Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden

Permafrost thaw in Arctic regions is increasing methane (CH4) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH4 emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH4 emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH4 emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH4 emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH4 emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH4 per season to emissions as high as 6500 to 13 000 kg CH4 per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 emissions from the Arctic over space and time with confidence

InSAR-measured permafrost degradation of palsa peatlands in northern Sweden

Climate warming is degrading palsa peatlands across the circumpolar permafrost region. Permafrost degradation may lead to ecosystem collapse and potentially strong climate feedbacks, as this ecosystem is an important carbon store and can transition to being a strong greenhouse gas emitter. Landscape-level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change in palsa degradation and can be monitored using interferometric synthetic-aperture radar (InSAR) satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with airborne optical and lidar data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55% of Sweden's eight largest palsa peatlands are currently subsiding, which can be attributed to the underlying permafrost landforms and their degradation. The most rapid degradation has occurred in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest percentage of palsa cover within the overall mapped wetland area. Further, higher degradation rates have been found in areas where winter precipitation has increased substantially. The roughness index calculated from a lidar-derived digital elevation model (DEM), used as a proxy for degradation, increases alongside subsidence rates and may be used as a complementary proxy for palsa degradation. We show that combining datasets captured using remote sensing enables regional-scale estimation of ongoing permafrost degradation, an important step towards estimating the future impact of climate change on permafrost-dependent ecosystems

Potential of APSIS-InSAR for measuring surface oscillations of tropical peatlands

Tropical peatland across Southeast Asia is drained extensively for production of pulpwood, palm oil and other food crops. Associated increases in peat decomposition have led to widespread subsidence, deterioration of peat condition and CO2 emissions. However, quantification of subsidence and peat condition from these processes is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. The development of satellite interferometric synthetic aperture radar (InSAR) has the potential to solve this problem. The Advanced Pixel System using Intermittent Baseline Subset (APSIS, formerly ISBAS) modelling technique provides improved coverage across almost all land surfaces irrespective of ground cover, enabling derivation of a time series of tropical peatland surface oscillations across whole catchments. This study aimed to establish the extent to which APSIS-InSAR can monitor seasonal patterns of tropical peat surface oscillations at North Selangor Peat Swamp Forest, Peninsular Malaysia. Results showed that C-band SAR could penetrate the forest canopy over tropical peat swamp forests intermittently and was applicable to a range of land covers. Therefore the APSIS technique has the potential for monitoring peat surface oscillations under tropical forest canopy using regularly acquired C-band Sentinel-1 InSAR data, enabling continuous monitoring of tropical peatland surface motion at a spatial resolution of 20 m

Exploring Spatial Patterns of Tropical Peatland Subsidence in Selangor, Malaysia Using the APSIS-DInSAR Technique

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts

Exploring spatial patterns of tropical peatland subsidence in Selangor, Malaysia using the APSIS-DInSAR technique

Tropical peatlands in Southeast Asia have experienced widespread subsidence due to forest clearance and drainage for agriculture, oil palm and pulp wood production, causing concerns about their function as a long-term carbon store. Peatland drainage leads to subsidence (lowering of peatland surface), an indicator of degraded peatlands, while stability/uplift indicates peatland accumulation and ecosystem health. We used the Advanced Pixel System using the Intermittent SBAS (ASPIS-DInSAR) technique with biophysical and geographical data to investigate the impact of peatland drainage and agriculture on spatial patterns of subsidence in Selangor, Malaysia. Results showed pronounced subsidence in areas subjected to drainage for agricultural and oil palm plantations, while stable areas were associated with intact forests. The most powerful predictors of subsidence rates were the distance from the drainage canal or peat boundary; however, other drivers such as soil properties and water table levels were also important. The maximum subsidence rate detected was lower than that documented by ground-based methods. Therefore, whilst the APSIS-DInSAR technique may underestimate absolute subsidence rates, it gives valuable information on the direction of motion and spatial variability of subsidence. The study confirms widespread and severe peatland degradation in Selangor, highlighting the value of DInSAR for identifying priority zones for restoration and emphasising the need for conservation and restoration efforts to preserve Selangor peatlands and prevent further environmental impacts

