Remote sensing-based forest aboveground tree biomass and uncertainty assessment through upscaling from single tree to stand level

Robust estimation of aboveground biomass (AGB) plays a pivotal role in forest resource management and carbon accounting. These estimates are especially relevant within the framework of climate mitigation strategies such as REDD+, yet direct tree-level estimates over large areas are still challenging to obtain. AGB predictions commonly rely on allometric models calibrated from destructive sampled trees. While diameter-based allometric models dominate, the high costs related to measuring diameters at tree-level have recently driven interest in alternative allometries. In this context, advances in remote-sensing technologies enable direct and spatially explicit characterization of three-dimensional forest structure, including tree height and crown attributes. Crown width continues to expand even when tree height growth slows, offering valuable information for AGB prediction. Together, these developments support diameter-independent, remotely sensed AGB models, though challenges remain in data availability, segmentation accuracy, and cross-site generalization.

Thus, the objective of this study was to develop an alternative methodological framework for assessing single-tree and stand AGB, along with its associated uncertainty, by upscaling predictions from field-calibrated terrestrial laser scanning (TLS) data to airborne laser scanning (ALS) data.
To achieve our objective, we conducted a case study at the Remningstorp study area in southern Sweden (58.5° N, 13.6° E), where the forest is dominated by Norway spruce (Picea abies), Scots pine (Pinus sylvestris), and birch (Betula spp.). In 2014, the site was surveyed collecting a combination of field measurements (diameter at breast height and tree height), TLS, and ALS data. In addition, destructively collected single-tree measurements from Marklund (1998) are being used to define diameter-independent models for AGB prediction, using crown-related features, such as tree height and crown diameter, as explanatory variables.

Our assumption is that crown related features can be reliably characterized from medium-density ALS data (approximately 10–100 points/m²). Thus, we will use the diameter-independent model to predict tree-level AGB from ALS and perform a rigorous assessment of associated uncertainties. This approach relies on accurate single-tree segmentation, the matching of field-measured trees with remotely sensed trees, and the extraction of crown and height metrics from TLS and ALS data. The accuracy of our method will be further tested comparing the proposed survey approach with the traditional Swedish AGB models, based on the tree height and DBH as predictors (Marklund 1998).

The expected results of this study are twofold: (i) the development of an AGB allometric model based on tree height and crown diameter, applicable to both field-measured and remotely sensed data, (ii) a comprehensive evaluation of the uncertainties inherent in upscaling this model from TLS to ALS data, and (iii) wall-to-wall AGB mapping with associated uncertainty analysis across the study area.