| Revue: | Anais da Academia Brasileira de Ciencias |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000412323 |
| ISSN: | 0001-3765 |
| Autores: | Silva, Carlos A1 Klauberg, Carine2 Hudak, Andrew T2 Vierling, Lee A1 Fennema, Scott J3 Corte, Ana Paula D4 |
| Instituciones: | 1University of Idaho, College of Natural Resources, Moscow, Idaho. Estados Unidos de América 2United States Department of Agriculture, Rocky Mountain Research Station, Moscow, Idaho. Estados Unidos de América 3University of Idaho, College of Agriculture and Life Sciences, Moscow, Idaho. Estados Unidos de América 4Universidade Federal do Parana, Departamento de Engenharia Florestal, Curitiba, Parana. Brasil |
| Año: | 2017 |
| Periodo: | Sep |
| Volumen: | 89 |
| Número: | 3 |
| Paginación: | 1895-1906 |
| País: | Brasil |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | Basal area (BA) is a good predictor of timber stand volume and forest growth. This study developed predictive models using field and airborne LiDAR (Light Detection and Ranging) data for estimation of basal area in Pinus taeda plantation in south Brazil. In the field, BA was collected from conventional forest inventory plots. Multiple linear regression models for predicting BA from LiDAR-derived metrics were developed and evaluated for predictive power and parsimony. The best model to predict BA from a family of six models was selected based on corrected Akaike Information Criterion (AICc) and assessed by the adjusted coefficient of determination (adj. R²) and root mean square error (RMSE). The best model revealed an adj. R²=0.93 and RMSE=7.74%. Leave one out cross-validation of the best regression model was also computed, and revealed an adj. R² and RMSE of 0.92 and 8.31%, respectively. This study showed that LiDAR-derived metrics can be used to predict BA in Pinus taeda plantations in south Brazil with high precision. We conclude that there is good potential to monitor growth in this type of plantations using airborne LiDAR. We hope that the promising results for BA modeling presented herein will stimulate to operate this technology in Brazil |
| Disciplinas: | Agrociencias, Biología |
| Palabras clave: | Silvicultura, Ecología, Inventario forestal, Area basal, Percepción remota, Regresión lineal múltiple |
| Keyword: | Agricultural sciences, Biology, Silviculture, Ecology, Forest inventory, Basal area, Remote sensing, Multiple linear regression |
| Texte intégral: | Texto completo (Ver HTML) |
