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Application of Remote Sensing for Monitoring of Peatlands

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 19155

Special Issue Editor

Department of Ecology and Environment Protection, Poznan University of Life Sciences, 60-637 Poznan, Poland
Interests: peatlands; GHG fluxes; remote sensing of peatlands; linking remote sensing and GHG fluxes; Sun Induced Fluorescence (SIF); ecosystem responses to climate change; climate change manipulation experiments
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Special Issue Information

Dear Colleagues,

We are pleased to announce that we are now accepting submissions for the upcoming Special Issue of Remote Sensing focused on peatlands.

Peatlands represent one of the most important ecosystems on Earth, mainly because of their huge carbon storage capacity and high vulnerability to climate change. Unfortunately, the majority of peatlands worldwide have been degraded and they are still under high anthropogenic pressure. On the other hand, increasing efforts are being devoted to the restoration of degraded peatlands and the recovery of their hydrology, biodiversity and climate-related functions. Non-degraded peatlands serve many environmental functions, not limited to their roles as sinks for atmospheric carbon and as huge natural pools of organic carbon. Peatlands regulate local hydrology, influence water quality and meso- and macro-climates, but they also play a major role in the conservation of biodiversity. However, due to climate change and the increasing occurrence and severity of heatwaves and droughts, these regulatory functions of peatlands are endangered.

Remote sensing (RS) is a powerful tool which can be used to monitor the regulatory functions of peatlands. Ground-, UAV-, airborne- or spaceborne-based RS approaches can be integrated with GHG flux towers and other ground-based monitoring datasets, while new remote sensing signals (e.g., sun-induced fluorescence), new retrieval methods, sensors and modelling approaches can be applied in order to make the monitoring of peatland status (e.g., rate of degradation, early stress indicators), water table dynamics, vegetation phenology and their productivity more efficient and complementary.

We are interested in high-quality submissions that use remote sensing to study the effects of weather and climate extremes and/or anthropogenic impact on any aspect of peatland functioning. Studies integrating remote sensing with ground-based monitoring data and modelling are particularly welcome.

We look forward to receiving your manuscript.

Sincerely,

Prof. Dr. Radoslaw Juszczak
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing of peatlands
  • monitoring of peatlands
  • peatlands productivity
  • carbon fluxes
  • vegetation phenology
  • water table depth dynamics
  • climate extremes
  • droughts
  • peatland degradation
  • peatland restoration

Published Papers (7 papers)

