Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 2 de 2
Filtrar
Mais filtros

País/Região como assunto
Ano de publicação
Tipo de documento
Assunto da revista
País de afiliação
Intervalo de ano de publicação
1.
An Acad Bras Cienc ; 90(1): 295-309, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-29641763

RESUMO

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.


Assuntos
Modelos Estatísticos , Pinus taeda/crescimento & desenvolvimento , Tecnologia de Sensoriamento Remoto/métodos , Árvores/crescimento & desenvolvimento , Algoritmos , Brasil , Confiabilidade dos Dados , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Agricultura Florestal/métodos
2.
An. acad. bras. ciênc ; 90(1): 295-309, Mar. 2018. tab, graf
Artigo em Inglês | LILACS | ID: biblio-886909

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.


Assuntos
Árvores/crescimento & desenvolvimento , Modelos Estatísticos , Pinus taeda/crescimento & desenvolvimento , Tecnologia de Sensoriamento Remoto/métodos , Algoritmos , Brasil , Monitoramento Ambiental/métodos , Monitoramento Ambiental/estatística & dados numéricos , Agricultura Florestal/métodos , Confiabilidade dos Dados
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA