Management impacts on Carex assiniboinensis and Cardamine concatenata on the Ottawa National Forest

Monitoring of herbaceous plants on the Ottawa National Forest (ONF) is used to understand the impact of forest management on understory composition and site conditions. In their planning, national forests are required to take into account management impacts on diversity and ecosystem health. The effect of management on understory species is dependent on various factors, including the intensity of disturbance and the biology of the plant. In the first study in this report, a population of Carex assiniboinensis, a Michigan state threatened species, was monitored for seven seasons including before logging commenced, in order to determine the sedge’s response to a single-tree selection harvest. Analyses provided insights for management of C. assiniboinensis at the stand level over the short-term. In the second study in this report, the use of the cutleaf toothwort (Cardamine concatenata) as a Management Indicator Species on the ONF was reviewed. Data were analyzed to determine the suitability of using C. concatenata to monitor impacts of forest management on site conditions. The various factors that affect understory species population dynamics illuminated the challenges of using indicator species to monitor site conditions. Insights from the study provide a greater understanding of management impacts on understory species across the Ottawa National Forest.

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.

APPLICATION OF AN IMPUTATION METHOD FOR GEOSPATIAL INVENTORY OF FOREST STRUCTURAL ATTRIBUTES ACROSS MULTIPLE SPATIAL SCALES IN THE LAKE STATES, U.S.A.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.