Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands

Dissanska, Maria; Bernier, Monique; Payette, Serge

doi:10.5589/m09-002

Cited by 46 publications

(52 citation statements)

References 35 publications

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“…Other multi-temporal C-band SAR applications in northern wetland [45,46] showed that it can distinguish inundation from areas highly saturated with water, although at a different scale platform (European Space Agency Environmental Satellite or ENVISAT ASAR) Wide Swath, spatial resolution of 150 m). With regard to optical images applications at the same study area [34,47], they provided a satisfactory level of accuracy both for the delineation of peatlands and for the distinction of their internal structures. Whereas Dribault et al [34] were able to relate the observed spatial dynamics to the evolution of the measured water levels (discharge), our approach with a multi-date and multi-polarimetric C-band SAR index allowed for quality estimations of surface soil moisture on the peatlands' internal structures.…”

Section: Cross-validation Of the Empirical Approachmentioning

confidence: 99%

Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data

et al. 2013

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Understanding the hydrological dynamics of boreal wetland ecosystems (peatlands) is essential in order to better manage hydropower inter-annual productivity at the La Grande basin (Northern Quebec, QC, Canada). Given the remoteness and the huge dimension of the La Grande basin, it is imperative to develop remote sensing monitoring techniques to retrieve hydrological parameters. The main objective of this study is to find out if multi-date and multi-polarization Radar Satellite 2 (RADARSAT-2) (C-band) image analysis could detect seasonal variations of surface soil moisture conditions of the acrotelm. A change detection approach through the use of multi temporal indexes was chosen based on the assumption that the temporal variability of surface roughness and natural vegetation biomass is generally at a much longer time scale than that of surface soil moisture (Δ-Index is based on a reference image that represents dry soil, in order to maximize the sensitivity of σ° to changes in soil moisture with respect to the same location when soil is wet). The Δ-Index approach was tested with each polarization: σ° for fully polarimetric mode (HH, HV, VV) and the cross-polarization coefficient (HV/HH). Results show that the best regression adjustment with regard to surface soil moisture content in boreal wetlands was obtained with the cross-polarization coefficient. The cross-polarization OPEN ACCESSRemote Sens. 2013, 5 4920 multi-temporal index enables precise volumetric surface soil moisture estimation and monitoring on boreal wetlands, regardless of the influence of vegetation cover and surface roughness conditions (bias was under 1%, standard deviation and RMSE were under 10% for almost all estimation errors). Surface soil moisture estimation was more precise over permanently flooded areas than seasonally flooded ones (standard deviation is systematically greater for the seasonally flooded areas, at all analyzed scales), although the overall quality of the estimation is still precise. Cross-polarization ratio image analysis appears to be a useful mean to exploit radar data spatially, as we were able to relate changes in wetland eco-hydrological dynamics to variations in the intensity of the ratio.

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Section: Cross-validation Of the Empirical Approachmentioning

confidence: 99%

Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data

et al. 2013

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“…An approach by region (object-based image analysis) [35] was chosen because it uses not only the spectral information contained in the image but also spatial and topological information. The procedure was inspired by the semi-automated approach developed by Dissanska et al [26] using panchromatic images at a multi-decadal scale.…”

Section: Methodsmentioning

confidence: 99%

“…Recent studies have shown that VHR imagery is suitable for the detailed mapping of temporary water bodies at a local scale [26,27]. Dissanska et al [26] developed a semi-automated approach for assessing the spatiotemporal development of terrestrial and aquatic compartments in patterned peatlands of the La Grande River watershed based on very high resolution panchromatic images. This study, which operated at a multi-decadal scale , proposed a robust method of classification with an overall accuracy of 81% that was able to detect significant changes in the aquatic areas of the studied peatlands.…”

