Mapping Above- and Below-Ground Carbon Pools in Boreal Forests: The Case for Airborne Lidar

Abstract

A large and growing body of evidence has demonstrated that airborne scanning light detection and ranging (lidar) systems can be an effective tool in measuring and monitoring above-ground forest tree biomass. However, the potential of lidar as an all-round tool for assisting in assessment of carbon (C) stocks in soil and non-tree vegetation components of the forest ecosystem has been given much less attention. Here we combine the use airborne small footprint scanning lidar with fine-scale spatial C data relating to vegetation and the soil surface to describe and contrast the size and spatial distribution of C pools within and among multilayered Norway spruce (Picea abies) stands. Predictor variables from lidar derived metrics delivered precise models of above- and below-ground tree C, which comprised the largest C pool in our study stands. We also found evidence that lidar canopy data correlated well with the variation in field layer C stock, consisting mainly of ericaceous dwarf shrubs and herbaceous plants. However, lidar metrics derived directly from understory echoes did not yield significant models. Furthermore, our results indicate that the variation in both the mosses and soil organic layer C stock plots appears less influenced by differences in stand structure properties than topographical gradients. By using topographical models from lidar ground returns we were able to establish a strong correlation between lidar data and the organic layer C stock at a stand level. Increasing the topographical resolution from plot averages (~2000 m2) towards individual grid cells (1 m2) did not yield consistent models. Our study demonstrates a connection between the size and distribution of different forest C pools and models derived from airborne lidar data, providing a foundation for future research concerning the use of lidar for assessing and monitoring boreal forest C.

Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

“…All subset selection Seqrep was the most powerful selection method in agreement with Hansen et al [84] who also used similar best subset regression procedures to estimate biomass with ALS data. Spearman rho coefficients, proposed as a good tool for determining the relationships between ALS and field metrics by Kristensen et al [85], also showed a good result, agreeing with our previous studies [37]. Furthermore, our results agree with García-Gutiérrez et al [78], who found that stepwise was a powerless technique.…”

An overview of ABoVE airborne campaign data acquisitions and science opportunities

“…(Kristensen et al 2015;Margolis et al 2015) and LIDAR in conjunction with SAR(Montesano et al 2014) or optical imagery (Goetz et al 2010; Bolton et al 2015; Langford et al 2016). LVIS data are also being used to characterize the tundra-taiga ecotone (after Callaghan et al 2002a, 2002b) and as control data in pan-Arctic digital elevation maps derived from high spatial resolution imagery such as ArcticDEM (https://pgc.umn.edu).…”

Coupling Fine-Scale Root and Canopy Structure Using Ground-Based Remote Sensing

“…Airborne LiDAR data products alone have been used to quantify root mass at sub-hectare (>30 m) spatial scales in boreal and subtropical forests [58,59], and to scale GPR-based root mass in an oak savanna [21], highlighting potential for joining LiDAR and radar technologies to more comprehensively quantify and couple above-and belowground ecosystem structure. Our findings build on and advance these prior results by showing that remote sensing applications of coupled ground-based LiDAR and ground penetrating radar, which both operate at finer spatial scales, may provide an order of magnitude higher (<10 m) resolution.…”