1924 UM Track Team

Top Row: Grant Hicks, Roy Callahan, William Aubry, Stewart Hulse, John Groshko

3rd Row: Nathan Feinsinger Glen Rearick, Halbert Loomis, George Griffin, Edward Huggins

2nd row: Leonard Goldwater, Harold Cochran ,Clayton Purdy, James Brooker, Richard Freyberg, William DeHart Hubbard

Front Row: Richard Doyle, Charles Reinke, Coach Steve Farrell, captain W. Homer Hattendorf, st. mngr. Arthur Graves, Raymond Smith, William Roesser

1925 UM Track Team

Top Row: Daniel Huff, Floyd McCaffree

3rd Row: Theodore Hornberger, Clayton Briggs, Guy Freeborn, Raymond Walk, John Schravesande, Harry Hawkins, Grant Hicks, Roy Callahan

2nd Row: George Likert, Harold Ohlheiser, William Herrnstein, Poorman Mueller, st. mngr. Archibald Herrick, coach Steve Farrell, Emil Voelker, Nathan Feinsinger, Raymond Hart

Front Row: David Weeks, Richard Freyberg, Stewart Hulse, William DeHart Hubbard, Lester Whittman, Charles Reinke, Richard Doyle, Philip Northrup

1922 Men's Cross country Team

Back Row: coach Steve Farrell, captain John Bowen. mngr. Lawrence. Snell

Front Row: Glenn Rearick, Charles Reinke, Egbert Isbel, John Shenefield, Raymond Arndt

1923 Men's Cross country Team

Back Row: Harry Davis, Charles Reinke, mngr John Morse, John Bowen, George Griffin

Front Row: John Shenefield, captain Raymond Arndt, coach Steve Farrell, Glenn Rearick

1924 Men's Cross country Team

Back Row: Charles E. Baker, Miles Reinke, Clayton Briggs, Theodore Hornberger

Front Row: Ira Mason, coach Steve Farrell, captain John Schenefield, mngr. Edwin Hartwick, Roy Callahan

1925 Men's Cross country Team

Back Row: Chester Jung, Clayton Briggs, Miles Reinke, Theodore Hornberger, Haig Iskenderian

Front Row: coach Steve Farrell, Roy Callahan, mngr. Robert Buick

Not pictured: Charles Baker, John H. Thoits, Miles Reinke

Festschrift der Universität Kiel zur feier des geburtsfestes Seiner Majestät des kaisers und königs Wilhelm II. Der álteste botanische garten Kiels. Urkundliche darstellung der begründung eines universitäts-instituts im siebzehnten jahrhundert.

Mode of access: Internet.

Botanisch-geologische streifzüge an den küsten des herzogtums Schleswig.

Mode of access: Internet.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.