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At least 793 records · Page 44Linked to original sources
USGS2013

Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska

Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes.

Alaska↗
USGS2025

Linking suspended sediment conditions to hyporheic dissolved oxygen and fine sediment deposition in salmonid spawning habitat below an irrigation dam, Park County, Wyoming

Dams are essential for water resources management but impose notable effects on fluvial sediment transport and downstream river morphology by reducing or altering the timing of sediment loads. We explored the relationship between dam sediment management and downstream sediment dynamics in the context of riverine fisheries management. We quantified the effects of dam sediment management operations on downstream salmonid spawning habitat during two fall water-level drawdown periods: an experimental drawdown leading to sediment release or a typical slower drawdown intended to minimize release of sediment. The experimental drawdown increased deposited fine sediment and decreased hyporheic dissolved oxygen levels. However, the typical drawdown did not increase fine sediment deposition or decrease hyporheic dissolved oxygen. We quantify the immediate impacts of dam operations using a number of water column and substrate metrics, and demonstrate the potential for sediment flushing operations to have short-term seasonally persistent effects on salmonid spawning habitat. Common surrogates of suspended sediment concentration (i.e. turbidity) were poor indicators of salmonid spawning habitat, especially when sand was the dominant grain size. Instead, measures of suspended sediment concentration such as acoustic backscatter sensors and depth-integrated samples, combined with discharge, appear to be better suited for monitoring and inferring the impacts of dam operations involving sediment releases on salmonid spawning habitat. We demonstrate the importance of understanding sediment particle sizes, monitoring relevant water column conditions in real-time, and provide options for effectively monitoring the downstream impact of dam operations. This work can help managers balance dam sediment management operations with ecological priorities.

Wyoming↗
USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗
USGS2022

Automating sandhill crane counts from nocturnal thermal aerial imagery using deep learning

Population monitoring is essential to management and conservation efforts for migratory birds, but traditional low-altitude aerial surveys with human observers are plagued by individual observer bias and risk to flight crews. Aerial surveys that use remote sensing can reduce bias and risk, but manual counting of wildlife in imagery is laborious and may be cost-prohibitive. Therefore, automated methods for counting are critical to cost-efficient application of remote sensing for wildlife surveys covering large areas. We conducted nocturnal surveys of sandhill cranes ( Antigone canadensis ) during spring migration in the Central Platte River Valley of Nebraska, USA, using midwave thermal infrared sensors. We developed a framework for automated counting of sandhill cranes from thermal imagery using deep learning, assessed and compared the performance of two automated counting models, and quantified the effect of spatial resolution on counting accuracy. Aerial thermal imagery data were collected in March 2018 and 2021; 40 images were analyzed. We applied two deep learning models: an object detection approach, Faster R-CNN and a recently developed pixel-density estimation approach, ASPDNet. Model performance was determined using data independent of the training imagery. The effect of spatial resolution was quantified with a beta regression on relative error. Our results showed model accuracy of 9% mean percent error for ASPDNet and 18% for Faster R-CNN. Most error was related to the undercounting of sandhill cranes. ASPDNet had < 50% of the error of Faster R-CNN as measured by mean percent error, root-mean-squared error and mean absolute error. Spatial resolution affected accuracy of both models, with error rate increasing with coarser resolution, particularly with Faster R-CNN. Deep learning models, particularly pixel-density estimators, can accurately automate counting of migratory birds in a dense, aggregate setting such as nocturnal roosting sites.

