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USGS2020

Reconstructing the velocity and deformation of a rapid landslide using multiview video

Noncontact measurements of spatially varied ground surface deformation during landslide motion can provide important constraints on landslide mechanics. Here, we present and test a new method for extracting measurements of rapid landslide surface displacement and velocity (accelerations of approximately 1 m/s 2 ) using sequences of stereo images obtained from a pair of inexpensive, stationary 4K video cameras with nominal frame rates of 29.97 Hz. The method combines elements of Structure from Motion with those of optical flow to extract data on 3‐D evolution of the ground surface during slope failure. We apply the method to an experiment at the U.S. Geological Survey debris‐flow flume in which a high‐speed, liquefying landslide was triggered by gradually adding water to a 6‐m 3 prism of loosely packed sediment on a 31° slope. Strip‐scanning lidar measurements made during the experiment corroborate our video‐based measurements, but the latter encompassed the entire landslide surface and were much lower in cost. Our video‐based measurements enabled computation of depth‐integrated landslide dilation/contraction rates. The range of computed rates was within the ranges inferred from independent measurements of evolving pore water pressures and reasonable estimates of the hydraulic permeability of the sediment. Dilation and contraction rates play a crucial role in landslide mechanics. The dilation and contraction we observe contradict the incompressible flow assumption used in many studies that have employed noncontact methods to infer landslide properties.

Oregon↗

USGS2011

Monitoring and inversion on land subsidence over mining area with InSAR technique

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism. ?? 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

Conference Paper↗

USGS2018

Sampling strategies to improve passive optical remote sensing of river bathymetry

Passive optical remote sensing of river bathymetry involves establishing a relation between depth and reflectance that can be applied throughout an image to produce a depth map. Building upon the Optimal Band Ratio Analysis (OBRA) framework, we introduce sampling strategies for constructing calibration data sets that lead to strong relationships between an image-derived quantity and depth across a range of depths. Progressively excluding observations that exceed a series of cutoff depths from the calibration process improved the accuracy of depth estimates and allowed the maximum detectable depth ($d_{max}$) to be inferred directly from an image. Depth retrieval in two distinct rivers also was enhanced by a stratified version of OBRA that partitions field measurements into a series of depth bins to avoid biases associated with under-representation of shallow areas in typical field data sets. In the shallower, clearer of the two rivers, including the deepest field observations in the calibration data set did not compromise depth retrieval accuracy, suggesting that $d_{max}$ was not exceeded and the reach could be mapped without gaps. Conversely, in the deeper and more turbid stream, progressive truncation of input depths yielded a plausible estimate of $d_{max}$ consistent with theoretical calculations based on field measurements of light attenuation by the water column. This result implied that the entire channel, including pools, could not be mapped remotely. However, truncation improved the accuracy of depth estimates in areas shallower than $d_{max}$, which comprise the majority of the channel and are of primary interest for many habitat-oriented applications.

Remote Sensing↗

USGS2010

Bayesian estimation of weak material dispersion: Theory and experiment

This work considers the estimation of dispersion in materials via an interferometric technique. At its core, the problem involves extracting the quadratic variation in phase over a range of wavelengths based on measured optical intensity. The estimation problem becomes extremely difficult for weakly dispersive materials where the quadratic nonlinearity is very small relative to the uncertainty inherent in experiment. This work provides a means of estimating dispersion in the face of such uncertainty. Specifically, we use a Markov Chain Monte Carlo implementation of Bayesian analysis to provide both the dispersion estimate and the associated confidence interval. The interplay between various system parameters and the size of the resulting confidence interval is discussed. The approach is then applied to several different experimental samples.

