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Geology topics

Bruce K. Wylie

Publications and source records attributed to Bruce K. Wylie.

At least 73 records · Page 4Linked to original sources
USGS2012

Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing

Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance ( P <0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

Oregon;Idaho↗
USGS2012

Mapping grasslands suitable for cellulosic biofuels in the Greater Platte River Basin, United States

Biofuels are an important component in the development of alternative energy supplies, which is needed to achieve national energy independence and security in the United States. The most common biofuel product today in the United States is corn-based ethanol; however, its development is limited because of concerns about global food shortages, livestock and food price increases, and water demand increases for irrigation and ethanol production. Corn-based ethanol also potentially contributes to soil erosion, and pesticides and fertilizers affect water quality. Studies indicate that future potential production of cellulosic ethanol is likely to be much greater than grain- or starch-based ethanol. As a result, economics and policy incentives could, in the near future, encourage expansion of cellulosic biofuels production from grasses, forest woody biomass, and agricultural and municipal wastes. If production expands, cultivation of cellulosic feedstock crops, such as switchgrass ( Panicum virgatum L. ) and miscanthus ( Miscanthus species ), is expected to increase dramatically. The main objective of this study is to identify grasslands in the Great Plains that are potentially suitable for cellulosic feedstock (such as switchgrass) production. Producing ethanol from noncropland holdings (such as grassland) will minimize the effects of biofuel developments on global food supplies. Our pilot study area is the Greater Platte River Basin, which includes a broad range of plant productivity from semiarid grasslands in the west to the fertile corn belt in the east. The Greater Platte River Basin was the subject of related U.S. Geological Survey (USGS) integrated research projects.

Colorado;Nebraska;South Dakota;Wyoming↗
USGS2011

Establishing water body areal extent trends in interior Alaska from multi-temporal Landsat data

An accurate approach is needed for monitoring, quantifying and understanding surface water variability due to climate change. Separating inter- and intra-annual variances from longer-term shifts in surface water extents due to contemporary climate warming requires repeat measurements spanning a several-decade period. Here, we show that trends developed from multi-date measurements of the extents of more than 15,000 water bodies in central Alaska using Landsat Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data (1979–2009) were highly influenced by the quantity and timing of the data. Over the 30-year period from 1979 to 2009, the study area had a net decrease ( p < 0.05) in the extents of 3.4% of water bodies whereas 86% of water bodies exhibited no significant change. The Landsat-derived dataset provides an opportunity for additional research assessing the drivers of lake and wetland change in this region.

Alaska↗
USGS2011

On the terminology of the spectral vegetation index (NIR – SWIR)/(NIR + SWIR)

The spectral vegetation index (&rho;NIR – &rho;SWIR)/(&rho;NIR + &rho;SWIR), where &rho;NIR and &rho;SWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI), and normalized burn ratio (NBR), etc. After reviewing each term’s definition, associated sensors, and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2–1.3 µm, 1.55–1.75 µm, or 2.05–2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term’s popularity, and the “rule of priority” in scientific nomenclature, NDWI, NDII, and NBR, each corresponding to the three SWIR regions, are more preferable terms.

International Journal of Remote Sensing↗
USGS2011

Response of spectral vegetation indices to soil moisture in grasslands and shrublands

The relationships between satellite-derived vegetation indices (VIs) and soil moisture are complicated because of the time lag of the vegetation response to soil moisture. In this study, we used a distributed lag regression model to evaluate the lag responses of VIs to soil moisture for grasslands and shrublands at Soil Climate Analysis Network sites in the central and western United States. We examined the relationships between Moderate Resolution Imaging Spectroradiometer (MODIS)-derived VIs and soil moisture measurements. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) showed significant lag responses to soil moisture. The lag length varies from 8 to 56 days for NDVI and from 16 to 56 days for NDWI. However, the lag response of NDVI and NDWI to soil moisture varied among the sites. Our study suggests that the lag effect needs to be taken into consideration when the VIs are used to estimate soil moisture.

