2021
DOI: 10.1186/s40663-021-00338-4
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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 lev… Show more

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Cited by 42 publications
(39 citation statements)
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References 33 publications
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“…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.…”
Section: Model Performance and Map Agreement Assessmentsupporting
confidence: 81%
See 1 more Smart Citation
“…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.…”
Section: Model Performance and Map Agreement Assessmentsupporting
confidence: 81%
“…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.…”
Section: Introductionmentioning
confidence: 99%
“…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].…”
Section: Introduction 1overviewmentioning
confidence: 99%
“…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.…”
Section: Description Class Terrain Criteriamentioning
confidence: 99%