Tropical peat surface oscillations are a function of peat condition at North Selangor peat swamp forest, Malaysia

Tropical peatland condition across southeast Asia is deteriorating as a result of conversion to agriculture and urban zones. Conversion begins by lowering the water table, which leads to peat decomposition, subsidence and increased risk of large-scale forest fires. Associated changes in mechanical peat properties impact the magnitude and timing of changes in peatland surface motion, making them a potential proxy for peatland condition. However, such a relationship is yet to be observed in a tropical peatland setting. This study aimed to establish whether patterns of tropical peatland surface motion were a function of peat condition at North Selangor Peat Swamp Forest in Selangor, Malaysia. Results showed that subsidence was greatest at fire-affected scrubland sites, whilst the lowest mean water table levels were found at smallholder oil palm sites. Peat condition and magnitude of tropical peat surface oscillation were significantly different between peat condition classes, whilst peat condition differed with depth. More degraded tropical peats with high bulk density throughout the peat profile due to high surface loading and low mean water table levels showed greater surface oscillation magnitudes. The dominant peat surface oscillation mechanisms present at all sites were compression and shrinkage from changes in water table level. Mean water table level and subsidence rate were related to surface oscillation magnitude. However further work towards measuring surface and within-water table range bulk densities and surface loading is required to better understand the controls on surface oscillation magnitudes

Monitoring surface oscillation dynamics of tropical peatlands: a novel approach using APSIS-InSAR

80% of tropical peatland in Indonesia and Malaysia (15% of Earth's soil carbon) is drained for production of pulpwood and palm oil. Associated increases in peat decomposition and large-scale forest fires have led to widespread subsidence and deterioration of peat condition. However, quantification of subsidence and peat condition from these processes across SE Asia is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. Space-based platforms offer the opportunity to monitor these inaccessible environments with regular and efficient pan- regional measurements. A development in satellite interferometric synthetic aperture radar (InSAR), a technique that measures surface motion, has the potential to solve this problem. A new ‘intermittent small baseline subset’ (APSIS, formerly ISBAS) modelling technique, developed at the University of Nottingham, provides excellent coverage across almost all land surfaces irrespective of ground cover. This enables derivation of a time series of tropical peatland surface oscillations across whole catchments, regions and countries. This project aimed to establish the extent to which APSIS InSAR can monitor seasonal patterns of tropical peat surface oscillations, and therefore physical peat condition, at North Selangor Peat Swamp Forest, Peninsular Malaysia. Firstly, a proof-of-concept study demonstrated how the APSIS technique can monitor peat surface oscillations under tropical forest canopy using C-band InSAR data, enabling continuous monitoring of tropical peatland surface motion ranging from 0.1 – 40 cm yr-1 at a spatial resolution of 20 m. Secondly, a ground-based study explored the relationship between tropical peat surface oscillations and physical peat condition across North Selangor. Thirdly, a statistical and conceptual comparison of surface motion time series produced by ground-based methodologies and the APSIS method was conducted. Finally, the spatial and temporal patterns of APSIS-derived surface motion was explored through use of machine learning to determine which peat swamp forest variables most strongly influenced surface oscillation patterns, and to what extent APSIS could inform physical peat condition at North Selangor. The proof-of-concept study showed that C-band Sentinel-1 SAR could penetrate the forest canopy over tropical peat swamp forests and was applicable to a wide range of land covers. This presented potential for monitoring tropical peatland degradation and the impacts of management strategies with greater accuracy than L-band SAR. Results from ground-based methodologies showed that peat condition and tropical peat surface oscillation magnitude were significantly different between peat swamp forest condition classes and at different depths. Links between peat condition and surface oscillation magnitude were found, whereby more degraded tropical peat had greater elastic potential and therefore greater surface oscillation magnitudes. However, a regional-scale investigation showed that above-ground biophysical variables were overall poor predictors of APSIS-InSAR surface oscillation patterns. Further, comparisons between surface oscillation time series from ground-based methodologies and APSIS-InSAR showed no significant relationships. Further work was recommended to extend the period of measurement for all methodologies to include more full oscillation cycles, as well as tease apart the different components that contribute to the surface oscillation signal of each of the methodological approaches. Regional-scale variables indicative of the peat profile should also be incorporated into future study, particularly the inclusion of water table change, which has demonstrated a strong control on peat surface amplitude at North Selangor. The inclusion of a undisturbed site as a benchmark would also enable real quantification of the extent of degradation across North Selangor. Continued development of remote sensing methods is recommended for effective and sustainable tropical peatland restoration and management at regional scales, including the support of global climate mitigation efforts. It is intended that the methodologies explored in this thesis will be built upon to better understand the regional-scale surface oscillation dynamics of tropical peat swamp forest environments

