Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping

• Published in: Remote sensing of environment 2021-02, Vol.253, p.112234, Article 112234
• Main Authors: Silva, Carlos Alberto, Duncanson, Laura, Hancock, Steven, Neuenschwander, Amy, Thomas, Nathan, Hofton, Michelle, Fatoyinbo, Lola, Simard, Marc, Marshak, Charles Z., Armston, John, Lutchke, Scott, Dubayah, Ralph
• Format: Article
• Language: English
• Subjects: Airborne lasers; Biomass; Carbon cycle; Data acquisition; Data integration; Data transmission; Forest biomass; Forests; Fusion; L-band SAR; Lidar; Mapping; Multisensor fusion; Radar; Remote sensing; Simulation; Spaceborne lidar; Temperate forest
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2020.112234

[Display omitted] Accurate mapping of forest aboveground biomass (AGB) is critical for better understanding the role of forests in the global carbon cycle. NASA's current GEDI and ICESat-2 missions as well as the upcoming NISAR mission will collect synergistic data with different coverage and sensitivity to AGB. In this study, we present a multi-sensor data fusion approach leveraging the strength of each mission to produce wall-to-wall AGB maps that are more accurate and spatially comprehensive than what is achievable with any one sensor alone. Specifically, we calibrate a regional L-band radar AGB model using the sparse, simulated spaceborne lidar AGB estimates. We assess our data fusion framework using simulations of GEDI, ICESat-2 and NISAR data from airborne laser scanning (ALS) and UAVSAR data acquired over the temperate high AGB forest and complex terrain in Sonoma County, California, USA. For ICESat-2 and GEDI missions, we simulate two years of data coverage and AGB at footprint level are estimated using realistic AGB models. We compare the performance of our fusion framework when different combinations of the sparse simulated GEDI and ICEsat-2 AGB estimates are used to calibrate our regional L-band AGB models. In addition, we test our framework at Sonoma using (a) 1-ha square grid cells and (b) similarly sized irregularly shaped objects. We demonstrate that the estimated mean AGB across Sonoma is more accurately estimated using our fusion framework than using GEDI or ICESat-2 mission data alone, either with a regular grid or with irregular segments as mapping units. This research highlights methodological opportunities for fusing new and upcoming active remote sensing data streams toward improved AGB mapping through data fusion. •GEDI, ICESat-2 and NISAR missions collect data sensitive to forest biomass.•Fusion of GEDI, ICESat-2 and NISAR data allows wall-to-wall biomass mapping.•ICESat-2 biomass estimates were unreliable when the photon return rate was low.•Grid and object-based approaches resulted in similar biomass estimation accuracy.