Revista: | Anais da Academia Brasileira de Ciencias |
Base de datos: | PERIÓDICA |
Número de sistema: | 000420607 |
ISSN: | 0001-3765 |
Autors: | Silva, Carlos Alberto1 Klauberg, Carine1 Hudak, Andrew T1 Vierling, Lee A2 Liesenberg, Veraldo3 Bernett, Luiz G4 Scheraiber, Clewerson F4 Schoeninger, Emerson R4 |
Institucions: | 1USDA Forest Service, Rocky Mountain Research Station, Moscow, Idaho. Estados Unidos de América 2University of Idaho, College of Natural Resources, Moscow, Idaho. Estados Unidos de América 3Universidade do Estado de Santa Catarina, Departamento de Engenharia Florestal, Lages, Santa Catarina. Brasil 4Klabin, S.A., Telemaco Borba, Parana. Brasil |
Any: | 2018 |
Període: | Mar |
Volum: | 90 |
Número: | 1 |
Paginació: | 295-310 |
País: | Brasil |
Idioma: | Inglés |
Tipo de documento: | Artículo |
Enfoque: | Analítico, descriptivo |
Resumen en inglés | 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 |
Disciplines | Agrociencias |
Paraules clau: | Silvicultura, Manejo forestal, Inventario forestal, Densidad poblacional, Altura de planta, Percepción remota, Pinus taeda |
Keyword: | Silviculture, Forest management, Forest inventory, Population density, Plant height, Remote sensing, Pinus taeda |
Text complet: | Texto completo (Ver HTML) |