Data/hora: 11/06/2024 04:28:43

Provedor de dados: | Anais da Academia Brasileira de Ciências
País: | Brazil
Título: | Estimating Stand Height and Tree Density in Pinus taeda plantations using in-situ data, airborne LiDAR and k-Nearest Neighbor Imputation
Autores: | SILVA,CARLOS ALBERTO; KLAUBERG,CARINE; HUDAK,ANDREW T.; VIERLING,LEE A.; LIESENBERG,VERALDO; BERNETT,LUIZ G.; SCHERAIBER,CLEWERSON F.; SCHOENINGER,EMERSON R.
Data: | 2018-03-01
Ano: | 2018
Palavras-chave: | Forest inventory; LiDAR metrics; K-NN Imputation; Remote Sensing.
Resumo: | 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 effective for retrieving height than tree density. The model coefficients of determination (adj.R2) and a root mean squared difference (RMSD) for HD, HM and TD were 0.90, 0.94, 0.38m and 6.99, 5.70, 12.92%, respectively. Our results show that LiDAR and k-NN imputation can be used to predict stand heights with high accuracy in Pinus taeda. However, furthers studies need to be realized to improve the accuracy prediction of TD and to evaluate and compare the cost of acquisition and processing of LiDAR data against the conventional inventory procedures.
Tipo: | Info:eu-repo/semantics/article
Idioma: | Inglês
Identificador: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652018000100295
Editor: | Academia Brasileira de Ciências
Relação: | 10.1590/0001-3765201820160071
Formato: | text/html
Fonte: | Anais da Academia Brasileira de Ciências  v.90 n.1 2018
Fonte: | info:eu-repo/semantics/openAccess