Section: Introductionmentioning

confidence: 99%

“…These images provide an attractive alternative to aerial photographs due to short satellite return intervals and digital data formats easily integrated into geographic information systems [13]. Recent studies have shown that VHR imagery is suitable for the detailed mapping of temporary water bodies at a local scale [26,27]. Dissanska et al [26] developed a semi-automated approach for assessing the spatiotemporal development of terrestrial and aquatic compartments in patterned peatlands of the La Grande River watershed based on very high resolution panchromatic images.…”

Section: Introductionmentioning

confidence: 99%

“…The emergence of new satellite sensors providing very high spatial resolution images (VHR) has raised new hopes for environmental and urban monitoring [24][25][26]. These images provide an attractive alternative to aerial photographs due to short satellite return intervals and digital data formats easily integrated into geographic information systems [13].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification

2012

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Abstract:The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological processes in these minerotrophic peatlands (fens) in order to provide effective monitoring tools to water managers. The objective of this study was to use VHR remote sensing data to understand the seasonal dynamics of the hydrology in fens. A series of 10 multispectral pan-sharpened GeoEye-1 images (with a spatial resolution of 40 cm) were acquired during the snow-free season (May to October) in 2009 and 2010, centered on two study sites in the Laforge sector (54°06'N; 72°30'W). These are two fens instrumented for continuous hydrometeorological monitoring (water level, discharge, precipitation, air temperature). An object-based classification procedure was set up and applied. It consisted of segmenting the imagery into objects using the multiresolution segmentation algorithm (MRIS) to delineate internal structures in the peatlands (aquatic, semi-aquatic, and terrestrial). Then, the objects were labeled using a fuzzy logic based algorithm. The overall classification accuracy of the 10 images was assessed to be 82%. The time series of the peatland mapping demonstrated the existence of important intra-seasonal spatial dynamics in the aquatic and semi-aquatic compartments. It was revealed that the dynamics amplitude depended on the morphological features of the OPEN ACCESS Remote Sens. 2012, 4 1888 fens. The observed spatial dynamics was also closely related to the evolution of the measured water levels.

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Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data

et al. 2014

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International audienceWe demonstrate the potential of using freely available satellite data from the Landsat ETM+ sensor for generating carbon balance estimates for lowland peatlands. We used a lowland ombrotrophic peatland in the UK as our test site representing a range of peatland conditions. A literature survey was undertaken to identify the simplest classification schema that could be used to distinguish ecohydrological classes for carbon sequestration on the peatland surface. These were defined as: active raised bog, Eriophorum-dominated bog, milled unvegetated peat and drained or degraded bog, with bracken and Carr woodland to define the bog edges. A maximum likelihood classifier (MLC) was used to map the spatial distribution of the six classes on the peatland surface. A Landsat ETM+ band-5 derived brightness-texture layer created using geostatistical methods greatly improved classification accuracies. The results showed the best accuracy of the MLC, when compared to finer scale methods, with Landsat ETM+ bands alone was 74%, which increased to 93% when including the brightness-texture layer. An estimate of carbon sequestration status of the site was performed that showed good agreement with the results of a finer-scale-based estimate. The coarse-scale map estimating −12 000 kg carbon and fine scale map estimating +23 000 kg carbon per annum. We conclude that with further development of our tool, if textural measures are used alongside optical data in MLC, it is possible to achieve good quality estimates of carbon balance status for peatland landscapes. This represents a potentially powerful operational toolkit for land managers and policy makers who require spatially distributed information on carbon storage and release for carbon pricing and effective land management. Copyright © 2014 John Wiley & Sons, Ltd

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Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands

Cited by 46 publications

References 35 publications

Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data

Monitoring Volumetric Surface Soil Moisture Content at the La Grande Basin Boreal Wetland by Radar Multi Polarization Data

Monitoring Seasonal Hydrological Dynamics of Minerotrophic Peatlands Using Multi-Date GeoEye-1 Very High Resolution Imagery and Object-Based Classification

Characterizing peatland carbon balance estimates using freely available Landsat ETM+ data

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