[1] This synthesis addresses the vulnerability of the North American high-latitude soil organic carbon (SOC) pool to climate change. Disturbances caused by climate warming in arctic, subarctic, and boreal environments can result in significant redistribution of C among major reservoirs with potential global impacts. We divide the current northern high-latitude SOC pools into (1) near-surface soils where SOC is affected by seasonal freeze-thaw processes and changes in moisture status, and (2) deeper permafrost and peatland strata down to several tens of meters depth where SOC is usually not affected by short-term changes. We address key factors (permafrost, vegetation, hydrology, paleoenvironmental history) and processes (C input, storage, decomposition, and output) responsible for the formation of the large high-latitude SOC pool in North America and highlight how climate-related disturbances could alter this pool's character and size. Press disturbances of relatively slow but persistent nature such as top-down thawing of permafrost, and changes in hydrology, microbiological communities, pedological processes, and vegetation types, as well as pulse disturbances of relatively rapid and local nature such as wildfires and thermokarst, could substantially impact SOC stocks. Ongoing climate warming in the North American high-latitude region could result in crossing environmental thresholds, thereby accelerating press disturbances and increasingly triggering pulse disturbances and eventually affecting the C source/sink net character of northern high-latitude soils. Finally, we assess postdisturbance feedbacks, models, and predictions for the northern high-latitude SOC pool, and discuss data and research gaps to be addressed by future research.
Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20-30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha,
OPEN ACCESSRemote Sens. 2015, 7
8656Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior).
The ability to distinguish peatland types at the landscape scale has implications for inventory, conservation, estimation of carbon storage, fuel loading, and postfire carbon emissions, among others. This paper presents a multisensor, multiseason remote sensing approach to delineate boreal peatland types (wooded bog, open fen, shrubby fen, treed fen) using a combination of multiple dates of L-band (24 cm) synthetic aperture radar (SAR) from ALOS PALSAR, C-band (∼5.6 cm) from ERS-1 or ERS-2, and Landsat 5 TM optical remote sensing data. Imagery was first evaluated over a small test area of boreal Alberta, Canada, to determine the feasibility of using multisensor SAR and optical data to discriminate peatland types. Then object-based and (or) machine-learning classification algorithms were applied to 3.4 million ha of peatland-rich subregions of Alberta, Canada, and the 4.24 million ha region of Michigan’s Upper Peninsula where peatlands are less dominant. Accuracy assessments based on field-sampled sites show high overall map accuracies (93%–94% for Alberta and Michigan), which exceed those of previous mapping efforts.
Wetlands (called bofedales in the Andes of Peru) are abundant and important components of many mountain ecosystems across the globe. They provide many benefits including water storage, high quality habitat, pasture, nutrient sinks and transformations, and carbon storage. The remote and rugged setting of mountain wetlands creates challenges for mapping, typically leading to misclassification and underestimates of wetland extent. We used multi-date, multi-sensor radar and optical imagery (Landsat TM/ PALSAR/RADARSAT-1/SRTM DEM-TPI) combined with ground truthing for mapping wetlands in Huascarán National Park, Peru. We mapped bofedales into major wetland types: 1) cushion plant peatlands, 2) cushion plant wet meadows, and 3) graminoid wet meadows with an overall accuracy of 92%. A fourth wetland type was found (graminoid peatlands) but was too rare to map accurately, thus it was combined with cushion peatland to form a single peatland class. Total wetland area mapped in the National Park is 38,444 ha, which is 11% of the park area. Peatlands were the most abundant wetland type occupying 6.3% of the park, followed by graminoid wet meadows (3.5%) and cushion wet meadows (1.3%). These maps will serve as the foundation for improved management, including restoration, and estimates of landscape carbon stocks.
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