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National Forest Biomass Mapping Using the Two-Level Model

Persson, Henrik and Soja, Maciej J. and Fransson, Johan and Ulander, Lars M. H. (2020). National Forest Biomass Mapping Using the Two-Level Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13 , 6391-6400
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This article uses the two-level model (TLM) to predict above-ground biomass (AGB) from TanDEM-X synthetic aperture radar (SAR) data for Sweden. The SAR data were acquired between October 2015 and January 2016 and consisted of 420 scenes. The AGB was estimated from forest height and canopy density estimates obtained from TLM inversion with a power law model. The model parameters were estimated separately for each satellite scene. The prediction accuracy at stand-level was evaluated using field inventoried references from entire Sweden 2017, provided by a forestry company. AGB estimation performance varied throughout the country, with smaller errors in the north and larger in the south, but when the errors were expressed in relative terms, this pattern vanished. The error in terms of root mean square error (RMSE) was 45.6 and 27.2 t/ha at the plot- and stand-level, respectively, and the corresponding biases were -8.80 and 11.2 t/ha. When the random errors related to using sampled field references were removed, the RMSE decreased about 24% to 20.7 t/ha at the stand-level. Overall, the RMSE was of similar order to that obtained in a previous study (27-30 t/ha), where one linear regression model was used for all scenes in Sweden. It is concluded that, using the power law model with parameters estimated for each scene, the scene-wise variations decreased.

Authors/Creators:Persson, Henrik and Soja, Maciej J. and Fransson, Johan and Ulander, Lars M. H.
Title:National Forest Biomass Mapping Using the Two-Level Model
Year of publishing :2020
Page range:6391-6400
Number of Pages:10
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 2 Engineering and Technology > 207 Environmental Engineering > Remote Sensing
(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 401 Agricultural, Forestry and Fisheries > Forest Science
Keywords:Forestry, Estimation, Data models, Biological system modeling, Predictive models, Synthetic aperture radar, Satellites, Forestry, interferometry, synthetic aperture radar (SAR), vegetation mapping
Permanent URL:
Additional ID:
Type of IDID
Web of Science (WoS)000589192200003
ID Code:19776
Faculty:S - Faculty of Forest Sciences
Department:(S) > Dept. of Forest Resource Management
(NL, NJ) > Dept. of Forest Resource Management
Deposited By: SLUpub Connector
Deposited On:28 Dec 2020 05:23
Metadata Last Modified:15 Jan 2021 19:45

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