At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from a...At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.展开更多
To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying...To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine,located in Fangshan District,Beijing,China.In the method,the vegetation is first removed using the normalized difference vegetation index(NDVI)on the GeoEye-1 data.Then,geological mining hazards interpretation features are determined after color enhancement using principal component analysis(PCA)transformation.Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software.Next,each bitmap is classified into a two-valued imagery using support vector machine algorithm.In the two-valued imagery,1 denotes the geological mining hazards,while 0 denotes none.Afterwards,the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually.Finally,the correlation between factors(such as mining activity,lithology,geological structure,and slope)and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built.The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1%and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards,while geological structure,slope,lithology,roughness of the surface,and aspect are the secondary.展开更多
基金funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09)
文摘At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.
基金This research was supported by the National Basic Research Program of China(973 Program,No.2009CB723906)National Natural Science Foundation of China(No.41171280)CEODE Program(No.DESP01-04-10).
文摘To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine,located in Fangshan District,Beijing,China.In the method,the vegetation is first removed using the normalized difference vegetation index(NDVI)on the GeoEye-1 data.Then,geological mining hazards interpretation features are determined after color enhancement using principal component analysis(PCA)transformation.Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software.Next,each bitmap is classified into a two-valued imagery using support vector machine algorithm.In the two-valued imagery,1 denotes the geological mining hazards,while 0 denotes none.Afterwards,the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually.Finally,the correlation between factors(such as mining activity,lithology,geological structure,and slope)and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built.The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1%and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards,while geological structure,slope,lithology,roughness of the surface,and aspect are the secondary.
