John S. Iiames

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Initiated in 1984, the Committee Earth Observing Satellites’ Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation(More)
This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250 m time-series data to develop an annual crop type mapping capability throughout the 480,000 km Great Lakes Basin (GLB). An ecoregion-stratified approach was developed using a two-step processing approach that included an(More)
The confounding effect of understory vegetation contributions to satellite-derived estimates of leaf area index (LAI) was investigated on two loblolly pine (Pinus taeda) forest stands located in Virginia and North Carolina. In order to separate NDVI contributions of the dominant-codominate crown class from that of the understory, two P. taeda 1 ha plots(More)
The validation process for a moderate resolution leaf area index (LAI) product (i.e., MODIS) involves the creation of a high spatial resolution LAI reference map (LaiRM), which when scaled to the moderate LAI resolution (i.e., >1 km) allows for comparison and analysis with this LAI product. This research addresses two major sources of uncertainty in the(More)
The U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL) is conducting hyperspectral remote sensing (imaging spectroscopy) methods development research in the Neuse River Basin, North Carolina. Science objectives have focused on the potential applications of hyperspectral imagery for vegetation discrimination in(More)
The ability to effectively use remotely sensed data for environmental analysis is dependent on understanding the underlying procedures and associated variances attributed to the data processing and image analysis technique. Equally important is understanding the error associated with the reference data used to assess the accuracy of image products. This(More)
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