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

Remote Sensing

Mauro Maesano¹  Giovanni Santopuoli²  Marco Marchetti³  Bruno Lasserre³  MirkoDi Febbraro³  Marco Balsi⁴ 

[1] Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, via San Camillo de Lellis snc, 01100 Viterbo, Italy;Dipartimento Agricoltura, Ambiente e Alimenti, Università degli Studi del Molise, Via De Sanctis s.n.c., 86100 Campobasso, Italy;Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Cda Fonte Lappone, s.n.c., 86090 Pesche (IS), Italy;Dipartimento di Ingegneria dell'Informazione, Elettronica e Telecomunicazioni, Università "La Sapienza", 00184 Roma, Italy;

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

DOI  :  10.3390/rs12132142

来源: DOAJ

【 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.

【 授权许可】

Unknown