Understanding Michigan nonindustrial private forest landowners attitudes and knowledge about forest management

Approximately one-fourth of the non-industrial private forestland (NIPF) owners in the state of Michigan, who collectively own approximately 50% of the private forested land, have conducted commercial timber harvest in recent years. Previous studies indicated that NIPFs preferred to manage their forest for a sustained yield of high-quality timber, but were limited to even-aged regeneration treatments or conversion for uneven-aged silviculture due to previous cuttings. Improved knowledge about NIPF’s intentions and forest management behavior could be useful for successful implementation of sustained yield management. This study’s objective was to identify more active NIPF’s attitudes towards timber management, their forest management practices and whether their forest management behavior leads or leads not to q management for sustained yield. Active NIPF’s intentions to harvest timber for biofuels and its suitability with NIPF’s forest management behavior will be discussed. Phone interviews of 30 NIPFs who have experience with commercial timber harvests were conducted between August and October 2011. All interviews were recorded, transcribed, and analyzed for identifying NIPF’s motivations, attitudes, forest management behavior and forestry related knowledge. Interviewees, whether consciously or not, tended to manage their land for a sustained yield and they would be willing to harvest timber for biofuels facility as long as it benefits landowners management goals.

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.