Abstract
We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborne ALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-day baseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low (≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e., <90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is <60% (or <100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.
Highlights
The sequestration of atmospheric carbon storage as Above Ground Biomass (AGB) is an important mitigating factor of the warming climate due to anthropogenic CO2 [1,2]
For UAVSAR, the coherence appears to be degraded by rain that occurred between the repeat-pass radar acquisitions, causing significant temporal decorrelation not associated with vegetation structure, which might explain this poor performance
For areas that are above 100 Mg/ha, our RMSE is approximately 10 Mg/ha lower than the standard deviation of AGB for all sites and sensors, indicating that our model outperforms the mean estimator in this AGB regime
Summary
The sequestration of atmospheric carbon storage as Above Ground Biomass (AGB) is an important mitigating factor of the warming climate due to anthropogenic CO2 [1,2]. NISAR will repeat its orbit within 350 m to observe changes in phase that result from surface displacement rather than elevation or volumes This so-called zero spatial baseline interferometric measurement is related to forest structure [15,16,17], due to the temporal decorrelation of the repeat-pass interferometric signal [12,13,18]. As noted in [15], the repeat-pass coherence is dominated by temporal decorrelation for shorter temporal baselines, barring significant weather or phenological events In such cases, we expect areas with larger, more complex canopy structures to have lower coherence precisely due to the inevitable motion of such canopy structures between image retrievals
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