Abstract:Satellite remote sensing provides global observations of the Earth's surface and provides useful information for monitoring smoke plumes emitted from forest fires. The aim of this study is to automatically separate smoke plumes from the background by analyzing the MODIS data. An identification algorithm was improved based on the spectral analysis among the smoke, cloud and underlying surface. In order to get satisfactory results, a multi-threshold method is used for extracting training sample sets to train back-propagation neural network (BPNN) classification for merging the smoke detection algorithm. The MODIS data from three forest fires were used to develop the algorithm and get parameter values. These fires occurred in (i) China on 16 October 2004, (ii) Northeast Asia on 29 April 2009 and (iii) Russia on 29 July 2010 in different seasons. Then, the data from four other fires were used to validate the algorithm. Results indicated that the algorithm captured both thick smoke and thin dispersed smoke over land, as well as the mixed pixels of smoke over the ocean. These results could provide valuable information concerning forest fire location, fire spreading and so on.

Intersecting pedestrian flows especially multi-directional ones are complicated in dynamics. People will face unavoidable head-on conflicts and obstruct each other. In this paper, controlled experiments of a four-directional intersecting pedestrian flow were conducted. Up to 364 university students took part in the experiments and their trajectories were extracted by a mean-shift algorithm. The global density-velocity relations in the cross area in different scenarios are compared. Moreover, local density-velocity and local density-flow relations in the cross area are investigated. In order to adapt the study of a fundamental diagram for four directional intersecting flows, a new coordinate system based on pedestrian motion is built. The results indicate that the coordinate system is suitable for the analysis of multi-directional flows. The local density-velocity relation seems consistent with previous results obtained from an actual high-density pedestrian flow. At high densities, the average local velocity in the cross area is a bit larger than a previous study. The reason may be due to the density difference between the cross area and the corridors, which can be observed in real life.

Corners can be commonly observed in most building facilities.However, pedestrians' turning behavior at the corners, especially in collective movements, is rarely studied and not fully understood. To investigate the eects of such configuration on pedestrian flow, both uni-and bidirectional experiments were conducted in a right-angled corridor. From the fundamental diagram, it is found that pedestrians in our experiments are less sensitive to high-density situations and the velocity at high densities tends to be larger than observed values in former studies. Besides, in our experiments, no noticeable dierence is observed between the fundamental diagrams in uni-and bidirectional scenarios for densities below 2 ped m −2 . According to the density profile, pedestrians in unidirectional turning movements tend to seek the shortest path, whereas their followed path is more influenced by the detour behavior against encounters when it comes to bidirectional scenarios. Besides, due to the collision avoidance behavior and lane formation phenomenon in bidirectional scenarios, the highest

Although bidirectional motion on stairs can be commonly observed

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In this paper, we introduce dyad social groups into the experiment to mimic uni- and bidirectional pedestrian flows that are closer to real life. According to the experimental videos, different strategies of collision avoidance for dyads are observed and classified. Moreover, we observe an interesting lane-merging phenomenon in bidirectional scenarios. Fundamental diagrams are calculated based on two measurement methods, and further compared with previous individual experiments (without dyads), confirming the negative impact of social groups due to their inherent cohesion during the movement process. Then, group characteristics such as group distance and spatial alignment are analyzed under the influence of different global densities. It is interesting to note that the effect of density on spatial alignment distribution is opposite in uni- and bidirectional scenarios, i.e. the increasing density will make the distribution less scattered in the unidirectional flow, which is contrary to the phenomenon in the bidirectional flow. Finally, based on the time evolution of congestion level and crowd danger, they can be good indicators of transitions from disorder to order, especially in bidirectional scenarios. Furthermore, the differences between uni- and bidirectional flows can be well distinguished through the relations between density and congestion level, as well as crowd danger.