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High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire
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2012 (English)In: Remote Sensing, ISSN 2072-4292, Vol. 4, no 7, 1974-1994 p.Article in journal (Refereed) Published
Abstract [en]

Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scattering mechanisms, and data interrelationships. A Classification and Regression Tree (CART), based on random forest, fused multi-mode (fine-beam single, dual, quad pol) Phased Array L-band Synthetic Aperture Radar (PALSAR) and LiDAR-derived elevation to effectively map hydroperiod attributes at the Swedish mire across an aggregated warm season (May-September, 2006-2010). Image derived estimates of water and peat moisture were sensitive (R-2 = 0.86) to field measurements of water table depth (cm). Peat areas that are underlain by permafrost were observed as areas with fluctuating soil moisture and water table changes.

Place, publisher, year, edition, pages
2012. Vol. 4, no 7, 1974-1994 p.
Keyword [en]
PALSAR, LiDAR, mire, hydroperiod, high latitude wetlands, permafrost
National Category
Remote Sensing
URN: urn:nbn:se:su:diva-81807DOI: 10.3390/rs4071974ISI: 000306759700006OAI: diva2:567473


Available from: 2012-11-13 Created: 2012-11-01 Last updated: 2012-11-13Bibliographically approved

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Crill, Patrick M.
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Department of Geological Sciences
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