No abstract

The utilization of high-resolution aerial imagery such as the National Agriculture Imagery Program (NAIP) data is often hampered by a lack of methods for retrieving surface reflectance from digital numbers. This study developed a new relative radiometric correction method to retrieve 1 m surface reflectance from NAIP imagery. The advantage of this method lies in the adaptive identification of pseudoinvariant (PIV) pixels from a time series of Landsat images that can fully characterize the temporally spectral variations of land surface. The identified PIV pixels allow for an effective conversion of digital numbers to surface reflectance, as demonstrated through the validation at 150 sites across the contiguous United States. The results show substantial improvement in the agreement of NAIP-derived normalized difference vegetation index (NDVI) values with Landsat-derived NDVI reference. Across the sites, root mean square error and mean absolute error were reduced from 0.37 ± 0.14 to 0.08 ± 0.07 and from 0.91 ± 0.64 to 0.18 ± 0.52, respectively. Over 70% PIV pixels on average were derived from vegetated areas, while water and developed areas together contributed 27% of the PIV pixels. As the NAIP program is continuing to generate new images across the country, the advantages of its high spatial resolution, national coverage, long time series, and regular revisits will make it an increasingly crucial data source for a variety of research and management applications. The proposed method could benefit many agricultural, hydrological, and urban studies that rely on NAIP imagery to quantify land surface patterns and dynamics. It could also be applied to improve the preprocessing of high-resolution aerial imagery in other countries.

No abstract

No abstract

The Probabilistic Balancing Rule (PBR) model addresses the problem of real-time (daily) reservoir system operations by defining the optimal set of release decisions as those which minimize the maximum non-exceedence probability values, for the respective decisions, within a specified forecast horizon. In contrast, penalty-based models define the optimal decisions as those which result in the minimum number of penalty points, for non-ideal operations, within a given forecast horizon. In this paper a rheoretical motiuation is provided for Probabilistic Balancing Rule models. The design and results of a broad-based test of the PBR models and penalty-based models is provided elsewhere by the authors. The analytical motivation for the PBR model follows from the inventory problem known as the Newsboy o r Christmas Tree problem. For a simple single reservoir system, the real-time operations problem is shown to be a Newsboy problem whose optimal solution can be found by solving the PBR model. For more complex system operation problems including hydroelectric energy production, water supply. flood damage mitigation, low flow augmentation and water quality improvement, the PBR model may be a close approximation of the real-time operations problem posed in the context of the Newsboy problem when marginal probability distribution functions of the decisions or control variables are used.