International Journal of Remote Sensing↗
USGS2011

Upscaling carbon fluxes over the Great Plains grasslands: Sinks and sources

Previous studies suggested that the grasslands may be carbon sinks or near equilibrium, and they often shift between carbon sources in drought years and carbon sinks in other years. It is important to understand the responses of net ecosystem production (NEP) to various climatic conditions across the U.S. Great Plains grasslands. Based on 15 grassland flux towers, we developed a piecewise regression model and mapped the grassland NEP at 250 m spatial resolution over the Great Plains from 2000 to 2008. The results showed that the Great Plains was a net sink with an averaged annual NEP of 24 ± 14 g C m −2 yr −1 , ranging from a low value of 0.3 g C m −2 yr −1 in 2002 to a high value of 47.7 g C m −2 yr −1 in 2005. The regional averaged NEP for the entire Great Plains grasslands was estimated to be 336 Tg C yr −1 from 2000 to 2008. In the 9 year period including 4 dry years, the annual NEP was very variable in both space and time. It appeared that the carbon gains for the Great Plains were more sensitive to droughts in the west than the east. The droughts in 2000, 2002, 2006, and 2008 resulted in increased carbon losses over drought-affected areas, and the Great Plains grasslands turned into a relatively low sink with NEP values of 15.8, 0.3, 20.1, and 10.2 g C m −2 yr −1 for the 4 years, respectively.

Journal of Geophysical Research: Biogeosciences↗
USGS2010

Detecting Ecosystem Performance Anomalies for Land Management in the Upper Colorado River Basin Using Satellite Observations, Climate Data, and Ecosystem Models

This study identifies areas with ecosystem performance anomalies (EPA) within the Upper Colorado River Basin (UCRB) during 2005–2007 using satellite observations, climate data, and ecosystem models. The final EPA maps with 250-m spatial resolution were categorized as normal performance, underperformance, and overperformance (observed performance relative to weather-based predictions) at the 90% level of confidence. The EPA maps were validated using “percentage of bare soil” ground observations. The validation results at locations with comparable site potential showed that regions identified as persistently underperforming (overperforming) tended to have a higher (lower) percentage of bare soil, suggesting that our preliminary EPA maps are reliable and agree with ground-based observations. The 3-year (2005–2007) persistent EPA map from this study provides the first quantitative evaluation of ecosystem performance anomalies within the UCRB and will help the Bureau of Land Management (BLM) identify potentially degraded lands. Results from this study can be used as a prototype by BLM and other land managers for making optimal land management decisions.

Remote Sensing↗
USGS2010

A self-trained classification technique for producing 30 m percent-water maps from Landsat data

Small bodies of water can be mapped with moderate-resolution satellite data using methods where water is mapped as subpixel fractions using field measurements or high-resolution images as training datasets. A new method, developed from a regression-tree technique, uses a 30 m Landsat image for training the regression tree that, in turn, is applied to the same image to map subpixel water. The self-trained method was evaluated by comparing the percent-water map with three other maps generated from established percent-water mapping methods: (1) a regression-tree model trained with a 5 m SPOT 5 image, (2) a regression-tree model based on endmembers and (3) a linear unmixing classification technique. The results suggest that subpixel water fractions can be accurately estimated when high-resolution satellite data or intensively interpreted training datasets are not available, which increases our ability to map small water bodies or small changes in lake size at a regional scale.

International Journal of Remote Sensing↗
USGS2010

Climate-driven interannual variability in net ecosystem exchange in the Northern Great Plains grasslands

The Northern Great Plains grasslands respond differently under various climatic conditions; however, there have been no detailed studies investigating the interannual variability in carbon exchange across the entire Northern Great Plains grassland ecosystem. We developed a piecewise regression model to integrate flux tower data with remotely sensed data and mapped the 8-d and 500-m net ecosystem exchange (NEE) for the years from 2000 to 2006. We studied the interannual variability of NEE, characterized the interannual NEE difference in climatically different years, and identified the drought impact on NEE. The results showed that NEE was highly variable in space and time across the 7 yr. Specifically, NEE was consistently low (−35 to 322 g C·m −2 ·yr −1 ) with an average annual NEE of −2 ± 242 g C·m −2 ·yr −1 and a cumulative flux of −152 g C·m −2 . The Northern Great Plains grassland was a weak source for carbon during 2000–2006 because of frequent droughts, which strongly affected the carbon balance, especially in the Western High Plains and Northwestern Great Plains. Comparison of the NEE map with a drought monitor map confirmed a substantial correlation between drought and carbon dynamics. If drought severity or frequency increases in the future, the Northern Great Plains grasslands may become an even greater carbon source.

