Modeling peatland carbon stock in a delineated portion of the Nayshkootayaow river watershed in Far North, Ontario using an integrated GIS and remote sensing approach

Akumu, Clement E.; McLaughlin, James W.

doi:10.1016/j.catena.2014.05.025

Cited by 25 publications

(12 citation statements)

References 15 publications

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“…Kumpula et al (2004) used Landsat 5 data to map land cover and distinguish peat in the eastern Tibetan Plateau. Akumu & McLaughlin (2014) also used SPOT 5 imagery to delineate and classify peat with some success.…”

Section: Introductionmentioning

confidence: 99%

Mapping peat in Scotland with remote sensing and site characteristics

Aitkenhead

2016

European J Soil Science

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Mapping the spatial distribution of organic soil is important for decision making in land management. Organic soil types have a strong effect on carbon storage, water availability and quality, biodiversity and many other ecosystem services. A large proportion of Scotland's soil is organic, and is classed as peats or organomineral. Existing soil maps for Scotland include a 1:25 000 map of eastern and southern parts of the country that identifies the major soil subgroup at each location, or a 1:250 000 map covering the whole country that gives proportions of the major soil group within mapping units. A detailed map of the organic soils of Scotland that disaggregates these mapping units is therefore required. Existing data were used from several Scottish soil surveys and remote sensing data (Landsat 8) to develop a model for predicting whether a site has mineral, organomineral (mineral with peaty topsoil) or organic (peat) soil. The model was applied across Scotland at a grid resolution of 100 m. Predictive accuracies of 68.1% (mineral), 61.3% (organomineral) and 81.8% (organic) were obtained, with an overall prediction accuracy of 69.8%. Training the model with two classes only, non‐organic (including mineral and organomineral) and organic, gave accuracies of 83.9 and 93.6%, respectively, and an overall accuracy of 86.4%. A map of prediction confidence was also produced. The results show how the approach can be used to disaggregate the existing map units and predict mineral and organic soils at locations within a map unit. The map is being used to prioritize monitoring efforts in catchments across Scotland. Highlights Use of remote sensing and spatial covariates to map peat in Scotland. Resulting peat maps will be useful for decision making on land management and carbon storage in soil. Peat is mapped accurately in most areas, although uncertainty is greater in complex landscapes. Combination of remote sensing and spatial covariates improves accuracy of mapping.

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Section: Introductionmentioning

confidence: 99%

Mapping peat in Scotland with remote sensing and site characteristics

Aitkenhead

2016

European J Soil Science

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“…Geostatistics estimates the values of properties (at unsampled places) that spatially vary from sparse sample data [8]. It has the capability in quantifying an unknown value, creating a map and validating sampling strategy and so improving the sampling [9]. It is here where this research wishes to perform spatial variability of peat depth by using the geostatistical method.…”

Section: Introductionmentioning

confidence: 99%

Performing Spatial Variabilityof Peat Depth by Using Geostatistics

et al. 2018

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Geostatistics has been knowns as a reliable tool to explore variability in space of any measured parameter. This research aims to study how peat depth change and vary in space using geostatistics aproach. The research took place in a peat land inMuaro Jambi district, Jambi province of Indonesia. The three different areas of peat depth [very deep (area A), deep (area B) and shallow (area C)] were purposely selected to investigate through borehole. From the total 120 boreholes, peat depth data were analyses using ArcGIS geostatistical analyses.The result showed that peat variability in shallow area is higher than that of deep and very deep area.It is also found that the reliable sampling distance in peat exploration should not be less than 230 meter in very deep area, 275 meter in deep area and 41 meter in shallow area.

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“…There are several approaches that can be used to estimate carbon storage. Lu (2006), Cantarello et al (2011), Sohl et al (2012), Humpenöder et al (2013), Akumu & McLaughlin (2014), Ardiansyah and Buchori (2014), Sun et al (2015), has reviewed and summarized some different approaches such as field measurement, remote sensing, and GIS based analysis for estimate carbon storage. Methodological approach using remote sensing has been successfully approved to measure carbon storage in low density boreal forests (Rosenqvist et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Increasing Accuracy Value in the Estimates of Carbon Stock by Using Vegetation Index From Alos Avnir 2 Satellite Imagery

Fardani

Darmawan²,

Hakim

et al. 2016

Geoplanning: Journal of Geomatics and Planning

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The existence of carbon stock began to be noticed by the public, especially after the global warming phenomenon, because one of the causes of global warming is the increasing amount of carbon in the atmosphere. There are several approaches that can be used to calculate carbon stocks, one of which is through remote sensing. In the study of carbon stocks in Meru Betiri National Park Indonesia, the vegetation index from ALOS-AVNIR satellite imagery is used to estimate carbon reserves by finding an exact equation. If it uses the Modified Soil Adjusted Vegetation Index (MSAVI) only, the correlation value is 0.49. Meanwhile, if Infrared Percentage Vegetation Index (IPVI) is used, the correlation value is 0.47. However, if some vegetation indices such as Soil-Adjusted Vegetation Index (SAVI), Normalize Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) are combined, the correlation value of the equation is 0.63. The comparison showed that by combining several variables of vegetation indices will increase the value of the correlation equation significantly.

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Modeling peatland carbon stock in a delineated portion of the Nayshkootayaow river watershed in Far North, Ontario using an integrated GIS and remote sensing approach

Cited by 25 publications

References 15 publications

Mapping peat in Scotland with remote sensing and site characteristics

Mapping peat in Scotland with remote sensing and site characteristics

Performing Spatial Variabilityof Peat Depth by Using Geostatistics

Increasing Accuracy Value in the Estimates of Carbon Stock by Using Vegetation Index From Alos Avnir 2 Satellite Imagery

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