Details

Autor(en) / Beteiligte

• Stovall, Atticus E.L.
• Fatoyinbo, Temilola
• Thomas, Nathan M.
• Armston, John
• Ebanega, Médard Obiang
• Simard, Marc
• Trettin, Carl
• Obiang Zogo, Robert Vancelas
• Aken, Igor Akendengue
• Debina, Michael
• Me Kemoe, Alphna Mekui
• Assoumou, Emmanuel Ondo
• Kim, Jun Su
• Lagomasino, David
• Lee, Seung-Kuk
• Ndong Obame, Jean Calvin
• Voubou, Geldin Derrick
• Essono, Chamberlain Zame

Titel

Comprehensive comparison of airborne and spaceborne SAR and LiDAR estimates of forest structure in the tallest mangrove forest on earth

Ist Teil von

• Science of Remote Sensing, 2021-12, Vol.4, p.100034, Article 100034

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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. [Display omitted] •Comparing height and biomass estimates for 17 products in tallest mangrove forest.•Height estimates are inconsistent across sensors in low and tall stature forests.•Radar performed well, but calibration with LiDAR improved height estimates.•Local calibrated biomass agreed within 15%, but global maps can have >50% error.•Future biomass maps should combine local and global calibration strategies.