Optics Express↗

USGS2019

Optical wave gauging using deep neural networks

We develop a remote wave gauging technique to estimate wave height and period from imagery of waves in the surf zone. In this proof-of-concept study, we apply the same framework to three datasets: the first, a set of close-range monochrome infrared (IR) images of individual nearshore waves at Duck, NC, USA; the second, a set of visible (i.e. RGB) band orthomosaics of a larger nearshore area near Santa Cruz, CA, USA; and the third, a set of oblique (unrectified) images from the same site. The network is trained using coincident images and in situ wave measurements. The optical wave gauge (OWG) consists of a deep convolutional neural network (CNN) to extract features from imagery — called a ‘base model’, with additional layers to distill the feature information into lower dimensional spaces, and a final layer of dense neurons to predict continuously varying quantities. Four base models are compared. The OWG is trained for both individual wave height and period, and statistical quantities like significant wave height and peak wave period. The best performing OWG on the IR dataset achieved RMS errors of 0.14 m and 0.41 s for height and period, respectively, capturing up to 98% of the variance in these quantities. The best performing OWG on the visible band rectified dataset achieved RMS errors of 0.08 m and 0.79 s, respectively, for height and period. The same values for the oblique RGB imagery were 0.11 m and 0.81 s for height and period, respectively. Overall, wave height and period accuracy is sensitive to choice of base model; OWGs built upon MobilenetV2 tend to perform worst and those built on Inception-ResnetV2 have the smallest RMS error. The presence or otherwise of residual layers in the model makes little systematic difference to the final OWG accuracy. Smaller batch sizes used in model training tend to result in more accurate OWGs. An out-of-calibration validation, using images associated with wave heights or periods outside the range of values represented in the training data, showed that the ability for OWGs to predict the bottom 5% of low wave heights and the top 5% of high wave heights was reasonably good, but the same was not generally true of wave period. The same framework, not optimized for either dataset, predicts both quantities with high accuracy when trained on imagery, despite the differences in electromagnetic band, perspective, and scale. The OWG estimates wave properties from an image in less than 100 ms on a modestly sized CPU, allowing for the possibility of continuous real-time wave estimates.

California, North Carolina↗

USGS2007

A unified equation for calculating methane vapor pressures in the CH4-H2O system with measured Raman shifts

A unified equation has been derived by using all available data for calculating methane vapor pressures with measured Raman shifts of C-H symmetric stretching band (??1) in the vapor phase of sample fluids near room temperature. This equation eliminates discrepancies among the existing data sets and can be applied at any Raman laboratory. Raman shifts of C-H symmetric stretching band of methane in the vapor phase of CH4-H2O mixtures prepared in a high-pressure optical cell were also measured at temperatures between room temperature and 200 ??C, and pressures up to 37 MPa. The results show that the CH4 ??1 band position shifts to higher wavenumber as temperature increases. We also demonstrated that this Raman band shift is a simple function of methane vapor density, and, therefore, when combined with equation of state of methane, methane vapor pressures in the sample fluids at elevated temperatures can be calculated from measured Raman peak positions. This method can be applied to determine the pressure of CH4-bearing systems, such as methane-rich fluid inclusions from sedimentary basins or experimental fluids in hydrothermal diamond-anvil cell or other types of optical cell. ?? 2007 Elsevier Ltd. All rights reserved.

Geochimica et Cosmochimica Acta↗

USGS2026

Short-term estuarine phytoplankton dynamics in response to hurricanes along the Gulf Coast of America: A Variational Autoencoder (VAE) approach with satellite and bio-optical observations