Northern Great Plains grasslands↗
USGS2010

Multitemporal L- and C-Band synthetic aperture radar to highlight differences in water status among boreal forest and wetland systems in the Yukon Flats, Interior Alaska

Tracking landscape-scale water status in high-latitude boreal systems is indispensable to understanding the fate of stored and sequestered carbon in a climate change scenario. Spaceborne synthetic aperture radar (SAR) imagery provides critical information for water and moisture status in Alaskan boreal environments at the landscape scale. When combined with results from optical sensor analyses, a complementary picture of vegetation, biomass, and water status emerges. Whereas L-band SAR showed better inherent capacity to map water status, C-band had much more temporal coverage in this study. Analysis through the use of L- and C-band SARs combined with Landsat Enhanced Thematic Mapper Plus (ETM+) enables landscape stratification by vegetation and by seasonal and interannual hydrology. Resultant classifications are highly relevant to biogeochemistry at the landscape scale. These results enhance our understanding of ecosystem processes relevant to carbon balance and may be scaled up to inform regional carbon flux estimates and better parameterize general circulation models (GCMs).

Open-File Report↗
USGS2008

Modeling and dynamic monitoring of ecosystem performance in the Yukon River Basin

Central Alaska is ecologically sensitive and experiencing stress in response to marked regional warming. Resource managers would benefit from an improved ability to monitor ecosystem processes in response to climate change, fire, insect damage, and management policies and to predict responses to future climate scenarios. We have developed a method for analyzing ecosystem performance as represented by the growing season integral of normalized difference vegetation index (NDVI), which is a measure of greenness that can be interpreted in terms of plant growth or photosynthetic activity (gross primary productivity). The approach illustrates the status and trends of ecosystem changes and separates the influences of climate and local site conditions from the influences of disturbances and land management. We emphasize the ability to quantify ecosystem processes, not simply changes in land cover, across the entire period of the remote sensing archive (Wylie and others, 2008). The method builds upon remotely sensed measures of vegetation greenness for each growing season. By itself, however, a time series of greenness often reflects annual climate variations in temperature and precipitation. Our method seeks to remove the influence of climate so that changes in underlying ecological conditions are identified and quantified. We define an "expected ecosystem performance" to represent the greenness response expected in a particular year given the climate of that year. We distinguish "performance anomalies" as cases where the ecosystem response is significantly different from the expected ecosystem performance. Maps of the performance anomalies (fig. 1) and trends in the anomalies give valuable information on the ecosystems for land managers and policy makers at a resolution of 1 km to 250 m.

Fact Sheet↗
USGS2007

Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands

Two spatially-explicit estimates of gross primary production (GPP) are available for the Northern Great Plains. An empirical piecewise regression (PWR) GPP model was developed from flux tower measurements to map carbon flux across the region. The Moderate Resolution Imaging Spectrometer (MODIS) GPP model is a process-based model that uses flux tower data to calibrate its parameters. Verification and comparison of the regional PWR GPP and the global MODIS GPP are important for the modeling of grassland carbon flux. This study compared GPP estimates from PWR and MODIS models with five towers in the grasslands. Among them, PWR GPP and MODIS GPP showed a good agreement with tower-based GPP at three towers. The global MODIS GPP, however, did not agree well with tower-based GPP at two other towers, probably because of the insensitivity of MODIS model to regional ecosystem and climate change and extreme soil moisture conditions. Cross-validation indicated that the PWR model is relatively robust for predicting regional grassland GPP. However, the PWR model should include a wide variety of flux tower data as the training data sets to obtain more accurate results. In addition, GPP maps based on the PWR and MODIS models were compared for the entire region. In the northwest and south, PWR GPP was much higher than MODIS GPP. These areas were characterized by the higher water holding capacity with a lower proportion of C 4 grasses in the northwest and a higher proportion of C 4 grasses in the south. In the central and southeastern regions, PWR GPP was much lower than MODIS GPP under complicated conditions with generally mixed C 3 /C 4 grasses. The analysis indicated that the global MODIS GPP model has some limitations on detecting moisture stress, which may have been caused by the facts that C 3 and C 4 grasses are not distinguished, water stress is driven by vapor pressure deficit (VPD) from coarse meteorological data, and MODIS land cover data are unable to differentiate the sub-pixel cropland components.

