Using Geographic Information System(GIS), based on wind speed, precipitation, topographic, soil, vegetation coverage and land use data of Inner Mongolia between 2001 and 2010, we applied the revised wind erosion equat...Using Geographic Information System(GIS), based on wind speed, precipitation, topographic, soil, vegetation coverage and land use data of Inner Mongolia between 2001 and 2010, we applied the revised wind erosion equation(RWEQ) model to simulate wind erosion intensity. The results showed that an area of approximately 47.8 × 10~4 km^2 experienced wind erosion in 2010, 23.2% of this erosion could be rated as severe, and 46.0% as moderate. Both the area and the intensity of wind erosion had decreased from 2001 to 2010, the wind erosion area reduced 10.1%, and wind erosion intensity decreased by 29.4%. Precipitation, wind speed, population size and urbanization in rural areas, and gross domestic product of primary industry(GDP1) were the main factors influencing wind erosion. Overall, these factors accounted for 88.8% of the wind erosion. These results indicated that the decrease in wind erosion over the past decade related to the increase in precipitation and the decrease in the number of windy days, while modest urban development and optimization of the economic structure might partially reduced the level of ecological pressure, highlighting the importance of human activities in controlling wind erosion.展开更多
Aim of this article, is to present a methodology for extracting macroseismic intensity information and producing seismic intensity maps from VGI （volunteered geographic information）. As a VGI source for obtaining an...Aim of this article, is to present a methodology for extracting macroseismic intensity information and producing seismic intensity maps from VGI （volunteered geographic information）. As a VGI source for obtaining and assessing macroseismic observations, the authors chose twitter. Our methodology is validated in two recent earthquakes occurred in Greece： the January 26, 2014 ML = 5.8 in Kefallinia, and the November 17, 2014 ML = 5.2 in Evoikos. Twitter data published within the first 6 h, 12 h, 24 h and 48 h after the earthquake occurrence were analyzed to develop seismic intensity maps. Those maps were evaluated through intensity maps for the same earthquakes, published by international institutes. Evaluation results provide a strong empiric evidence for the credibility of our methodology, the accuracy of the produced seismic intensity maps and accentuate VGI, generated by twitter, as an adequate alternative source for collecting macroseismic information.展开更多
基金Under the auspices of National Key Technology Research and Development Program of China(No.2011BAC09B08)Special Issue of National Remote Sensing Survey and Assessment of Eco-Environment Change Between 2000 and 2010(No.STSN-04-01)
文摘Using Geographic Information System(GIS), based on wind speed, precipitation, topographic, soil, vegetation coverage and land use data of Inner Mongolia between 2001 and 2010, we applied the revised wind erosion equation(RWEQ) model to simulate wind erosion intensity. The results showed that an area of approximately 47.8 × 10~4 km^2 experienced wind erosion in 2010, 23.2% of this erosion could be rated as severe, and 46.0% as moderate. Both the area and the intensity of wind erosion had decreased from 2001 to 2010, the wind erosion area reduced 10.1%, and wind erosion intensity decreased by 29.4%. Precipitation, wind speed, population size and urbanization in rural areas, and gross domestic product of primary industry(GDP1) were the main factors influencing wind erosion. Overall, these factors accounted for 88.8% of the wind erosion. These results indicated that the decrease in wind erosion over the past decade related to the increase in precipitation and the decrease in the number of windy days, while modest urban development and optimization of the economic structure might partially reduced the level of ecological pressure, highlighting the importance of human activities in controlling wind erosion.
文摘Aim of this article, is to present a methodology for extracting macroseismic intensity information and producing seismic intensity maps from VGI （volunteered geographic information）. As a VGI source for obtaining and assessing macroseismic observations, the authors chose twitter. Our methodology is validated in two recent earthquakes occurred in Greece： the January 26, 2014 ML = 5.8 in Kefallinia, and the November 17, 2014 ML = 5.2 in Evoikos. Twitter data published within the first 6 h, 12 h, 24 h and 48 h after the earthquake occurrence were analyzed to develop seismic intensity maps. Those maps were evaluated through intensity maps for the same earthquakes, published by international institutes. Evaluation results provide a strong empiric evidence for the credibility of our methodology, the accuracy of the produced seismic intensity maps and accentuate VGI, generated by twitter, as an adequate alternative source for collecting macroseismic information.
