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Remote Sensing of Peatlands II
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Remote Sensing of Peatlands II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 34741

Special Issue Editors

Dr. Tristan Quaife

E-Mail Website
Guest Editor
National Centre for Earth Observation, University of Reading, Reading, UK
Interests: remote sensing; carbon cycle; land surface modelling; data assimilation
Dr. Kirsten Lees

E-Mail Website
Guest Editor
University of Reading & The James Hutton Institute
Interests: peatlands; remote sensing; carbon cycle

Special Issue Information

Dear Colleagues,

We invite you to submit a manuscript to our Special Issue of Remote Sensing on Peatlands.

Peatlands are an important store of carbon, yet this store is vulnerable to climate change. As temperatures rise, carbon could be released to the atmosphere, thus acting as a positive feedback to global warming. Restoration of peatland, on the other hand, holds significant potential to sequester carbon and meet national greenhouse gas reduction targets under the Paris Agreement. Its capacity to do this is intrinsically linked to its hydrological status, and hence understanding the dynamics of water in peatland is also critical.

The condition and health of global peatlands is difficult to assess using any mechanism other than remote sensing due the vast area of the land surface they cover. However, there remain numerous technical challenges to facilitate the accurate monitoring of relevant variables.

We are interested in receiving high-quality submissions that use remote sensing to study any aspect of peatlands. This includes, but is not limited to, estimating carbon fluxes and storage, peatland hydrology and water table dynamics, the modelling of all aspects of peatland, species discrimination and mapping, data assimilation, monitoring of restoration and/or degradation, the scaling-up of field observations, and the development of new retrieval techniques. In addition, manuscripts that examine the synergy of multiple sensors are particularly welcome, such as those that combine different wavelength domains (e.g., SAR and optical data) or the utilization of data on different spatial scales and temporal frequencies (e.g., the combination of Landsat and MODIS data).

We look forward to receiving your manuscript.

Sincerely,

Dr. Tristan Quaife
Dr. Kirsten Lees
Guest Editors

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

  • peatland
  • carbon
  • hydrology
  • restoration
  • degradation
  • climate change

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Published Papers (6 papers)

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Research

23 pages, 4873 KiB  
Article
The Least Square Adjustment for Estimating the Tropical Peat Depth Using LiDAR Data
by Bambang Kun Cahyono, Trias Aditya and Istarno
Remote Sens. 2020, 12(5), 875; https://doi.org/10.3390/rs12050875 - 9 Mar 2020
Cited by 8 | Viewed by 4935
Abstract
High-accuracy peat maps are essential for peatland restoration management, but costly, labor-intensive, and require an extensive amount of peat drilling data. This study offers a new method to create an accurate peat depth map while reducing field drilling data up to 75%. Ordinary [...] Read more.
High-accuracy peat maps are essential for peatland restoration management, but costly, labor-intensive, and require an extensive amount of peat drilling data. This study offers a new method to create an accurate peat depth map while reducing field drilling data up to 75%. Ordinary least square (OLS) adjustments were used to estimate the elevation of the mineral soil surface based on the surrounding soil parameters. Orthophoto and Digital Terrain Models (DTMs) from LiDAR data of Tebing Tinggi Island, Riau, were used to determine morphology, topography, and spatial position parameters to define the DTM and its coefficients. Peat depth prediction models involving 100%, 50%, and 25% of the field points were developed using the OLS computations, and compared against the field survey data. Raster operations in a GIS were used in processing the DTM, to produce peat depth estimations. The results show that the soil map produced from OLS provided peat depth estimations with no significant difference from the field depth data at a mean absolute error of ±1 meter. The use of LiDAR data and the OLS method provides a cost-effective methodology for estimating peat depth and mapping for the purpose of supporting peat restoration. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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24 pages, 20821 KiB  
Article
Airborne Electromagnetic and Radiometric Peat Thickness Mapping of a Bog in Northwest Germany (Ahlen-Falkenberger Moor)
by Bernhard Siemon, Malte Ibs-von Seht and Stefan Frank
Remote Sens. 2020, 12(2), 203; https://doi.org/10.3390/rs12020203 - 7 Jan 2020
Cited by 17 | Viewed by 4297
Abstract
Knowledge on peat volumes is essential to estimate carbon stocks accurately and to facilitate appropriate peatland management. This study used airborne electromagnetic and radiometric data to estimate the volume of a bog. Airborne methods provide an alternative to ground-based methods, which are labor [...] Read more.
Knowledge on peat volumes is essential to estimate carbon stocks accurately and to facilitate appropriate peatland management. This study used airborne electromagnetic and radiometric data to estimate the volume of a bog. Airborne methods provide an alternative to ground-based methods, which are labor intensive and unfeasible to capture large-scale (>10 km2) spatial information. An airborne geophysical survey conducted in 2004 covered large parts of the Ahlen-Falkenberger Moor, an Atlantic peat bog (39 km2) close to the German North Sea coast. The lateral extent of the bog was derived from low radiometric and elevated surface data. The vertical extent resulted from smooth resistivity models derived from 1D inversion of airborne electromagnetic data, in combination with a steepest gradient approach, which indicated the base of the less resistive peat. Relative peat thicknesses were also derived from decreasing radiation over peatlands. The scaling factor (µa = 0.28 m−1) required to transform the exposure rates (negative log-values) to thicknesses was calculated using the electromagnetic results as reference. The mean difference of combined airborne results and peat thicknesses of about 100 boreholes is very small (0.0 ± 1.1 m). Although locally some (5%) deviations (>2 m) from the borehole results do occur, the approach presented here enables fast peat volume mapping of large areas without an imperative necessity of borehole data. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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Graphical abstract

