Spatial Forecast of Landslides in Three Gorges Based On Spatial Data Mining

The Three Gorges is a region with a very high landslide distribution density and a concentrated population. In Three Gorges there are often landslide disasters, and the potential risk of landslides is tremendous. In this paper, focusing on Three Gorges, which has a complicated landform, spatial forecasting of landslides is studied by establishing 20 forecast factors (spectra, texture, vegetation coverage, water level of reservoir, slope structure, engineering rock group, elevation, slope, aspect, etc). China-Brazil Earth Resources Satellite (Cbers) images were adopted based on C4.5 decision tree to mine spatial forecast landslide criteria in Guojiaba Town (Zhigui County) in Three Gorges and based on this knowledge, perform intelligent spatial landslide forecasts for Guojiaba Town. All landslides lie in the dangerous and unstable regions, so the forecast result is good. The method proposed in the paper is compared with seven other methods: IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Parallelepiped and Information Content Model. The experimental results show that the method proposed in this paper has a high forecast precision, noticeably higher than that of the other seven methods

Landslide Prediction in the Village Min-Kush of Kyrgyz Republic

The work is devoted to the actual problem of landslide prediction in the mountain zone and the development of measures for engineering protection of the landslide hazardous area. The main task of the presented work was to calculate the stability of the landslide slope within the MinKush village of the Jumgal district of the Naryn region of the Kyrgyz Republic. A preliminary assessment of the natural factors of the development of landslide processes on the territory of the Jumgal district was carried out, danger zones were identified and predictions were made on the possible activation of the landslide process. Engineering protection measures for the studied area are recommended

Wireless Sensor Technologies and Applications

Recent years have witnessed tremendous advances in the design and applications of wirelessly networked and embedded sensors. Wireless sensor nodes are typically low-cost, low-power, small devices equipped with limited sensing, data processing and wireless communication capabilities, as well as power supplies. They leverage the concept of wireless sensor networks (WSNs), in which a large (possibly huge) number of collaborative sensor nodes could be deployed. As an outcome of the convergence of micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics, WSNs represent a significant improvement over traditional sensors. In fact, the rapid evolution of WSN technology has accelerated the development and deployment of various novel types of wireless sensors, e.g., multimedia sensors. Fulfilling Moore’s law, wireless sensors are becoming smaller and cheaper, and at the same time more powerful and ubiquitous. [...

Landslide Hazard Zonation Using Expert Evaluation Technique: A Case Study of the Area Between Gohatsion Town and The Abay (Blue Nile) River, Central Ethiopia

The area between Gohatsion town and the Abay River in Central Ethiopia is witnessing severe problems of landslides during rainy seasons. These landslides in the area affect the safe functioning of the road, which is an essential link between Addis Ababa and the northwestern part of the country. In the present study, an attempt is made to delineate the area into landslide hazard zones (lhz). The landslide hazard zonation was carried out by “Landslide Hazard Evaluation Factor” (lhef) rating scheme. The lhef is an expert evaluation technique that is based on the observational past experience gained over causative factors and their contribution for instability of slopes in the area. The causative factors responsible for landslide activity, which were considered during the present study, are: relative relief, slope morphometry, geology, groundwater and land use/ land cover. The information pertaining to these causative factors was collected from the field and analyzed as per the lhef scheme. The evaluated lhz revealed that most of the study area falls within the moderate and high hazard zones. The existing road that links Addis Ababa with the northwestern part of the country mostly passes through high hazard zones and some of it passes through moderate hazard zones. This seems to be the main reason for frequent landslides along the road during the rainy season. Thus, it is imperative to conduct detailed investigations to suggest proper remedial measures for slope stabilization along the road section or to realign the road section to avoid such critical slope sections

Development and validation of the terrain stability model for assessing landslide instability during heavy rain infiltration

Slope stability is a key topic, not only for engineers but also for politicians, due to the considerable monetary and human losses that landslides can cause every year. In fact, it is estimated that landslides have caused thousands of deaths and economic losses amounting to tens of billions of euros per year around the world. The geological stability of slopes is affected by several factors, such as climate, earthquakes, lithology and rock structures, among others. Climate is one of the main factors, especially when large amounts of rainwater are absorbed in short periods of time. Taking this issue into account, we developed an innovative analytical model using the limit equilibrium method supported by a geographic information system (GIS). This model is especially useful for predicting the risk of landslides in scenarios of heavy unpredictable rainfall. The model, hereafter named terrain stability (or TS) is a 2-D model, is programed in MATLAB and includes a steady-state hydrological term. Many variables measured in the field – topography, precipitation and type of soil – can be added, changed or updated using simple input parameters. To validate the model, we applied it to a real example – that of a landslide which resulted in human and material losses (collapse of a building) at Hundidero, La Viñuela (Málaga), Spain, in February 2010.</p

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin