Heikinpäivä in Hancock

Each January in Hancock, Michigan, the Heikinpäivä Midwinter Festival offers local residents three weeks of activities highlighting the continuing role Finnish culture has in the area. Utilizing a set of fading Finnish midwinter traditions surrounding the day of Heikki, or Henrik, this festival has grown from a brief day-long gathering to a long period of activity incorporating films, craft and cooking classes, religious services, and more traditional festival events such as a parade, games, feasting, music, and dancing. This festival has complex origins in more commonplace agricultural traditions brought from Finland by immigrants, which are often no longer commonly remembered in Finland to this day. In this paper, I will examine the complex history of this festival both through its Finnish origins and through its current incarnation in Michigan. Through this festival, we can see the role Finnish heritage has as a simultaneous marker of cultural pride and deprecation. The place Finnish heritage has as a tool in community and economic development in the City of Hancock and the wider region will also be explored. Finally, the function of the festival as a means of maintaining traditions seemingly doomed to fade with time will also be explored.

Applications of LiDAR remote sensing of forest structure in the Upper Great Lakes region, USA

We used active remote sensing technology to characterize forest structure in a northern temperate forest on a landscape- and local-level in the Upper Peninsula of Michigan. Specifically, we used a form of active remote sensing called light detection and ranging (e.g., LiDAR) to aid in the depiction of current forest structural stages and total canopy gap area estimation. On a landscape-level, LiDAR data are shown not only to be a useful tool in characterizing forest structure, in both coniferous and deciduous forest cover types, but also as an effective basis for data-driven surrogates for classification of forest structure. On a local-level, LiDAR data are shown to be a benchmark reference point to evaluate field-based canopy gap area estimations, due to the highly accurate nature of such remotely sensed data. The application of LiDAR remote sensed data can help facilitate current and future sustainable forest management.