James W. Merchant

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 Commissions 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.

Measuring phenological variability from satellite imagery

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administrations Advanced Very High Resolution Radiom- eter (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variabil- ity of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and pre- dicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demon- strated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particu- larly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

Effects of Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper plus radiometric and geometric calibrations and corrections on landscape characterization

The Thematic Mapper (TM) instruments onboard Landsats 4 and 5 provide high-quality imagery appropriate for many different applications, including land cover mapping, landscape ecology, and change detection. Precise calibration was considered to be critical to the success of the Landsat 7 mission and, thus, issues of calibration were given high priority during the development of the Enhanced Thematic Mapper Plus (ETM+). Data sets from the Landsat 5 TM are not routinely corrected for a number of radiometric and geometric artifacts, including memory effect, gain/bias, and interfocal plane misalignment. In the current investigation, the effects of correcting vs. not correcting these factors were investigated for several applications. Gain/bias calibrations were found to have a greater impact on most applications than did memory effect calibrations. Correcting interfocal plane offsets was found to have a moderate effect on applications. On June 2, 1999, Landsats 5 and 7 data were acquired nearly simultaneously over a study site in the Niobrara, NE area. Field radiometer data acquired at that site were used to facilitate crosscalibrations of Landsats 5 and 7 data. Current findings and results from previous investigations indicate that the internal calibrator of Landsat 5 TM tracked instrument gain well until 1988. After this, the internal calibrator diverged from the data derived from vicarious calibrations. Results from this study also indicate very good agreement between prelaunch measurements and vicarious calibration data for all Landsat 7 reflective bands except Band 4. Values are within about 3.5% of each other, except for Band 4, which differs by 10%. Coefficient of variation (CV) values derived from selected targets in the imagery were also analyzed. The Niobrara Landsat 7 imagery was found to have lower CV values than Landsat 5 data, implying that lower levels of noise characterize Landsat 7 data than current Landsat 5 data. It was also found that following radiometric normalization, the Normalized Difference Vegetation Index (NDVI) imagery and classification products of Landsats 5 and 7 were very similar. This implies that data from the two sensors can be used to measure and monitor the same landscape phenomena and that Landsats 5 and 7 data can be used interchangeably with proper caution. In addition, it was found that difference imagery produced using Landsat 7 ETM+ data are of excellent quality.

An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A.

Abstract Research designed to better define relations between 1-km multitemporal AVHRR-derived NDVI data and selected climatological parameters, soil hydrological properties and land cover characteristics is summarized. Bi-weekly maximum value composite NDVI data and concurrently measured meteorological data acquired in 1990 and 1991 for Nebraska were utilized to study relations between NDVI and accumulated growing degree days,soil temperature, precipitation and potential evapotranspiration. Temporal change in NDVI was found to be closely linked with the temperature regime. NDVI-precipitation and NDVI-potential evapotranspiration relations exhibited time lags, although the length of lag varied with land cover type, precipitation, and soil hydrologic properties. NDVI response to precipitation was stronger in natural grasslands and grassland/wet meadows than in areas of irrigated cropland and mixed crop/ grass. NDVI-climate relations were strongest where vegetation was developed on soils with low root zone ...

Modeling vulnerability of groundwater to pollution under future scenarios of climate change and biofuels-related land use change: A case study in North Dakota, USA

Modeling groundwater vulnerability to pollution is critical for implementing programs to protect groundwater quality. Most groundwater vulnerability modeling has been based on current hydrogeology and land use conditions. However, groundwater vulnerability is strongly dependent on factors such as depth-to-water, recharge and land use conditions that may change in response to future changes in climate and/or socio-economic conditions. In this research, a modeling framework, which employs three sets of models linked within a geographic information system (GIS) environment, was used to evaluate groundwater pollution risks under future climate and land use changes in North Dakota. The results showed that areas with high vulnerability will expand northward and/or northwestward in Eastern North Dakota under different scenarios. GIS-based models that account for future changes in climate and land use can help decision-makers identify potential future threats to groundwater quality and take early steps to protect this critical resource.

Impacts of upscaling techniques on land cover representation in Nebraska, U.S.A.

Abstract Land cover information is required for a host of environmental applications. Such information is needed at a variety of spatial resolutions ranging from meters to, on a global scale, hundreds of kilometers. This research explores the impacts of selected upscaling methods and landscape structure on land cover representation at multiple spatial resolutions. A land cover dataset over a range of spatial resolutions was derived from Landsat Thematic Mapper data. Comparisons of class areas among different resolutions and between maps obtained from two different upscaling methods are conducted. Effects of landscape structure on class area estimation are also examined using landscape metrics. Results indicate that class areas change substantially through resolutions, but that patterns of change are different for different classes and upscaling approaches. While class areas at coarse resolutions are determined predominantly by initial areas on the full resolution map, they are also related to several metr...

A Geospatial Approach for Assessing Groundwater Vulnerability to Nitrate Contamination in Agricultural Settings

Groundwater is the principal source of drinking water for at least one third of Earth’s human inhabitants. Thus, protection of groundwater is a critical issue in many locales. Nitrates and other contaminants that impact human health are of particular concern. Mapping of aquifer vulnerability to pollution is a critical first step in implementing groundwater management protection programs; however, mapping is often constrained by generalizations inherent in model formulation and availability of data. In this study, a groundwater vulnerability model, which employs data extracted from widely available national and statewide geospatial datasets, is used to evaluate regional groundwater pollution risk in the Elkhorn River Basin, Nebraska, USA. The model, implemented in a geographic information system (GIS), is specifically structured to address risks of nitrate contamination in agricultural landscapes; thus, land use is a key factor. Modeled groundwater vulnerability was found to be positively correlated with nitrate concentrations obtained from sampled wells. The results suggest that the approach documented here could be used effectively to model regional groundwater pollution risk in other areas.

Employing a Geographic Information System for Wetlands Management in Nebraska’s Rainwater Basin

Wetlands are critical components of most ecosystems, providing a broad array of services including support for biodiversity, enhancement of water quality and flood abatement. In spite of their ecological importance, wetlands have been degraded and lost at alarming rates. In the Rainwater Basin (RWB) of south-central Nebraska, an internationally significant staging area for migratory water birds, the adoption of geographic information system (GIS) technology has been critical to managing wetlands. Efforts to employ GIS began with assemblage of dozens of geospatial datasets including digital elevation models, soils data, digital orthophotography, historic aerial photography and land ownership data. Land use and land cover data, which did not exist, were developed using satellite remote sensing. Subsequently, the Revised Universal Soil Loss Equation (RUSLE) was implemented via GIS to assess how sediment was impacting wetlands. All geospatial data, RUSLE modeling results, metadata and documentation were made available on a WWW site designed to facilitate both data download and mapping. During the past decade the RWB has developed an independent, robust and forward-looking program to exploit GIS and related technologies. Many new and improved datasets, including LiDAR data, have been developed, and increasingly sophisticated GIS-based models have been implemented for decision support. Examples includes models to establish priorities for wetlands management, to facilitate evaluation of eligibility of candidate sites for enrollment in programs such as the USDA Wetlands Reserve Program, and to assess the RWB landscape’s capacity to provide food energy for migrating waterfowl.