Zhang C.,CAS Institute of Remote Sensing |
Guan Y.,CAS Institute of Remote Sensing |
Guo S.,CAS Institute of Remote Sensing |
Li J.,HIGH-TECH |
And 6 more authors.
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011
Coal fire generates a number of environmental problems and results in disorderly changes of landcover. Detecting the change of Land-cover is an important scientific issue of the land evaluation and the eco-environmental change forecasting. The temporal land cover maps with high accuracy make it possible to explore the eco-environmental changes of coal fire area. In thispaper, the multi-layer segmentation-based classification approach, Markov Transition Matrix methodology and Dynamic indexesby using Landsat TM data was carried out. The results reveal that coal mine and resident change are mostly in recent decades among all land cover types. Private coal mining exploitation and government administrative measures are the deriving factors. © 2011 SPIE.
Zhang C.-Y.,CAS Institute of Remote Sensing |
Guo S.,CAS Institute of Remote Sensing |
Guan Y.-N.,CAS Institute of Remote Sensing |
Kong B.,Shenhua Beijing Remote Sensing and GEO Engineering Co. |
And 8 more authors.
Meitan Xuebao/Journal of the China Coal Society | Year: 2012
Wuda coal fire area, where the coal fire is serious, was chosen as the study area, the diffusion of gases in atmosphere, such as CO, CO2, CH4 and SO2, were studied with the Gaussian plume Model on the ArcGIS platform. The study indicates that the gases diffuses along the downwind, and theirs concentrations takes on Gaussian distribution where is in the prevailing westerly winds Wuda, and the urban area of Wuda city is in a higher concentration and the gases have a severely impaction on the residents.
Jiang L.,Chinese University of Hong Kong |
Jiang L.,CAS Wuhan Institute of Geodesy and Geophysics |
Lin H.,Chinese University of Hong Kong |
Ma J.,Shenhua Beijing Remote Sensing and Geo engineering Co. |
And 2 more authors.
Remote Sensing of Environment | Year: 2011
Uncontrolled coal fires can result in massive surface displacements due to the change in volume of burning coal and thermal effects in the adjacent rock mass; simultaneously, the resultant surface breakings provide greater access to air and water that in turn can aggravate the problem of underground coal seam burning. In this case study, we have investigated the feasibility and potential of detecting the land subsidence accompanying coal fires by means of satellite InSAR observations. Three groups of small-baseline InSAR approaches (PSI, stacking and 2-passDInSAR) were applied to the Wuda coalfield (Northern China) to reveal the spatial and temporal signals of the land subsidence in the areas affected by the coal fires. The interferometric results agree well with GPS observations and coal fire data obtained by field investigation, which demonstrates that the small-baseline InSAR techniques have remarkable potential to detect this land subsidence of interest. In particular, our results show that the development of coal fires can lead to new subsiding areas and also accelerate the ongoing surface subsidence, typically within the areas of mature coal fires, through a comparison of the interferometric observations and the multi-temporal coal fire maps. This timely and reliable information on land subsidence will be useful for the detection and mapping of the coal fire affected regions and thereby assist in fighting and controlling coal seam burning. © 2010 Elsevier Inc.
Yang F.,China University of Mining and Technology |
Peng S.-P.,China University of Mining and Technology |
Ma J.-W.,Shenhua Beijing Remote Sensing and Geo engineering Co. |
He S.,China University of Mining and Technology
Meitan Xuebao/Journal of the China Coal Society | Year: 2010
The studies show that the ARMA (Auto-Regressive and Moving Average Model) spectral is sensitivity for the weak change of signal and there is significant difference in response characteristics of ARMA spectrum when radar wave travels in fragmentation and loose zone. On the basis of these studies, the ARMA spectral density algorithm was given and the spectral section was constructed through rolling short time windows. By using the spectral section, the different abnormal zone can be detected.