Hurricanes drive diverse estuarine phytoplankton responses and can trigger cascading ecological and physicochemical impacts. Capturing these short-term dynamics requires high spatiotemporal resolution. Here, we applied a globally-applicable coastal ocean color algorithm, Variational Autoencoder (VAE), to Sentinel-2 MSI imagery for chlorophyll- a (Chl- a ) estimation and validated its strong performance across the northern Gulf coast of America (GoA) estuaries, including Galveston Bay (TX), Barataria-Terrebonne Estuary (LA), Apalachicola Estuary (FL) and Tampa Bay (FL). The test set showed strong performance (MAE: 1.44 mg m −3 ; RMSE: 17.7 mg m −3 ; slope: 0.86; median symmetric accuracy: 30.33%). The validated VAE was then applied to 76 Sentinel-2 MSI images to assess phytoplankton biomass responses to hurricanes Harvey (2017), Michael (2018), Ida (2021), Francine (2024), Helene (2024), and Milton (2024) in the GoA estuaries. Results showed that hurricane disturbances on Chl- a typically lasted 3–5 weeks. Estuarine waters west (left) of hurricane tracks showed a rapid decline in Chl- a (∼5 mg m −3 ) due to elevated turbidity from heavy rainfall, and wind-driven flushing in the estuary, followed by a rebound over about two weeks, with Chl- a increasing approximately 10–15 mg m −3 above pre-storm levels. In contrast, right-side waters showed a slower response, likely from oligotrophic seawater intrusion driven by the hurricane's counterclockwise rotation. Post-storm observations showed increased freshwater phytoplankton like chlorophytes and cyanobacteria dominating estuaries, while shelf-waters exhibited elevated dinoflagellates (e.g., Karenia brevis bloom after Hurricane Milton). These results highlight the spatial heterogeneity of hurricane impacts on estuarine phytoplankton dynamics, which may trigger cascading effects on biogeochemical cycling and food webs, potentially prolonging ecosystem recovery.

Alabama, Florida, Louisiana, Mississippi, Texas↗

USGS2025

Hyperspectral imaging of river bathymetry using an ensemble of regression trees

Remote sensing has emerged as an effective tool for characterizing river systems, and machine learning (ML) techniques could make this approach even more powerful. To explore this possibility, we developed an ML-based workflow for hyperspectral imaging of river bathymetry using an ensemble of regression trees (HIRBERT). This approach involves using paired observations of depth and reflectance to select wavelength bands as predictors and then train a depth retrieval model; applying the model to the image yields a spatially continuous bathymetric map. We used data from five rivers with diverse morphologies and optical characteristics to assess whether HIRBERT can (1) provide more accurate depth estimates than a band ratio-based algorithm and (2) extend the range of depths detectable via remote sensing. Relative to single band combinations identified via optimal band ratio analysis (OBRA), regression tree ensembles improved depth retrieval performance, with observed versus predicted (OP) regression R 2 values increasing for all five sites. Similarly, HIRBERT provided more reliable depth estimates than OBRA over the full range of depths present along each river. These results suggest that by incorporating additional spectral information from multiple wavelength bands, ML could enhance bathymetric mapping across a range of river environments. In addition, we show how graphical tools can facilitate interpretation of ML-based depth retrieval models and yield insight regarding relationships between depth and reflectance. The HIRBERT workflow is packaged in free, standalone software developed to support applications in river research and management. Although ML can enhance remote sensing of river bathymetry, the limitations of this approach must also be acknowledged: Field measurements of water depth are required to train a depth retrieval model and the resulting model should only be applied to the image from which the training data were derived. The inherently image-specific nature of this approach implies that developing generalized regression tree ensembles that could be applied at larger scales would require additional research.

California, Idaho, Nebraska, Oregon, Wyoming↗

USGS2026

Detecting polar bears on land using satellite imagery and deep learning

Summer land use is increasing for polar bears in the Beaufort Sea, which may lead to increases in human-bear interactions. Remote methods may enable noninvasive approaches to monitor polar bear abundance and distribution on land. We examined the potential of using very high-resolution optical satellite imagery to detect polar bears on land from images collected at three sites on the Beaufort Sea coast of Alaska. Additionally, we evaluated the use of deep learning models to automate detection of polar bears in these images and the degree to which detection of polar bears differed among three very high-resolution optical satellite missions. Pléiades Neo images exhibited 90% average precision in validation analyses using a faster region-based convolutional neural network model, which was 55% and 11% greater than the average precision using WorldView-2 or -3 images, respectively. Applying the Pléiades Neo training set to new imagery exhibited lower precision (35%), but high recall (100%), which indicates that deep learning could be used to expedite the visual detection of polar bears in imagery. Very high-resolution optical satellite imagery provides a remote method for monitoring polar bears on land where they occur in high densities, but satellite mission, off-nadir angle, pansharpening methods, and image resolution are important considerations.

