| 1. |
- A'Campo, Willeke, et al.
(författare)
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Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery
- 2021
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Ingår i: Remote Sensing. - : MDPI AG. - 2072-4292. ; 13:23
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Tidskriftsartikel (refereegranskat)abstract
- Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.
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| 2. |
- Bartsch, Annett, et al.
(författare)
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Can C-band synthetic aperture radar be used to estimate soil organic carbon storage in tundra?
- 2016
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 13:19, s. 5453-5470
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Tidskriftsartikel (refereegranskat)abstract
- A new approach for the estimation of soil organic carbon (SOC) pools north of the tree line has been developed based on synthetic aperture radar (SAR; ENVISAT Advanced SAR Global Monitoring mode) data. SOC values are directly determined from backscatter values instead of upscaling using land cover or soil classes. The multi-mode capability of SAR allows application across scales. It can be shown that measurements in C band under frozen conditions represent vegetation and surface structure properties which relate to soil properties, specifically SOC. It is estimated that at least 29 Pg C is stored in the upper 30 cm of soils north of the tree line. This is approximately 25% less than stocks derived from the soil-map-based Northern Circumpolar Soil Carbon Database (NCSCD). The total stored carbon is underestimated since the established empirical relationship is not valid for peatlands or strongly cryoturbated soils. The approach does, however, provide the first spatially consistent account of soil organic carbon across the Arctic. Furthermore, it could be shown that values obtained from 1 km resolution SAR correspond to accounts based on a high spatial resolution (2 m) land cover map over a study area of about 7 x 7 km in NE Siberia. The approach can be also potentially transferred to medium-resolution C-band SAR data such as ENVISAT ASAR Wide Swath with similar to 120m resolution but it is in general limited to regions without woody vegetation. Global Monitoring-mode-derived SOC increases with unfrozen period length. This indicates the importance of this parameter for modelling of the spatial distribution of soil organic carbon storage.
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| 3. |
- Bartsch, Annett, et al.
(författare)
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Circumarctic land cover diversity considering wetness gradients
- 2024
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Ingår i: Hydrology and Earth System Sciences. - 1027-5606 .- 1607-7938. ; 28:11, s. 2421-2481
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Tidskriftsartikel (refereegranskat)abstract
- Land cover heterogeneity information considering soil wetness across the entire Arctic tundra is of interest for a wide range of applications targeting climate change impacts and ecological research questions. Patterns are potentially linked to permafrost degradation and affect carbon fluxes. First, a land cover unit retrieval scheme which provides unprecedented detail by fusion of satellite data using Sentinel-1 (synthetic aperture radar) and Sentinel-2 (multispectral) was adapted. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are interpreted at 10 m nominal resolution. Units with similar patterns were identified with a k-means approach and documented through statistics derived from comprehensive in situ records for soils and vegetation (more than 3500 samples). The result goes beyond the capability of existing land cover maps which have deficiencies in spatial resolution, thematic content and accuracy, although landscape heterogeneity related to moisture gradients cannot be fully resolved at 10 m. Wetness gradients were assessed, and measures for landscape heterogeneity were derived north of the treeline. About 40 % of the area north of the treeline falls into three units of dry types with limited shrub growth. Wetter regions have higher land cover diversity than drier regions. An area of 66 % of the analysed Arctic landscape is highly heterogeneous with respect to wetness at a 1 km scale (representative scale of frequently used regional land cover and permafrost modelling products). Wetland areas cover 9 % and moist tundra types 32 %, which is of relevance for methane flux upscaling.
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| 4. |
- Chadburn, Sarah E., et al.
(författare)
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Carbon stocks and fluxes in the high latitudes : using site-level data to evaluate Earth system models
- 2017
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Ingår i: Biogeosciences. - : Copernicus GmbH. - 1726-4170 .- 1726-4189. ; 14:22, s. 5143-5169
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Tidskriftsartikel (refereegranskat)abstract
- It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow. We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI. The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models. Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.
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| 5. |
- Muster, Sina, et al.
(författare)
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PeRL : a circum-Arctic Permafrost Region Pond and Lake database
- 2017
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Ingår i: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 9:1, s. 317-348
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Tidskriftsartikel (refereegranskat)abstract
- Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i. e., waterbodies with surface areas smaller than 1.0 x 10(4) m(2), have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002-2013) high-resolution aerial and satellite imagery with a resolution of 5m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1.4 x 10(6) km(2) across the Arctic, about 17% of the Arctic lowland (<300ma. s.l.) land surface area. PeRL waterbodies with sizes of 1.0 x 10(6) m(2) down to 1.0 x 10(2) m(2) contributed up to 21% to the total water fraction. Waterbody density ranged from 1.0 x 10 to 9.4 x 10(1) km(-2). Ponds are the dominant waterbody type by number in all landscapes representing 45-99% of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area boundaries, and maps of regional permafrost landscapes including detailed metadata are available at https://doi.pangaea.de/10.1594/PANGAEA.868349.
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| 6. |
- Wagner, Julia, et al.
(författare)
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High resolution mapping shows differences in soil carbon and nitrogen stocks in areas of varying landscape history in Canadian lowland tundra
- 2023
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Ingår i: Geoderma. - 0016-7061 .- 1872-6259. ; 438
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Tidskriftsartikel (refereegranskat)abstract
- Soil organic carbon (SOC) in Arctic coastal polygonal tundra is vulnerable to climate change, especially in soils with occurrence of large amounts of ground ice. Pan-arctic studies of mapping SOC exist, yet they fail to describe the high spatial variability of SOC storage in permafrost landscapes. An important factor is the landscape history which determines landform development and consequently the spatial variability of SOC. Our aim was to map SOC stocks, and which environmental variables that determine SOC, in two adjacent coastal areas along Canadian Beaufort Sea coast with different glacial history. We used the machine learning technique random forest and environmental variables to map the spatial distribution of SOC stocks down to 1 m depth at a spatial resolution of 2 m for depth increments of 0-5, 5-15, 15-30, 30-60 and 60-100 cm. The results show that the two study areas had large differences in SOC stocks in the depth 60-100 cm due to high amounts of ground ice in one of the study areas. There are also differences in variable importance of the explanatory variables between the two areas. The area low in ground ice content had with 66.6 kg C/m(-2) more stored SOC than the area rich in ground ice content with 40.0 kg C/m(-2). However, this SOC stock could be potentially more vulnerable to climate change if ground ice melts and the ground subsides. The average N stock of the area low in ground ice is 3.77 kg m(-2) and of the area rich in ground ice is 3.83 kg m(-2). These findings support that there is a strong correlation between ground ice and SOC, with less SOC in ice-rich layers on a small scale. In addition to small scale studies of SOC mapping, detailed maps of ground ice content and distribution are needed for a validation of large-scale quantifications of SOC stocks and transferability of models.
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| 7. |
- Zhang, Zhen, et al.
(författare)
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Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)
- 2021
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Ingår i: Earth System Science Data. - : Copernicus GmbH. - 1866-3508 .- 1866-3516. ; 13:5, s. 2001-2023
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Tidskriftsartikel (refereegranskat)abstract
- Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a similar to 25 km resolution at the Equator (0.25 degrees) at a monthly time step for 2000-2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotem-poral patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13 :0 x 106 km(2) (13.0Mkm(2)), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5Mkm(2), seasonally inundated wetlands at 4.0Mkm(2) (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5Mkm(2) (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Nino-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).
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| 8. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 9. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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