Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach

Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions

Landslide monitoring techniques in the Geological Surveys of Europe

ABSTRACT: Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020).info:eu-repo/semantics/publishedVersio

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

The SWADE model for landslide dating in time series of optical satellite imagery

Landslides are destructive natural hazards that cause substantial loss of life and impact on natural and built environments. Landslide frequencies are important inputs for hazard assessments. However, dating landslides in remote areas is often challenging. We propose a novel landslide dating technique based on Segmented WAvelet-DEnoising and stepwise linear fitting (SWADE), using the Landsat archive (1985–2017). SWADE employs the principle that vegetation is often removed by landsliding in vegetated areas, causing a temporal decrease in normalized difference vegetation index (NDVI). The applicability of SWADE and two previously published methods for landslide dating, harmonic modelling and LandTrendr, are evaluated using 66 known landslides in the Buckinghorse River area, northeastern British Columbia, Canada. SWADE identifies sudden changes of NDVI values in the time series and this may result in one or more probable landslide occurrence dates. The most-probable date range identified by SWADE detects 52% of the landslides within a maximum error of 1 year, and 62% of the landslides within a maximum error of 2 years. Comparatively, these numbers increase to 68% and 80% when including the two most-probable landslide date ranges, respectively. Harmonic modelling detects 79% of the landslides with a maximum error of 1 year, and 82% of the landslides with a maximum error of 2 years, but requires expert judgement and a well-developed seasonal vegetation cycle in contrast to SWADE. LandTrendr, originally developed for mapping deforestation, only detects 42% of landslides within a maximum error of 2 years. SWADE provides a promising fully automatic method for landslide dating, which can contribute to constructing landslide frequency-magnitude distributions in remote areas

Spatial Statistical Modeling of Rockfall Hazard in a Mountainous Road in Cantabria (Spain)

Rockfall events are one of the most frequent types of mass wasting in mountainous areas, causing service and traffic disruption, as well as infrastructure and human damage. Hence, having accurate tools to model these hazards becomes crucial to prevent fatalities, especially in a context of climate change whereby the effects of these phenomena might be exacerbated. Under this premise, this article concerned the development of a framework for assessing rockfall hazard in mountainous areas. First, a set of factors expected to favor rockfalls were processed and aggregated using spatial analysis tools, yielding a series of hazard maps with which to fit observed data through statistical modeling. The validation process was undertaken with the support of a database containing the number of rocks removed from a mountainous road section located in Cantabria, northern Spain. The results achieved, which demonstrated the accuracy of the proposed approach to reproduce rockfall hazard using frequency data, highlighted the primary role played by factors such as slope, runoff threshold, and precipitation to explain the occurrence of these events. The effects of climate change were considerably influenced by the fluctuations in the projections of precipitation, which limited the variations in the spatial distribution and magnitude of rockfall hazard.This work was supported in part by the Spanish Ministry of Science, Innovation, and Universities, in part by the State General Budget (PGE), and in part by the European Regional Development Fund (ERDF)under Grant RTI2018-094217-B-C32 (MCIU/AEI/FEDER, UE). The work of Alejandro Roldan-Valcarce was supported by the Spanish Ministry of Science, Innovation and Universities through a Researcher Formation Fellowship under Grant PRE2019-08945