Alaska↗

USGS2011

Nanoparticles formed from bacterial oxyanion reduction of toxic Group 15 and 16 metalloids

This chapter presents some examples of nanoparticles formed by only a few microbial species that are cultivated in only a handful of laboratories worldwide. The investigations so far have just scratched the surface of the potential of the natural world to yield bionanomineral producers. While future research should involve screening surveys of the prokaryotes for this biomineralizing phenomenon, more detailed investigations are justified. The chapter discusses microbial Interaction with Group 15 and 16 Toxic Metalloids. The toxicity of the metalloids Se, Te, and As is due to the disruption of thiol intracellular biochemistry through the formation of stable, long-lived sulfur complexes. Selenium oxyanion reduction occurs in a wide range of microbes, including representatives of the Wolinella , Pseudomonas , Sulfurospirillum , Enterobacter , Thaurea , Bacillus , Shewanella , and Citrobacter genera. Technological applications of Se(0) and Te(0) nanoparticles include their use in photocopiers, microelectronic circuits, and solar cells as a result of their photo-optical and semiconducting physical properties. Nonetheless, once novel Se, Te, and As bionanoparticles are identified as having significant technical applications, applied research into their practical commercial production will without doubt ensue rapidly.

Book chapter↗

USGS2026

Thermal infrared and ultraviolet remote sensing of sulfur dioxide gas emitted during the 2018 lower East Rift Zone eruption of Kilauea, Hawaiʻi

Ultraviolet (UV) remote sensing is widely used to detect volcanic sulfur dioxide (SO₂) due to its high sensitivity and favorable spatial and temporal resolution. However, significant discrepancies have been reported between ground-based and satellite-based UV observations of dense volcanic plumes. A notable example is the 2018 lower East Rift Zone eruption of Kīlauea, where SO₂ emission rates derived from ground-based Differential Optical Absorption Spectroscopy (DOAS) measurements differed substantially from those obtained by the spaceborne Tropospheric Monitoring Instrument (TROPOMI). In this study, we investigate these differences by applying thermal infrared (TIR) satellite retrievals using a modified version of the SO₂-ALTA algorithm to Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The resulting TIR-derived SO₂ fluxes are compared with ground-based DOAS data, satellite UV observations, and petrological estimates of gas emissions. Our results show strong agreement between TIR-derived fluxes, ground-based DOAS measurements, and petrological estimates, particularly during the peak and plateau phases of the eruption. In contrast, satellite UV-derived SO₂ emissions are systematically lower. We find that TIR observations are more effective in quantifying high-concentration SO₂ plumes in the near-vent region, while UV measurements are more sensitive under lower-concentration conditions but more affected by scattering in optically dense plumes. These findings highlight the complementary strengths of UV and TIR remote sensing techniques and emphasize the importance of accounting for plume density and observation geometry when interpreting satellite SO₂ retrievals.

Hawaii↗

USGS2009

Synergistic use of optical and InSAR data for urban impervious surface mapping: A case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.

International Journal of Remote Sensing↗

USGS2019

Vertical coseismic offsets from differential high-resolution stereogrammetric DSMs: The 2013 Baluchistan, Pakistan earthquake

The recent proliferation of high-resolution (< 3-m spatial resolution) digital topography datasets opens a spectrum of geodetic applications in differential topography, including the quantification of coseismic vertical displacement fields. Most investigations of coseismic vertical displacements to date rely, in part, on pre- or post-event lidar surveys that are intractable or non-existent in many locales. Stereogrammetric digital surface models (DSMs) derived from high-resolution satellite optical imagery provide a new avenue for the retrieval of spatially-dense vertical coseismic displacements on a global scale. In this study, we generated 2-m resolution pre- and post-seismic DSMs from satellite optical imagery spanning the 2013 Mw7.7 Baluchistan strike-slip earthquake that occurred on the Hoshab fault in southern Pakistan. We applied the Iterative Closest Point algorithm to the DSMs to quantify the coseismic vertical displacement field at a spatial resolution of 10-30 m and to generate 3D coseismic strain tensors. We found that across-fault vertical offsets alternated between uplift and subsidence and varied between ~1-3 m in a non-systematic manner along the Hoshab fault. We show that the pre-existing topography and near-fault geomorphology are variably consistent and inconsistent with the displacement kinematics of the 2013 earthquake, and we argue that these relationships highlight varied slip sense history along the Hoshab fault. Notably, topography along the southern extents of the Hoshab fault requires different surface displacement kinematics than occurred in the 2013 earthquake, suggesting that the Hoshab fault accommodates varying senses of slip (bimodal slip) through time.

