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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 methane emitter. Landscape level measurement of permafrost degradation is needed to monitor this impact of warming. Surface subsidence is a useful metric of change and can be monitored using InSAR satellite technology. We combined InSAR data, processed using the ASPIS algorithm to monitor ground motion between 2017 and 2021, with optical and LiDAR data to investigate the rate of subsidence across palsa peatlands in northern Sweden. We show that 55% of the area of Sweden's eight largest palsa peatlands is currently subsiding, which can be attributed to these permafrost landforms and their degradation. The most rapid degradation occurring in the largest palsa complexes in the most northern part of the region of study, also corresponding to the areas with the highest % palsa cover within the overall mapped wetland area. Further, higher degradation rates were found in areas where winter precipitation has increased substantially. The roughness index calculated from a LiDAR-derived 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 to-wards estimating the future impact of climate change on permafrost-dependent ecosystems. [ABSTRACT FROM AUTHOR]

Permafrost thaw in Arctic regions is increasing methane (CH4) emissions to 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 both 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 - that landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions and the difficulty in detecting 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, together with Sentinel-1 and -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 Sentinel-1 and Sentinel-2 can be used. 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 matched closely that generated using the UAV data. 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. We estimate that a transition of an area currently experiencing subsidence to fen type vegetation are estimated to increase emissions from 116 kg CH4 season-1 from the 50 ha study area, to emissions as high as 6500 to 13000 kg CH4 season-1. The key outcome from this study is that a fusion of EO data types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types and vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 from the Arctic over space and time with confidence. [ABSTRACT FROM AUTHOR]

A comprehensive multilayer second-order radiative transfer model, driven entirely by intensive field observations, is used to show that second order terms contribute at most 0.5 dB to the backscattering coefficient at all polarizations from wheat and barley throughout the growing season and 1 dB to the copolar response of oilseed rape. Under these circumstances, an equivalent integrable first-order model, with coefficients derived from full model runs, can be formulated. This allows the role of each of the plant components in attenuating and scattering the radar signal to be clarified, and provides a basis for quantitative comparison of observed ERS-2 backscatter values with model calculations, taking full account of measurement uncertainties. Discrepancies between the two suggest that effective attenuation through mature cereal crops is overestimated by the model. This appears to be either an intrinsic failure of the radiative transfer formulation or (more likely) due to an inadequate adaptation of the notion of crop coverage to the microwave case. Nonplanarity of leaves is an important source of model error for oilseed rape. Index Terms--Author, please supply index terms. E-mail keywords@ieee.org for info.

In this paper, TLS error sources are discussed and the types and magnitudes of these errors are observed. Furthermore, a procedure for eliminating laser scanner outliers and reducing noise is introduced. For this purpose, two TLS instruments have been examined: Leica Scanstation P20 and P40. The results of the tests reveal the average noise level (mean errors) between two scans can be improved by using a surface model rather than direct point to point differencing, with improvements from 2.8 mm to 1.7 mm and from 0.7 mm to 0.2 mm for P20 and P40 respectively. In addition, the best fit local planes technique can remove outliers without reducing point density. Therefore, the maximum errors decreased from 50 mm (due to outliers) to 11 mm and from 13 mm (due to outliers) to 1 mm for P20 and P40 respectively. [ABSTRACT FROM AUTHOR]

The advent of new satellite and data processing techniques have meant that routine, operational and reliable surveys of land motion on a regional and national scale are now possible. In this paper, we apply a novel satellite remote sensing technique, the Intermittent Small Baseline Subset method, to data from a new satellite mission, Sentinel-1, and demonstrate that a wide area map of ground deformation can be generated that supports the regulation of a range of energy-related activities. The area for the demonstration is mainland Scotland (∼75,000 km2) and the land motion map required the processing of some 627 images acquired from March 2015 to April 2017. The results show that land motion is encountered almost everywhere across Scotland, dominated by subsidence over peatland areas. However, many other phenomena are also encountered including landslides and deformation associated with mining and civil engineering activities. Considering specifically Petroleum Exploration and Development Licence areas offered under the 14th Onshore Licensing Round in the UK, examples of the types of land motion are shown, including an example related to soil restoration by a wind farm. It is demonstrated that, in Scotland at least, almost all licence areas contain deformation of one form or another and, furthermore, the causes of that subsidence are dynamic and likely to be changing from year-to-year. Therefore, maps like this are likely to be of enormous use in a regulatory framework to scope out pre-existing problems in a licence area and to ensure that the correct monitoring framework is put in place once activities begin. They can also provide evidence of good practice and give assurance against litigation by third parties. [ABSTRACT FROM AUTHOR]