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Special Issue "Remote Sensing of Peatlands"

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 (28 February 2018).

Special Issue Editor

Guest Editor
Prof. Dr. Kevin Tansey

Centre for Landscape & Climate Research, Leicester Institute for Space & Earth Observation, School of Geography, Geology & the Environment, University of Leicester, Leicester, UK
Website | E-Mail
Phone: 00447770748990
Interests: radar; InSAR; LiDAR; multispectral; hyperspectral; lithological mapping; image classification; structural mapping; vegetation mapping; hydrocarbon seep mapping; landscape modelling

Special Issue Information

Dear Colleagues,

Peatlands are landscapes that have naturally-accumulated layers of partially-decayed vegetation or organic matter on the land surface. They are distributed across the Earth, from high latitudes to the tropics. They account for between 50 and 70% of global wetlands. They are a huge store of soil carbon under conditions of almost permanent water saturation. They play an important role in the carbon cycle, water cycle and are habitats for some very important species of animals and plants, from tiny insectivorous sundews to clouded leopards and orangutan, for example.

Peatlands are sources of fuel that humans burn. Peatlands are being drained and used to grow crops such as the oil palm tree (Elaeis guineensis jacq.). Peatlands are ecosystems that are very sensitive to climate change and weather patterns, for example El Nino reduces rainfall that impacts on the water table making them susceptible to fire and erosion. The threats to peatlands and remedies to manage them sustainably into the future require an understanding of the physical, environmental, political, and social environment.

Remote sensing of peatlands can reveal a great deal of information to help develop this understanding. Satellite data can be used to establish the extent of peatlands, their elevation and topographic characteristics, the land use/land cover change history, the diversity of the vegetation, the fire disturbance impacts and various measurements associated with the atmosphere, such as emissions, smoke and air quality.

This Special Issue will establish the state-of-the-art with respect to the remote sensing of peatlands and determine if current observational capacity is meeting needs or whether further capability is required.

Prof. Kevin Tansey
Guest Editor

Manuscript Submission Information

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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 1800 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

  • Peatlands
  • Peatswamp forest
  • Disturbance
  • Emissions
  • Carbon loss, sinks and sources
  • Climate change
  • Mires
  • Organic soils
  • Orang-utan

Published Papers (9 papers)

