National Forest Biomass Mapping Using the Two-Level Model
Journal article, 2020

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.

interferometry

synthetic aperture radar (SAR)

vegetation mapping

Forestry

Author

Maciej Soja

MJ Soja Consulting

University of Tasmania

J.E.S. Fransson

Swedish University of Agricultural Sciences (SLU)

Lars Ulander

University of Tasmania

Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing, Optical Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

1939-1404 (ISSN)

Vol. 13 6391-6400 9222480

Subject Categories

Geophysics

Probability Theory and Statistics

Control Engineering

DOI

10.1109/JSTARS.2020.3030591

More information

Latest update

12/21/2020