Baluchistan Province↗

USGS2025

Earthquake magnitude and source parameter estimation with a distributed acoustic sensing dataset in the Gorda subduction zone

Distributed acoustic sensing (DAS) systems offer a cost‐effective way to create large‐scale strainmeter arrays for seismological applications using fiber‐optic cables. DAS‐based strain measurements are known to be influenced by various factors, bringing into question their general reliability for accurate earthquake characterization. A 15‐km‐long DAS deployment in northern California was operational within 3 days of the 2022 M w 6.4 Ferndale earthquake and ran continuously throughout the aftershock sequence. We utilize these aftershock data to validate DAS‐based strain measurements in two ways. We first test the accuracy of DAS‐based magnitude estimates from peak dynamic strains by comparing them with magnitude and attenuation scaling relations derived independently from traditional borehole strainmeter (BSM) data. We demonstrate that DAS‐based magnitudes are comparable to BSM‐based magnitudes when corrections for variations in site response along the fiber‐optic cable are properly made. Magnitude errors are spatially correlated, potentially because of factors such as finite‐fault effects (e.g., stress drop) or more complex, unmodeled path attenuation or because of wave propagation effects in heterogeneous media. We then apply more advanced source characterization methodology to the DAS data using a time‐domain empirical Green’s function (EGF) deconvolution approach to measure details of the moment rate history. The EGF approach using DAS data depends on careful treatment of distorting factors such as anthropogenic sources of noise and optical phase wrapping but successfully isolates source spectra for moderate‐magnitude earthquakes: source spectral ratios obtained from DAS data, broadband seismometer data, and BSM data in the same region show consistent results, revealing differences in directivity and spectral shape among earthquakes. Although further research is needed to refine source‐time‐function estimation techniques for DAS data, particularly for larger magnitude events, these case studies demonstrate the clear potential of DAS for earthquake source characterization.

California↗

USGS2020

High-resolution imaging of hydrothermal heat flux using optical and thermal Structure-from-Motion photogrammetry

Quantifying hydrothermal heat flux at meter-scale resolution over N0.25 km2 is required to bridge in-situ heat flux and satellite-based measurements. We advance a methodology that blends ground-based daytime optical and nighttime thermal infrared (TIR) imagery using Structure-from-Motion photogrammetry to map radiant hydrothermal heat flux over these scales at sites with low signal-to-noise ratios that would otherwise be difficult to characterize using, for example, unmanned aerial systems. The improved method uses a computerized telescopic mount to relocate and align daytime optical acquisitions with nighttime TIR imagery, thereby enabling TIR acquisition from multiple camera orientations positioned throughout a study area. This facilitates mapping of thermal features at sites of varying size and complexity and helps to ameliorate topographic occlusion effects and geometric distortions that can bias radiant hydrothermal heat flux estimates derived from the resulting orthorectified thermal maps. We assessed detection thresholds of this method at three sites across central California, which range in size, topography, and heat flux conditions. We found that blending of optical and thermal acquisitions successfully detected anomalous heat flux, even in cases where temperatures were only slightly greater than background. This approach might be applied to a variety of volcanic and hydrothermal systems to quantify the spatial distribution of heat flux, and how this may relate to factors such as the distribution of ground fractures and lava flow rheology.

