Applications of artificial intelligence and LiDAR in forest inventories: A Systematic Literature Review

Abstract

Forest inventory is a crucial tool for managing forest resources by providing quantitative and qualitative information about a particular region, much of which is collected manually in the field. Using devices such as Light Detection and Ranging (LiDAR) assists in collecting and analyzing various parameters of forest inventory. Adopting artificial intelligence (AI) techniques has sparked interest among forestry engineers seeking to work with forest LiDAR data. In this context, this study presents a Systematic Literature Review (SLR) to identify, evaluate, and interpret the results of primary studies related to the intersection between AI and Forestry Engineering. The automated search strategy retrieved 218 studies, of which 46 were selected after applying inclusion and exclusion criteria and quality assessment. After analyzing and synthesizing the data, the results showed that deep learning is becoming an increasing trend in recent research and that the direct estimation of tree diameter from aerial scans, although critical, has been minimally explored, highlighting an open field for future research.