Spatial distribution prediction of growing stock volume (GSV) for supporting the sustainable management forest ecosystems, is one most widespread applications remote sensing. For this purpose, sensing data were used as predictor variables in combination with ground obtained from field sample plots. However, increase GSV values, spectral reflectance imagery often saturated or less sensitive to c...

Seedlings, rooted cuttings from juvenile stock plants, and cotyledon-derived tissue culture plantlets were propagated from several coastal Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) full-sib families so that the rooted cuttings and plantlets were clonally identical. The stock types (seedlings, rooted cuttings and plantlets) were planted in the field in spring 1987. In fall 1991, after f...

Aggregate statistics readily indicate why so many observers of financial markets in the United States are concerned about the balance between debt and equity in the corporate financial decision. Exceeding zero in most years before 1984, net equity issues by U.S. nonfinancial corporations have been negative in each year since. Net redemptions averaged approximately $80 billion annually during th...

There is a large and growing literature that investigates evidence for mean reversion in stock prices. Empirically, there is no consensus as to whether stock prices are mean reverting or random walk processes; at best, the results are mixed. In this paper, we provide further evidence on the mean reversion hypothesis for seventeen European countries using the Levin and Lin (1992), seemingly unre...

The views and opinions expressed or implied in this article are those of the author (or authors) and do not necessarily reflect the position of the National Marine Fisheries Service, NOAA. The relationship between reproductive output and subsequent recruitment of new individuals is fundamental to population dynamics. In stock assessments, this relationship is typically quantified by using a spa...

Abstract An automated region growing algorithm has been adapted for multi-class segmentation. The algorithm does not need initial parameters. It automatically finds a start region which is required by each of the region growing processes performed during multi-class segmentation. A new method for finding start region is proposed. The multi-class region growing algorithm was tested on CT and MRI...