16 research outputs found

    Rapid Detection of Earthquake-triggered Landslides from Satellite Radar

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    Triggered landslides pose a major risk following large earthquakes in mountainous areas and disrupt emergency response efforts. If information on the spatial distribution of these landslides can be generated quickly enough, it is therefore invaluable to emergency response coordinators. At present, this information is commonly generated manually from optical satellite imagery, which is labour-intensive and can be delayed or left incomplete due to cloud cover. This means a complete picture of triggered landsliding is often unavailable within the time frame of the emergency response. Alternatively, empirical models can predict landslide probability based on factors such as shaking intensity and slope steepness within hours of an earthquake, but these models are static in time and not always reliable as they do not contain any observations of landslides. Satellite radar offers a third alternative method of generating landslide information for emergency response. These data can be acquired through cloud and are now available within days of any continental earthquake. Radar data are sensitive to landslides, which alter the scattering properties of the Earth’s surface, so could be used to generate all-weather information on landslide spatial distributions within days of an earthquake. Satellite radar data are routinely used to generate other products for emergency response, but for landslide detection, the testing and development of radar methods is not yet sufficiently advanced for them to be widely applied. In this thesis, I present new methods of landslide detection based on satellite radar coherence, a measure of the level of noise in an interferogram that reflects physical changes in the Earth’s surface such as landslides. I carry out systematic testing of new and existing methods of coherence-based landslide detection across four case study earthquakes using data from two satellite radar sensors, allowing identification of which method is preferable depending on the data available after an earthquake. Finally I experiment with combining empirical models and radar coherence methods. Overall, I demonstrate that useful information on landslide intensity can be generated within two weeks of an earthquake using satellite radar data, and that the addition of these data to empirical models can significantly improve their performance

    HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery

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    Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth observation (EO) data, several gaps and uncertainties remain with developing models to be operational at the global scale. The lack of a high-resolution globally distributed and event-diverse dataset for landslide segmentation poses a challenge in developing machine learning models that can accurately and robustly detect landslides in various regions, as the limited representation of landslide and background classes can result in poor generalization performance of the models. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD), a high-resolution (HR) satellite dataset (PlanetScope, 3 m pixel resolution) for landslide mapping composed of landslide instances from 10 different physiographical regions globally in South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall-triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions in the form of standardized image patches containing four PlanetScope image bands (red, green, blue, and NIR) and a binary mask for landslide detection. The HR-GLDD can be accessed through this link: https://doi.org/10.5281/zenodo.7189381 (Meena et al., 2022a, c). HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, three recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results when testing the HR-GLDD at individual test sites, thereby indicating the robustness of the dataset for such purposes. The HR-GLDD is open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.</p

    ALOS-2/PALSAR-2 Calibration, Validation, Science and Applications

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    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

    Combining remote sensing techniques and field surveys for post-earthquake reconnaissance missions

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    Remote reconnaissance missions are promising solutions for the assessment of earthquake-induced structural damage and cascading geological hazards. Space-borne remote sensing can complement in-field missions when safety and accessibility concerns limit post-earthquake operations on the ground. However, the implementation of remote sensing techniques in post-disaster missions is limited by the lack of methods that combine different techniques and integrate them with field survey data. This paper presents a new approach for rapid post-earthquake building damage assessment and landslide mapping, based on Synthetic Aperture Radar (SAR) data. The proposed texture-based building damage classification approach exploits very high resolution post-earthquake SAR data integrated with building survey data. For landslide mapping, a backscatter intensity-based landslide detection approach, which also includes the separation between landslides and flooded areas, is combined with optical-based manual inventories. The approach was implemented during the joint Structural Extreme Event Reconnaissance, GeoHazards International and Earthquake Engineering Field Investigation Team mission that followed the 2021 Haiti Earthquake and Tropical Cyclone Grace

    Earthquake-triggered landslides and Environmental Seismic Intensity: insights from the 2018 Papua New Guinea earthquake (Mw 7.5)

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    On the 25 February 2018, an earthquake of magnitude M(w)7.5 struck the region of Porgera in Papua New Guinea (PNG), triggering numerous landslides. Planetscope images are used to derive a partial inventory of 2941 landslides in a cloud-free area of 2686 km(2). The average area of landslides in the study area is 18,500 m(2). We use the Environmental Seismic Intensity (ESI) scale to assess the damage due to the triggered landslides. Local intensity values are assigned to individual landslides by calculating their volume using various area-volume relations. We observe that different empirical relations yield similar volume values for individual landslides (local ESI intensity &amp; GE; X). The spatial variation of landslide density and areal coverage within the study area in cells of 1 km(2) is investigated and compared to the probability predicted by the USGS model. We observe that high probability corresponds to a significant number of landslides. An ESI epicentral intensity of XI is estimated based on primary and secondary effects. This study represents the first application of the ESI scale to an earthquake in PNG. The Porgera earthquake fits well with past case studies worldwide in terms of ESI scale epicentral intensity and triggered landslide number as a function of earthquake magnitude