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Research

Open AccessArticle
Long-Term Peatland Condition Assessment via Surface Motion Monitoring Using the ISBAS DInSAR Technique over the Flow Country, Scotland
Remote Sens. 2018, 10(7), 1103; https://doi.org/10.3390/rs10071103
Received: 2 May 2018 / Revised: 28 June 2018 / Accepted: 6 July 2018 / Published: 11 July 2018
Cited by 4 | PDF Full-text (7105 KB) | HTML Full-text | XML Full-text
Abstract
Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical [...] Read more.
Satellite Earth Observation (EO) is often used as a cost-effective method to report on the condition of remote and inaccessible peatland areas. Current EO techniques are primarily limited to reporting on the vegetation classes and properties of the immediate peat surface using optical data, which can be used to infer peatland condition. Another useful indicator of peatland condition is that of surface motion, which has the potential to report on mass accumulation and loss of peat. Interferometic SAR (InSAR) techniques can provide this using data from space. However, the most common InSAR techniques for information extraction, such as Persistent Scatterers’ Interferometry (PSI), have seen limited application over peat as they are primarily tuned to work in areas of high coherence (i.e., on hard, non-vegetated surfaces only). A new InSAR technique, called the Intermittent Small BAseline Subset (ISBAS) method, has been recently developed to provide measurements over vegetated areas from SAR data acquired by satellite sensors. This paper examines the feasibility of the ISBAS technique for monitoring long-term surface motion over peatland areas of the Flow Country, in the northeast of Scotland. In particular, the surface motions estimated are compared with ground data over a small forested area (namely the Bad a Cheo forest Reserve). Two sets of satellite SAR data are used: ERS C-band images, covering the period 1992–2000, and Sentinel-1 C-band images, covering the period 2015–2016. We show that the ISBAS measurements are able to identify surface motion over peatland areas, where subsidence is a consequence of known land cover/land use. In particular, the ISBAS products agree with the trend of surface motion, but there are uncertainties with their magnitude and direction (vertical). It is concluded that there is a potential for the ISBAS method to be able to report on trends in subsidence and uplift over peatland areas, and this paper suggests avenues for further investigation, but this requires a well-resourced validation campaign. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Soil Moisture Monitoring in a Temperate Peatland Using Multi-Sensor Remote Sensing and Linear Mixed Effects
Remote Sens. 2018, 10(6), 903; https://doi.org/10.3390/rs10060903
Received: 28 February 2018 / Revised: 31 May 2018 / Accepted: 4 June 2018 / Published: 8 June 2018
PDF Full-text (7866 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of this research was to use empirical models to monitor temporal dynamics of soil moisture in a peatland using remotely sensed imagery, and to determine the predictive accuracy of the approach on dates outside the time series through statistically independent validation. [...] Read more.
The purpose of this research was to use empirical models to monitor temporal dynamics of soil moisture in a peatland using remotely sensed imagery, and to determine the predictive accuracy of the approach on dates outside the time series through statistically independent validation. A time series of seven Moderate Resolution Imaging Spectroradiometer (MODIS) and Synthetic Aperture Radar (SAR) images were collected along with concurrent field measurements of soil moisture over one growing season, and soil moisture retrieval was tested using Linear Mixed Effects models (LMEs). A single-date airborne Light Detection and Ranging (LiDAR) survey was incorporated into the analysis, along with temporally varying environmental covariates (Drought Code, Time Since Last Rain, Day of Year). LMEs allowed repeated measures to be accounted for at individual sampling sites, as well as soil moisture differences associated with peatland classes. Covariates provided a large amount of explanatory power in models; however, SAR imagery contributed to only a moderate improvement in soil moisture predictions (marginal R2 = 0.07; conditional R2 = 0.7, independently validated R2 = 0.36). The use of LMEs allows for a more accurate characterization of soil moisture as a function of specific measurement sites, peatland classes and measurement dates on model strength and predictive power. For intensively monitored peatlands, SAR data is best analyzed in conjunction with peatland Class (e.g., derived from an ecosystem classification map) to estimate the spatial distribution of surface soil moisture, provided there is a ground-based monitoring network with a sufficiently fine spatial and temporal resolution to fit the LME models. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Estimating Peatland Water Table Depth and Net Ecosystem Exchange: A Comparison between Satellite and Airborne Imagery
Remote Sens. 2018, 10(5), 687; https://doi.org/10.3390/rs10050687
Received: 23 February 2018 / Revised: 24 April 2018 / Accepted: 24 April 2018 / Published: 29 April 2018
Cited by 3 | PDF Full-text (5903 KB) | HTML Full-text | XML Full-text
Abstract
Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, [...] Read more.
Peatlands play a fundamental role in climate regulation through their long-term accumulation of atmospheric carbon. Despite their resilience, peatlands are vulnerable to climate change. Remote sensing offers the opportunity to better understand these ecosystems at large spatial scales through time. In this study, we estimated water table depth from a 6-year time sequence of airborne shortwave infrared (SWIR) hyperspectral imagery. We found that the narrowband index NDWI1240 is a strong predictor of water table position. However, we illustrate the importance of considering peatland anisotropy on SWIR imagery from the summer months when the vascular plants are in full foliage, as not all illumination conditions are suitable for retrieving water table position. We also model net ecosystem exchange (NEE) from 10 years of Landsat TM5 imagery and from 4 years of Landsat OLI 8 imagery. Our results show the transferability of the model between imagery from sensors with similar spectral and radiometric properties such as Landsat 8 and Sentinel-2. NEE modeled from airborne hyperspectral imagery more closely correlated to eddy covariance tower measurements than did models based on satellite images. With fine spectral, spatial and radiometric resolutions, new generation satellite imagery and airborne hyperspectral imagery allow for monitoring the response of peatlands to both allogenic and autogenic factors. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Tropical Peatland Vegetation Structure and Biomass: Optimal Exploitation of Airborne Laser Scanning
