Abstract
The estimation of forest biomass by remote sensing is constrained by different uncertainties. An important source of uncertainty is the border effect, as tree crowns are not constrained by plot borders. Lidar remote sensing systems record the canopy height within a certain area, while the ground-truth is commonly the aboveground biomass of inventory trees geolocated at their stem positions. Hence, tree crowns reaching out of or into the observed area are contributing to the uncertainty in canopy-height–based biomass estimation. In this study, forest inventory data and simulations of a tropical rainforest’s canopy were used to quantify the amount of incoming and outgoing canopy volume and surface at different plot sizes (10, 20, 50, and 100 m). This was performed with a bottom-up approach entirely based on forest inventory data and allometric relationships, from which idealized lidar canopy heights were simulated by representing the forest canopy as a 3D voxel space. In this voxel space, the position of each voxel is known, and it is also known to which tree each voxel belongs and where the stem of this tree is located. This knowledge was used to analyze the role of incoming and outgoing crowns. The contribution of the border effects to the biomass estimation uncertainty was quantified for the case of small-footprint lidar (a simulated canopy height model, CHM) and large-footprint lidar (simulated waveforms with footprint sizes of 23 and 65 m, corresponding to the GEDI and ICESat GLAS sensors). A strong effect of spatial scale was found: e.g., for 20-m plots, on average, 16% of the CHM surface belonged to trees located outside of the plots, while for 100-m plots this incoming CHM fraction was only 3%. The border effects accounted for 40% of the biomass estimation uncertainty at the 20-m scale, but had no contribution at the 100-m scale. For GEDI- and GLAS-based biomass estimates, the contributions of border effects were 23% and 6%, respectively. This study presents a novel approach for disentangling the sources of uncertainty in the remote sensing of forest structures using virtual canopy modeling.
Highlights
Biomass maps derived from remote sensing methods are an effective way to conduct detailed, spatially explicit carbon accounting of vegetation over large areas
At the 10-m scale, the overall nRMSE was 121%, with a contribution of 53% caused by border effects, and a residual nRMSE of 68% when border effects were excluded by periodic boundary conditions
At the 20-m scale, the overall nRMSE was 48%, of which 19% could be attributed to border effects and 29% was residual uncertainty
Summary
Biomass maps derived from remote sensing methods are an effective way to conduct detailed, spatially explicit carbon accounting of vegetation over large areas. A frequently applied method for aboveground biomass (AGB) estimation uses remote sensing data from active sensor systems, such as light detection and ranging (lidar) or synthetic aperture radar [2]. Such data are used to generate metrics of canopy height, which are linked to AGB via regression models [3]. The area sizes used depend on the size of the available
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