Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning

编号 030025301

推送时间 20200824

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Machine Learning Algorithms to Predict Tree-Related Microhabitats using Airborne Laser Scanning

来源期刊 REMOTE SENSING

期 第253期

发表时间 20200703

关键词 forest biodiversity;  habitat trees;  LiDAR;  remote sensing;  forestry; 

摘要 In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.

服务人员 付贺龙

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