Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Efficient dam break flood simulation methods for developing a preliminary evacuation plan after the Wenchuan Earthquake

The Xiaojiaqiao barrier lake, which was the second largest barrier lake formed by the Wenchuan Earthquake had seriously threatened the lives and property of the population downstream. The lake was finally dredged successfully on 7 June 2008. Because of the limited time available to conduct an inundation potential analysis and make an evacuation plan, barrier lake information extraction and real-time dam break flood simulation should be carried out quickly, integrating remote sensing and geographic information system (GIS) techniques with hydrologic/hydraulic analysis. In this paper, a technical framework and several key techniques for this real-time preliminary evacuation planning are introduced. An object-oriented method was used to extract hydrological information on the barrier lake from unmanned aerial vehicle (UAV) remote sensing images. The real-time flood routine was calculated by using shallow-water equations, which were solved by means of a finite volume scheme on multiblock structured grids. The results of the hydraulic computations are visualized and analyzed in a 3-D geographic information system for inundation potential analysis, and an emergency response plan is made. The results show that if either a full-break or a half-break situation had occurred for the Chapinghe barrier lake on 19 May 2008, then the Xiaoba Town region and the Sangzao Town region would have been affected, but the downstream towns would have been less influenced. Preliminary evacuation plans under different dam break situations can be effectively made using these methods

A COMPARISON OF PIXEL-BASED VERSUS OBJECT ORIENTED ANALYSIS OF LANDSLIDES USING HISTORICAL REMOTE SENSING DATA

With recent advances in remote sensing processing technology, it has become more feasible to begin analysis of the enormous historic archive of remotely sensed data. This historical data provides valuable information on a wide variety of topics which can influence the lives of millions of people if processed correctly and in a timely manner. One such field of benefit is that of landslide mapping and inventory. This data provides a historical reference to those who live near high risk areas so future disasters may be avoided. In order to properly map landslides remotely, an optimum method must first be determined. Historically, mapping has been attempted using pixel based methods such as unsupervised and supervised classification. These methods are limited by their ability to only characterize an image spectrally based on single pixel values. This creates a result prone to false positives and often without meaningful objects created. Recently, several reliable methods of Object Oriented Analysis (OOA) have been developed which utilize a full range of spectral, spatial, textural, and contextual parameters to delineate regions of interest. A comparison of these two methods on a historical dataset of the landslide affected city of San Juan La Laguna, Guatemala has proven the benefits of OOA methods over those of unsupervised classification. Overall accuracies of 96.5% and 94.3% and F-score of 84.3% and 77.9% were achieved for OOA and unsupervised classification methods respectively. The greater difference in F-score is a result of the low precision values of unsupervised classification caused by poor false positive removal, the greatest shortcoming of this method

An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data

© 2018 Landslides are natural disasters that cause environmental and infrastructure damage worldwide. They are difficult to be recognized, particularly in densely vegetated regions of the tropical forest areas. Consequently, an accurate inventory map is required to analyze landslides susceptibility, hazard, and risk. Several studies were done to differentiate between different types of landslide (i.e. shallow and deep-seated); however, none of them utilized any feature selection techniques. Thus, in this study, three feature selection techniques were used (i.e. correlation-based feature selection (CFS), random forest (RF), and ant colony optimization (ACO)). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Random forest (RF) was used to evaluate the performance of each feature selection algorithms. The overall accuracies of the RF classifier revealed that CFS algorithm exhibited higher ranks in differentiation landslide types. Moreover, the results of the transferability showed that this method is easy, accurate, and highly suitable for differentiating between types of landslides (shallow and deep-seated). In summary, the study recommends that the outlined approaches are significant to improve in distinguishing between shallow and deep-seated landslide in the tropical areas, such as; Malaysia

Landslide mapping from multi-sensor data through improved change detection-based Markov random field

Abstract Accurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved δNDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data

Vulnerability assessment using remote sensing: The earthquake prone megacity Istanbul, Turkey

Hazards like earthquakes are natural, disasters are not. Disasters result from the impact of a hazard on a vulnerable system or society at a specific location. The framework of vulnerability aims at a holistic concept taking physical, environmental, socio-economic and political components into account. This paper focuses on the capabilities of remote sensing to contribute up-to-date spatial information to the physical dimension of vulnerability for the complex urban system of the megacity Istanbul, Turkey. An urban land cover classification based on high resolution satellite data establishes the basis to analyse the spatial distribution of different types of buildings, the carrying capacity of the street network or the identification of open spaces. In addition, a DEM (Digital Elevation Model) enables a localization of potential landslide areas. A methodology to combine these attributes related to the physical dimension of vulnerability is presented. In this process an n-dimensional coordinate system plots the variables describing vulnerability against each other. This enables identification of the degree of vulnerability and the vulnerability-determining factors for a specific location. This assessment of vulnerability provides a broad spatial information basis for decision-makers to develop mitigation strategies

Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification

Landslide mapping and monitoring by using radar and optical remote sensing: examples from the EC-FP7 project SAFER

This paper focuses on the Landslide Thematic services of the EU-funded FP7-SPACE project SAFER (Services and Applications For Emergency Response) for inventory mapping, monitoring and rapid mapping by using Earth Observation (EO). We exploited satellite Interferometric Synthetic Aperture Radar (InSAR) and Object-Based Image Analysis (OBIA), and discuss example applications in South Tyrol and Abruzzo (Italy), Lower Austria (Austria), Lubietova (Slovakia) and the Kaohsiung County (Taiwan). These case studies showcase the significance of radar and optical EO data, InSAR and OBIA methods for landslide mapping and monitoring in different geological environments and during all phases of emergency management: mitigation, preparedness, crisis and recovery

Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia