Detecting anomalous CO2 flux using space borne spectroscopy

Over the time-scale, earth's atmospheric CO₂ concentration has varied and that is mostly determined by balance among the geochemical processes including burial of organic carbon in sediments, silicate rock weathering and volcanic activity. The best recorded atmospheric CO₂ variability is derived from Vostok ice core that records last four glacial/interglacial cycles. The present CO₂ concentration of earth's atmosphere has exceeded far that it was predicted from the ice core data. Other than rapid industrialization and urbanization since last century, geo-natural hazards such as volcanic activity, leakage from hydrocarbon reservoirs and spontaneous combustion of coal contribute a considerable amount of CO₂ to the atmosphere. Spontaneous combustion of coal is common occurrence in most coal producing countries and sometimes it could be in an enormous scale. Remote sensing has already proved to be a significant tool in coalfire identification and monitoring studies. However, coalfire related CO₂ quantification from remote sensing data has not endeavoured yet by scientific communities because of low spectral resolution of commercially available remote sensing data and relatively sparse CO₂ plume than other geological hazards like volcanic activity. The present research has attempted two methods to identify the CO₂ flux emitted from coalfires in a coalmining region in north China. Firstly, a band rationing method was used for column atmospheric retrieval of CO₂ and secondly atmospheric models were simulated in fast atmospheric signature code (FASCOD) to understand the local radiation transport and then the model was implemented with the inputs from hyperspectral remote sensing data. It was observed that retrieval of columnar abundance of CO₂ with the band rationing method is faster as less simulation required in FASCOD. Alternatively, the inversion model could retrieve CO₂ concentration from a (certain) source because it excludes the uncertainties in the higher altitude.     

Application of remote sensing to identify coalfires in the Raniganj Coalbelt,India

Raniganj and Jharia regions together have been for long the single largest coal supplier in India, now contributing about a quarter of the total output in the country. Numerous reasons such as improper mining techniques and policy, as well as unauthorized mining caused surface and subsurface coalfires in these areas. These coalfires burn millions of tonnes of valuable coal resources, creating severe environmental problems and posing enormous operational difficulties of mining. After first use of remote sensing as a tool to identify coalfires in 1960s, with the time, the efficiency of remote sensing to identify and monitoring coalfires has been well established by several researchers. With the knowledge of local geological setting and density sliced surface temperature image the spatial distribution of coalfires can be revealed. The present paper makes an attempt to identify temperature anomalies of the Raniganj coalbelt to locate the spatial distribution of coalfires. Landsat Thematic Mapper (TM) thermal band data was used to calculate surface temperature along with NDVI (normalized vegetation index) derived emissivity.