GIS technology can be used to identify potential NO 3 -N hot spots in shallow alluvial aquifers under irrigated agriculture. The NLEAP NO 3 -N Leached (NL) index provided the most promising single index followed by NO 3 -N Available for Leaching (NAL). The same combined technology also shows promise in identifying Best Management Practice (BMP) methods that help minimize NO 3 -N leaching in vulnerable areas. Future plans call for linkage of the NLEAP/GIS procedures with groundwater modeling to establish a mechanistic analysis of agriculture-aquifer interactions at a regional scale.

Book chapter↗
USGS2012

Airborne electromagnetic imaging of discontinuous permafrost

The evolution of permafrost in cold regions is inextricably connected to hydrogeologic processes, climate, and ecosystems. Permafrost thawing has been linked to changes in wetland and lake areas, alteration of the groundwater contribution to streamflow, carbon release, and increased fire frequency. But detailed knowledge about the dynamic state of permafrost in relation to surface and groundwater systems remains an enigma. Here, we present the results of a pioneering ∼1,800 line-kilometer airborne electromagnetic survey that shows sediments deposited over the past ∼4 million years and the configuration of permafrost to depths of ∼100 meters in the Yukon Flats area near Fort Yukon, Alaska. The Yukon Flats is near the boundary between continuous permafrost to the north and discontinuous permafrost to the south, making it an important location for examining permafrost dynamics. Our results not only provide a detailed snapshot of the present-day configuration of permafrost, but they also expose previously unseen details about potential surface – groundwater connections and the thermal legacy of surface water features that has been recorded in the permafrost over the past ∼1,000 years. This work will be a critical baseline for future permafrost studies aimed at exploring the connections between hydrogeologic, climatic, and ecological processes, and has significant implications for the stewardship of Arctic environments.

Alaska↗
USGS2011

Using normalized difference vegetation index to estimate carbon fluxes from small rotationally grazed pastures

Satellite-based normalized difference vegetation index (NDVI) data have been extensively used for estimating gross primary productivity (GPP) and yield of grazing lands throughout the world. However, the usefulness of satellite-based images for monitoring rotationally-grazed pastures in the northeastern United States might be limited because paddock size is often smaller than the resolution limits of the satellite image. This research compared NDVI data from satellites with data obtained using a ground-based system capable of fine-scale (submeter) NDVI measurements. Gross primary productivity was measured by eddy covariance on two pastures in central Pennsylvania from 2003 to 2008. Weekly 250-m resolution satellite NDVI estimates were also obtained for each pasture from the moderate resolution imaging spectroradiometer (MODIS) sensor. Ground-based NDVI data were periodically collected in 2006, 2007, and 2008 from one of the two pastures. Multiple-regression and regression-tree estimates of GPP, based primarily on MODIS 7-d NDVI and on-site measurements of photosynthetically active radiation (PAR), were generally able to predict growing-season GPP to within an average of 3% of measured values. The exception was drought years when estimated and measured GPP differed from each other by 11 to 13%. Ground-based measurements improved the ability of vegetation indices to capture short-term grazing management effects on GPP. However, the eMODIS product appeared to be adequate for regional GPP estimates where total growing-season GPP across a wide area would be of greater interest than short-term management-induced changes in GPP at individual sites.

Pennsylvania↗
USGS2008

Integrating modelling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River Basin, Alaska

High-latitude ecosystems are exposed to more pronounced warming effects than other parts of the globe. We develop a technique to monitor ecological changes in a way that distinguishes climate influences from disturbances. In this study, we account for climatic influences on Alaskan boreal forest performance with a data-driven model. We defined ecosystem performance anomalies (EPA) using the residuals of the model and made annual maps of EPA. Most areas (88%) did not have anomalous ecosystem performance for at least 6 of 8 years between 1996 and 2004. Areas with underperforming EPA (10%) often indicate areas associated with recent fires and areas of possible insect infestation or drying soil related to permafrost degradation. Overperforming areas (2%) occurred in older fire recovery areas where increased deciduous vegetation components are expected. The EPA measure was validated with composite burn index data and Landsat vegetation indices near and within burned areas.