    Vulnerabilidad de la zona del Eje Cafetero por sismos y por deslizamientos

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    A nivel general se define la vulnerabilidad como el grado de resiliencia que tiene un objeto, persona o sistema ante cualquier evento o fenómeno amenazante, y en cuanto al Eje Cafetero es el nivel de exposición frente a riesgos sísmicos y deslizamientos de esta zona, situada en la subducción de la placa Nazca y atravesada por la falla del Romeral, una de la más activa de América. El objetivo del trabajo fue diagnosticar el estado el arte de la información científica en torno al tema de la afectación del riesgo sísmicos y deslizamientos en el Eje Cafetero, para la realización del trabajo, se investigaron los trabajos que de acuerdo al objetivo guardasen concordancia, para ello y con el uso de nubes de palabras se recopilo la literatura de bases de datos tanto de Scopus, como en repositorios institucionales dejando como resultado la compilación de 89 con los que se inició el procesos de análisis y resultados estadísticos. En cuanto los resultados obtenidos, muestran que el tema de los sismos, asociado a deslizamientos está suficientemente documentado. Para Colombia, se presentan numerosas investigaciones, la mayoría editados por entidades de vigilancia y control de riesgo. Sobre el Eje Cafetero es mínima la información que traten el tema de las vulnerabilidades frente a este tipo de eventos. La información de la literatura muestra que es evidente amenaza de la región, la fragilidad de las comunidades y de la infraestructura es alta, en la literatura investigada no se evidencian trabajos que presenten herramientas, modelos o métodos dirigidas a las comunidades urbanas que les permitan afrontar estos eventos de tal forma que su uso evite o minimice las pérdidas de vías humanas y daños físicos. En cuanto a la región rural, es mínima la información, pero de la investigada, en la literatura se sostiene que se encuentra en igual de condición de desamparo, con el agravante que la deforestación e invasión de áreas sensibles a deslizamientos y una infraestructura precaria los hace más vulnerables frente a este tipo de amenazas. Por último como reflexión del trabajo queda que, es urgente e importante por parte de las instituciones de prevención y control proponer planes y acciones encaminadas a la prevención de estas catástrofes, que involucren la concientización de la comunidad en estas situaciones que entiendan que la posición geográfica del país es propensa a los movimientos telúricos, pero que existen metodologías, modelos alertas tempranas y políticas públicas que aplicadas en los tiempos reales, minimizaran el efecto e impacto de estos.At a general level, vulnerability is defined as the degree of resilience that an object, person or system has in the face of any threatening event or phenomenon, and as regards the Eje Cafetero, it is the level of exposure to seismic risks and landslides in this area. located in the subduction of the Nazca plate and crossed by the Romeral fault, one of the most active in America. The objective of the work was to diagnose the state of the art of scientific information on the issue of the affectation of seismic risk and landslides in the Eje Cafetero, to carry out the work, the works that according to the objective were consistent, were investigated, to This and with the use of word clouds, the literature was compiled from both Scopus databases and institutional repositories, leaving as a result the compilation of 89 with which the analysis processes and statistical results were started. Regarding the results obtained, it shows that the issue of earthquakes, associated with slopes slides, is sufficiently documented. For Colombia, numerous investigations are presented, the majority edited by risk surveillance and control entities. The information on the issue of vulnerabilities in the face of this type of event is minimal on the Coffee Axis. The information in the literature shows that there is an evident threat to the region, the fragility of the communities and of the infrastructure is high, in the researched literature there are no works that present tools, models or methods aimed at urban communities that allow them to face these events in such a way that their use avoids or minimizes the loss of human routes and physical damage. Regarding the rural region, the information is minimal, but of the investigated, in the literature it is maintained that it is in the same condition of helplessness, with the aggravating factor that deforestation and invasion of areas sensitive to landslides and a precarious infrastructure makes them more vulnerable to these types of threats. Finally, as a reflection of the work, it is urgent and important on the part of the prevention and control institutions to propose plans and actions aimed at preventing these catastrophes, which involve the awareness of the community in these situations that understand that the geographical position The country is prone to earthquakes, but there are methodologies, early warning models and public policies that, applied in real times, will minimize their effect and impact

    Automated determination of landslide locations after large trigger events: advantages and disadvantages compared to manual mapping

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    Earthquakes in mountainous areas can trigger thousands of co-seismic landslides, causing significant damage, hampering relief efforts, and rapidly redistributing sediment across the landscape. Efforts to understand the controls on these landslides rely heavily on manually mapped landslide inventories, but these are costly and time-consuming to collect, and their reproducibility is not typically well constrained. Here we develop a new automated landslide detection algorithm (ALDI) based on pixel-wise NDVI differencing of Landsat time series within Google Earth Engine accounting for seasonality. We compare classified inventories to manually mapped inventories from five recent earthquakes: 2005 Kashmir, 2007 Aisen, 2008 Wenchuan, 2010 Haiti, and 2015 Gorkha. We test the ability of ALDI to recover landslide locations (using ROC curves) and landslide sizes (in terms of landslide area-frequency statistics). We find that ALDI more skilfully identifies landslides than published inventories in 10 of 14 cases when ALDI is locally optimised, and in 8 of 14 cases both when ALDI is globally optimised and in holdback testing. These results reflect both good performance of the automated approach but also surprisingly poor performance of manual mapping, which has implications not only for how future classifiers are tested but also for the interpretations that are based on these inventories. We conclude that ALDI already represents a viable alternative to manual mapping in terms of its ability to identify landslide-affected image pixels. Its fast run-time, cost-free image requirements and near-global coverage make it an attractive alternative with the potential to significantly improve the coverage and quantity of landslide inventories. Its simplicity (pixel-wise analysis only) and parsimony of inputs (optical imagery only) suggests that considerable further improvement should be possible
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