FOUR YEARS OF UNMANNED AERIAL SYSTEM IMAGERY REVEALS VEGETATION CHANGE IN A SUB-ARCTIC MIRE DUE TO PERMAFROST THAW

Warming trends in sub-arctic regions have resulted in thawing of permafrost which in turn induces change in vegetation across peatlands both in areal extent and composition. Collapse of palsas (i.e. permafrost plateaus) has also been correlated with increases in methane (CH4) emission to the atmosphere. Vegetation change provides new microenvironments that promote CH4 production and emission, specifically through plant interactions and structure. By quantifying the changes in vegetation at the landscape scale, we will be able to scale the impact of thaw on CH4 emissions in these complex climate-sensitive northern ecosystems. We combine field-based measurements of vegetation composition and Unmanned Aerial Systems (UAS) high resolution (3 cm) imagery to characterize vegetation change in a sub-arctic mire. The objective of this study is to analyze how vegetation from Stordalen Mire, Abisko, Sweden, has changed over time in response to permafrost thaw. At Stordalen Mire, we flew a fixed-wing UAS in July of each of four years, 2014 through 2017, over a 1 km x 0.5 km area. High precision GPS ground control points were used to georeference the imagery. Randomized square-meter plots were measured for vegetation composition and individually classified into one of five vegetation cover types, each representing a different stage of permafrost degradation. Using these training data, each year of imagery was classified by cover type in Google Earth Engine using a Random Forest Classifier. Textural information was extracted from the imagery, which provided additional spatial context information and improved classification accuracy. Twenty five percent of the training data were held back from the classification and used for validation, while the remaining seventy five percent of the training data were used to classify the imagery. The overall classification accuracy for 2014-2017 was 80.6%, 79.1%, 82.0%, and 82.9%, respectively. Percent cover across the landscape was calculated from each classification map and compared between years. Hummock sites, representing intact permafrost, decreased coverage by 9% from 2014-2017, while semi-wet sites increased coverage by 18%. This four-year comparison of vegetation cover indicated a rapid response to permafrost thaw. The use of a UAS allowed us to effectively capture the spatial heterogeneity of a northern peatland ecosystem. Estimation of vegetation cover types is vital in our understanding of the evolution of northern peatlands and their future role in the global carbon cycle

Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery

The focus of this work is to investigate and apply the remote sensing method of object-based image analysis (OBIA) for vegetation classification of a permafrost underlain peatland in sub-arctic Sweden, by using aerial imagery of high resolution. Since the northern landscapes are an important source of naturally stored CH4 and CO2, their contribution to the global carbon cycle is a focus in research about climate change and the global methane exchange. Climate change affects permafrost soils by future increases in the mean temperature and precipitation. It further influences the depth of the frozen layer, and the thickness of the active layer above permafrost increases. This complex relationship results into a changing future landscape distribution of the vegetation at permafrost peatlands. The change has an effect on the exchange of CH4 in particular permafrost areas. For that reason, knowledge about the vegetation distribution of plant communities is interesting for ecological studies. In this work, the observed area is Stordalen mire, which is situated in Swedish Lapland. At this peatland, a landscape change is currently visible as it occurs as variations in the vegetation pattern above the permafrost and by an obvious permafrost thaw. A number of studies focus on the mire and the place has a long history of research in climate change. So far, there is no detailed vegetation map of Stordalen available, indicating the relative spatial distribution of vegetation. Therefore, the main aim is to use a suitable technique to derive a detailed vegetation map by supervised classification. To carry out the information needed, digital aerial photography of high spatial resolution was used. The extraction of thematic information from that data was done by a combination of OBIA methods. Remote sensing has the capability to explore distant regions, and the usage of digital aerial imagery of high resolution allowed to captures the small size of structures of vegetation. It was possible to identify single plant communities from the data, and this information was taken out as vegetation classes. The presented work includes the preprocessing of the data, the segmentation of image objects, establishment of classification controls, set up of training areas, and the image classification to the vegetation map, finally with an evaluation of the results. The resulted map is a contribution to apply OBIA as a method for landscape analysis in future research about northern peatlands. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 323Northern peatlands play an important role in the observation of global climate variations. Permafrost in the ground is melting slowly, because the earth becomes warmer. This tends to result in increasing methane gas emissions from the ground. The impact of degradation is visible in a changing land cover distribution at the peatlands, because the landscape faces a change into moister conditions. Understanding the relation to climate conditions of the future is a challenge. No accurate information about the particular vegetation of peatlands exists. More knowledge about the vegetation cover would help to understand the effects from loosing permafrost in peatlands. It is also a useful source for a long- term observation of landscape changes. In our work, the objective was to derive a map that shows a detailed vegetation distribution of a permafrost underlain area. This was done by a classification of an aerial photography. Because Swedish Lapland is far from populated places, remote sensing is a welcome method to observe the ground vegetation from above. This is commonly done by interpreting photos taken by an aircraft. Our method involves an approach of object- based image analysis. We classify the information from an aircraft-taken image in a meaningful context into groups of vegetation covering the land surface. We produce a detailed vegetation map that helps to explore the vegetation and to observe changes. Background Remains from the last ice age characterize the subarctic landscape of Swedish Lapland. Large areas are still underlain by a constantly frozen ground, permafrost. The regions of permafrost exhibit wetlands that are often peatlands. We know that the permafrost ground layer is sensitive to changes in local climates. An obvious effect, the trend to warmer temperatures and to more rainfall, is that the landscape changes slowly by that. Landscape transformation is highly visible by ongoing ground degradation. This causes an increase in the disturbance of wetlands. The plant cover is affected, as vegetation pattern is connected to the conditions in the ground. In subarctic regions, the plant species distribution covering a peatland is defined by water access. The production of methane, a greenhouse gas, is also connected to water accessibility. Water covered permafrost drives the release of methane to the atmosphere. A better knowledge about ongoing processes within the peatland ecosystems and permafrost disappearance is important. Study Site and Data Northernmost Sweden, Lapland, is a region where peatlands are commonly found. We use an aerial photography that shows the area of a peatland that is underlain by permafrost. The digital image was taken during the growing season. Our study site, Stordalen, is a peatland close to the Abisko Scientific Research Station near lake Torneträsk. Stordalen is a peatland which has a frozen permafrost ground at different stages of degradation. Permafrost is already melting in some places, which suits the area for a closer observation. The used image has a high pixel resolution of 8 cm, which is in higher resolution than other available data of the area. Such high resolution enables to identify a high level of details of the vegetation cover. Image Classification A classification based on aerial imagery is a common technique. Traditional methods are based on a classification of every single pixel the image contains, and hereby considering the spectral information only. But this approach may lead to some problems, as one plant growing naturally consists of more than one pixel in a high- resolution image, as ours. The imagination that most plants covering the ground, e.g. patches of mosses, are naturally larger than 8 cm helps to understand this. We used the approach of an object-based interpretation, which enables a deeper influence on the classification, on the other hand. By the help of parameters and rule setting, object-based allows to capture typical pixel values that form a plant. Our method of object creation enables to include structures of different and very small sizes, so the plant cover is described following the natural shape. Main plant species are collected in vegetation groups that describe the type of plants by their growth form. Such a group is based on common characteristics. This classification method proposes the comparability of the vegetation classes. Using objects also enables new possibilities to analyze the vegetation. Results and Conclusions A map showing the distribution of vegetation at Stordalen peatland was obtained from the image. The work included tests of different parameters, the adjustment of the object size, shape and location. Typically, a vegetation object was identified by the spectral response of the plant surface in the image. A visual inspection, based on field knowledge, helped to determine the best parameters. The evaluation was done by comparing information from a field survey to the classification results. It was possible to produce a map that contains vegetation classes that follow the natural growth. Therefore, the combination of an object-based image analysis with high resolution data is of use to map the vegetation of a peatland. More knowledge about the actual vegetation, to evaluate the classification result, could improve future map results. Key words: Physical Geography and Ecosystem Analysis, Object-Based Image Analysis, OBIA, Vegetation Classification, Permafrost, Arctic Peatland, Remote Sensing, Aerial Photography, Environmental Monitoring, Landscape Analysis Original title: Marco Giljum (2014) Object-Based Classification of Vegetation at Stordalen Mire near Abisko by using High-Resolution Aerial Imagery Advisor: Andreas Persson Master degree project 30 credits in Geomatics, 2014 Department of Physical Geography and Ecosystems Science, Lund University Student thesis series INES nr 32

