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Surface coal fire detection using viirs and landsat 8 oli data

Ghosh, Raktim (2019) Surface coal fire detection using viirs and landsat 8 oli data.

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Abstract:Coal fire hasbeen a major concern for the coal industries, environmental departments, and other national agencies in India. The vulnerability associated with the coal fire is inextricably linked withenvironmentalimpacts. Systematic monitoring is of paramount importance concerning the impacts of habitats living within the close proximity of the coal fire-affected regions. Remote sensing provides a cost-effective solutionin detecting and monitoring the coal fire-affectedareas. In this study, the potential of channel-specific surface reflectance of Landsat-8 OLI and VIIRSdatahave beenexplored in detecting and delineating the regions affected by surfacecoal fires. The core objective of this researchisto formulate a methodology by blending Landsat-8 and VIIRS data with a view to generating a high-resolution and high-frequency synthetic coal fire products.The Jharia coalfield, India has been chosen as a study area for the current research. It is majorly affected by surface and sub-surface coal fires in Indiaand a significant amount of potential coal resources have been depleted. In detecting the coal fire,the reflectance-based active fire detection method was incorporated to check its fidelity in delineating surface coal fire affected pixels. After observing the underestimation caused by the existing active fire detection method, a normalised reflectance-based active fire detection method was establishedand the fidelity of this algorithm was tested for other actual Landsat scenes over the similar region in Jharia coalfield. On the other hand, in view of generating the high-frequency and high-resolution synthetic coal fire products, the current study explored severalspatiotemporal fusion methodswithin the framework of weight function based techniques to blend the high spatial resolution Landsat-8 data with the high temporal resolutionVIIRS data within thesimilar spectral domains broadly matches with each other. The fire responsive spectral channels lying within the domain ofNIR and SWIR regions were employed for spatiotemporalfusionmethods. Consequently, the methods named STARFM and ESTARFM were explicitly implemented in this research.The performance of these spatiotemporal fusion methods wasevaluatedqualitatively and quantitativelyusing several assessment metrics. With aview to improving the accuraciesfurther, the modified STARFM and the modified ESTARFM methods were established. In order to generate a high-resolution and high-frequency synthetic coal fire products, this study executed anovel reflectance-based active fire detection method on synthetically predicted Landsat like imagesderived from the spatiotemporalfusionmethods. Also, the accuracy assessmentsof the synthetic coal fire products werecarried out byassessment metricslinkedwith the corresponding confusion matrix. Moreover, a coal fire product quality index(CFPQI)wasdesignedto designatethe overall quality of the synthetic product. It was observed that the modified ESTARFM outperformed all other fusion in terms of spatiotemporalfusion methods as well as for the synthetic coalfire products generated from it.In light of the abovediscussions, this studybuilt an overall framework for generating the high-frequency and high-resolution synthetic coal fire productswhich could be used for systematic mapping and monitoring of the regions affected by surface coal fire. Interestingly, the established novel coal fire detection method was successful in resolving the underestimation caused by the existing active fire detection method. In future studies, the fidelity of the novel coal fire detection techniques couldbe testedfor othercoal fire-affected regions such as in China, Australia and the USA. Also, a fusion-based neural network could be designedfor locating the fire pixels more accurately in the synthetic images.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
Link to this item:https://purl.utwente.nl/essays/85875
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