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Remote Sensing of Peatlands I
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Remote Sensing of Peatlands I

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 October 2013) | Viewed by 50394

Special Issue Editor

Prof. Dr. Florian Siegert

E-Mail Website
Guest Editor
Biology Department II, Ludwig-Maximilians-Universitaet Munich, Grosshadenerstr. 2, 82152 Planegg-Martinsried, Germany
Interests: earth observation; global change; climate change; ecology; biodiversity; nature conservation; natural disasters; digital image processing; remote sensing; Geographical Information system (GIS)

Special Issue Information

Dear Collegues,

Peat is dead organic matter occurring largely in poorly draining environments. It forms at all altitudes and climates. Peatlands cover only approx. 3% of the global land surface (about 4 million km²) but store one-third of the global soil carbon.
Worldwide peatlands are drained in order to expand agricultural land or for the plantation industry. If peat is drained for agriculture or plantations it quickly decomposes, resulting in large emissions of CO2 and N2O into the atmosphere. Drained and degrading peatlands produce 6% of all global anthropogenic CO2 emissions.
Of special importance in the context of GHG emissions are tropical peat swamp forests because of their huge carbon store. Due to fast conversion to oil palm and other huge emissions results from tropical peatlands.
Remote sensing as an advanced technique has been more and more applied to peatlands studies. This special issue is dedicated to publish state-of-the-art studies on remote sensing in peatlands as well as comprehensive literature reviews.

Prof. Dr. Florian Siegert
Guest Editor

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Keywords

  • mapping of peatlands and/or land cover
  • mapping of peatlands drainage
  • peat carbon store
  • emission factors
  • peat thickness measurements
  • monitoring
  • fires on peatlands
  • impact of fire, drainage
  • SAR
  • LiDAR
  • 3D modelling
  • hydrology
  • restoration

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Published Papers (5 papers)

