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Bruce K. Wylie

Publications and source records attributed to Bruce K. Wylie.

84 records · Page 5Linked to original sources
USGS2004

Development of a 2001 National Land Cover Database for the United States

Multi-Resolution Land Characterization 2001 (MRLC 2001) is a second-generation Federal consortium designed to create an updated pool of nation-wide Landsat 5 and 7 imagery and derive a second-generation National Land Cover Database (NLCD 2001). The objectives of this multi-layer, multi-source database are two fold: first, to provide consistent land cover for all 50 States, and second, to provide a data framework which allows flexibility in developing and applying each independent data component to a wide variety of other applications. Components in the database include the following: (1) normalized imagery for three time periods per path/row, (2) ancillary data, including a 30 m Digital Elevation Model (DEM) derived into slope, aspect and slope position, (3) perpixel estimates of percent imperviousness and percent tree canopy, (4) 29 classes of land cover data derived from the imagery, ancillary data, and derivatives, (5) classification rules, confidence estimates, and metadata from the land cover classification. This database is now being developed using a Mapping Zone approach, with 66 Zones in the continental United States and 23 Zones in Alaska. Results from three initial mapping Zones show single-pixel land cover accuracies ranging from 73 to 77 percent, imperviousness accuracies ranging from 83 to 91 percent, tree canopy accuracies ranging from 78 to 93 percent, and an estimated 50 percent increase in mapping efficiency over previous methods. The database has now entered the production phase and is being created using extensive partnering in the Federal government with planned completion by 2006.

Photogrammetric Engineering and Remote Sensing↗
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&ndash;energy balance (BREB) technique in a sagebrush ( Artemisia spp.)&ndash;steppe ecosystem in northeast Idaho, USA, during 1996&ndash;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&ndash;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&ndash;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&ndash;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↗
USGS2001

Sky type discrimination using a ground-based sun photometer

A 2-year feasibility study was conducted at the USGS EROS Data Center, South Dakota (43.733&deg;N, 96.6167&deg;W) to assess whether a four-band, ground-based, sun photometer could be used to discriminate sky types. The results indicate that unique spectral signatures do exist between sunny skies (including clear and hazy skies) and cirrus, and cirrostratus, altocumulus or fair-weather cumulus, and thin stratocumulus or altostratus, and fog/fractostratus skies. There were insufficient data points to represent other cloud types at a statistically significant level.

Atmospheric Research↗
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↗
USGS2001

A strategy for estimating tree canopy density using Landsat 7 ETM+ and high resolution images over large areas

Forest cover is of great interest to a variety of scientific and land management applications, many of which require not only information on forest categories, but also tree canopy density. In previous studies, large area tree canopy density had been estimated at spatial resolutions of 1km or coarser using coarse resolution satellite images. In this study, a strategy is developed for estimating tree canopy density at a spatial resolution of 30 m. This strategy is based on empirical relationships between tree canopy density and Landsat data, established using linear regression and regression tree techniques. One-meter digital orthophoto quadrangles were used to derive reference tree canopy density data needed for calibrating the relationships between canopy density and Landsat spectral data. This strategy was tested over three areas of the United States. In general, models derived using both linear regression and regression tree techniques were statistically significant. The regression tree was found more robust than linear regression, primary due to its capability of approximating complex non-linear relationships using a set of linear equations. This strategy will be recommended for use in developing a nation wide tree canopy density data set at a 30 m resolution as part of the Multi-Resolution Land Characteristics 2000 project.

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

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↗
USGS1996

Grassland canopy parameters and their relationships to remotely sensed vegetation indices in the Nebraska Sand Hills

Relationships among spectral vegetation indices and grassland biophysical parameters including the effects of varying levels of standing dead vegetation, range sites, and range plant communities were examined. Range plant communities consisting of northern mixed grass prairie and a smooth brome field as well as range sites and management in a Sand Hills bluestem prairie were sampled with a ground radiometer and for LAI, biomass, chlorophy]] and nitrogen amounts. Live above ground (herbaceous and shrub leaf) biomass quadrat estimates in early June, 1994, ranged from a mean value of 35,9 g/m 2 in the burned area to 128.0 g/m 2 in the mixed grass prairie. Shrubs, when present, had a stronger effect on the ground radiometer NDVI and MSAVI relationships with live biomass than either standing dead vegetation or plant community composition. Predictive relationships for live biomass from ground radiometer spectral data allowed rapid nondestructive estimation of live biomass for eleven 30 m by 30 m plots. Strong (r 2 ‐ 0.81 to 0.87) predictive relationships for live biomass and SPOT vegetation indices at the 30 m by 30 m scale were developed and applied to estimate live biomass for entire site areas.

Geocarto International↗
USGSDate not supplied

Comparative analysis of GPP products estimated from an empirical model and MODIS

Carbon-cycle models have uncertainties associated with data inputs, parameters, and model algorithms. The prerequisite for an applicable model is that it should perform at an acceptable level of accuracy and uncertainties should be documented. In this study, we validated the gross primary productivity (GPP) data from a piecewise regression (PWR) model and the MODIS GPP model at five grassland flux towers in the Northern Great Plains. The results showed a good agreement of GPP values (agreement coefficient d = 0.88–0.98) among PWR, MODIS, and tower measurements at Fort Peck, Mandan, and Cheyenne sites; but MODIS GPP did not agree well ( d = 0.62–0.79) with tower measurements at Miles City and Lethbridge sites. Additionally, we compared PWR GPP and MODIS GPP for grasslands in the entire study area. We found that the PWR GPP was lower than or similar to the MODIS GPP in the east and higher in the west and south. We explored possible factors that may cause the GPP difference in spatial distribution between the two models.

Conference Paper↗