Forest Volume and Biomass Estimation Using Small-Footprint Lidar-Distributional Parameters on a Per-Segment Basis
نویسندگان
چکیده
This study assessed a lidar-based, object-oriented (segmentation) approach to forest volume and aboveground biomass modeling. The study area in the Piedmont physiographic region of Virginia is composed of temperate coniferous, deciduous, and mixed stands. Segmentation objects, hierarchical in terms of area and ranging from 0.035 to 5.632 ha/object, were created using a lidar-derived canopy height model. Horizontal point (basal area) samples were used to calculate volume and aboveground biomass. Per-object lidar point (per return height and intensity) distributional parameters were extracted from small-footprint lidar. Adjusted R and Mallow’s Cp metrics were used to select models for the range of segmentation results. Selected variables included intensity-based and structurally related first through fifth return height parameters. Object-based modeling (adjusted R 0.58–0.79; various object sizes) resulted in distinct improvements over stand-based attempts (adjusted R 0.40–0.73; majority adjusted R 0.50). Adjusted R and RMSE values for deciduous volume (0.59; 51.15 m/ha) and biomass (0.58; 37.41 Mg/ha) were better than those found for another, plot-based study in the study area. Coniferous R values for volume (0.66) and biomass (0.59) were lower than previous studies, which was attributed to variability within the relatively narrow volume range (6.94–50.93 m/ha). FOR. SCI. 52(6):636–649.
منابع مشابه
Improvement of Biomass Estimation in Forest Areas based on Polarimetric Parameters Optimization of SETHI airborne Data using Particle Swarm Optimization Method
Estimation of forest biomass has received much attention in recent decades. Airborne and spaceborne (SAR) have a great potential to quantify biomass and structural diversity because of its penetration capability. Polarizations are important elements in SAR systems due to sensitivity of them to backscattering mechanisms and can be useful to estimate biomass. Full Polarimetric Synthetic Aperture ...
متن کاملEstimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors
متن کامل
Estimating plot-level tree height and volume of Eucalyptus grandis plantations using small-footprint, discrete return lidar data
This study explores the utility of small-footprint, discrete return lidar data in deriving important forest structural attributes with the primary objective of estimating plot-level mean tree height, dominant height, and volume of Eucalyptus grandis plantations. The secondary objectives of the study were related to investigating the effect of lidar point densities (1 point/m, 3 points/m, and 5 ...
متن کاملEstimation of tropical forest structural characteristics using large-footprint lidar
Quantification of forest structure is important for developing a better understanding of how forest ecosystems function. Additionally, estimation of forest structural attributes, such as aboveground biomass (AGBM), is an important step in identifying the amount of carbon in terrestrial vegetation pools and is central to global carbon cycle studies. Although current remote sensing techniques rec...
متن کاملMapping Above- and Below-Ground Biomass Components in Subtropical Forests Using Small-Footprint LiDAR
In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map aboveand below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots (30 × 30 m) were modeled using a range o...
متن کامل