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Quantification of Peat Thickness and Stored Carbon at the Landscape Scale in Tropical Peatlands: A Comparison of Airborne Geophysics and an Empirical Topographic Method
Author(s) -
Silvestri Sonia,
Knight Rosemary,
Viezzoli Andrea,
Richardson Curtis J.,
Anshari Gusti Z.,
Dewar Noah,
Flanagan Neal,
Comas Xavier
Publication year - 2019
Publication title -
journal of geophysical research: earth surface
Language(s) - English
Resource type - Journals
eISSN - 2169-9011
pISSN - 2169-9003
DOI - 10.1029/2019jf005273
Subject(s) - peat , carbon fibers , environmental science , greenhouse gas , soil science , geology , carbon cycle , scale (ratio) , earth science , mineralogy , remote sensing , atmospheric sciences , geography , ecology , computer science , algorithm , cartography , archaeology , composite number , oceanography , ecosystem , biology
Peatlands play a key role in the global carbon cycle, sequestering and releasing large amounts of carbon. Despite their importance, a reliable method for the quantification of peatland thickness and volume is still missing, particularly for peat deposits located in the tropics given their limited accessibility, and for scales of measurement representative of peatland environments (i.e., of hundreds of km 2 ). This limitation also prevents the accurate quantification of the stored carbon as well as future greenhouse gas emissions due to ongoing peat degradation. Here we present the results obtained using the airborne electromagnetic (AEM) method, a geophysical surveying tool, for peat thickness detection at the landscape scale. Based on a large amount of data collected on an Indonesian peatland, our results show that the AEM method provides a reliable and accurate 3‐D model of peatlands, allowing the quantification of their volume and carbon storage. A comparison with the often used empirical topographic approach, which is based on an assumed correlation between peat thickness and surface topography, revealed larger errors across the landscape associated with the empirical approach than the AEM method when predicting the peat thickness. As a result, the AEM method provides higher estimates (22%) of organic carbon pools than the empirical method. We show how in our case study the empirical method tends to underestimate the peat thickness due to its inability to accurately detect the large variability in the elevation of the peat/mineral substrate interface, which is better quantified by the AEM method.