A method for detecting miners based on helmets detection in underground coal mine videos

In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.

Spatial pattern of grassland aboveground biomass and its environmental controls in the Eurasian steppe

Vegetation biomass is an important component of terrestrial ecosystem carbon stocks. Grasslands are one of the most widespread biomes worldwideplaying an important role in global carbon cycling. Thereforestudying spatial patterns of biomass and their correlations to environment in grasslands is fundamental to quantifying terrestrial carbon budgets. The Eurasian steppean important part of global grasslandsis the largest and relatively well preserved grassland in the world. In this studywe analyzed the spatial pattern of aboveground biomass（AGB）and correlations of AGB to its environment in the Eurasian steppe by meta-analysis. AGB data used in this study were derived from the harvesting method and were obtained from three data sources（literatureglobal NPP database at the Oak Ridge National Laboratory Distributed Active Archive Center（ORNL）some data provided by other researchers）. Our results demonstrated that：（1） as for the Eurasian steppe overallthe spatial variation in AGB exhibited significant horizontal and vertical zonality. In detailAGB showed an inverted parabola curve with the latitude and with the elevationwhile a parabola curve with the longitude. In additionthe spatial pattern of AGB had marked horizontal zonality in the Black Sea-Kazakhstan steppe subregion and the Mongolian Plateau steppe subregionwhile horizontal and vertical zonality in the Tibetan Plateau alpine steppe subregion.（2） Of the examined environmental variablesthe spatial variation of AGB was related to mean annual precipitation（MAP）mean annual temperature（MAT）mean annual solar radiation（MAR）soil Gravel contentsoil p H and soil organic content（SOC） at the depth of 0–30 cm. NeverthelessMAP dominated spatial patterns of AGB in the Eurasian steppe and its three subregions.（3） A Gaussian function was found between AGB and MAP in the Eurasian steppe overallwhich was primarily determined by unique patterns of grasslands and environment in the Tibetan Plateau. AGB was significantly positively related to MAP in the Black Sea-Kazakhstan steppe subregion（elevation 〈 3000 m）the Mongolian Plateau steppe subregion（elevation 〈 3000 m） and the surface（elevation ≥ 4800 m） of the Tibetan Plateau. Neverthelessthe spatial variation in AGB exhibited a Gaussian function curve with the increasing MAP in the east and southeast margins（elevation 〈 4800 m） of the Tibetan Plateau. This study provided more knowledge of spatial patterns of AGB and their environmental controls in grasslands than previous studies only conducted in local regions like the Inner Mongolian temperate grasslandthe Tibetan Plateau alpine grasslandetc.