Nebraska↗
USGS2025

Object detection-assisted workflow facilitates cryptic snake monitoring

Camera traps are an important tool used to study rare and cryptic animals, including snakes. Time-lapse photography can be particularly useful for studying snakes that often fail to trigger a camera's infrared motion sensor due to their ectothermic nature. However, the large datasets produced by time-lapse photography require labor-intensive classification, limiting their use in large-scale studies. While many artificial intelligence-based object detection models are effective at identifying mammals in images, their ability to detect snakes is unproven. Here, we used camera data to evaluate the efficacy of an object detection model to rapidly and accurately detect snakes. We classified images manually to the species level and compared this with a hybrid review workflow where the model removed blank images followed by a manual review. Using a ≥0.05 model confidence threshold, our hybrid review workflow correctly identified 94.5% of blank images, completed image classification 6× faster, and detected large (>66 cm) snakes as well as manual review. Conversely, the hybrid review method often failed to detect all instances of a snake in a string of images and detected fewer small (<66 cm) snakes than manual review. However, most relevant ecological information requires only a single detection in a sequence of images, and study design changes could likely improve the detection of smaller snakes. Our findings suggest that an object detection-assisted hybrid workflow can greatly reduce time spent manually classifying data-heavy time-lapse snake studies and facilitate ecological monitoring for large snakes.

Florida↗
USGS2026

Network-wide assessment of soil water content calibration and sensitivity to biomass proxies using cosmic-ray neutron sensing in the Roaring Fork Basin, Colorado

Soil water content (SWC) is a key state variable of the climate system but is often uncertain in water balance monitoring, especially in alpine environments. SWC measurements can be challenging in alpine environments due to the topography and rocky soils. In 2022, the US Geological Survey's Next Generation Water Observing System Program began research to evaluate water balance monitoring technologies, including cosmic-ray neutron sensors (CRNS). This work evaluated the uncertainty resulting from network-wide calibration of CRNS for SWC monitoring in an alpine watershed and investigated the stability of the calibration parameters across space and time, focusing on potential influence of biomass dynamics. Fifteen stations with moderated and unmoderated (bare) CRNS were deployed and made operational within the Roaring Fork Basin in west-central Colorado. The root mean squared error of the network-wide calibration using the moderated CRNS was 0.042 or 0.047 cm 3 cm −3 , depending on the calibration equation used. Relative SWC dynamics from CRNS were correlated with the in situ probes with a correlation coefficient of 0.91 or 0.87 (depending on calibration equation). We did not find significant relationships between the calibration parameters and stationary site-specific variables. However, the calibration parameters derived from in situ probe SWC dynamics varied over time and were correlated with biomass proxies of cumulative growing degree-day, cumulative growing season index, and bare neutron counts. Future use of the CRNS network can leverage the reliable relative SWC data from network-wide calibration for watershed modeling and continue to research sensitivity of bare neutron measurements to biomass dynamics.

Colorado↗
USGS2019

Monitoring the Riverine Pulse: Applying high-frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes

Widespread deployment of sensors that measure river nitrate (NO3-) concentrations has led to many recent publications in water resources journals including review papers focused on data quality assurance, improved load calculations, and better nutrient management. The principal objective of this paper is to review and synthesize studies of high-frequency NO3- data that have aimed to improve understanding of the hydrologic and biogeochemical processes underlying episodic, diel, and long-term stream NO3- dynamics. Investigations have provided unprecedented detail on hysteresis and flushing patterns during high flow, seasonal variation during baseflow, and responses to multi-year climate variation. Analyses of high-frequency data have led to notable advances in understanding how climate variation affects spatial and temporal NO3- patterns, especially dry-wet cycles and antecedent moisture. Further advances have been limited by few investigations that include high-frequency measurements outside the channel and the short duration of many records. High-frequency data for multiple constituents have provided new insight to the relative roles of hydrology and biogeochemistry as highlighted by studies of the roles of autotrophic uptake, denitrification, riparian evapotranspiration, and temperature-driven changes in viscosity as drivers of diel patterns. Comparisons of short-duration high-frequency data with long-duration low frequency data have described similarities and differences in concentration – discharge patterns and highlighted the role of legacy stores. Investigators have applied innovative analysis approaches not previously possible with low-frequency or temporally-irregular data. Future availability of long-duration high-frequency data will provide new insight to processes, resulting in improved conceptual models and a deeper understanding of the role of climate variation.

