A hyperspectral detection model for permeability coefficient of debris flow fine-grained sediments, Southwestern China

Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.

Application of remote sensing for investigating mining geological hazards

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.