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Research

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23 pages, 8479 KiB  
Article
Potential for Peatland Water Table Depth Monitoring Using Sentinel-1 SAR Backscatter: Case Study of Forsinard Flows, Scotland, UK
by Linda Toca, Rebekka R. E. Artz, Catherine Smart, Tristan Quaife, Keith Morrison, Alessandro Gimona, Robert Hughes, Mark H. Hancock and Daniela Klein
Remote Sens. 2023, 15(7), 1900; https://doi.org/10.3390/rs15071900 - 31 Mar 2023
Cited by 2 | Viewed by 3216
Abstract
Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of the key monitoring elements, where it is used as a proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data [...] Read more.
Peatland restoration has become a common land-use management practice in recent years, with the water table depth (WTD) being one of the key monitoring elements, where it is used as a proxy for various ecosystem functions. Regular, uninterrupted, and spatially representative WTD data in situ can be difficult to collect, and therefore, remotely sensed data offer an attractive alternative for landscape-scale monitoring. In this study, we illustrate the application of Sentinel-1 SAR backscatter for water table depth monitoring in near-natural and restored blanket bogs in the Flow Country of northern Scotland. Among the study sites, the near-natural peatlands presented the smallest fluctuations in the WTD (with depths typically between 0 and 15 cm) and had the most stable radar signal throughout the year (~3 to 4 dB amplitude). Previously drained and afforested peatlands undergoing restoration management were found to have higher WTD fluctuations (depths up to 35 cm), which were also reflected in higher shifts in the radar backscatter (up to a ~6 dB difference within a year). Sites where more advanced restoration methods have been applied, however, were associated with shallower water table depths and smoother surfaces. Three models—simple linear regression, multiple linear regression, and the random forest model—were evaluated for their potential to predict water table dynamics in peatlands using Sentinel-1 SAR backscatter. The random forest model was found to be the most suited, with the highest correlation scores, lowest RMSE values, and overall good temporal fit (R2 = 0.66, RMSE = 2.1 cm), and multiple linear regression came in a close second (R2 = 0.59, RMSE = 4.5 cm). The impact of standing water, terrain ruggedness, and the ridge and furrow aspect on the model correlation scores was tested but found not to have a statistically significant influence. We propose that this approach, using Sentinel-1 and random forest models to predict the WTD, has strong potential and should be tested in a wider range of peatland sites. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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21 pages, 4915 KiB  
Article
The Spatial Analysis of Vegetation Cover and Permafrost Degradation for a Subarctic Palsa Mire Based on UAS Photogrammetry and GPR Data in the Kola Peninsula
by Natalya Krutskikh, Pavel Ryazantsev, Pavel Ignashov and Alexey Kabonen
Remote Sens. 2023, 15(7), 1896; https://doi.org/10.3390/rs15071896 - 31 Mar 2023
Viewed by 1568
Abstract
Subarctic palsa mires undergo substantial transformation under climate impacts, and today a reliable marker of their degradation is the vegetation cover. We studied the correspondence between the surface traits of palsa degradation, as expressed in the vegetation composition, and the interior condition of [...] Read more.
Subarctic palsa mires undergo substantial transformation under climate impacts, and today a reliable marker of their degradation is the vegetation cover. We studied the correspondence between the surface traits of palsa degradation, as expressed in the vegetation composition, and the interior condition of permafrost within subarctic palsa mires in the central part of the Kola Peninsula. We have employed a set of methods to collect the data, including geobotanical relevés, unmanned aerial system (UAS) photogrammetry, and ground-penetrating radar (GPR) survey. Based on RGB orthophoto values and morphometric variables, we produced a land cover classification (LCC) consistent with the vegetation classes identified during field measurements. The outcome proves that the additional morphometric predictors improve the accuracy of classification algorithms. We identified three major patterns in GPR cross-sections defining (i) permafrost in palsas, (ii) water saturated peat, and (iii) the regular peat layer. As a result, our GPR data demonstrated a high correlation with land cover classes and pointed to some vegetation features controlled by the peat deposit inner structure. Under our results, palsas with thawing permafrost can be appraised using sequences of LCC. This is primarily the lichen hummock—tall shrub—carpet vegetation (LH–TSh–C) sequence from palsa top to foot. We have also detected an asymmetric configuration of permafrost in some palsas in the west-to-east direction and hypothesized that it can relate to the wind regime of the area and snow accumulation on the eastern slopes. Our results highlight that the combined application of the remote UAS photogrammetry and GPR survey enables a more precise delineation of the lateral degradation of palsas. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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19 pages, 2089 KiB  
Article
Mapping Fractional Vegetation Coverage across Wetland Classes of Sub-Arctic Peatlands Using Combined Partial Least Squares Regression and Multiple Endmember Spectral Unmixing
by Heidi Cunnick, Joan M. Ramage, Dawn Magness and Stephen C. Peters
Remote Sens. 2023, 15(5), 1440; https://doi.org/10.3390/rs15051440 - 04 Mar 2023
Cited by 4 | Viewed by 1668
Abstract
Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember [...] Read more.
Vegetation communities play a key role in governing the atmospheric-terrestrial fluxes of water, carbon, nutrients, and energy. The expanse and heterogeneity of vegetation in sub-arctic peatland systems makes monitoring change at meaningful spatial resolutions and extents challenging. We use a field-collected spectral endmember reference library to unmix hyperspectral imagery and map vegetation coverage at the level of plant functional type (PFT), across three wetland sites in sub-arctic Alaska. This study explores the optimization and parametrization of multiple endmember spectral mixture analysis (MESMA) models to estimate coverage of PFTs across wetland classes. We use partial least squares regression (PLSR) to identify a parsimonious set of critical bands for unmixing and compare the reference and modeled coverage. Unmixing, using a full set of 110-bands and a smaller set of 4-bands, results in maps that effectively discriminate between PFTs, indicating a small investment in fieldwork results in maps mirroring the true ground cover. Both sets of spectral bands differentiate between PFTs, but the 4-band unmixing library results in more accurate predictive mapping with lower computational cost. Reducing the unmixing reference dataset by constraining the PFT endmembers to those identified in the field-site produces only a small advantage for mapping, suggesting extensive fieldwork may not be necessary for MESMA to have a high explanatory value in these remote environments. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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16 pages, 4657 KiB  
