National Forest Biomass Mapping Using the Two-Level Model

编号 030027004

推送时间 20201221

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 National Forest Biomass Mapping Using the Two-Level Model

来源期刊 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

期 第270期

发表时间 20200226

关键词 Forestry;  Estimation;  Data models;  Biological system modeling;  Predictive models;  Synthetic aperture radar;  Satellites;  interferometry;  synthetic aperture radar (SAR);  vegetation mapping; 

摘要 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.

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