Remote Sens. 2018, 10(5), 671; https://doi.org/10.3390/rs10050671
Received: 19 March 2018 / Revised: 16 April 2018 / Accepted: 24 April 2018 / Published: 25 April 2018
Cited by 2 | PDF Full-text (12251 KB) | HTML Full-text | XML Full-text
Abstract
Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB [...] Read more.
Accurate estimation of above ground biomass (AGB) is required to better understand the variability and dynamics of tropical peat swamp forest (PSF) ecosystem function and resilience to disturbance events. The objective of this work is to examine the relationship between tropical PSF AGB and small-footprint airborne Light Detection and Ranging (LiDAR) discrete return (DR) and full waveform (FW) derived metrics, with a view to establishing the optimal use of this technology in this environment. The study was undertaken in North Selangor peat swamp forest (NSPSF) reserve, Peninsular Malaysia. Plot-based multiple regression analysis was performed to established the strongest predictive models of PSF AGB using DR metrics (only), FW metrics (only), and a combination of DR and FW metrics. Overall, the results demonstrate that a Combination-model, coupling the benefits derived from both DR and FW metrics, had the best performance in modelling AGB for tropical PSF (R2 = 0.77, RMSE = 36.4, rRMSE = 10.8%); however, no statistical difference was found between the rRMSE of this model and the best models using only DR and FW metrics. We conclude that the optimal approach to using airborne LiDAR for the estimation of PSF AGB is to use LiDAR metrics that relate to the description of the mid-canopy. This should inform the use of remote sensing in this ecosystem and how innovation in LiDAR-based technology could be usefully deployed. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates
Remote Sens. 2018, 10(4), 611; https://doi.org/10.3390/rs10040611
Received: 28 February 2018 / Revised: 9 April 2018 / Accepted: 12 April 2018 / Published: 15 April 2018
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Abstract
A soil wetness index for optical satellite images, the Transformed Wetness Index (TWI) is defined and evaluated against ground sampled soil moisture. Conceptually, TWI is formulated as a non-linear normalized difference index from orthogonalized vectors representing soil and water conditions, with the vegetation [...] Read more.
A soil wetness index for optical satellite images, the Transformed Wetness Index (TWI) is defined and evaluated against ground sampled soil moisture. Conceptually, TWI is formulated as a non-linear normalized difference index from orthogonalized vectors representing soil and water conditions, with the vegetation signal removed. Compared to 745 ground sites with in situ measured soil moisture, TWI has a globally estimated Random Mean Square Error of 14.0 (v/v expressed as percentage), which reduces to 8.5 for unbiased data. The temporal variation in soil moisture is significantly captured at 4 out of 10 stations, but also fails for 2 to 3 out of 10 stations. TWI is biased by different soil mineral compositions, dense vegetation and shadows, with the latter two most likely also causing the failure of TWI to capture soil moisture dynamics. Compared to soil moisture products from microwave brightness temperature data, TWI performs slightly worse, but has the advantages of not requiring ancillary data, higher spatial resolution and a relatively simple application. TWI has been used for wetland and peatland mapping in previously published studies but is presented in detail in this article, and then applied for detecting changes in soil moisture for selected tropical regions between 2001 and 2016. Sites with significant changes are compared to a published map of global tropical wetlands and peatlands. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Airborne Hyperspectral Evaluation of Maximum Gross Photosynthesis, Gravimetric Water Content, and CO2 Uptake Efficiency of the Mer Bleue Ombrotrophic Peatland
Remote Sens. 2018, 10(4), 565; https://doi.org/10.3390/rs10040565
Received: 19 February 2018 / Revised: 31 March 2018 / Accepted: 4 April 2018 / Published: 6 April 2018
Cited by 4 | PDF Full-text (79087 KB) | HTML Full-text | XML Full-text
Abstract
Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required [...] Read more.
Peatlands cover a large area in Canada and globally (12% and 3% of the landmass, respectively). These ecosystems play an important role in climate regulation through the sequestration of carbon dioxide from, and the release of methane to, the atmosphere. Monitoring approaches, required to understand the response of peatlands to climate change at large spatial scales, are challenged by their unique vegetation characteristics, intrinsic hydrological complexity, and rapid changes over short periods of time (e.g., seasonality). In this study, we demonstrate the use of multitemporal, high spatial resolution (1 m2) hyperspectral airborne imagery (Compact Airborne Spectrographic Imager (CASI) and Shortwave Airborne Spectrographic Imager (SASI) sensors) for assessing maximum instantaneous gross photosynthesis (PGmax) in hummocks, and gravimetric water content (GWC) and carbon uptake efficiency in hollows, at the Mer Bleue ombrotrophic bog. We applied empirical models (i.e., in situ data and spectral indices) and we derived spatial and temporal trends for the aforementioned variables. Our findings revealed the distribution of hummocks (51.2%), hollows (12.7%), and tree cover (33.6%), which is the first high spatial resolution map of this nature at Mer Bleue. For hummocks, we found growing season PGmax values between 8 μmol m−2 s−1 and 12 μmol m−2 s−1 were predominant (86.3% of the total area). For hollows, our results revealed, for the first time, the spatial heterogeneity and seasonal trends for gravimetric water content and carbon uptake efficiency for the whole bog. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessEditor’s ChoiceArticle
Inferring Water Table Depth Dynamics from ENVISAT-ASAR C-Band Backscatter over a Range of Peatlands from Deeply-Drained to Natural Conditions
Remote Sens. 2018, 10(4), 536; https://doi.org/10.3390/rs10040536
Received: 28 February 2018 / Revised: 21 March 2018 / Accepted: 29 March 2018 / Published: 31 March 2018
Cited by 3 | PDF Full-text (10964 KB) | HTML Full-text | XML Full-text
Abstract
