High-Resolution Digital Mapping of Soil Organic Carbon in Permafrost Terrain Using Machine Learning: a Case Study in a Sub-Arctic Peatland Environment
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Biogeosciences, 15, 1663–1682, 2018 https://doi.org/10.5194/bg-15-1663-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment Matthias B. Siewert1,2 1Department of Physical Geography, Stockholm University, Stockholm, 106 91, Sweden 2Department of Ecology and Environmental Science, Umeå University, Umeå, 901 87, Sweden Correspondence: Matthias B. Siewert ([email protected]) Received: 25 July 2017 – Discussion started: 4 August 2017 Revised: 11 January 2018 – Accepted: 13 February 2018 – Published: 21 March 2018 Abstract. Soil organic carbon (SOC) stored in northern peat- ture research needs to investigate the geomorphic response of lands and permafrost-affected soils are key components in permafrost degradation and the fate of SOC across all land- the global carbon cycle. This article quantifies SOC stocks scape compartments in post-permafrost landscapes. in a sub-Arctic mountainous peatland environment in the discontinuous permafrost zone in Abisko, northern Sweden. Four machine-learning techniques are evaluated for SOC quantification: multiple linear regression, artificial neural networks, support vector machine and random forest. The 1 Introduction random forest model performed best and was used to pre- dict SOC for several depth increments at a spatial resolution Northern high latitudes are among the regions most af- of 1 m (1 × 1 m). A high-resolution (1 m) land cover classifi- fected by increasing temperatures and climate change (IPCC, cation generated for this study is the most relevant predictive 2013). Large amounts of soil organic carbon (SOC) and the variable. The landscape mean SOC storage (0–150 cm) is es- abundance of wetlands as a substantial source of methane −2 timated to be 8.3 ± 8.0 kg C m and the SOC stored in the (CH4/, are factors that make this region a key component in top meter (0–100 cm) to be 7.7 ± 6.2 kg C m−2. The predic- the global carbon (C) cycle (McGuire et al., 2009). Frozen tive modeling highlights the relative importance of wetland conditions, cold temperatures and waterlogging are char- areas and in particular peat plateaus for the landscape’s SOC acteristics of wetlands, peatlands and permafrost-affected storage. The total SOC was also predicted at reduced spatial soils that reduce decomposition rates of SOC (Davidson and resolutions of 2, 10, 30, 100, 250 and 1000 m and shows a Janssens, 2006; Ping et al., 2015). This has led to the ac- significant drop in land cover class detail and a tendency to cumulation of large stocks of SOC in high-latitude ecosys- underestimate the SOC at resolutions > 30 m. This is associ- tems (Tarnocai et al., 2009). SOC stocks in the circumpo- ated with the occurrence of many small-scale wetlands form- lar permafrost region are estimated to be ∼ 1300 Pg, includ- ing local hot-spots of SOC storage that are omitted at coarse ing soils to a depth of 3 m and other unconsolidated deposits resolutions. Sharp transitions in SOC storage associated with (Hugelius et al., 2014) . This corresponds to around half of land cover and permafrost distribution are the most challeng- the global SOC stocks (Köchy et al., 2015). A significant pro- ing methodological aspect. However, in this study, at local, portion of this SOC is stored in northern wetland and peat- regional and circum-Arctic scales, the main factor limiting land areas (Gorham, 1991). However, warming temperatures robust SOC mapping efforts is the scarcity of soil pedon data and environmental changes caused by warming of soils and from across the entire environmental space. For the Abisko consequent permafrost degradation are projected to lead to region, past SOC and permafrost dynamics indicate that most a gradual and prolonged release of greenhouse gases in the of the SOC is barely 2000 years old and very dynamic. Fu- future (Schuur et al., 2015). Published by Copernicus Publications on behalf of the European Geosciences Union. 1664 M. B. Siewert: A case study in a sub-Arctic peatland environment Circumpolar mapping efforts of SOC provide important (Åkerman and Johansson, 2008; Johansson et al., 2011), input data for Earth system models. At the same time, high- significant changes in surface structure in peat mires and resolution mapping efforts are necessary to understand the changes in vegetation (Malmer et al., 2005). These changes substantial local-scale spatial and vertical variability of SOC can be associated with increases in landscape-scale CH4 in permafrost-affected soils (Siewert et al., 2015, 2016). The- emissions (Christensen et al., 2004). Analysis of present- matic maps are commonly used to map SOC from point mea- day C fluxes indicate that losses from soil and over the surements to a landscape scale in permafrost environments hydrosphere currently offset C accumulation