Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data

Abstract

Background The Norwegian forest resource map (SR16) maps forest attributes by combining national forest inventory (NFI), airborne laser scanning (ALS) and other remotely sensed data. While the ALS data were acquired over a time interval of 10 years using various sensors and settings, the NFI data are continuously collected. Aims of this study were to analyze the effects of stratification on models linking remotely sensed and field data, and assess the accuracy overall and at the ALS project level. Materials and methods The model dataset consisted of 9203 NFI field plots and data from 367 ALS projects, covering 17 Mha and 2/3 of the productive forest in Norway. Mixed-effects regression models were used to account for differences among ALS projects. Two types of stratification were used to fit models: 1) stratification by the three main tree species groups spruce, pine and deciduous resulted in species-specific models that can utilize a satellite-based species map for improving predictions, and 2) stratification by species and maturity class resulted in stratum-specific models that can be used in forest management inventories where each stand regularly is visually stratified accordingly. Stratified models were compared to general models that were fit without stratifying the data. Results The species-specific models had relative root-mean-squared errors (RMSEs) of 35%, 34%, 31%, and 12% for volume, aboveground biomass, basal area, and Lorey’s height, respectively. These RMSEs were 2–7 percentage points (pp) smaller than those of general models. When validating using predicted species, RMSEs were 0–4 pp. smaller than those of general models. Models stratified by main species and maturity class further improved RMSEs compared to species-specific models by up to 1.8 pp. Using mixed-effects models over ordinary least squares models resulted in a decrease of RMSE for timber volume of 1.0–3.9 pp., depending on the main tree species. RMSEs for timber volume ranged between 19%–59% among individual ALS projects. Conclusions The stratification by tree species considerably improved models of forest structural variables. A further stratification by maturity class improved these models only moderately. The accuracy of the models utilized in SR16 were within the range reported from other ALS-based forest inventories, but local variations are apparent.

Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages

“…Our model accuracy against the 20% testing partition of our model dataset was favorably comparable to previous LiDAR-AGB mapping studies (Huang et al 2019;Nilsson et al 2017;Ayrey et al 2021;Hauglin et al 2021). Using a set of FIA-developed methods Menlove and Healey 2020) we further demonstrated a strong agreement between our map-based estimates and FIA-derived estimates.…”

“…Huang et al (2019) pooled by ecoregion. Both Ayrey et al (2021) and Hauglin et al (2021) pooled all coverages but used a convolutional neural network and a mixed-effects model respectively, with differing protocols for inventory plot selection.…”

Synthesizing Disparate LiDAR and Satellite Datasets through Deep Learning to Generate Wall-to-Wall Regional Inventories for the Complex, Mixed-Species Forests of the Eastern United States

“…While LiDAR data are becoming increasingly available to the public, few studies have emphasized mapping whole regions (e.g., [12]) while focusing instead on specific municipalities or individual parcels. One example of regional LiDAR modeling occurred in Sweden, which recently developed nation-wide forest inventory maps at a 12.5 m resolution [13], while similar maps have also been generated in Finland [14].…”

The utility of forest attribute maps for automated Avalanche Terrain Exposure Scale (ATES) modelling

“…To achieve this mapping of forest attributes, the entire area is gridded into aerial units of the same size as used for model fitting on NFI plots (250 m 2 ), and the same remote sensing variables are extracted for each unit. Following this approach, various forest attributes were modelled and mapped for the Norwegian forest resource map SR16 (Astrup et al 2019;Hauglin et al 2021), which is a national map at spatial resolution of 16 m x 16 m.…”