Estimation of forest cover extent over peatlands and paludified shallow-peat lands in Russia

Vompersky, Stanislav E.; Sirin, Andrey; Salnikov, A.; Tsyganova, O. P.; Valyaeva, N. A.

doi:10.1134/s1995425511070058

Cited by 51 publications

(29 citation statements)

References 2 publications

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“…The model predictions for Russia (2.17 million km 2 ) appear to agree well spatially with C-WS-Y but are lower than Vompersky et al (2011) (3.69 million km 2 ). The estimate of Vompersky et al (2011) includes both peatlands (defined by a peat layer >30 cm) and paludified shallow peatlands (defined by a peat layer <30 cm). In Indonesia, the model predicts similar locations but larger coverage of peatlands than C-WS-Y (Fig.…”

Section: Peatland Distributions Globally and Regionallymentioning

confidence: 71%

A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models

Wu¹,

Chan

Melton

et al. 2017

Preprint

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Abstract. Peatlands store large amounts of soil carbon and constitute an important component of the global carbon cycle. Accurate information on the global extent and distribution of peatlands is presently lacking but it important for earth system models (ESMs) to be able to simulate the effects of climate change on the global carbon balance. The most comprehensive peatland map produced to date is a qualitative presence/absence product. Here, we present a spatially continuous global map of peatland fractional coverage using the extremely randomized tree machine learning method suitable for use as a prescribed geophysical field in an ESM. Inputs to our statistical model include spatially distributed climate data, soil data and topographical slopes. Available maps of peatland fractional coverage for Canada and West Siberia were used along with a proxy for non-peatland areas to train and test the statistical model. Regions where the peatland fraction is expected to be zero were estimated from a map of topsoil organic carbon content below a threshold value of 13 kg/m2. The modelled coverage of peatlands yields a root mean square error of 4 % and a coefficient of determination of 0.91 for the 10,978 tested 0.5 degree grid cells. We then generated a complete global peatland fractional coverage map. In comparison with earlier qualitative estimates, our global modelled peatland map is able to reproduce peatland distributions in places remote from the training areas and capture peatland hot spots in both boreal and tropical regions, as well as in the southern hemisphere. Additionally we demonstrate that our machine-learning method has greater skill than solely setting peatland areas based on histosols from a soil database.

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Section: Peatland Distributions Globally and Regionallymentioning

confidence: 71%

A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models

Wu¹,

Chan

Melton

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Canada, Eastern Europe, Russia or Indonesia), usually on the margins of deeper peat reserves (i.e. Jaenicke et al, 2008;JNCC, 2011;Vompersky et al, 2011). However, extensive literature review has highlighted that information on the restoration of shallow peatlands remains sparse, as efforts tend to be concentrated on deeper peat reserves.…”

Section: Introductionmentioning

confidence: 98%

New approaches to the restoration of shallow marginal peatlands

Grand-Clément

Anderson

Smith³

et al. 2015

Journal of Environmental Management

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Globally, the historic and recent exploitation of peatlands through management practices such as agricultural reclamation, peat harvesting or forestry, have caused extensive damage to these ecosystems. Their value is now increasingly recognised, and restoration and rehabilitation programmes are underway to improve some of the ecosystem services provided by peatlands: blocking drainage ditches in deep peat has been shown to improve the storage of water, decrease carbon losses in the long-term, and improve biodiversity. However, whilst the restoration process has benefitted from experience and technical advice gained from restoration of deep peatlands, shallow peatlands have received less attention in the literature, despite being extensive in both uplands and lowlands. Using the experience gained from the restoration of the shallow peatlands of Exmoor National Park (UK), and two test catchments in particular, this paper provides technical guidance which can be applied to the restoration of other shallow peatlands worldwide. Experience showed that integrating knowledge of the historical environment at the planning stage of restoration was essential, as it enabled the effective mitigation of any threat to archaeological features and sites. The use of bales, commonly employed in other upland ecosystems, was found to be problematic. Instead, 'leaky dams' or wood and peat combination dams were used, which are both more efficient at reducing and diverting the flow, and longer lasting than bale dams. Finally, an average restoration cost (£306 ha(-1)) for Exmoor, below the median national value across the whole of the UK, demonstrates the cost-effectiveness of these techniques. However, local differences in peat depth and ditch characteristics (i.e. length, depth and width) between sites affect both the feasibility and the cost of restoration. Overall, the restoration of shallow peatlands is shown to be technically viable; this paper provides a template for such process over analogous landscapes.

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“…For example, in order to integrate the available diverse information on peatbog ecosystems and assess the impact of environmental changes and human activities, the Institute of Forest Science (Russian Academy of Sciences) has developed the Geographic Information System, of ′peatland ecosystems of Russia′ to provide the most important basic data for Russian wetland studies. Based on the GIS ′peatland ecosystems of Russia′, the areas of forest cover on peatlands and on paludified shallow-peatlands were estimated, showing that the distribution of forested, sparsely forested, and open peatlands are controlled by the changes in latitude in some degree (Vompersky et al, 2011). However, GIS ′peatland ecosystems of Russia′ largely relied on peatland soil inventories, which are expensive, and the data acquisition from them can be inefficient.…”

Section: Introductionmentioning

confidence: 99%

Mapping deciduous broad-leaved forested swamps using ALOS/Palsar data

et al. 2016

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Accurate, updated information on the distribution of wetlands is essential for estimating net fluxes of greenhouse gases and for effectively protecting and managing wetlands. Because of their complex community structure and rich surface vegetation, deciduous broad-leaved forested swamps are considered to be one of the most difficult types of wetland to classify. In this research, with the support of remote sensing and geographic information system, multi-temporal radar images L-Palsar were used initially to extract the forest hydrological layer and phenology phase change layer as two variables through image analysis. Second, based on the environmental characteristics of forested swamps, three decision tree classifiers derived from the two variables were constructed to explore effective methods to identify deciduous broad-leaved forested swamps. Third, this study focused on analyzing the classification process between flat-forests, which are the most severely disturbed elements, and forested swamps. Finally, the application of the decision tree model will be discussed. The results showed that: 1) L-HH band (a L band with wavelength of 0-235 m in HH polarization mode; HH means Synthetic Aperture Radars transmit pulses in horizontal polarization and receive in horizontal polarization) in the leaf-off season is shown to be capable of detecting hydrologic conditions beneath the forest; 2) the accuracy of the classification (forested swamp and forest plat) was 81.5% based on hydrologic features, and 83.5% was achieved by combining hydrologic features and phenology response features, which indicated that hydrological characteristics under the forest played a key role. The HHOJ (refers to the band created by the subtraction with HH band in October and HH band in July) achieved by multi-temporal radar images did improve the classification accuracy, but not significantly, and more leaf-off radar images may be more efficient than multi-seasonal radar images for inland forested swamp mapping; 3) the lower separability between forested swamps dominated by vegetated surfaces and forest plat covered with litter was the main cause of the uncertainty in classification, which led to misleading interpretations of the pixel-based classification. Finally, through the analysis with kappa coefficients, it was shown that the value of the intersection point was an ideal choice for the variable.

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Estimation of forest cover extent over peatlands and paludified shallow-peat lands in Russia

Cited by 51 publications

References 2 publications

A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models

A map of global peatland distribution created using machine learning for use in terrestrial ecosystem and earth system models

New approaches to the restoration of shallow marginal peatlands

Mapping deciduous broad-leaved forested swamps using ALOS/Palsar data

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