The Potential of Sentinel Satellites for Large Area Aboveground Forest Biomass Mapping

Estimation of aboveground forest biomass is critical for regional carbon policies and sustainable forest management. Both passive optical remote sensing and active microwave remote sensing can play an important role in the monitoring of forest biomass. In this study, the recently launched Sentinel-2 Multi Spectral Instrument satellite and Sentinel-1 SAR satellite systems were evaluated and integrated to investigate the relative strengths of each sensor for mapping aboveground forest biomass at a regional scale. The Australian state of Victoria, with its wide range of forest vegetation was chosen as the study area to demonstrate the scalability and transferability of the approach. In this study aboveground forest biomass (AGB) was defined as the tons of carbon per hectare for the aboveground components (stem, branches, leaves) of all live large trees greater than 10 cm in diameter at breast height (DBHOB). Sentinel-2 and Sentinel-1 data were fused within a machine learning framework using a boosted regression tree model and high-quality ground survey data. Multicriteria evaluations showed the use of the two independent and fundamentally different Sentinel satellite systems were able to provide robust estimates (R2 of 0.62, RMSE of 32.2 t.C.ha-l) of aboveground forest biomass, with each sensor compensating for the weakness (cloud perturbations and spectral saturation for Sentinel 2, and sensitivity to ground moisture for Sentinel 1) of each other. As archives for Sentinel-2 and Sentinel-1 continue to grow, mapping aboveground forest biomass and dynamics at moderate resolution over large regions should become increasingly feasible.

• Publication year

  2018

• Venue

  IEEE International Geoscience and Remote Sensing Symposium

• Publication date

  2018-07-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/IGARSS.2018.8517597
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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