The ability to distinguish peatland types at the landscape scale has implications for inventory, conservation, estimation of carbon storage, fuel loading, and postfire carbon emissions, among others. This paper presents a multisensor, multiseason remote sensing approach to delineate boreal peatland types (wooded bog, open fen, shrubby fen, treed fen) using a combination of multiple dates of L-band (24 cm) synthetic aperture radar (SAR) from ALOS PALSAR, C-band (∼5.6 cm) from ERS-1 or ERS-2, and Landsat 5 TM optical remote sensing data. Imagery was first evaluated over a small test area of boreal Alberta, Canada, to determine the feasibility of using multisensor SAR and optical data to discriminate peatland types. Then object-based and (or) machine-learning classification algorithms were applied to 3.4 million ha of peatland-rich subregions of Alberta, Canada, and the 4.24 million ha region of Michigan’s Upper Peninsula where peatlands are less dominant. Accuracy assessments based on field-sampled sites show high overall map accuracies (93%–94% for Alberta and Michigan), which exceed those of previous mapping efforts.
Characteristics of school bullies and victims identified in the professional literature vary widely. This study turned these characteristics into a survey that included 70 potential characteristics. A world-wide group of 14 experts were asked to rate the importance of each characteristic in identifying either bullies or victims. The results demonstrated strong agreement on 19 characteristics for bullies and 19 for victims. Results of the study are discussed as well as the potential value of this data for increasing understanding of these groups, early identification of potential problem students, and the relationship of the results to prevention and intervention models.
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