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Research

2585 KiB  
Article
Monitoring Wetlands Ecosystems Using ALOS PALSAR (L-Band, HV) Supplemented by Optical Data: A Case Study of Biebrza Wetlands in Northeast Poland
by Katarzyna Dabrowska-Zielinska, Maria Budzynska, Monika Tomaszewska, Maciej Bartold, Martyna Gatkowska, Iwona Malek, Konrad Turlej and Milena Napiorkowska
Remote Sens. 2014, 6(2), 1605-1633; https://doi.org/10.3390/rs6021605 - 20 Feb 2014
Cited by 40 | Viewed by 9833
Abstract
The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. [...] Read more.
The aim of the study was to elaborate the remote sensing methods for monitoring wetlands ecosystems. The investigation was carried out during the years 2002–2010 in the Biebrza Wetlands. The meteorological conditions at the test site varied from extremely dry to very wet. The authors propose applying satellite remote sensing data acquired in the optical and microwave spectrums to classify wetlands vegetation habitats for the assessment of vegetation changes and estimation of wetlands’ biophysical properties to improve monitoring of these unique, very often physically impenetrable, areas. The backscattering coefficients (σ°) calculated from ALOS PALSAR FBD (Advanced Land Observing Satellite, Phased Array type L-band Synthetic Aperture Radar, Fine Beam Dual Mode) images registered at cross polarization HV on 12 May 2008 were used to classify the main wetland communities using ground truth observations and the visual interpretation method. As a result, the σ° values were distributed among the six wetlands’ vegetation classes: scrubs, sedges-scrubs, sedges, reeds, sedges-reeds, rushes, and the areas of each community and changes were assessed. Also, the change in the biophysical variable as Leaf Area Index (LAI) is described using the information from PALSAR data. Strong linear relationships have been found between LAI and σ° derived for particular wetland classes, which then were applied to elaborate the maps of LAI distribution. The other variables used to characterize the changing environmental conditions are: surface temperature (Ts) calculated from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer) and Normalized Difference Vegetation Index (NDVI) from ENVISAT MERIS (ENVIronmental SATellite MEdium Resolution Imaging Spectrometer). Differences of almost double Ts between “dry” and “wet” years were noticed that reflect observed weather conditions. The highest values of NDVI occurred in years with a sufficient amount of precipitation with the lowest in “dry” years. NDVI values variances within the same wetlands class resulted mainly from the differences in soil moisture. The results of this study show that the satellite data from microwave and optical spectrum gave the repetitive spatial information about vegetation growth conditions and could be used for monitoring wetland ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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6736 KiB  
Article
Empirical Modelling of Vegetation Abundance from Airborne Hyperspectral Data for Upland Peatland Restoration Monitoring
by Beth Cole, Julia McMorrow and Martin Evans
Remote Sens. 2014, 6(1), 716-739; https://doi.org/10.3390/rs6010716 - 9 Jan 2014
Cited by 38 | Viewed by 9311
Abstract
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the [...] Read more.
Peatlands are important terrestrial carbon stores. Restoration of degraded peatlands to restore ecosystem services is a major area of conservation effort. Monitoring is crucial to judge the success of this restoration. Remote sensing is a potential tool to provide landscape-scale information on the habitat condition. Using an empirical modelling approach, this paper aims to use airborne hyperspectral image data with ground vegetation survey data to model vegetation abundance for a degraded upland blanket bog in the United Kingdom (UK), which is undergoing restoration. A predictive model for vegetation abundance of Plant Functional Types (PFT) was produced using a Partial Least Squares Regression (PLSR) and applied to the whole restoration site. A sensitivity test on the relationships between spectral data and vegetation abundance at PFT and single species level confirmed that PFT was the correct scale for analysis. The PLSR modelling allows selection of variables based upon the weighted regression coefficient of the individual spectral bands, showing which bands have the most influence on the model. These results suggest that the SWIR has less value for monitoring peatland vegetation from hyperspectral images than initially predicted. RMSE values for the validation data range between 10% and 16% cover, indicating that the models can be used as an operational tool, considering the subjective nature of existing vegetation survey results. These predicted coverage images are the first quantitative landscape scale monitoring results to be produced for the site. High resolution hyperspectral mapping of PFTs has the potential to assess recovery of peatland systems at landscape scale for the first time. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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3862 KiB  
Article
Peat Mapping Associations of Airborne Radiometric Survey Data
by David Beamish
Remote Sens. 2014, 6(1), 521-539; https://doi.org/10.3390/rs6010521 - 3 Jan 2014
Cited by 27 | Viewed by 9849
Abstract
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are [...] Read more.