Journal of Volcanology and Geothermal Research↗

USGS1987

Image restoration techniques as applied to Landsat MSS and TM data

Two factors are primarily responsible for the loss of image sharpness in processing digital Landsat images. The first factor is inherent in the data because the sensor's optics and electronics, along with other sensor elements, blur and smear the data. Digital image restoration can be used to reduce this degradation. The second factor, which further degrades by blurring or aliasing, is the resampling performed during geometric correction. An image restoration procedure, when used in place of typical resampled techniques, reduces sensor degradation without introducing the artifacts associated with resampling. The EROS Data Center (EDC) has implemented the restoration proceed for Landsat multispectral scanner (MSS) and thematic mapper (TM) data. This capability, developed at the University of Arizona by Dr. Robert Schowengerdt and Lynette Wood, combines restoration and resampling in a single step to produce geometrically corrected MSS and TM imagery. As with resampling, restoration demands a tradeoff be made between aliasing, which occurs when attempting to extract maximum sharpness from an image, and blurring, which reduces the aliasing problem but sacrifices image sharpness. The restoration procedure used at EDC minimizes these artifacts by being adaptive, tailoring the tradeoff to be optimal for individual images.

Pecora XI Symposium↗

USGS2014

Modeling future scenarios of light attenuation and potential seagrass success in a eutrophic estuary

Estuarine eutrophication has led to numerous ecological changes, including loss of seagrass beds. One potential cause of these losses is a reduction in light availability due to increased attenuation by phytoplankton. Future sea level rise will also tend to reduce light penetration and modify seagrass habitat. In the present study, we integrate a spectral irradiance model into a biogeochemical model coupled to the Regional Ocean Model System (ROMS). It is linked to a bio-optical seagrass model to assess potential seagrass habitat in a eutrophic estuary under future nitrate loading and sea-level rise scenarios. The model was applied to West Falmouth Harbor, a shallow estuary located on Cape Cod (Massachusetts) where nitrate from groundwater has led to eutrophication and seagrass loss in landward portions of the estuary. Measurements of chlorophyll, turbidity, light attenuation, and seagrass coverage were used to assess the model accuracy. Mean chlorophyll based on uncalibrated in-situ fluorometry varied from 28 μg L −1 at the landward-most site to 6.5 μg L −1 at the seaward site, while light attenuation ranged from 0.86 to 0.45 m -1 . The model reproduced the spatial variability in chlorophyll and light attenuation with RMS errors of 3.72 μg L −1 and 0.07 m -1 respectively. Scenarios of future nitrate reduction and sea-level rise suggest an improvement in light climate in the landward basin with a 75% reduction in nitrate loading. This coupled model may be useful to assess habitat availability changes due to eutrophication and sediment resuspension and fully considers spatial variability on the tidal timescale.

Massachusetts↗

USGS2026

Rapid seismic and infrasound assessment of large landslides: A case study from Denali National Park and Preserve (Alaska)

Large, rapid landslides are a global hazard that can occur in remote, mountainous areas. Eyewitness reports of landslides and satellite imagery can often be limited or delayed, particularly during inclement weather. However, landslide-generated seismic and infrasound (low-frequency atmospheric sound) waves can be remotely detected in near real-time. This information can significantly expedite characterization and possible landslide response activities. Here, we highlight these capabilities using a > 4 million m³ ice–rock avalanche in Denali National Park and Preserve (Alaska). This event was detected via a landslide-specific seismic location and volume estimation algorithm deployed in Alaska, and — notably — by standard earthquake monitoring systems. Following rapid detection of this event, we combined its seismic and infrasound dataset with optical, synthetic aperture radar, and oblique aerial imagery, multitemporal digital elevation models, and a numerical flow model to reconstruct its failure timeline and dynamics. We apply array processing to infrasound signals traveling > 250 km and find that two precursory events occurred minutes prior to the main failure. We use long-period seismic signals to infer the force exerted by the landslide on the Earth and constrain the rheological parameters of our numerical flow simulation with this result and deposit morphology. The main failure produced a steeply-dipping impulsive initial downward force and reached speeds exceeding 60 m/s. This impulsive force generated relatively strong seismic body waves, which contributed to the earthquake system detection. This large, remote Alaska landslide underscores the key value of seismic and infrasound analysis for rapid landslide assessment and motivates efforts to further operationalize these approaches.

Alaska↗