Abstract
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.
Highlights
Forest ecosystems cover approximately one third of the Earth’s land and play a major role in the global carbon budget (FAO, 2018)
The number of Global Ecosystem Dynamics Investigation Lidar (GEDI), ICESat-2l and ICESat-2h ob servations falling within 1-ha-grid cells or 1-ha segment objects over the simulated two years of data acquisition were higher than 10 observa tions in some areas
We investigated a regional data fusion framework to extend reliable GEDI and ICESat-2 aboveground biomass (AGB) estimates with NISAR-like data to obtain an approximately 1-ha resolution wall-to-wall AGB map
Summary
Forest ecosystems cover approximately one third of the Earth’s land and play a major role in the global carbon budget (FAO, 2018). Accurate measurements of forest aboveground biomass (AGB) over large spatial scales are crucial to improve our understanding of the global carbon cycle and achieve effective carbon emission mitigation strategies (Chen et al, 2016; Hese et al, 2005; Houghton et al, 2009), and for many other societal and scientific tasks such as sustainable forest management and monitoring forest ecosystem productivity and con servation (Hudak et al, 2009; Tian et al, 2012; Silva et al, 2016) It is not practical nor cost effective to use field studies for large regional AGB estimates (Hummel et al, 2011). The Advanced Topographic Laser Altimeter System (ATLAS), instrument on ICESat-2 collects global photon counting lidar data that are used to measure forest structure (e.g. height) and can potentially be used for forest AGB monitoring (Mon tesano et al, 2015; Neuenschwander and Pitts, 2019; Narine et al, 2019a; Narine et al, 2019b)
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