Aijun Su

Mapping Urban Bare Land Automatically from Landsat Imagery with a Simple Index

In recent years, hundreds of Earth observation satellites have been launched to collect massive amounts of remote sensing images. However, the considerable cost and time to process the significant amount of data have become the greatest obstacle between data and knowledge. In order to accelerate the transformation from remote sensing images to urban thematic maps, a strategy to map the bare land automatically from Landsat imagery was developed and assessed in this study. First, a normalized difference bare land index (NBLI) was presented to maximally differentiate bare land from other land types in Wuhan City, China. Then, an unsupervised classifier was employed to extract the bare land from the NBLI image without training samples or self-assigned thresholds. Experimental results showed good performance on overall accuracy (92%), kappa coefficient (0.84), area ratio (1.1321), and match rate (83.96%), respectively. Results in multiple years disclosed that bare lands in the study site gradually moved from inner loops to the outer loops since 2007, in two main directions. This study demonstrated that the proposed method was an accurate and reliable option to extract the bare land, which led to a promising approach to mapping urban land use/land cover (LULC) automatically with simple indices.

Prediction of landslide runout based on influencing factor analysis

Landslide runout predictions are essential for producing hazard maps and siting mitigation structures. Data from 55 catastrophic historical landslides in the mountainous area of China are used to determine how topography, material, triggering factors, volume, and initial posture of the slide mass influence runout. Plots and regressions between slide mobility, expressed in terms of the equivalent coefficient of friction (ratio of the fall height to the runout distance), and the slide volume show the necessity to exclude data from zones where motion is blocked by steep orthogonal slopes (“T-type topography”) in defining meaningful empirical relationships. Landslide material exerts strong control on runout, with colluvium showing the strongest dependence on slide volume. Rainfall-induced rockslides have higher mobility than gravity-induced rockslides, but rockslides triggered by great earthquakes may have lower mobility than gravity-induced rockslides. The extra sum of squares analysis results demonstrate that slide volume is the dominant predictor for estimating runout, with posture also influencing the runout distance of non-seismic rockslides. These results provide the basis for our new, multiple-factor predictive method for runout distance. The predicted runout distance in the test case was about 115% of the actual distance. This method may enhance the accuracy of runout distance estimates for landslides in the mountainous area of China.

Introduction to the Badong Field Test Site for Landslide Research in the Three Gorges Reservoir Area of China

The initial impoundment and periodic water level variation may change the balance of geological environment of the reservoir area and bring different kinds of geohazards. Reservoir induced landslides typify such hazards that present challenges to the long-time operation of the Three Gorges Water Conservancy and Hydropower Project of China. The Huangtupo landslide, with multiple sliding stages and masses, is one of the largest and most destructive landslides still deforming in the Three Gorges Reservoir area. From 2008, we take the Huangtupo landslide as a typical case, and build a large field test site to provide an unprecedented opportunity for the research on the evaluation and prevention of reservoir induced landslides. The test site is composed with a tunnel group with a total length of 1.1 km and a series of monitoring system on both earth surface and underground. Through the tunnel group and multiple monitoring systems, visitors can directly enter the Huangtupo No.1 riverside sliding mass to closely observe the bedrock, sliding zone, and sliding mass, and also operate related scientific experiments and deep monitoring, such as large scale in-situ mechanical tests, underground hydrological tests, deep stress, deformation and environment monitoring. Now, the test site becomes an important site for international research, education, and academic communication about geohazards.

Multi-sensor fusion of data for monitoring of Huangtupo landslide in the three Gorges Reservoir (China)

Abstract There hides a certain relationship among various monitoring data in a landslide, and the mining of this relationship is of significance to landslide research. In this paper, we first collect multiple monitoring data of riverside 1# slump-mass of Huangtupo landslide, the Three Gorges Reservoir Region, China, including Global Positioning System (GPS) monitoring data, inclinometer data, reservoir water level, rainfall, water content, crack width, groundwater level and temperature data, etc. By adopting the combination of quantitative statistics and qualitative simulation method for multi-sensor fusion monitoring data analysis, we overcome the one-sidedness of using a single method or single data type. The result of fusion analysis has indicated that in time periods with low rainfall or when the rainfall is not the major factor, main factors affecting landslide movement are crack development, water content of the landslide and water level of the Three Gorges Reservoir. Compared with the actual monitoring data, the fusion analysis results has a maximum error of 1.9%, which shows a good effect.

Comprehensive study of landslides through the integration of multi remote sensing techniques: Framework and latest advances

Detecting the timing and amount of deformation is critical for understanding the physical causes and eventually warning of possible landslide hazards. Monitoring of deformation of structures and ground surface displacements during landslides can be accomplished by using different types of systems and techniques. Besides geotechnical or physical techniques, remote sensing techniques can be classified as satellite techniques, photogrammetric techniques, geodetic techniques, ground based techniques, and so on. To study and govern growing geological disasters in China, especially in the Three Gorges area, Three Gorges Research Center for Geo-hazard (TGRG) is establishing an infra structure to effectively and comprehensively analyze the mechanism of landslide deformation, focused on the Huangtupo landslide, using of various advanced monitoring systems and techniques. In this article, the framework and latest advances of integration of multi remote sensing techniques in the infrastructure are presented. Different remote sensing techniques, data processing and integrating methods, and the latest results are discussed in detail. At last, reviews on current work and suggestions for further work are put forward.