This study considers recent airborne radiometric (gamma ray) survey data, obtained at high-resolution, across various regions of the UK. The datasets all display a very evident attenuation of signal in association with peat, and intra-peat variations are observed. The geophysical response variations are examined in detail using example data sets across lowland areas (raised bogs, meres, fens and afforested peat) and upland areas of blanket bog, together with associated wetland zones. The radiometric data do not map soils per se. The bedrock (the radiogenic parent) provides a specific amplitude level. Attenuation of this signal level is then controlled by moisture content in conjunction with the density and porosity of the soil cover. Both soil and bedrock variations need to be jointly assessed. The attenuation theory, reviewed here, predicts that the behaviour of wet peat is distinct from most other soil types. Theory also predicts that the attenuation levels observed across wet peatlands cannot be generally used to map variations in peat thickness. Four survey areas at various scales, across England, Scotland, Wales and Ireland are used to demonstrate the ability of the airborne data to map peat zones. A 1:50 k national mapping of deep peat is used to provide control although variability in the definition of peat zones across existing databases is also demonstrated. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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3155 KiB  
Communication
Videographic Analysis of Eriophorum Vaginatum Spatial Coverage in an Ombotrophic Bog
by Margaret Kalacska, J. Pablo Arroyo-Mora, Julie De Gea, Eva Snirer, Carrie Herzog and Tim R. Moore
Remote Sens. 2013, 5(12), 6501-6512; https://doi.org/10.3390/rs5126501 - 2 Dec 2013
Cited by 26 | Viewed by 8860
Abstract
The use of Remotely Piloted Aircraft Systems (RPAS) as well as newer automated unmanned aerial vehicles is becoming a standard method in remote sensing studies requiring high spatial resolution (<1 m) and very precise temporal data to capture phenological events. In this study [...] Read more.
The use of Remotely Piloted Aircraft Systems (RPAS) as well as newer automated unmanned aerial vehicles is becoming a standard method in remote sensing studies requiring high spatial resolution (<1 m) and very precise temporal data to capture phenological events. In this study we use a low cost rotorcraft to map Eriophorum vaginatum at Mer Bleue, an ombrotrophic bog located east of Ottawa, ON, Canada. We focus on E. vaginatum because this sedge plays an important role in methane (CH4) gas exchange in peatlands. Using the remote controlled rotorcraft we were able to record, process, and mosaic 11.1 hectares of 4.5 cm spatial resolution imagery extracted from individual frames of video recordings (post georegistration RMSE 4.90 ± 4.95 cm). Our results, based on a supervised classification (96% accuracy) of the red, green, blue image planes, indicate a total tussock cover of 2,417 m2. Because the basal area of the plant is more relevant for calculating its contribution to the CH4 flux, the tussock area was related to the basal area from field data (R2 = 0.88, p < 0.0001). Our final results indicate a total basal area of 1,786 ± 62.8 m2. Based on temporal measurements of CH4 flux from the peatland as a whole that vary over the growing season, we estimate the E. vaginatum contribution to range from 3.0% to 17.3% of that total. Overall, our low cost approach was an effective non-destructive way to derive E. vaginatum coverage and estimate CH4 exchange over the growing season. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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6136 KiB  
Article
Quantifying Dynamics in Tropical Peat Swamp Forest Biomass with Multi-Temporal LiDAR Datasets
by Sandra Englhart, Juilson Jubanski and Florian Siegert
Remote Sens. 2013, 5(5), 2368-2388; https://doi.org/10.3390/rs5052368 - 14 May 2013
Cited by 56 | Viewed by 11581
Abstract
Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the [...] Read more.
Tropical peat swamp forests in Indonesia store huge amounts of carbon and are responsible for enormous carbon emissions every year due to forest degradation and deforestation. These forest areas are in the focus of REDD+ (reducing emissions from deforestation, forest degradation, and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks) projects, which require an accurate monitoring of their carbon stocks or aboveground biomass (AGB). Our study objective was to evaluate multi-temporal LiDAR measurements of a tropical forested peatland area in Central Kalimantan on Borneo. Canopy height and AGB dynamics were quantified with a special focus on unaffected, selective logged and burned forests. More than 11,000 ha were surveyed with airborne LiDAR in 2007 and 2011. In a first step, the comparability of these datasets was examined and canopy height models were created. Novel AGB regression models were developed on the basis of field inventory measurements and LiDAR derived height histograms for 2007 (r2 = 0.77, n = 79) and 2011 (r2 = 0.81, n = 53), taking the different point densities into account. Changes in peat swamp forests were identified by analyzing multispectral imagery. Unaffected forests accumulated on average 20 t/ha AGB with a canopy height increase of 2.3 m over the four year time period. Selective logged forests experienced an average AGB loss of 55 t/ha within 30 m and 42 t/ha within 50 m of detected logging trails, although the mean canopy height increased by 0.5 m and 1.0 m, respectively. Burned forests lost 92% of the initial biomass. These results demonstrate the great potential of repetitive airborne LiDAR surveys to precisely quantify even small scale AGB and canopy height dynamics in remote tropical forests, thereby featuring the needs of REDD+. Full article
(This article belongs to the Special Issue Remote Sensing of Peatlands I)
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