Abstract
National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) provides rich insights over the Earth’s surface through elevation data collected by its Advanced Topographic Laser Altimeter System (ATLAS) since its launch in September 2018. While this mission is primarily aimed at capturing ice measurements, ICESat-2 also provides data over vegetated areas, offering the capability to gain insights into ecosystem structure and the potential to contribute to the sustainable management of forests. This study involved an examination of the utility of ICESat-2 for estimating forest aboveground biomass (AGB). The objectives of this study were to: (1) investigate the use of canopy metrics for estimating AGB, using data extracted from an ICESat-2 transect over forests in south-east Texas; (2) compare the accuracy for estimating AGB using data from the strong beam and weak beam; and (3) upscale predicted AGB estimates using variables from Landsat multispectral imagery and land cover and canopy cover maps, to generate a 30 m spatial resolution AGB map. Methods previously developed with simulated ICESat-2 data over Sam Houston National Forest (SHNF) in southeast Texas were adapted using actual data from an adjacent ICESat-2 transect over similar vegetation conditions. Custom noise filtering and photon classification algorithms were applied to ICESat-2’s geolocated photon data (ATL03) for one beam pair, consisting of a strong and weak beam, and canopy height estimates were retrieved. Canopy height parameters were extracted from 100 m segments in the along-track direction for estimating AGB, using regression analysis. ICESat-2-derived AGB estimates were then extrapolated to develop a 30 m AGB map for the study area, using vegetation indices from Landsat 8 Operational Land Imager (OLI), National Land Cover Database (NLCD) landcover and canopy cover, with random forests (RF). The AGB estimation models used few canopy parameters and suggest the possibility for applying well-developed methods for modeling AGB with airborne light detection and ranging (lidar) data, using processed ICESat-2 data. The final regression model achieved a R2 and root mean square error (RMSE) value of 0.62 and 24.63 Mg/ha for estimating AGB and RF model evaluation with a separate test set yielded a R2 of 0.58 and RMSE of 23.89 Mg/ha. Findings provide an initial look at the ability of ICESat-2 to estimate AGB and serve as a basis for further upscaling efforts.
Highlights
Information about the three-dimensional structure of forests enables critical measurements related to ecosystem health and functioning [1] and facilitates the sustainable management of forests
The three-predictor model explained 61% of the variance in the reference aboveground biomass (AGB) used in this study and yielded a root mean square error (RMSE) of 22.15 Mg/ha (Table 2, Figure 4a,b), representing 36% of the dependent mean of 61.81 Mg/ha
The final regression model using AGB estimated with processed ICESat-2 data from the weak beam did not perform as well (Figure 4c,d)
Summary
Information about the three-dimensional structure of forests enables critical measurements related to ecosystem health and functioning [1] and facilitates the sustainable management of forests. With National Aeronautics and Space Administration’s (NASA’s) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) directly capturing this information, a plethora of indicators of ecosystem health and function as well as key surrogate measures relevant to the scientific community, such as forest aboveground biomass (AGB), can potentially be estimated. ICESat-2 began its 3-year mission in September 2018 and carries a photon-counting light detection and ranging (PCL) instrument, which records elevation measurements through transects along the Earth’s surface. This mission serves to continue the work of its predecessor, ICESat, with a primary aim of capturing observations of sea ice freeboard and ice sheets to assess changes in ice cover [2]. Some of the limitations encountered with this mission were used to inform the development of the instrument onboard ICESat-2, resulting in improved spatial resolution and denser ground track coverage over non-polar regions [2]
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