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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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23 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Mangrove Mapping with the Freeman-Durden Polarimetric Decomposition and Insar Coherence from ALOS-2

We map mangrove extents in Pongara National Park, Gabon using the Freeman-Durden Decomposition and InSAR Coherence derived from ALOS-2 imagery. Specifically, we obtain a land cover map derived from both this polarimetric decom-position and a 14-day repeat-pass coherence. Our classification model and results are highly interpretable based on a depth 2 decision tree. We further illustrate the correlation between InSAR coherence and height obtaining rough man-grove height estimates from TanDEM-X data. From our results, we observe that repeat-pass interferometric coherence provides invaluable information about mangrove extents and coastal forests. The clear identification of mangrove extents presents a significant opportunity for NISAR, which will pro-vide 12-day repeat pass images over coastal areas globally.

Tien-Hao Liao↗
NASA NTRS2020

Mangrove Mapping With the Freeman-Durden Polarimetric Decomposition and InSAR Coherence From ALOS-2

We map mangrove extents in Pongara National Park, Gabon using the Freeman-Durden Decomposition and InSAR Coherence derived from ALOS-2 imagery. Specifically, we obtain a land cover map derived from both this polarimetric decomposition and a 14-day repeat-pass coherence. Our classification model and results are highly interpretable based on a depth 2 decision tree. We further illustrate the correlation between InSAR coherence and height obtaining rough mangrove height estimates from TanDEM-X data. From our results, we observe that repeat-pass interferometric coherence provides invaluable information about mangrove extents and coastal forests. The clear identification of mangrove extents presents a significant opportunity for NISAR, which will provide 12-day repeat pass images over coastal areas globally.

Thomas, Nathan↗
NASA NTRS2021

Comprehensive Comparison of Airborne and Spaceborne SAR and LiDAR Estimates of Forest Structure in the Tallest Mangrove Forest on Earth

A recent suite of new global-scale satellite sensors and regional-scale airborne campaigns are providing a wealth of remote sensing data capable of dramatically advancing our current understanding of the spatial distribution of forest structure and carbon stocks. However, a baseline for forest stature and biomass estimates has yet to be established for the wide array of available remote sensing products. At present, it remains unclear how the estimates from these sensors compare to one another in terrestrial forests, with a clear dearth of studies in high carbon density mangrove ecosystems. In the tallest mangrove forest on Earth (Pongara National Park, Gabon), we leverage the data collected during the AfriSAR campaign to evaluate 17 state-of-the-art sensor data products across the full range of height and biomass known to exist globally in mangrove forest ecosystems, providing a much-needed baseline for sensor performance. Our major findings are: (Houghton, Hall, Goetz) height estimates are not consistent across products, with opposing trends in relative and absolute errors, highlighting the need for an adaptive approach to constraining height estimates (Panet al., 2011); radar height estimates had the lowest calibration error and bias, with further improvements using LiDAR fusion (Bonan, 2008); biomass variability and uncertainty strongly depends on forest stature, with variation across products increasing with canopy height, while relative biomass variation was highest in low-stature stands (Le Quéréet al., 2017); a remote sensing product's sensitivity to variations in canopy structure is more important than the absolute accuracy of height estimates (Mitchardet al., 2014); locally-calibrated area-wide totals are more representative than generalized global biomass models for high-precision biomass estimates. The findings presented here provide critical baseline expectations for height and biomass predictions across the full range of mangrove forest stature, which can be directly applied to current (TanDEM-X, GEDI, ICESat-2) and future (NISAR, BIOMASS) global-scale forest monitoring missions.

Atticus E.L. Stovall↗
NASA NTRS2018

Mangrove Canopy Height Globally Related to Precipitation and Cyclone Frequency

Mangrove wetlands are among the most productive and carbon-dense ecosystems in the world. Their structural attributes vary considerably across spatial scales, yielding large uncertainties in regional and global estimates of carbon stocks. Here, we present a global analysis of mangrove canopy height gradients and aboveground carbon stocks based on remotely sensed measurements and field data. Our study highlights that precipitation, temperature and cyclone frequency explain 74% of the global trends in maximum canopy height, with other geophysical factors influencing the observed variability at local and regional scales. We find the tallest mangrove forests in Gabon, equatorial Africa, where stands attain 62.8 m. The total global mangrove carbon stock (above- and belowground biomass, and soil) is estimated at 5.03 Pg, with a quarter of this value stored in Indonesia. Our analysis implies sensitivity of mangrove structure to climate change, and offers a baseline to monitor national and regional trends in mangrove carbon stocks.

Carbon cycle↗
NASA NTRS2020

Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

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 spaceborneALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-daybaseline. 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.

NISAR↗