The Geological Environment Monitoring Station

Chengdu, China

The Geological Environment Monitoring Station

Chengdu, China
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Yu H.,CAS Chengdu Institute of Mountain Hazards and Environment | Yu H.,University of Chinese Academy of Sciences | Liu S.,CAS Chengdu Institute of Mountain Hazards and Environment | Guo S.,CAS Chengdu Institute of Mountain Hazards and Environment | And 4 more authors.
Journal of Mountain Science | Year: 2012

A Ms 8. 0 large earthquake occurred in Sichuan, China on May 12, 2008 (hereafter called 5. 12 Earthquake), and then a large debris flow happened in the quake-hit Qingping Township of Mianzhu county on August 13, 2008 (hereafter called 8. 13 Debris Flow). The influence of two disasters on the changes in land use were analyzed by using high-resolution aerial photos and satellite remote sensing images taken before and after the 5. 12 Earthquake and 8. 13 Debris Flow, the selection of suitable construction land were studied by learning experiences and lessons from the selection of resettlement areas and through field surveys and with land use transfer model and analytical model in combination with RS and GIS. The results showed that the influence of the 5. 12 Earthquake on ecological environment was far greater than that of the 8. 13 Debris Flow; there were more salient conflicts between population and land after the earthquake. Sites for post-disaster reconstruction should not be in disaster-prone areas or in gully-facing areas. Suitable land for settlement construction in I-1~I-5 low-hazard zones is optimal settlement areas for post-disaster reconstruction. © 2012 Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg.


Luo Y.,Chengdu University of Technology | Yu H.,CAS Chengdu Institute of Mountain Hazards and Environment | Wang Y.,Sichuan Institute of Land Planning and Survey | Zheng Z.-J.,The Geological Environment Monitoring Station | Wang X.,China Southwest Geotechnical Investigation & Design Institute Co.
Journal of Donghua University (English Edition) | Year: 2015

Compared with previous studies, the research attempted to establish the appropriate quantitative models to explain the relations between settlement density Di and geographic factors, which could make a scientific guidance to the mountain settlements planning. Five factors, including slope, relief amplitude, distance to river, distance to cultivated land, and distance to road, were identified through principal component analysis (PCA). The inherent relations between five factors and Di (settlement density) were modeled by regression analysis. The results are as follows. (1) The associations among Di and slope, relief amplitude, river, road are better modeled by the exponential decay line; with the buffer distance of slope, relief amplitude, distance to river and distance to road increasing, Di decreases. (2) The associations between Di and cultivated land are better modeled by the quadratic polynomial line; with the buffer distance of cultivated land increasing, Di increases first, and then dramatically decreases. (3) The area within 500 m from the road, within 500 m from the cultivated land, within 1600 m from the river, within the relief amplitude of 30-200 m, and the area within the slope of 0°-10° are the fitting land for settlements, and it is very important to lay the mountain settlements on those optimized regions. Copyright © 2015 Editorial Department of Journal of Donghua University, Shanghai, China. All rights reserved.


Luo Y.,CAS Chengdu Institute of Mountain Hazards and Environment | Luo Y.,University of Chinese Academy of Sciences | Yu H.,CAS Chengdu Institute of Mountain Hazards and Environment | Yu H.,University of Chinese Academy of Sciences | And 2 more authors.
International Journal of Remote Sensing | Year: 2012

This article discusses the integration of remote sensing (RS), geographic information system (GIS), GPS and ground survey technologies to establish the accuracy of supervised classification. N e, the proportion of earthquake-damaged trace in a specific region, as a relative value expresses the influence of the characteristics of an area on the distribution of earthquake-damaged trace; it reflects the earthquake-damaged trace's distribution characteristics better. Four factors - distance to central fault, slope, elevation and distance to river - were selected to quantify the association between environmental factors and N e by regression analysis. The results show the following. (1) The association between N e and distance to river (R 2 = 0.99) and between N e and distance to central fault (R 2 = 1.00) is better modelled by an inverse second-order line. N e is largest within 0.5 km of the river and within 5 km of the central fault. When the distance is more than 2.0 km from the river and more than 30 km from the central fault, N e dramatically decreases. (2) The association between N e and elevation (R 2 = 0.94) is better modelled by the exponential decay line. With an increasing elevation, N e decreases with the weakening of human activities. (3) The association between N e and slope (R 2 = 0.93) is better modelled by exponential growth. With an increasing slope, N e first decreases and then increases. (4) A possible mechanism is that the earthquake energy first acts on the area near the central fault. The erosion of the river leads to a steep slope; the unstable geologic structure is easily destroyed by the effect of seismic waves. Consequently, earthquake-damaged trace and N e are greater in regions near the central fault and river bank than in other regions. © 2012 Copyright Taylor and Francis Group, LLC.

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