{"title":"Assessment of residual slash coverage using UAVs and implications for aspen regeneration","authors":"L. Sealey, K. Rees","doi":"10.1139/juvs-2019-0001","DOIUrl":null,"url":null,"abstract":"Proper redistribution of residual slash following harvesting is crucial for ensuring successful regeneration and continued health in trembling aspen (Populus tremuloides) forests. As traditional methods of measuring residual slash are a strenuous and tedious process, the objective of this study was to develop a new, faster, and more detailed method to assess residual slash distribution for entire harvested blocks. This study also aimed to assess the influence residual slash coverage had on the success of aspen regeneration 1 year after winter harvesting. Using high-resolution UAV imagery and maximum likelihood supervised image classification, residual slash was differentiated from the underlying forest floor. Overall, classification accuracy ranged between 85% and 96% with the highest accuracy occurring when aerial imagery was collected at the beginning of the second spring following winter harvesting. Slash distribution was quite consistent across harvested blocks, with 92% of harvested blocks experiencing <33% coverage. There was no relationship between the level of aspen regeneration following 1 year of growth and percentage slash coverage up to 60%. No vegetation plots occurred in areas with >60% slash coverage; therefore, it is unknown whether aspen regeneration will be affected in areas with higher slash coverage.","PeriodicalId":45619,"journal":{"name":"Journal of Unmanned Vehicle Systems","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1139/juvs-2019-0001","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Unmanned Vehicle Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1139/juvs-2019-0001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 3
Abstract
Proper redistribution of residual slash following harvesting is crucial for ensuring successful regeneration and continued health in trembling aspen (Populus tremuloides) forests. As traditional methods of measuring residual slash are a strenuous and tedious process, the objective of this study was to develop a new, faster, and more detailed method to assess residual slash distribution for entire harvested blocks. This study also aimed to assess the influence residual slash coverage had on the success of aspen regeneration 1 year after winter harvesting. Using high-resolution UAV imagery and maximum likelihood supervised image classification, residual slash was differentiated from the underlying forest floor. Overall, classification accuracy ranged between 85% and 96% with the highest accuracy occurring when aerial imagery was collected at the beginning of the second spring following winter harvesting. Slash distribution was quite consistent across harvested blocks, with 92% of harvested blocks experiencing <33% coverage. There was no relationship between the level of aspen regeneration following 1 year of growth and percentage slash coverage up to 60%. No vegetation plots occurred in areas with >60% slash coverage; therefore, it is unknown whether aspen regeneration will be affected in areas with higher slash coverage.