23 pages, 10589 KiB  
Article
A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery
by Roberto O. Chávez, Duncan A. Christie, Matías Olea and Talia G. Anderson
Remote Sens. 2019, 11(24), 2955; https://doi.org/10.3390/rs11242955 - 10 Dec 2019
Cited by 16 | Viewed by 5855
Abstract
The high Andean peatlands, locally known as “bofedales”, are a unique type of wetland distributed across the high-elevation South American Altiplano plateau. This extensive peatland network stores significant amounts of carbon, regulates local and regional hydrological cycles, supports habitats for a variety of [...] Read more.
The high Andean peatlands, locally known as “bofedales”, are a unique type of wetland distributed across the high-elevation South American Altiplano plateau. This extensive peatland network stores significant amounts of carbon, regulates local and regional hydrological cycles, supports habitats for a variety of plant and animal species, and has provided critical water and forage resources for the livestock of the indigenous Aymara communities for thousands of years. Nevertheless, little is known about the productivity dynamics of the high Andean peatlands, particularly in the drier western Altiplano region bordering the Atacama desert. Here, we provide the first digital peatland inventory and multiscale productivity assessment for the entire western Altiplano (63,705 km2) using 31 years of Landsat data (about 9000 scenes) and a non-parametric approach for estimating phenological metrics. We identified 5665 peatland units, covering an area of 510 km2, and evaluated the spatiotemporal productivity patterns at the regional, peatland polygon, and individual pixel scales. The regional assessment shows that the peatland areas and peatlands with higher productivity are concentrated towards the northern part of our study region, which is consistent with the Altiplano north–south aridity gradient. Regional patterns further reveal that the last seven years (2011–2017) have been the most productive period over the past three decades. While individual pixels show contrasting patterns of reductions and gains in local productivity during the most recent time period, most of the study area has experienced increases in annual productivity, supporting the regional results. Our novel database can be used not only to explore future research questions related to the social, biological, and hydrological influences on peatland productivity patterns, but also to provide technical support for the sustainable development of livestock practices and conservation and water management policy in the Altiplano region. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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Graphical abstract

