THE REMOTE SENSING IN COAL GEOLOGY——A study on detection of coal spontanous burning by remote sensing in the coal fields in the north of China

The spontaneous burning has been lasting for thousands of years in the coal fields in the north of China. It spreads from the west (Tianshan coal field) to the east (Huolinhe coal field). Its E-W extension is up to 3750km, concentrating in N35°toN45°, its vertical depth up to 260m, and the surface temprature locally up to 270℃. Annually, it burns out 0, 250-300 million tones of coal, causing economic loss equivalent to 2-3 billion R.M.B. Yuan.It destroies coal resources and causes hazards in coal mines. In order to locate the extent and the direction in coal burning areas, the remote sensing technique has heen used and has produced an obvious benefit.

TEST STUDY OF DETECTING OIL AND GAS BY USING REMOTE SENSING TECHNIQUE

Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process

Monitoring of winter wheat distribution and phenological phases based on MODIS time-series： A case study in the Yellow River Delta, China

Accurate winter wheat identification and phenology extraction are essential for field management and agricultural policy making. Here, we present mechanisms of winter wheat discrimination and phenological detection in the Yellow River Delta（YRD） region using moderate resolution imaging spectroradiometer（MODIS） time-series data. The normalized difference vegetation index（NDVI） was obtained by calculating the surface reflectance in red and infrared. We used the Savitzky-Golay filter to smooth time series NDVI curves. We adopted a two-step classification to identify winter wheat. The first step was designed to mask out non-vegetation classes, and the second step aimed to identify winter wheat from other vegetation based on its phenological features. We used the double Gaussian model and the maximum curvature method to extract phenology. Due to the characteristics of the time-series profiles for winter wheat, a double Gaussian function method was selected to fit the temporal profile. A maximum curvature method was performed to extract phenological phases. Phenological phases such as the green-up, heading and harvesting phases were detected when the NDVI curvature exhibited local maximum values. The extracted phenological dates then were validated with records of the ground observations. The spatial patterns of phenological phases were investigated. This study concluded that, for winter wheat, the accuracy of classification is 87.07%, and the accuracy of planting acreage is 90.09%. The phenological result was comparable to the ground observation at the municipal level. The average green-up date for the whole region occurred on March 5, the average heading date occurred on May 9, and the average harvesting date occurred on June 5. The spatial distribution of the phenology for winter wheat showed a significant gradual delay from the southwest to the northeast. This study demonstrates the effectiveness of our proposed method for winter wheat classification and phenology detection.

RS analysis of glaciers change in the Heihe River Basin, Northwest China, during the recent decades

The Heihe River Basin is the second largest inland river basin in Northwest China and it is also a hotspot in arid hydrology, water resources and other aspects of researches in cold regions. In addition, the Heihe River Basin has complete landscape, moderate watershed size, and typical social ecological environmental problems. So far, there has been no detailed assessment of glaciers change information of the whole river basin. 1：50,000 topographic map data, Landsat TM/ETM＋ remote sensing images and digital elevation model data were used in this research. Through integrated computer automatic interpretation and visual interpretation methods, the object-oriented image feature extraction method was applied to extract glacier outline information. Glaciers change data were derived from analysis, and the glacier variation and its response to climate change in the period 1956/1963–2007/ 2011 were also analyzed. The results show that：（1） In the period 1956/1963–2007/2011, the Heihe River Basin＇s glaciers had an evident retreat trend, the total area of glaciers decreased from 361.69 km2 to 231.17 km^2; shrinking at a rate of 36.08%, with average single glacier area decrease 0.14 km^2; the total number of the glaciers decreased from 967 to 800.（2） Glaciers in this basin are mainly distributed at elevations of 4300–4400 m, 4400–4500 m and 4500–4600 m; and there are significant regional differences in glaciers distribution and glaciers change.（3） Compared with other western mountain glaciers, glaciers retreat in the Heihe River Basin has a higher rate.（4） Analysis of the six meteorological stations＇ annual average temperature and precipitation data from 1960 to 2010 suggests that the mean annual temperature increased significantly and the annual precipitation also showed an increasing trend. It is concluded that glacier shrinkage is closely related with temperature rising, besides, glacier melting caused by rising temperatures greater than glacier mass supply by increased precipitation to some extent.