There is a data gap in our current knowledge of the geospatial distribution, type and extent of C rich peatlands across the globe. The Pastaza Marañón Foreland Basin (PMFB), within the Peruvian Amazon, is known to store large amounts of peat, but the remoteness of the region makes field data collection and mapping the distribution of peatland ecotypes challenging. Here we review methods for developing high accuracy peatland maps for the PMFB using a combination of multi-temporal synthetic aperture radar (SAR) and optical remote sensing in a machine learning classifier. The new map produced has 95% overall accuracy with low errors of commission (1–6%) and errors of omission (0–15%) for individual peatland classes. We attribute this improvement in map accuracy over previous maps of the region to the inclusion of high and low water season SAR images which provides information about seasonal hydrological dynamics. The new multi-date map showed an increase in area of more than 200% for pole forest peatland (6% error) compared to previous maps, which had high errors for that ecotype (20–36%). Likewise, estimates of C stocks were 35% greater than previously reported (3.238 Pg in Draper et al. (2014) to 4.360 Pg in our study). Most of the increase is attributed to pole forest peatland which contributed 58% (2.551 Pg) of total C, followed by palm swamp (34%, 1.476 Pg). In an assessment of deforestation from 2010 to 2018 in the PMFB, we found 89% of the deforestation was in seasonally flooded forest and 43% of deforestation was occurring within 1 km of a river or road. Peatlands were found the least affected by deforestation and there was not a noticeable trend over time. With development of improved transportation routes and population pressures, future land use change is likely to put South American tropical peatlands at risk, making continued monitoring a necessity. Accurate mapping of peatland ecotypes with high resolution (<30 m) sensors linked with field data are needed to reduce uncertainties in estimates of the distribution of C stocks, and to aid in deforestation monitoring.

<p>Eighty percent of global peatlands are distributed across the boreal and subarctic regions, storing an estimated 30% of earth’s soil organic carbon (1,016 to 1,105 Gt C) despite representing only about 3% of the global land surface. The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk of changing from a C sink to a source.  Recent changes in climate including earlier springs, longer summers and changes in moisture patterns across the landscape, are affecting wildfire regimes of the boreal region including intensity, severity and frequency of wildfires. This in turn has potential to cause shifts in successional trajectories.  Understanding how these changes in climate are affecting peatlands and their vulnerability to wildfire has been a focus of study of the research team since 2009.  Soil moisture is one variable which can provide information to understand wildfire behavior including the depth of peat consumption in these wildfires but it also has a direct effect on post-fire successional trajectories. Further it is needed to understand methane emissions from peatlands.  To develop the soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions from 2012-2014.  We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) synthetic aperture radar (SAR) data.  Peatlands have low enough aboveground biomass (<3.0 kg/m<sup>2</sup>) to allow this shorter wavelength SAR to penetrate the canopy to reach the ground surface.  Data from over 60, 4 ha sites were collected over 3 seasons from Alaska and Michigan USA and Alberta Canada.  Both multi-linear regressions and general additive models (GAM) were developed.  Using both polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results.  GAM models were tested in an independent study area, Northwest Territories (NWT), Canada.  The sites of NWT were sampled in 2016-2019 coincident to Radarsat-2 polarimetric image collections.  The high accuracy results will be presented as well as methods developed to use multidate C-band data from Sentinel-1 to classify soil drainage (well drained to poorly drained) in recently burned peatlands.  These products are being used in a fire effects and emissions model, CanFIRE, as we parameterize it for peatlands; as well as the Functionally-Assembled Terrestrial Ecosystem Simulator <strong>(</strong>FATES) to understand the effects of wildfire and hydrology on peatland ecosystems.  Characterization and quantification of boreal peatlands in global C cycling is critical for proper accounting given that peatlands play a significant role in sequestering and releasing large amounts of C. The ability to retrieve soil moisture from C-band SAR, therefore, provides a means to monitor a key variable in scaling C flux estimates as well as understanding the vulnerability and resiliency of boreal peatlands to climate change.</p><p> </p>

No abstract