A brief survey of GIS in mass-movement studies, with reflections on theory and methods

Alexander, David

doi:10.1016/j.geomorph.2006.09.022

Cited by 55 publications

(23 citation statements)

References 27 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Landslides are one of the most destructive natural hazards, and they often cause substantial damage to societies worldwide every year [1,2]. The intensity of landslides results in more substantial injuries and loss of life than any other type of natural disaster, including earthquakes, hurricanes, tsunamis, and floods [3].…”

Section: Introductionmentioning

confidence: 99%

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

et al. 2015

View full text Add to dashboard Cite

This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA), a genetic algorithm (GA), and a case-based reasoning (CBR) technique. It consists of three main phases: (1) image processing and multi-image segmentation; (2) feature optimization; and (3) detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87) demonstrates higher classification performance than the stand-alone OOIA (0.75) method for detecting landslides. The area under curve (AUC) value was also higher than that of the OPEN ACCESS Remote Sens. 2015, 7 4319 simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The remaining three MR LSMs are found to be ideal for subsequent landslide risk assessment. The three MR LSMs were found useful (AUC > 0.9); nonetheless the practical implementation of a specific LSM depends on resource availability of concerned authority, cultural status of the society and their risk perception [63][64][65][66]. For preparing a landslide risk map, a landslide hazard assessment should be …”

Section: Discussionmentioning

confidence: 99%

Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh

Ahmed

Dewan

2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid urbanization through hill-cutting is another factor, which is believed to have a significant impact on the occurrence of landslides. This study aims to develop landslide susceptibility maps (LSMs) through the use of Dempster-Shafer weights of evidence (WoE) and the multiple regression (MR) method. Three different combinations with principal component analysis (PCA) and fuzzy membership techniques were used and tested. Twelve factor maps (i.e., slope, hill-cutting, geology, geomorphology, NDVI, soil moisture, precipitation and distance from existing buildings, stream, road and drainage network, and faults-lineaments) were prepared based on their association with historical landslide events. A landslide inventory map was prepared through field surveys for model simulation and validation purposes. The performance of the predicted LSMs was validated using the area under the relative operating characteristic (ROC) curve method. The overall success rates were 87.3%, 90.9%, 91.3%, and 93.9%, respectively for the WoE, MR with all the layers, MR with PCA layers, and MR with fuzzy probability layers.

show abstract

“…The Earthquake triggered landslides are highly concentrated in specific zones associated with the lithology, structure, geomorphology, topography and human presence (Keefer 1994, 2002, Owen et al 2008, Sato, Harp 2009, Yin et al 2009). The mass-movement classification problem has been more complex than it has for many other types of extreme natural phenomena (Alexander 2008). Different researches provided model being able to identify those areas in greater risk of slope failure, and to differentiate between stable and landslide-prone ground (Mason, Rosenbaum 2002, Liu et al 2004, Fourniadis et al 2007.…”

Section: Introductionmentioning

confidence: 99%

Assessment of slope instability and its impact on land status: a case study from Central Himalaya, India

Pande

2017

Landform Analysis

View full text Add to dashboard Cite

Tectonic instability, geological sensitivity along with human intrusion in Himalaya has greatly exacerbated the occurrence of hazardous situation. Dynamics of slope instability have been evaluated under three processes leading to geomorphic instability, viz. i. erosion ii. mass wasting and iii. anthropogenic. Their causative factors have been identified under Ghuniyoli Gad watershed. The measurement of the intensity, magnitude and nature of instability factors were done within the units of 1 km 2 under 21 units of watershed. Each unit was evaluated in terms of type, extent and corresponding degree of instability along with their potential assessment. The stage of erosion reveals that Ghuniyoli Gad watershed experiences instability. Maximum units fall under instability of degree 1 and degree 2 (38% and 38%) while minimum units belong to instability of degree 4 (4.76%). The instability of degree 3 contributes only about 19.04%. Appropriate mitigation measures to overcome hazardous calamities are needed to be introduced therein.

show abstract

A brief survey of GIS in mass-movement studies, with reflections on theory and methods

Cited by 55 publications

References 27 publications

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh

Assessment of slope instability and its impact on land status: a case study from Central Himalaya, India

Contact Info

Product

Resources

About