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Impact of local slope and aspect assessed from LiDAR records on tree diameter in radiata pine (Pinus radiata D. Don) plantations
Author(s) -
Hanieh Saremi,
Lalit Kumar,
Russell Turner,
Christine Stone,
Gavin J. Melville
Publication year - 2014
Publication title -
annals of forest science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.763
H-Index - 77
eISSN - 1297-966X
pISSN - 1286-4560
DOI - 10.1007/s13595-014-0374-4
Subject(s) - diameter at breast height , pinus radiata , lidar , radiata , biomass (ecology) , environmental science , forestry , tree (set theory) , sampling (signal processing) , remote sensing , geography , ecology , mathematics , biology , agronomy , mathematical analysis , vigna , filter (signal processing) , computer science , computer vision
International audience& Context Reliable information on tree stem diameter varia-tion at local spatial scales and on the factors controlling it could potentially lead to improved biomass estimation over pine plantations. & Aims This study addressed the relationship between local topography and tree diameter at breast height (DBH) within two even-aged radiata pine plantation sites in New South Wales, Australia. & Methods A total of 85 plots were established, and 1,302 trees were sampled from the two sites. Airborne light detection and ranging (LiDAR) was used to derive slope and aspect and to link them to each individual tree. & Results The results showed a significant relationship be-tween DBH and local topography factors. At both sites, trees on slopes below 20° and on southerly aspects displayed sig-nificantly larger DBHs than trees on steeper slopes and north-erly aspects. Older trees with similar heights also exhibited a significant relationship between DBH and aspect factor, where greater DBHs were found on southerly aspects. & Conclusions The observed correlation between tree DBH and LiDAR-derived slope and aspect could contribute to the development of improved biomass estimation approaches in pine plantations. These topographical variables are easily attained with airborne LiDAR, and they could potentially improve DBH predictions in resource inventories (e.g. stand volume or biomass) and support field sampling design

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