1,233 research outputs found
Automated spatiotemporal landslide mapping over large areas using RapidEye time series data
In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale
Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
We develop a stochastic modeling approach based on spatial point processes of
log-Gaussian Cox type for a collection of around 5000 landslide events provoked
by a precipitation trigger in Sicily, Italy. Through the embedding into a
hierarchical Bayesian estimation framework, we can use the Integrated Nested
Laplace Approximation methodology to make inference and obtain the posterior
estimates. Several mapping units are useful to partition a given study area in
landslide prediction studies. These units hierarchically subdivide the
geographic space from the highest grid-based resolution to the stronger
morphodynamic-oriented slope units. Here we integrate both mapping units into a
single hierarchical model, by treating the landslide triggering locations as a
random point pattern. This approach diverges fundamentally from the unanimously
used presence-absence structure for areal units since we focus on modeling the
expected landslide count jointly within the two mapping units. Predicting this
landslide intensity provides more detailed and complete information as compared
to the classically used susceptibility mapping approach based on relative
probabilities. To illustrate the model's versatility, we compute absolute
probability maps of landslide occurrences and check its predictive power over
space. While the landslide community typically produces spatial predictive
models for landslides only in the sense that covariates are spatially
distributed, no actual spatial dependence has been explicitly integrated so far
for landslide susceptibility. Our novel approach features a spatial latent
effect defined at the slope unit level, allowing us to assess the spatial
influence that remains unexplained by the covariates in the model
Surface motion prediction and mapping for road infrastructures management by PS-InSAR measurements and machine learning algorithms
This paper introduces a methodology for predicting and mapping surface motion beneath road pavement structures caused by environmental factors. Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) measurements, geospatial analyses, and Machine Learning Algorithms (MLAs) are employed for achieving the purpose. Two single learners, i.e., Regression Tree (RT) and Support Vector Machine (SVM), and two ensemble learners, i.e., Boosted Regression Trees (BRT) and Random Forest (RF) are utilized for estimating the surface motion ratio in terms of mm/year over the Province of Pistoia (Tuscany Region, central Italy, 964 km2), in which strong subsidence phenomena have occurred. The interferometric process of 210 Sentinel-1 images from 2014 to 2019 allows exploiting the average displacements of 52,257 Persistent Scatterers as output targets to predict. A set of 29 environmental-related factors are preprocessed by SAGA-GIS, version 2.3.2, and ESRI ArcGIS, version 10.5, and employed as input features. Once the dataset has been prepared, three wrapper feature selection approaches (backward, forward, and bi-directional) are used for recognizing the set of most relevant features to be used in the modeling. A random splitting of the dataset in 70% and 30% is implemented to identify the training and test set. Through a Bayesian Optimization Algorithm (BOA) and a 10-Fold Cross-Validation (CV), the algorithms are trained and validated. Therefore, the Predictive Performance of MLAs is evaluated and compared by plotting the Taylor Diagram. Outcomes show that SVM and BRT are the most suitable algorithms; in the test phase, BRT has the highest Correlation Coefficient (0.96) and the lowest Root Mean Square Error (0.44 mm/year), while the SVM has the lowest difference between the standard deviation of its predictions (2.05 mm/year) and that of the reference samples (2.09 mm/year). Finally, algorithms are used for mapping surface motion over the study area. We propose three case studies on critical stretches of two-lane rural roads for evaluating the reliability of the procedure. Road authorities could consider the proposed methodology for their monitoring, management, and planning activities
Novel Approaches in Landslide Monitoring and Data Analysis
Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis
ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications
Twelve edited original papers on the latest and state-of-art results of topics ranging from calibration, validation, and science to a wide range of applications using ALOS-2/PALSAR-2. We hope you will find them useful for your future research
Landslide Mapping and Susceptibility Assessment of Chittagong Hilly Areas, Bangladesh
Landslides are natural phenomena in mountainous areas that cause damage to properties and death to people around the world. In Bangladesh, landslides have caused enormous economic loss and casualty in Chittagong Hilly Areas (CHA). In this dissertation, a landslide inventory of CHA was prepared using Google Earth and field mapping. Google Earth-based mapping helped in recording landslides in inaccessible areas like forests. In contrast, field mapping helped in mapping landslides in accessible areas like areas near road networks. For absence data sampling of landslide susceptibility mapping, this research proposed the Mahalanobis distance (MD) based absence data sampling and compared it with the slope-based absence data sampling. Three Upazilas (subdistricts) of Rangamati district, Bangladesh was used as the study area. Fifteen landslide causal factors, including slope aspect, plan curvature, and geology, were used in the random forest model for landslide susceptibility mapping. The area under the success and prediction rate curves, statistical indices including the Kappa index, showed that both the absence data sampling method provided similar accuracy. But based on the Seed Cell Area Index (SCAI) MD based landslide susceptibility map was more consistent and did not overestimate the landslide susceptibility like the slope-based model. Finally, this study assessed the impact of three land use/land cover (LULC) scenarios: a. existing (2018); b. Proposed LULC (Planned); and c. Simulated (2028) LULC on landslide susceptibility of Rangamati municipality of Rangamati district. The random forest model was used, and it showed that high susceptibility zones would increase in both proposed and simulated LULC scenarios. It indicated that LULC change would increase the landslide susceptibility in the study area. The increase of landslide susceptibility is comparatively low in the proposed LULC, indicating the importance of implementing planned LULC in the study
- …