Remote Sensing of Environment↗
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,↗
USGS2006

Scaling-up of CO2 fluxes to assess carbon sequestration in rangelands of Central Asia

Flux towers provide temporal quantification of local carbon dynamics at specific sites. The number and distribution of flux towers, however, are generally inadequate to quantify carbon fluxes across a landscape or ecoregion. Thus, scaling up of flux tower measurements through use of algorithms developed from remote sensing and GIS data is needed for spatial extrapolation of carbon fluxes and to identify regional sinks and sources of carbon. Spatial and temporal quantification of carbon dynamics are useful in understanding the biophysical factors that cause regions to be sinks or sources of carbon. We analyzed data sets from the Northern Great Plains and the Kazakh Steppe and found similarities in latitude, precipitation, and carbon fluxes between the two regions. These similarities allowed us to pool carbon flux data, remotely sensed data, and GIS data from these two regions to map gross primary productivity (Pg), total ecosystem respiration (Re), and net ecosystem exchange (NEE) for Kazakh Steppe for 2001 using regression tree techniques. We estimated 10-day Pg and Re with mean absolute errors of 3.2 and 2.7 g CO 2 /m 2 /day, respectively. The NEE for grasslands in the Kazakh Steppe during the growing season (April through October 2001) was 0.79 t C/ha. Localized carbon sinks and sources were positively correlated with growing season precipitation and Pg. The regression tree technique provided an effective method for the regional mapping of carbon dynamics as seasonally quantified by flux towers in the Northern Great Plains of North America and the Kazakh Steppe of Central Asia.

Conference Paper↗
USGS2005

Integration of CO2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Aim&ensp; Extrapolation of tower CO 2 fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO 2 fluxes and photosynthetically active radiation ( Q ), other on-site factors, and Normalized Difference Vegetation Index ( NDVI ) from the SPOT VEGETATION data set. Location&ensp; CO 2 flux data from the following stations and years were analysed: Lethbridge, Alberta 1998&ndash;2001; Fort Peck, MT 2000, 2002; Miles City, MT 2000&ndash;01; Mandan, ND 1999&ndash;2001; and Cheyenne, WY 1997&ndash;98. Results&ensp; Analyses based on light-response functions allowed partitioning net CO 2 flux ( F ) into gross primary productivity ( P g ) and ecosystem respiration ( R e ). Weekly averages of daytime respiration, &gamma; day , estimated from light responses were closely correlated with weekly averages of measured night-time respiration, &gamma; night ( R 2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of &gamma; day on &gamma; night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO 2 m &minus;2 year &minus;1 , ecosystem respiration from 996 to 1881 g CO 2 m &minus;2 year &minus;1 , and net ecosystem exchange from &minus;537 (source) to +610 g CO 2 m &minus;2 year &minus;1 (sink). Maximum daily ecological light-use efficiencies, ɛ d , max = P g /Q , were in the range 0.014 to 0.032 mol CO 2 (mol incident quanta) &minus;1 . Main conclusions&ensp; Ten-day average P g was significantly more highly correlated with NDVI than 10-day average daytime flux, P d ( R 2 = 0.46 to 0.77 for P g -NDVI and 0.05 to 0.58 for P d -NDVI relationships). Ten-day average R e was also positively correlated with NDVI , with R 2 values from 0.57 to 0.77. Patterns of the relationships of P g and R e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.

Global Ecology and Biogeography↗
USGS2004

Gross primary productivity of the true steppe in central Asia in relation to NDVI: scaling up CO 2 fluxes

Compared to other characteristics of CO 2 exchange, gross primary productivity ( P g ) is most directly related to photosynthetic activity. Until recently, it was considered difficult to obtain measurement-based P g . The objective of our study was to evaluate if P g can be estimated from continuous CO 2 flux measurements using nonlinear identification of the nonrectangular hyperbolic model of ecosystem-scale, light-response curves. Estimates of P g and ecosystem respiration ( R e ) were obtained using Bowen ratio&ndash; energy-balance measurements of CO 2 exchange in a true-steppe ecosystem in northern Kazakhstan during four growing seasons (1998&ndash;2001). The maximum mean weekly apparent quantum yield (&alpha; max ) was 0.0388 mol CO 2 mol photons and the maximum mean weekly P g was 28 g CO 2 /m 2 /day in July 2000. The highest mean weekly R e max (20 g CO 2 m 2 /day) was observed in July of both 1999 and 2000. Nighttime respiration calculated from daily respiration corrected for length of the dark period and temperature (using Q 10 = 2) was closely associated with measured nighttime respiration ( R 2 = 0.67 to 0.93). The 4-year average annual gross primary production (GPP) was 1617 g CO 2 /m 2 / year (range = 1308&ndash;1957). Ten-day normalized difference vegetation index corrected for the start of the season (NDVI sos ) was closely associated with 10-day average P g ( R 2 = 0.66 to 0.83), which was higher than R 2 values for regressions of mean 10-day net daytime fluxes on NDVI sos (0.55&ndash;0.72). This demonstrates the advantage of using P g in scaling up flux-tower measurements compared to other characteristics (net daytime flux or net 24-h flux).

Environmental Management↗