A Spatial Ensemble Model for Rockfall Source Identification From High Resolution LiDAR Data and GIS

Abstract

Rockfall source identification is the most challenging task in rockfall hazard and risk assessment. This difficulty rises in the areas where there is a presence of other types of the landslide, such as shallow landslide and debris flow. The aim of this paper is to develop and test a hybrid model that can accurately identify the source areas. High-resolution light detection and ranging data (LiDAR) was employed to derive the digital terrain model (DTM), from which several conditioning factors were extracted. These conditioning factors were optimized utilizing the ant colony optimization (ACO). Different machine learning algorithms, namely, logistic regression (LR), random tree (RT), random forest (RF), support vector machine (SVM), and artificial neural network (ANN), in addition to their ensemble models (stacking, bagging, and voting), were examined. This is based on the selected best subset of conditioning factors and inventory dataset. Stacking LR-RT (the best fit model) was then utilized to produce the probabilities of different landslide types. On the other hand, the Gaussian mixture model (GMM) was optimized and applied for automatically identifying the slope threshold of the occurrence of the different landslide types. In order to reduce the model sensitivity to the alteration in various conditioning factors and to improve the model computations performance, land use probability area was formed. The rockfall sources were identified by integrating the probability maps and the reclassified slope raster based on the GMM results. The accuracy assessment reveals that the developed hybrid model can identify the probable rockfall regions with an accuracy of 0.95 based on the validation dataset and 0.94 on based the training dataset. The slope thresholds calculated by GMM were found to be > 58 • , 22 •-58 • , and 9 •-22 • for rockfall, shallow landslide, and debris flow, respectively. This indicates that the model can be generalized and replicated in different regions, and the proposed method can be applied in various landslides studies.

Rockfall Research: A Bibliometric Analysis and Future Trends

“…Rapid developments in remote sensing techniques, "GIS" and "LiDAR" tools made quantitative analysis of rock landslide assessment, analysis and monitoring are more feasible [117,140]. Recently, the availability of GIS and remote sensing data has provided many advantages in quantifying topographic attributes related to rock slides.…”

“…This phase stands out because its publications have incorporated new technological trends for remote monitoring. The most outstanding are: (i) UAV-based mapping, back analysis, and trajectory modeling of a co-seismic rockfall in Lefkada island, Greece [116], (ii) A Spatial Ensemble Model for Rockfall Source Identification from High-Resolution LiDAR Data and GIS [117], and (iii) Managing rockfall risk through baseline monitoring of precursors using a terrestrial laser scanner Phase III: Maturation (2017-2019).-This trend is marked from 2017; it can be considered that this topic is calling the attention of scientists since, in this short period of 3 years (until 2019), it has 206 publications, representing 25.40% of the total and with an average of 68.67 documents/years. The number of documents is increasing rapidly.…”

“…This phase stands out because its publications have incorporated new technological trends for remote monitoring. The most outstanding are: (i) UAV-based mapping, back analysis, and trajectory modeling of a co-seismic rockfall in Lefkada island, Greece [116], (ii) A Spatial Ensemble Model for Rockfall Source Identification from High-Resolution LiDAR Data and GIS [117], and (iii) Managing rockfall risk through baseline monitoring of precursors using a terrestrial laser scanner [118]. The number of citations is 884, equivalent to 5.10% of the total.…”

An Integration of UAV-Based Photogrammetry and 3D Modelling for Rockfall Hazard Assessment: The Cárcavos Case in 2018 (Spain)

“…Among remote acquisition techniques, one of the most used is Light Detection and Ranging (LiDAR), which provides a 3DPC of the scanned surface. This type of technique is widely used, both for characterization of rockfalls and for related risk assessment, as well as for monitoring and modelling purposes [45,46,53]. In this regard, despite the LiDAR technique allowing for the obtaining of greater accuracy, UAV photogrammetry was used for detailed investigations of rockfalls in remote areas characterized by difficult access-as in the case of Cárcavos 80 m vertical scarp.…”

Landslide Detection Using a Saliency Feature Enhancement Technique From LiDAR-Derived DEM and Orthophotos

“…Additionally, the landslide inventories which are needed for validation purposes were obtained and implemented through remote sensing techniques and technical researches according to previous studies for Kinta Valley and entire Malaysia. The data was collected from archive data of the Mineral and Geoscience Department of Malaysia [42], [43], [44]. A total of 13 and 21 landslides were identified in the training and test areas, respectively.…”