Water table depth (WTD) is one of the key variables controlling many processes in peatlands. Reliable WTD estimates based on remote sensing data would advance peatland research from global-scale climate monitoring to field-scale ecosystem management. Here, we evaluate the relationship between ENVISAT Advanced [...] Read more.
Water table depth (WTD) is one of the key variables controlling many processes in peatlands. Reliable WTD estimates based on remote sensing data would advance peatland research from global-scale climate monitoring to field-scale ecosystem management. Here, we evaluate the relationship between ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-band backscatter (σ°) and in situ observed WTD dynamics over 17 peatlands in Germany covering deeply-drained to natural conditions, excluding peatlands dominated by forest or inundation periods. The results show increasing σ° with shallower WTD (=wetter conditions), with average temporal Pearson correlation coefficients of 0.38 and 0.54 (-) for natural (also including disturbed and rewetted/restored states) and agriculturally-used drained peatlands, respectively. The anomaly correlation further highlights the potential of ASAR backscatter to capture interannual variations with values of 0.33 and 0.43 (-), for natural and drained peatlands. The skill metrics, which are similar to those for evaluations of top soil moisture from C-band over mineral soils, indicate a strong capillary connection between WTD and the ‘C-band-sensitive’ top 1–2 cm of peat soils, even during dry periods with WTD at around −1 m. Various backscatter processing algorithms were tested without significant differences. The cross-over angle concept for correcting dynamical vegetation effects was tested, but not superior, to constant incidence angle correction. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
Passive L-Band Microwave Remote Sensing of Organic Soil Surface Layers: A Tower-Based Experiment
Remote Sens. 2018, 10(2), 304; https://doi.org/10.3390/rs10020304
Received: 30 November 2017 / Revised: 2 February 2018 / Accepted: 7 February 2018 / Published: 16 February 2018
Cited by 4 | PDF Full-text (4192 KB) | HTML Full-text | XML Full-text
Abstract
Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their [...] Read more.
Organic soils play a key role in global warming because they store large amount of soil carbon which might be degraded with changing soil temperatures or soil water contents. There is thus a strong need to monitor these soils and, in particular, their hydrological characteristics using, for instance, space-borne L-band brightness temperature observations. However, there are still open issues with respect to soil moisture retrieval techniques over organic soils. In view of this, organic soil blocks with their vegetation cover were collected from a heathland in the Skjern River catchment in western Denmark and then transported to a remote sensing field laboratory in Germany where their structure was reconstituted. The controlled conditions at this field laboratory made it possible to perform tower-based L-band radiometer measurements of the soils over a period of two months. Brightness temperature data were inverted using a radiative transfer (RT) model for estimating the time variations in the soil dielectric permittivity and the vegetation optical depth. In addition, the effective vegetation scattering albedo parameter of the RT model was retrieved based on a two-step inversion approach. The remote estimations of the dielectric permittivity were compared to in situ measurements. The results indicated that the radiometer-derived dielectric permittivities were significantly correlated with the in situ measurements, but their values were systematically lower compared to the in situ ones. This could be explained by the difference between the operating frequency of the L-band radiometer (1.4 GHz) and that of the in situ sensors (70 MHz). The effective vegetation scattering albedo parameter was found to be polarization dependent. While the scattering effect within the vegetation could be neglected at horizontal polarization, it was found to be important at vertical polarization. The vegetation optical depth estimated values over time oscillated between 0.10 and 0.19 with a mean value of 0.13. This study provides further insights into the characterization of the L-band brightness temperature signatures of organic soil surface layers and, in particular, into the parametrization of the RT model for these specific soils. Therefore, the results of this study are expected to improve the performance of space-borne remote sensing soil moisture products over areas dominated by organic soils. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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Open AccessArticle
A New Method to Map Groundwater Table in Peatlands Using Unmanned Aerial Vehicles
Remote Sens. 2017, 9(10), 1057; https://doi.org/10.3390/rs9101057
Received: 16 August 2017 / Revised: 30 September 2017 / Accepted: 13 October 2017 / Published: 17 October 2017
Cited by 9 | PDF Full-text (7435 KB) | HTML Full-text | XML Full-text
Abstract
Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they [...] Read more.
Groundwater level (GWL) and depth to water (DTW) are related metrics aimed at characterizing groundwater-table positions in peatlands, and two of the most common variables collected by researchers working in these ecosystems. While well-established field techniques exist for measuring GWL and DTW, they are generally difficult to scale. In this study, we present a novel workflow for mapping groundwater using orthophotography and photogrammetric point clouds acquired from unmanned aerial vehicles. Our approach takes advantage of the fact that pockets of surface water are normally abundant in peatlands, which we assume to be reflective of GWL in these porous, gently sloping environments. By first classifying surface water and then extracting a sample of water elevations, we can generate continuous models of GWL through interpolation. Estimates of DTW can then be obtained through additional efforts to characterize terrain. We demonstrate our methodology across a complex, 61-ha treed bog in northern Alberta, Canada. An independent accuracy assessment using 31 temporally coincident water-well measurements revealed accuracies (root mean square error) in the 20-cm range, though errors were concentrated in small upland pockets in the study area, and areas of dense tree covers. Model estimates in the open peatland areas were considerably better. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands)
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