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.
Highlights
Light Detection and Ranging (LIDAR) is an optical remote sensing technology which measures properties of scattered light to find range and/ or other information of a distant target
Young forest stands of Pinus taeda are normally (Eq.2) defined by a lower canopy coverage, high tree density and low height values
Intermediate and advanced stands, present higher canopy coverage values, higher height values and lower tree density when compared with young stands due to forest thinning and mortality
Summary
Light Detection and Ranging (LIDAR) is an optical remote sensing technology which measures properties of scattered light to find range and/ or other information of a distant target. Several LiDAR-derived metrics can be derived out from these point clouds (McGaughey 2015) These metrics can be used indirectly to predict several other parameters of the forests using either regression or classification approaches to spatially represent these selected attribute over large areas (Dubayah and Drake 2000). Pine plantations are the most important long fiber source for pulp and paper production in South Brazil. It covers nowadays an area of 1.59 million hectares, accounting for approximately 20.54% of the country’s total reforested area (Ibá 2015). It has high economic importance due to its high volumetric increment in the colder regions of the southern plateau (Kohler et al 2014) It has fast growing rates presenting increments up to 50 m3·ha-1·year-1 (Ibá 2015)
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