Article
Peatland Plant Spectral Response as a Proxy for Peat Health, Analysis Using Low-Cost Hyperspectral Imaging Techniques
by Mary B. Stuart, Matthew Davies, Matthew J. Hobbs, Andrew J. S. McGonigle and Jon R. Willmott
Remote Sens. 2022, 14(16), 3846; https://doi.org/10.3390/rs14163846 - 09 Aug 2022
Cited by 4 | Viewed by 2069
Abstract
Peatland habitats represent key environmental resources that are a critical component in climate change mitigation strategies. However, many of these environmental settings are facing significant levels of erosion and degradation which, over time, will result in the loss of these key environments. Traditional [...] Read more.
Peatland habitats represent key environmental resources that are a critical component in climate change mitigation strategies. However, many of these environmental settings are facing significant levels of erosion and degradation which, over time, will result in the loss of these key environments. Traditional monitoring techniques for these settings require invasive methods, disrupting the natural environment and potentially leading to further losses if incorrectly administered. In this article, we provide a non-invasive, cost-effective alternative to peatland health monitoring through the implementation of low-cost hyperspectral imaging techniques. Using common peatland plant species as a proxy for underlying peat health, we monitor the spectral response of Sphagnum plants under varying degrees of water stress to document their spectral response under these conditions. For this research, we utilise a low-cost, semi-portable High-Resolution Hyperspectral Imager capable of resolving mm-scale targets in conjunction with the ultra-low-cost Hyperspectral Smartphone that represents a completely accessible fully field portable instrument allowing for rapid and accurate on-site measurements. Both instruments are shown to provide accurate and robust results, capturing subtle changes in spectral response prior to their appearance within visual datasets enabling the use of mitigation and restoration techniques before the onset of more damaging conditions. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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19 pages, 105265 KiB  
Article
Towards a Monitoring Approach for Understanding Permafrost Degradation and Linked Subsidence in Arctic Peatlands
by Betsabe de la Barreda-Bautista, Doreen S. Boyd, Martha Ledger, Matthias B. Siewert, Chris Chandler, Andrew V. Bradley, David Gee, David J. Large, Johan Olofsson, Andrew Sowter and Sofie Sjögersten
Remote Sens. 2022, 14(3), 444; https://doi.org/10.3390/rs14030444 - 18 Jan 2022
Cited by 10 | Viewed by 3721
Abstract
Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and [...] Read more.
Permafrost thaw resulting from climate warming is threatening to release carbon from high latitude peatlands. The aim of this research was to determine subsidence rates linked to permafrost thaw in sub-Arctic peatlands in Sweden using historical orthophotographic (orthophotos), Unoccupied Aerial Vehicle (UAV), and Interferometric Synthetic Aperture Radar (InSAR) data. The orthophotos showed that the permafrost palsa on the study sites have been contracting in their areal extent, with the greatest rates of loss between 2002 and 2008. The surface motion estimated from differential digital elevation models from the UAV data showed high levels of subsidence (maximum of −25 cm between 2017 and 2020) around the edges of the raised palsa plateaus. The InSAR data analysis showed that raised palsa areas had the greatest subsidence rates, with maximum subsidence rates of 1.5 cm between 2017 and 2020; however, all wetland vegetation types showed subsidence. We suggest that the difference in spatial units associated with each sensor explains parts of the variation in the subsidence levels recorded. We conclude that InSAR was able to identify the areas most at risk of subsidence and that it can be used to investigate subsidence over large spatial extents, whereas UAV data can be used to better understand the dynamics of permafrost degradation at a local level. These findings underpin a monitoring approach for these peatlands. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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21 pages, 3707 KiB  
Article
Impact of Atmospheric Optical Properties on Net Ecosystem Productivity of Peatland in Poland
by Kamila M. Harenda, Mateusz Samson, Radosław Juszczak, Krzysztof M. Markowicz, Iwona S. Stachlewska, Małgorzata Kleniewska, Alasdair MacArthur, Dirk Schüttemeyer and Bogdan H. Chojnicki
Remote Sens. 2021, 13(11), 2124; https://doi.org/10.3390/rs13112124 - 28 May 2021
Cited by 10 | Viewed by 2607
Abstract
Peatlands play an important role in the global carbon cycle due to the high carbon storage in the substrate. Ecosystem production depends, for example, on the solar energy amount that reaches the vegetation, however the diffuse component of this flux can substantially increase [...] Read more.
Peatlands play an important role in the global carbon cycle due to the high carbon storage in the substrate. Ecosystem production depends, for example, on the solar energy amount that reaches the vegetation, however the diffuse component of this flux can substantially increase ecosystem net productivity. This phenomenon is observed in different ecosystems, but the study of the atmosphere optical properties on peatland production is lacking. In this paper, the presented methodology allowed us to disentangle the diffuse radiation impact on the net ecosystem production (NEP) of Rzecin peatland, Poland. It allowed us to assess the impact of the atmospheric scattering process determined by the aerosol presence in the air mass. An application of atmospheric radiation transfer (ART) and ecosystem production (EP) models showed that the increase of aerosol optical thickness from 0.09 to 0.17 caused NEP to rise by 3.4–5.7%. An increase of the diffusion index (DI) by 0.1 resulted in an NEP increase of 6.1–42.3%, while a DI decrease of 0.1 determined an NEP reduction of −49.0 to −10.5%. These results show that low peatland vegetation responds to changes in light scattering. This phenomenon should be taken into account when calculating the global CO2 uptake estimation of such ecosystems. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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Review

