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/métodosRESUMO
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 DadosRESUMO
ABSTRACT 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.
Assuntos
Florestas , Pinus taeda/crescimento & desenvolvimento , Clima Tropical , Brasil , Biomassa , Tecnologia de Sensoriamento Remoto , Modelos TeóricosRESUMO
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.
Assuntos
Florestas , Pinus taeda/crescimento & desenvolvimento , Biomassa , Brasil , Modelos Teóricos , Tecnologia de Sensoriamento Remoto , Clima TropicalRESUMO
Forest fragmentation affects the heterogeneity of accumulated fuels by increasing the diversity of forest types and by increasing forest edges. This heterogeneity has implications in how we manage fuels, fire, and forests. Understanding the relative importance of fragmentation on woody biomass within a single climatic regime, and along climatic gradients, will improve our ability to manage forest fuels and predict fire behavior. In this study we assessed forest fuel characteristics in stands of differing moisture, i.e., dry and moist forests, structure, i.e., open canopy (typically younger) vs. closed canopy (typically older) stands, and size, i.e., small (10-14 ha), medium (33 to 60 ha), and large (100-240 ha) along a climatic gradient of boreal, temperate, and tropical forests. We measured duff, litter, fine and coarse woody debris, standing dead, and live biomass in a series of plots along a transect from outside the forest edge to the fragment interior. The goal was to determine how forest structure and fuel characteristics varied along this transect and whether this variation differed with temperature, moisture, structure, and fragment size. We found nonlinear relationships of coarse woody debris, fine woody debris, standing dead and live tree biomass with mean annual median temperature. Biomass for these variables was greatest in temperate sites. Forest floor fuels (duff and litter) had a linear relationship with temperature and biomass was greatest in boreal sites. In a five-way multivariate analysis of variance we found that temperature, moisture, and age/structure had significant effects on forest floor fuels, downed woody debris, and live tree biomass. Fragment size had an effect on forest floor fuels and live tree biomass. Distance from forest edge had significant effects for only a few subgroups sampled. With some exceptions edges were not distinguishable from interiors in terms of fuels.
Assuntos
Biomassa , Ecossistema , Árvores , Conservação dos Recursos Naturais , Incêndios , América do Norte , Porto Rico , Análise de Regressão , Clima Tropical , MadeiraRESUMO
In this study, we set up a wood decomposition experiment to i) quantify the percent of mass remaining, decay constant and performance strength of aspen stakes (Populus tremuloides) in dry and moist boreal (Alaska and Minnesota, USA), temperate (Washington and Idaho, USA), and tropical (Puerto Rico) forest types, and ii) determine the effects of fragmentation on wood decomposition rates as related to fragment size, forest age (and/or structure) and climate at the macro- and meso-scales. Fragment sizes represented the landscape variability within a climatic region. Overall, the mean small fragments area ranged from 10-14 ha, medium-sized fragments 33 to 60 ha, and large fragments 100-240 ha. We found that: i) aspen stakes decayed fastest in the tropical sites, and the slowest in the temperate forest fragments, ii) the percent of mass remaining was significantly greater in dry than in moist forests in boreal and temperate fragments, while the opposite was true for the tropical forest fragments, iii) no effect of fragment size on the percent of mass remaining of aspen stakes in the boreal sites, temperate dry, and tropical moist forests, and iv) no significant differences of aspen wood decay between forest edges and interior forest in boreal, temperate and tropical fragments. We conclude that: i) moisture condition is an important control over wood decomposition over broad climate gradients; and that such relationship can be non linear, and ii) the presence of a particular group of organism (termites) can significantly alter the decay rates of wood more than what might be predicted based on climatic factors alone. Biotic controls on wood decay might be more important predictors of wood decay in tropical regions, while abiotic constraints seems to be important determinants of decay in cold forested fragments.