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| SILVA,CARLOS ALBERTO; KLAUBERG,CARINE; HUDAK,ANDREW T.; VIERLING,LEE A.; LIESENBERG,VERALDO; BERNETT,LUIZ G.; SCHERAIBER,CLEWERSON F.; SCHOENINGER,EMERSON R.. |
| ABSTRACT Accurate forest inventory is of great economic importance to optimize the entire supply chain management in pulp and paper companies. The aim of this study was to estimate stand dominate and mean heights (HD and HM) and tree density (TD) of Pinus taeda plantations located in South Brazil using in-situ measurements, airborne Light Detection and Ranging (LiDAR) data and the non- k-nearest neighbor (k-NN) imputation. Forest inventory attributes and LiDAR derived metrics were calculated at 53 regular sample plots and we used imputation models to retrieve the forest attributes at plot and landscape-levels. The best LiDAR-derived metrics to predict HD, HM and TD were H99TH, HSD, SKE and HMIN. The Imputation model using the selected metrics was more... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Forest inventory; LiDAR metrics; K-NN Imputation; Remote Sensing. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000100295 |
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