Use of Surface Motion Characteristics Determined by InSAR to Assess Peatland Condition. Issue 1 (17th January 2020)

• Record Type: Journal Article
• Authors: Alshammari, Lubna; Boyd, Doreen S.; Sowter, Andrew; Marshall, Chris; Andersen, Roxane; Gilbert, Peter; Marsh, Stuart; Large, David J.
• Is Part Of: Journal of geophysical research. Volume 125:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-17
• Subjects: peat -- InSAR -- plant functional type -- surface motion -- seasonal cycles -- peatland condition; Geobiology -- Periodicals; Biogeochemistry -- Periodicals; Biotic communities -- Periodicals; Geophysics -- Periodicals; 577.14
• DOI: 10.1029/2018JG004953
• Languages: English
• ISSNs: 2169-8953

Abstract

Abstract: Peatland surface motion is a key property of peatland that relates to condition. However, field‐based techniques to measure surface motion are not cost‐effective over large areas and long time periods. An alternative method that can quantify peatland surface motion over large areas is interferometric synthetic aperture radar. Although field validation of the accuracy of this method is difficult, the value of interferometric synthetic aperture radar (InSAR) as a means of quantifying peat condition can be tested. To achieve this, the characteristics of InSAR time series measured over an18‐month period at 22 peatland sites in the Flow Country northern Scotland were compared to site condition assessment based on plant functional type and site management history. Sites in good condition dominated by Sphagnum display long‐term stability or growth and a seasonal cycle with maximum uplift and subsidence in August–November and April–June, respectively. Drier and partially drained sites dominated by shrubs display long‐term subsidence with maximum uplift and subsidence in July–October and February–June, respectively. Heavily degraded sites with large bare peat extent display subsidence with no distinct seasonal oscillations. Seasonal oscillation in surface motion at sites with a dominant nonvascular plant community is interpreted as resulting from changes in seasonal evaporative demand. On sites with extensive vascular plants cover and falling water table, surface oscillations are interpreted as representing sustained drawdown during the growing season and subsequent recharge in late winter. This study highlights the potential to use InSAR to characterize peatland condition and provide a new view of the surface dynamics of peatland landscapes. Plain Language Summary: Peatlands contain one third of all soil carbon despite covering only 3% of the Earth's land area. Peatlands in good condition cool climate through carbon sequestration and provide a range of other benefits such as water regulation and support of biodiversity. All these are compromised by peatland degradation, with severe costs to society. Given the global extent and remoteness of many peatlands, tools are needed to reliably assess peatland condition and inform future management. This research assesses the potential to use remote sensing of surface motion to measure peatland condition. The surface of peatland is known to move in response to change in water or gas in the peat but is notoriously difficult to measure. Our research provides the first indication that surface motion measured by satellite radar can differentiate peatland condition categories effectively. Wet, mossy peatland in good condition displays a strong and distinctive seasonal cycle falling mid summer and rising in midwinter. Drier shrubby peatland has a completely different pattern of motion, with the surface falling in late summer and rising to a peak in late spring. This evidence opens new possibilities of using satellite radar to provide a global view of peatland. With further development, this technique could enable rapid and continuous assessment of the condition of peatland and therefore inform management decisions. Key Points: Interferometric satellite radar time series can be used to characterize peat surface motion Peatland sites conditions can consistently and reliably be inferred on the basis of these time series properties This technique brings about a new view of peatland dynamics, and our approach paves the way for peatland condition monitoring over whole regions and countries