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Meng D.,State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation | Wang M.,Beijing Key Laboratory of Resource Environment and Geographic Information System | Li X.,Key Laboratory of Dimensional Information Acquisition and Application | Gong H.,Capital Normal University
Shengtai Xuebao/ Acta Ecologica Sinica | Year: 2013

The urban thermal environment is an important element for the urban ecological environment, urban climate and urban disasters. This paper selected MOD11A2, the MODIS LST night data to study the thermal environment evolution in Beijing, Shanghai and Guangzhou, which are the three major cities of China in the past decade. Three methods have been applied in the paper, Landscape centroid evolution, Landscape pattern index and spatial autocorrelation. Three main conclusions have been drawn as follows. Firstly the thermal landscape distributions in the three cities have moved from the suburb to the downtown. And the evolution trend of the thermal landscape is changed from the low temperature region, sub- middle temperature region to middle temperature region, sub-high temperature region and high temperature region. Secondly, among these five types of thermal landscape, the middle temperature region is the most prevalent. The urban thermal landscape fragmentation was highest in Shanghai among the three cities, and sub-middle and high temperature region has the highest fragmentation. The urban thermal landscape dispersion was highest in Beijing, and the dispersion of low and high temperature region was higher than the other types of thermal landscapes. Thirdly, thermal environment spatial autocorrelation analysis showed that the high-high temperature zones were adjacent, low-low temperature areas were adjacent, which are the main types in the temperature spatial agglomeration. And for Beijing and Guangzhou city, the high- high temperature zone located in the south of the city, the low-low temperature region located in the north. While, the spatial autocorrelation distribution of LST in Shanghai is more complicated. The distribution areas of high-high temperature varied among the three cities in the past decade. In Beijing, the distribution area increased shortly after decreasing, and in Guangzhou, the distribution area continued to decline, which preliminary reflects the heat island effect problem aggravated in Beijing, while weakened in Shanghai and Guangzhou. Through comparisons and analysis, the paper has provided a reference for urban planning and urban living environment improvements, but there are still some inadequacies to be further studied. Firstly, this study only selected the January night LST data in the three cities. Because the time factors, such as season, daytime and nocturne, will affect the urban heat environment pattern, the comprehensiveness of the thermal environment pattern changes need to be improved. In addition, the paper only selected the data in the period of three years, the evolution regulation of the urban thermal environment pattern is not precise. Secondly, the landscape of urban heat environment were impacted by many factors, including the pattern of landuse, urban surface construction, weather conditions, terrain, anthropogenic heat emissions factors and so on. The analysis between the urban heat environment and impact factors will help reveal the mechanism of urban heat environment and which will be studied further. Source


Li S.,Beijing Municipal Research Institute of Environmental Protection | Li S.,Center for Industrial Wastewater Pollution Control | Huang X.,Chinese Research Academy of Environmental Sciences | Gong H.,Beijing Normal University | And 5 more authors.
Journal of Natural Disasters | Year: 2015

By the impact of the natural environmental factors (such as topography, stratum lithology, geological structure, etc.) and the inducing factors (such as rainfall, earthquakes, etc.), the characteristics of the landslides distribution in space are extremely complex, however, its spatial distribution still has internal rules. This paper takes the time of "Wenchuan earthquake" extreme event as the time phasing point, quadrat-based point pattern analysis of landslide point space distribution indicates that the landslide point group showes a spatial agglomeration model before and after the earthquake. This results demonstrate that the occurrence of landslide in the study area are not random events before and after the earthquake, but the results of the natural environmental factors, or factors combination; and the landslide point spatial distribution of Wenchuan earthquake heavy disaster area has self-similar structure in statistical sence, no characteristic scale. Source


Wei L.,Capital Normal University | Wei L.,Laboratory of 3D Information Acquisition and Application | Hu Z.,Capital Normal University | Hu Z.,Laboratory of 3D Information Acquisition and Application | And 6 more authors.
Marine Pollution Bulletin | Year: 2015

Oil spills are one of the major sources of marine pollution; it is important to conduct comprehensive assessment of losses that occur as a result of these events. Traditional methods are required to assess the three parts of losses including cleanup, socioeconomic losses, and environmental costs. It is relatively slow because assessment is complex and time consuming. A relatively quick method was developed to improve the efficiency of assessment, and then applied to the Penglai 19-3 accident. This paper uses an SAR image to calculate the oil spill area through Neural Network Classification, and uses historical oil-spill data to build the relationship between loss and other factors including sea-surface wind speed, and distance to the coast. A multiple regression equation was used to assess oil spill damage as a function of the independent variables. Results of this study can be used for regulating and quickly dealing with oil spill assessment. © 2014 The Authors. Source


Huili G.,State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation | Huili G.,Beijing Key Laboratory of Resource Environment and Geographic Information System | Huili G.,Key Laboratory of 3 Dimensional Information Acquisition and Application | Huili G.,Capital Normal University | And 9 more authors.
European Space Agency, (Special Publication) ESA SP | Year: 2013

Inhalable particulate matter (IPM) is one of the principal pollutants in Beijing. Sand weather in spring and winter seasons partly because of regional airflow, in most cases it is results from autochthonic pollution, especially in heating season of winter. In this paper, the law of temporal spatial distribution of IPM and the relationship between IPM and influence factors were studied combing RS techniques with ground-based monitoring. The change of underlying surface which were obtained from high resolution Remote Sensing images in different periods was analyzed; the content of different diameter of particles were collected by ground observation instrument and chemical composition were analyzed; the relationship of distribution of IPM and underlying surface was studied using spatial analysis of GIS. The results indicate that the pollution distribution of IPM has a very close relation with underlying surface, man-made pollution sources, population density and meteorological factors. Source


Huili G.,State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation | Huili G.,Beijing Key Laboratory of Resource Environment | Huili G.,Key Laboratory of 3 Dimensional Information Acquisition and Application | Huili G.,Capital Normal University | And 9 more authors.
European Space Agency, (Special Publication) ESA SP | Year: 2013

Inhalable particulate matter (IPM) is one of the principal pollutants in Beijing. Sand weather in spring and winter seasons partly because of regional airflow, in most cases it is results from autochthonic pollution, especially in heating season of winter. In this paper, the law of temporal spatial distribution of IPM and the relationship between IPM and influence factors were studied combing RS techniques with ground-based monitoring. The change of underlying surface which were obtained from high resolution Remote Sensing images in different periods was analyzed; the content of different diameter of particles were collected by ground observation instrument and chemical composition were analyzed; the relationship of distribution of IPM and underlying surface was studied using spatial analysis of GIS. The results indicate that the pollution distribution of IPM has a very close relation with underlying surface, man-made pollution sources, population density and meteorological factors. Source

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