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

被引:1
作者
Rodrigues, Welington G. [1 ,2 ]
Vieira, Gabriel S. [2 ,3 ]
Cabacinha, Christian D. [4 ]
Bulcao-Neto, Renato F. [2 ]
Soares, Fabrizzio [2 ]
机构
[1] Inst Fed Maranhao, Campus Acailandia, Acailandia, MA, Brazil
[2] Univ Fed Goias, Inst Informat, Ave Esperanca s-n,Campus Samambaia Predio Reitoria, BR-74690900 Goiania, Go, Brazil
[3] Inst Fed Goiano, Lab Visao Computac, Urutai, Go, Brazil
[4] Univ Fed Minas Gerais, Inst Ciencias Agr ICA UFMG, Montes Claros, MG, Brazil
关键词
Artificial intelligence; Systematic literature review; Forest measurements; LiDAR; Deep learning; AIRBORNE LIDAR; HARVESTER DATA; VOLUME; BIOMASS; HEIGHT; TREES;
D O I
10.1016/j.compeleceng.2024.109793
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页数:17
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