Monitoring surface oscillation dynamics of tropical peatlands: a novel approach using APSIS-InSAR

80% of tropical peatland in Indonesia and Malaysia (15% of Earth's soil carbon) is drained for production of pulpwood and palm oil. Associated increases in peat decomposition and large-scale forest fires have led to widespread subsidence and deterioration of peat condition. However, quantification of subsidence and peat condition from these processes across SE Asia is challenging due to the scale and inaccessibility of dense tropical peat swamp forests. Space-based platforms offer the opportunity to monitor these inaccessible environments with regular and efficient pan- regional measurements. A development in satellite interferometric synthetic aperture radar (InSAR), a technique that measures surface motion, has the potential to solve this problem. A new ‘intermittent small baseline subset’ (APSIS, formerly ISBAS) modelling technique, developed at the University of Nottingham, provides excellent coverage across almost all land surfaces irrespective of ground cover. This enables derivation of a time series of tropical peatland surface oscillations across whole catchments, regions and countries. This project aimed to establish the extent to which APSIS InSAR can monitor seasonal patterns of tropical peat surface oscillations, and therefore physical peat condition, at North Selangor Peat Swamp Forest, Peninsular Malaysia. Firstly, a proof-of-concept study demonstrated how the APSIS technique can monitor peat surface oscillations under tropical forest canopy using C-band InSAR data, enabling continuous monitoring of tropical peatland surface motion ranging from 0.1 – 40 cm yr-1 at a spatial resolution of 20 m. Secondly, a ground-based study explored the relationship between tropical peat surface oscillations and physical peat condition across North Selangor. Thirdly, a statistical and conceptual comparison of surface motion time series produced by ground-based methodologies and the APSIS method was conducted. Finally, the spatial and temporal patterns of APSIS-derived surface motion was explored through use of machine learning to determine which peat swamp forest variables most strongly influenced surface oscillation patterns, and to what extent APSIS could inform physical peat condition at North Selangor. The proof-of-concept study showed that C-band Sentinel-1 SAR could penetrate the forest canopy over tropical peat swamp forests and was applicable to a wide range of land covers. This presented potential for monitoring tropical peatland degradation and the impacts of management strategies with greater accuracy than L-band SAR. Results from ground-based methodologies showed that peat condition and tropical peat surface oscillation magnitude were significantly different between peat swamp forest condition classes and at different depths. Links between peat condition and surface oscillation magnitude were found, whereby more degraded tropical peat had greater elastic potential and therefore greater surface oscillation magnitudes. However, a regional-scale investigation showed that above-ground biophysical variables were overall poor predictors of APSIS-InSAR surface oscillation patterns. Further, comparisons between surface oscillation time series from ground-based methodologies and APSIS-InSAR showed no significant relationships. Further work was recommended to extend the period of measurement for all methodologies to include more full oscillation cycles, as well as tease apart the different components that contribute to the surface oscillation signal of each of the methodological approaches. Regional-scale variables indicative of the peat profile should also be incorporated into future study, particularly the inclusion of water table change, which has demonstrated a strong control on peat surface amplitude at North Selangor. The inclusion of a undisturbed site as a benchmark would also enable real quantification of the extent of degradation across North Selangor. Continued development of remote sensing methods is recommended for effective and sustainable tropical peatland restoration and management at regional scales, including the support of global climate mitigation efforts. It is intended that the methodologies explored in this thesis will be built upon to better understand the regional-scale surface oscillation dynamics of tropical peat swamp forest environments