Greenhouse gas emission factors associated with rewetting of organic soils

Drained organic soils are a significant source of greenhouse gas (GHG) emissions to the atmosphere. Rewetting these soils may reduce GHG emissions and could also create suitable conditions for return of the carbon (C) sink function characteristic of undrained organic soils. In this article we expand on the work relating to rewetted organic soils that was carried out for the 2014 Intergovernmental Panel on Climate Change (IPCC) Wetlands Supplement. We describe the methods and scientific approach used to derive the Tier 1 emission factors (the rate of emission per unit of activity) for the full suite of GHG and waterborne C fluxes associated with rewetting of organic soils. We recorded a total of 352 GHG and waterborne annual flux data points from an extensive literature search and these were disaggregated by flux type (i.e. CO2, CH4, N2O and DOC), climate zone and nutrient status. Our results showed fundamental differences between the GHG dynamics of drained and rewetted organic soils and, based on the 100 year global warming potential of each gas, indicated that rewetting of drained organic soils leads to: net annual removals of CO2 in the majority of organic soil classes; an increase in annual CH4 emissions; a decrease in N2O and DOC losses; and a lowering of net GHG emissions. Data published since the Wetlands Supplement (n = 58) generally support our derivations. Significant data gaps exist, particularly with regard to tropical organic soils, DOC and N2O. We propose that the uncertainty associated with our derivations could be significantly reduced by the development of country specific emission factors that could in turn be disaggregated by factors such as vegetation composition, water table level, time since rewetting and previous land use history

High Resolution Mapping of Peatland Hydroperiod at a High-Latitude Swedish Mire

Abstract: Monitoring high latitude wetlands is required to understand feedbacks between terrestrial carbon pools and climate change. Hydrological variability is a key factor driving biogeochemical processes in these ecosystems and effective assessment tools are critical for accurate characterization of surface hydrology, soil moisture, and water table fluctuations. Operational satellite platforms provide opportunities to systematically monitor hydrological variability in high latitude wetlands. The objective of this research application was to integrate high temporal frequency Synthetic Aperture Radar (SAR) and high spatial resolution Light Detection and Ranging (LiDAR) observations to assess hydroperiod at a mire in northern Sweden. Geostatistical and polarimetric (PLR) techniques were applied to determine spatial structure of the wetland and imagery at respective scales (0.5 m to 25 m). Variogram, spatial regression, and decomposition approaches characterized the sensitivity of the two platforms (SAR and LiDAR) to wetland hydrogeomorphology, scatterin

Modelling peatland water table depth using remotely sensed satellite data

Peatlands are carbon-rich wetland ecosystems and represent the largest terrestrial carbon store. Although they are natural carbon sinks, damage, drainage and extraction over past decades have turned peatlands into a global carbon source. To tackle this nearly irreversible loss, peatland conservation and restoration projects on global and national levels have been increasing in numbers. High water table depth (WTD) is a highly important factor that influences peatland condition, resilience and ability to accumulate carbon. Given the extent of peatlands, a regular physical collection of data in situ, looking forward, would be impractical and difficult to accomplish, and the development of a remote sensing methods for peatland WTD monitoring would be highly beneficial. The accessibility to satellite data along with advancements in sensors, both in variety - optical, microwave, thermal, and their resolutions - spatial, spectral, and temporal, has greatly increased in the last decade. Combined with advances in image processing using cloud computing and machine learning, it has made it easier to access and process remotely sensed data. Synthetic aperture radar (SAR), with its ability to provide data regardless of the weather, has emerged as an important source of data for environmental applications. This project aimed to advance the usage of remotely sensed SAR data to predict peatland water table depth. First, a unique high resolution laboratory study was completed confirming SAR backscatter sensitivity to changes in peatland soil moisture and water table depth. This was followed by a case study for the Forsinard Flows area, where Sentinel-1 SAR data were used to build and test three models of different complexity for WTD prediction. The random forest model was found to be the most suited with an overall good temporal fit, highest correlation scores and lowest RMSE values. The model was later tested on a wider Peatland ACTION dataset, reaching an even higher score, affirming its applicability to peatlands in various conditions (near natural, degraded and undergoing restoration). In the final section of the thesis, up to twenty year-long time series of remote sensing data were analysed to investigate trends and change points in peatland restoration areas. The trends found using lower resolution satellite data from MODIS gave mixed results and would only be indicative of very abrupt changes, such as tree felling. The trends from the modelled WTD series based on Sentinel-1 data were indicative of positive trajectories towards higher WTD, following restoration. The results from this thesis suggest that remotely sensed data can be informative about changes in the WTD and overall peatland condition, can be used to look at seasonal change, and can be indicative of restoration progress and response to droughts. Recent studies have shown a close link between greenhouse gasses and peatland WTD, therefore, if methods of predicting WTD based on remotely sensed data are developed further, they ultimately could be used as a proxy for greenhouse gas emission reporting