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20 pages, 3749 KiB  
Review
Cloud-Based Remote Sensing for Wetland Monitoring—A Review
by Abdallah Yussuf Ali Abdelmajeed, Mar Albert-Saiz, Anshu Rastogi and Radosław Juszczak
Remote Sens. 2023, 15(6), 1660; https://doi.org/10.3390/rs15061660 - 19 Mar 2023
Cited by 3 | Viewed by 2493
Abstract
The rapid expansion of remote sensing provides recent and developed advances in monitoring wetlands. Integrating cloud computing with these techniques has been identified as an effective tool, especially for dealing with heterogeneous datasets. In this study, we conducted a systematic literature review (SLR) [...] Read more.
The rapid expansion of remote sensing provides recent and developed advances in monitoring wetlands. Integrating cloud computing with these techniques has been identified as an effective tool, especially for dealing with heterogeneous datasets. In this study, we conducted a systematic literature review (SLR) to determine the current state-of-the-art knowledge for integrating remote sensing and cloud computing in the monitoring of wetlands. The results of this SLR revealed that platform-as-a-service was the only cloud computing service model implemented in practice for wetland monitoring. Remote sensing applications for wetland monitoring included prediction, time series analysis, mapping, classification, and change detection. Only 51% of the reviewed literature, focused on the regional scale, used satellite data. Additionally, the SLR found that current cloud computing and remote sensing technologies are not integrated enough to benefit from their potential in wetland monitoring. Despite these gaps, the analysis revealed that economic benefits could be achieved by implementing cloud computing and remote sensing for wetland monitoring. To address these gaps and pave the way for further research, we propose integrating cloud computing and remote sensing technologies with the Internet of Things (IoT) to monitor wetlands effectively. Full article
(This article belongs to the Special Issue Application of Remote Sensing for Monitoring of Peatlands)
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