International Journal of Digital Earth↗
USGS2007

Regional assessment of NLEAP NO 3- N leaching indices

Nonpoint source ground water contamination by nitrate nitrogen (NO 3 -N) leached from agricultural lands can be substantial and increase health risks to humans and animals. Accurate and rapid methods are needed to identify and map localities that have a high potential for contamination of shallow aquifers with NO 3 -N leached from agriculture. Evaluation of Nitrate Leaching and Economic Analysis Package (NLEAP) indices and input variables across an irrigated agricultural area on an alluvial aquifer in Colorado indicated that all leaching indices tested were more strongly correlated with aquifer NO 3 -N concentration than with aquifer N mass. Of the indices and variables tested, the NO 3 -N Leached (NL) index was the NLEAP index most strongly associated with groundwater NO 3 -N concentration (r 2 values from 0.37 to 0.39). NO 3 -N concentration of the leachate was less well correlated with ground water NO 3 -N concentration (r 2 values from 0.21 to 0.22). Stepwise regression analysis indicated that, although inorganic and organic/inorganic fertilizer scenarios had similar r 2 values, the Feedlot Indicator (proximity) variable was significant over and above the NO 3 -N Leached index for the inorganic scenario. The analysis also showed that combination of either Movement Risk Index (MIRI) or NO 3 -N concentration of the leachate with the NO 3 -N Leached index leads to an improved regression, which provides insight into area-wide associations between agricultural activities and ground water NO 3 -N concentration.

Journal of the American Water Resources Associatio↗
USGS2007

Adaptive data-driven models for estimating carbon fluxes in the Northern Great Plains

Rangeland carbon fluxes are highly variable in both space and time. Given the expansive areas of rangelands, how rangelands respond to climatic variation, management, and soil potential is important to understanding carbon dynamics. Rangeland carbon fluxes associated with Net Ecosystem Exchange (NEE) were measured from multiple year data sets at five flux tower locations in the Northern Great Plains. These flux tower measurements were combined with 1-km2 spatial data sets of Photosynthetically Active Radiation (PAR), Normalized Difference Vegetation Index (NDVI), temperature, precipitation, seasonal NDVI metrics, and soil characteristics. Flux tower measurements were used to train and select variables for a rule-based piece-wise regression model. The accuracy and stability of the model were assessed through random cross-validation and cross-validation by site and year. Estimates of NEE were produced for each 10-day period during each growing season from 1998 to 2001. Growing season carbon flux estimates were combined with winter flux estimates to derive and map annual estimates of NEE. The rule-based piece-wise regression model is a dynamic, adaptive model that captures the relationships of the spatial data to NEE as conditions evolve throughout the growing season. The carbon dynamics in the Northern Great Plains proved to be in near equilibrium, serving as a small carbon sink in 1999 and as a small carbon source in 1998, 2000, and 2001. Patterns of carbon sinks and sources are very complex, with the carbon dynamics tilting toward sources in the drier west and toward sinks in the east and near the mountains in the extreme west. Significant local variability exists, which initial investigations suggest are likely related to local climate variability, soil properties, and management.

Remote Sensing of Environment↗
USGS2006

Long-term dynamics of production, respiration, and net CO 2 exchange in two sagebrush-steppe ecosystems

We present a synthesis of long-term measurements of CO 2 exchange in 2 US Intermountain West sagebrush-steppe ecosystems. The locations near Burns, Oregon (1995–2001), and Dubois, Idaho (1996–2001), are part of the AgriFlux Network of the Agricultural Research Service, United States Department of Agriculture. Measurements of net ecosystem CO 2 exchange ( F c ) during the growing season were continuously recorded at flux towers using the Bowen ratio-energy balance technique. Data were partitioned into gross primary productivity ( P g ) and ecosystem respiration ( R e ) using the light-response function method. Wintertime fluxes were measured during 1999/2000 and 2000/2001 and used to model fluxes in other winters. Comparison of daytime respiration derived from light-response analysis with nighttime tower measurements showed close correlation, with daytime respiration being on the average higher than nighttime respiration. Maxima of P g and R e at Burns were both 20 g CO 2 ·m −2 ·d −1 in 1998. Maxima of P g and R e at Dubois were 37 and 35 g CO 2 ·m −2 ·d −1 , respectively, in 1997. Mean annual gross primary production at Burns was 1 111 (range 475–1 715) g CO 2 ·m −2 ·y −1 or about 30% lower than that at Dubois (1 602, range 963–2 162 g CO 2 ·m −2 ·y −1 ). Across the years, both ecosystems were net sinks for atmospheric CO 2 with a mean net ecosystem CO 2 exchange of 82 g CO 2 ·m −2 ·y −1 at Burns and 253 g CO 2 ·m −2 ·y −1 at Dubois, but on a yearly basis either site could be a C sink or source, mostly depending on precipitation timing and amount. Total annual precipitation is not a good predictor of carbon sequestration across sites. Our results suggest that F c should be partitioned into P g and R e components to allow prediction of seasonal and yearly dynamics of CO 2 fluxes.

Rangeland Ecology and Management↗
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

Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study

Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fPAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study ( r 2 =.66–.85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 “ground pixels” (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30×30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI ( r 2 =.92–.94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km 2 ) was also investigated.

Remote Sensing of Environment↗