15 pages, 3268 KiB  
Article
InSAR Time Series Analysis of L-Band Data for Understanding Tropical Peatland Degradation and Restoration
by Zhiwei Zhou, Zhenhong Li, Susan Waldron and Akiko Tanaka
Remote Sens. 2019, 11(21), 2592; https://doi.org/10.3390/rs11212592 - 5 Nov 2019
Cited by 19 | Viewed by 5434
Abstract
In this study, satellite radar observations are employed to reveal spatiotemporal changes in ground surface height of peatlands that have, and have not, undergone restoration in Central Kalimantan, Indonesia. Our time series analysis of 26 scenes of Advanced Land Observation Satellite-1 (ALOS-1) Phased-Array [...] Read more.
In this study, satellite radar observations are employed to reveal spatiotemporal changes in ground surface height of peatlands that have, and have not, undergone restoration in Central Kalimantan, Indonesia. Our time series analysis of 26 scenes of Advanced Land Observation Satellite-1 (ALOS-1) Phased-Array L-band Synthetic-Aperture Radar (PALSAR) images acquired between 2006 and 2010 suggests that peatland restoration was positively affected by the construction time of dams—the earlier the dam was constructed, the more significant the restoration appears. The results also suggest that the dams resulted in an increase of ground water level, which in turn stopped peat losing height. For peatland areas without restoration, the peatland continuously lost peat height by up to 7.7 cm/yr. InSAR-derived peat height changes allow the investigation of restoration effects over a wide area and can also be used to indirectly assess the relative magnitude and spatial pattern of peatland damage caused by drainage and fires. Such an assessment can provide key information for guiding future restoration activities. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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22 pages, 5432 KiB  
Article
Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing
by Mara Y. McPartland, Michael J. Falkowski, Jason R. Reinhardt, Evan S. Kane, Randy Kolka, Merritt R. Turetsky, Thomas A. Douglas, John Anderson, Jarrod D. Edwards, Brian Palik and Rebecca A. Montgomery
Remote Sens. 2019, 11(14), 1685; https://doi.org/10.3390/rs11141685 - 16 Jul 2019
Cited by 36 | Viewed by 7031
Abstract
Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising [...] Read more.
Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO2. Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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19 pages, 3739 KiB  
Article
On the Potential of Sentinel-1 for High Resolution Monitoring of Water Table Dynamics in Grasslands on Organic Soils
by Tina Asmuß, Michel Bechtold and Bärbel Tiemeyer
Remote Sens. 2019, 11(14), 1659; https://doi.org/10.3390/rs11141659 - 11 Jul 2019
Cited by 30 | Viewed by 6354
Abstract
For soils with shallow groundwater and high organic carbon content, water table depth (WTD) is a key parameter to describe their hydrologic state and to estimate greenhouse gas emissions (GHG). Since the microwave backscatter coefficient (σ0) is sensitive to soil moisture, [...] Read more.
For soils with shallow groundwater and high organic carbon content, water table depth (WTD) is a key parameter to describe their hydrologic state and to estimate greenhouse gas emissions (GHG). Since the microwave backscatter coefficient (σ0) is sensitive to soil moisture, the application of Sentinel-1 satellite data might support the monitoring of these climate-relevant soils at high spatial resolution (~100 m) by detecting spatial and temporal changes in local field and water management. Despite the low penetration depth of the C-band, σ0 is influenced by shallow WTD fluctuations via the soil hydraulic connection between the water table and surface soil. Here, we analyzed σ0 at 60 monitoring wells in a drained temperate peatland with degraded organic soils used as extensive grassland. We evaluated temporal Spearman correlation coefficients between σ0 and WTD considering the soil and vegetation information. To account for the effects of seasonal vegetation changes, we used the cross-over (incidence) angle method. Climatologies of the slope of the incidence angle dependency derived from two years of Sentinel-1 data and their application to the cross-over angle method did improve correlations, though the effect was minor. Overall, averaged over all sites, a temporal Spearman correlation coefficient of 0.45 (±0.17) was obtained. The loss of correlation during summer (higher vegetation, deeper WTD) and the effects of cuts and grazing are discussed. The site-specific general wetness level, described by the mean WTD of each site was shown to be a major factor controlling the strength of the correlation. Mean WTD deeper than about −0.60 m lowered the correlations across sites, which might indicate an important limit of the application. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands II)
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