Mapping Peatlands in Boreal and Tropical Ecoregions

Bourgeau-Chavez, L. L.; Endres, Sarah L.; Graham, Jeremy A.; Hribljan, John A.; Chimner, Rodney A.; Lillieskov, E.A.; Battaglia, M.J.

doi:10.1016/b978-0-12-409548-9.10544-5

Cited by 17 publications

(23 citation statements)

References 121 publications

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“…Since peatlands are often small and interspersed with upland and other wetland types, it is essential to use finer resolution data (30 m or better) to distinguish peatland types. Further, hybrid remote sensing methods that use a combination of data sources and imagery from multiple seasons are necessary to capture the hydrologic and phenological variation that characterizes the diversity of peatlands that exist on the landscape (Bourgeau-Chavez et al, 2017;Bourgeau-Chavez et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

et al. 2021

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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.

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Section: Introductionmentioning

confidence: 99%

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

et al. 2021

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“…Northern peatlands are often small and heterogeneous, thus posing challenge for SAR in terms of spatial resolution [15,17,22], also due to loss of temporal coherence caused by vegetation [23][24][25]. However, SAR Interferometry (InSAR) has proven feasibility for monitoring long-term elevation changes in northern peatlands.…”

Section: Introductionmentioning

confidence: 99%

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

et al. 2020

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Peatlands of northern temperate and cold climates are significant pools of stored carbon. Understanding seasonal dynamics of peatland surface height and volume, often referred to as mire breathing or oscillation, is the key to improve spatial models of material flow and gas exchange. The monitoring of this type of dynamics over large areas is only feasible by remote sensing instruments. The objective of this study is to examine the applicability of Sentinel-1 synthetic aperture radar interferometry (InSAR) to characterize seasonal dynamics of peatland surface height and water table (WT) over open raised bog areas in Endla mire complex in central Estonia, characteristic for northern temperate bogs. Our results show that InSAR temporal coherence, sufficient for differential InSAR (DInSAR), is preserved in the open bog over more than six months of temporal baseline. Moreover, the coherence which is lost in a dry summer, make a recovery in autumn correlate with WT dynamics. The relationship between the coherence from a single master image and the corresponding WT difference is described by the second degree polynomial regression model (Root Mean Squared Error RMSE = 0.041 for coherence magnitude). It is also demonstrated that DInSAR phase is connected to bog surface dynamics and reveals differences between bogs and for ecotopes within a bog. These findings suggest that InSAR long term temporal coherence could be used to describe seasonal bog WT dynamics and differentiate between mire types and ecotopes within a bog. Moreover, DInSAR analysis has the potential to characterize seasonal mire surface oscillation which may be important for assessing the capacity of water storage or restoration success in northern temperate bogs.

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“…Minasny et al detail an open data and mapping methodology with the ability to predict peat depth at a lower cost than lidar [308]. SAR particularly useful in tropical peatlands due to cloud and forest canopy penetration and its sensitivity to inundation and biomass [290,296]. SAR applications included dinSAR to map subsidence across Southeast Asia [309] and predict AGB [310].…”

Section: Tropicalmentioning

confidence: 99%

“…The focus on topography and landform included identifying permafrost peat mound degradation with aerial and high-resolution imagery [348], classifying boreal bogs with microtopographic variation from lidar [349], mapping thermokarst lakes with spectral imagery [350,351], detecting freeze thaw dynamics with SAR [352], detecting permafrost extent with electromagnetic imaging [353], and mapping Extrapolation [344] Permafrost Not available lake extent with multispectral imagery [354]. An integration of multi-season SAR and multispectral imagery was complementary in detecting vegetation and hydrologic differences in bogs versus fens in the boreal zone [290,355]. Carbon monitoring efforts included modeling gas fluxes [272,328,356], upscaling in situ emission estimates with land cover maps [350,329,330,357], and peat extent [358].…”

Section: Boreal and Permafrost Peatlandsmentioning

confidence: 99%

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A review of carbon monitoring in wet carbon systems using remote sensing

Campbell

Fatoyinbo

Charles

et al. 2022

Environ. Res. Lett.

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Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1,622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry.

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Mapping Peatlands in Boreal and Tropical Ecoregions

Cited by 17 publications

References 121 publications

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

Advances in Amazonian Peatland Discrimination With Multi-Temporal PALSAR Refines Estimates of Peatland Distribution, C Stocks and Deforestation

Long Term Interferometric Temporal Coherence and DInSAR Phase in Northern Peatlands

A review of carbon monitoring in wet carbon systems using remote sensing

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