in peatlands (Hugelius, 2012). This method is used in combination with and aboveground biomass making these ecosystems a C soil maps to estimate SOC storage in the circumpolar per- source (Lundin et al., 2016). To improve our understand- mafrost region using the Northern Circumpolar Soil Carbon ing of these long-term permafrost-region C dynamics, high- Database (NCSCD; Hugelius et al., 2014; Tarnocai et al., resolution maps of landscape distribution and partitioning of 2009). Land cover maps are also used at local to regional SOC are necessary. This should include the vertical parti- scales to estimate SOC values in numerous circumpolar en- tioning of SOC and provide data that can be integrated into vironments (Fuchs et al., 2015; Hugelius et al., 2010, 2011; numerical models (Mishra et al., 2013; Schuur et al., 2015). Hugelius and Kuhry, 2009; Palmtag et al., 2015; Siewert et Combined with a better temporal framework of past C dy- al., 2015; Zubrzycki et al., 2013). While soil maps may bet- namics, this will improve the projection of future climatic ter reflect soil properties and soil forming processes, a land changes. cover classification (LCC) has the advantage that it can be This study aims to compare four different machine- readily generated from remote sensing data using the spa- learning techniques for the prediction of SOC in a high- tial resolution of the respective sensor. However, thematic latitude permafrost and peatland environment. The mapping mapping also represents a strong generalization, as equal soil approach will be discussed with regard to its suitability to properties are assumed for all areas covered by the same estimate SOC stocks in permafrost environments at local to mapping class. Furthermore, for land cover maps there is circumpolar scales using different spatial resolutions. The re- an implicit assumption that land cover alone reflects below- sults will provide high-resolution SOC storage data for a key ground soil properties (Hugelius, 2012). sub-Arctic research site located in Abisko, northern Sweden. An alternative to thematic mapping is the use of predic- The study will give insights into the spatial and vertical par- tive modeling methods. These can yield well-resolved pixel- titioning of the SOC under different land covers and its asso- based estimates of SOC. A comprehensive summary of these ciation with different environmental variables. The temporal methods, commonly called digital soil mapping, is published evolution of the SOC stocks over the Holocene will be inter- by McBratney et al. (2003) and many examples are available, preted from eight radiocarbon dates and the future develop- e.g., in Boettinger et al. (2010). However, in sub-Arctic and ment of SOC stocks and potential C release in high-latitude Arctic permafrost environments, the adoption of predictive environments will be addressed. modeling methods to map soil properties has been limited. Some examples include Bartsch et al. (2016), Baughman et al. (2015), Ding et al. (2016), Mishra and Riley (2012, 2014), 2 Study area and Pastick et al. (2014). The limited adoption has several reasons including the limited availability of environmental The study area is a sub-Arctic mountain environment in the input data, the limited amount of soil pedon data (Mishra et Abisko region near Stordalen, northernmost Sweden (Figs. 1 al., 2013) and the large local-scale variability of permafrost- and 2). Environmental monitoring and research has been con- affected soils (Siewert et al., 2016). To cope with these lim- ducted for more than a century in this region and a par- itations new mapping methods for permafrost environments ticular interest has been the main peatland complex called are necessary to better constrain SOC stocks in the northern Stordalen mire (Callaghan et al., 2013; Jonasson et al., 2012). circumpolar permafrost region (Mishra et al., 2013). Such The mapping extent covers two major peatland complexes, new methods include the use of machine learning in digi- Stordalen and Storflaket, the surrounding birch forest and the tal soil mapping (Hastie et al., 2009; Li et al., 2011). Ma- adjacent alpine tundra zone. The altitude ranges from 342 to chine learning in soil science covers a set of data-mining 932 m a.s.l. The total mapping area is 65 km2. techniques that can recognize patterns in datasets and learn A mean annual air temperature of 0.5 ◦C and a mean an- from these to predict quantitative soil variables. Many algo- nual precipitation of 332 mm have been measured for the rithms are available and robust prediction results are possible period 2002–2011 in Abisko (Callaghan et al., 2013). The (Hastie et al., 2009; Li and Heap, 2008; Li et al., 2011). Test- study area is located in the zone of discontinuous permafrost ing these methods at different spatial resolutions can eventu- (Brown et al., 1997). The onset of late Holocene permafrost ally improve local- and circumpolar-scale estimates of SOC.