WIREs Water↗
USGS2022

Evaluating the efficacy of aerial infrared surveys to detect artificial polar bear dens

The need to balance economic development with impacts to Arctic wildlife has been a prominent subject since petroleum exploration began on the North Slope of Alaska, USA, in the late 1950s. The North Slope region includes polar bears ( Ursus maritimus ) of the southern Beaufort Sea subpopulation, which has experienced a long-term decline in abundance. Pregnant polar bears dig dens in snow drifts during winter and are vulnerable to disturbance, as den abandonment and mortality of neonates may result. Maternal denning coincides with the peak season of petroleum exploration and construction, raising concerns that human activities may disrupt denning. To minimize disturbance of denning polar bears, aerial infrared (AIR) surveys are routinely used to search for dens within planned industry activity areas and that information is used to implement mitigation. Aerial infrared surveys target the heat signature emanating from dens. Despite use by industry for >15 years, the efficacy of AIR and the factors that impact its ability to detect dens remains uncertain. Here, we evaluate AIR using artificial dens and observers naïve to locations to estimate detection probability and its relationship with covariates including weather variables, den characteristics, infrared sensor and altitude, and survey order to identify potential evidence of in-flight observer learning occurring between surveys. In December 2019 we constructed 14 dens (each with an artificial heat source), and 11 control sites (disturbed sites without dens). Between December 2019 and January 2020, 3 survey crews flew 6 independent AIR surveys within the vicinity of dens and control sites and video-recorded AIR imagery. Observers identified putative dens either in flight or during post-flight review of recordings. We assessed detection probability with a simple Bayesian model using 3 subsets of data: 1) all detection/non-detection data; 2) detection/non-detection data restricted to instances where sample sites were confirmed to have been properly scanned by AIR during post-study verification (i.e., when den locations were known); and 3) all dens visible on the recorded imagery during post-study verification, even if they were not seen during the survey or during post-flight review. Subsets 1 and 2 most closely resembled den surveys flown for oil and gas industry and had detection probabilities of 0.15 (95% CI = 0.08–0.23) and 0.24 (95% CI = 0.13–0.37), respectively. Detection probability was 0.41 (95% CI = 0.25–0.58) for subset 3. Higher wind speeds and larger den volume negatively influenced detection probability. Our low detection rate compared to previous studies could partially be the result of differences in study design, such as survey flight patterns. Our results suggest that AIR, as it is currently used, is unlikely to detect most polar bear dens in surveyed areas. Resource managers who use AIR should consider a suite of additional methods (e.g., habitat mapping, probabilistic den distribution, AIR methodology improvements) for minimizing impacts of industry on denning polar bears.

Alaska↗
USGS2005

Multi-scale remote sensing of coral reefs

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

Florida↗
USGS2016

Rotational seismology

Rotational seismology is an emerging study of all aspects of rotational motions induced by earthquakes, explosions, and ambient vibrations. It is of interest to several disciplines, including seismology, earthquake engineering, geodesy, and earth-based detection of Einstein’s gravitation waves. Rotational effects of seismic waves, together with rotations caused by soil–structure interaction, have been observed for centuries (e.g., rotated chimneys, monuments, and tombstones). Figure 1a shows the rotated monument to George Inglis observed after the 1897 Great Shillong earthquake. This monument had the form of an obelisk rising over 19 metres high from a 4 metre base. During the earthquake, the top part broke off and the remnant of some 6 metres rotated about 15° relative to the base. The study of rotational seismology began only recently when sensitive rotational sensors became available due to advances in aeronautical and astronomical instrumentations.

Book chapter↗
USGS2010

The ASTER data system: An overview of the data products in Japan and in the United States

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data system is a cooperative system, which is operated jointly by Japan’s Ministry of Economy, Trade, and Industry (METI) through its Earth Remote Sensing Data Analysis Center (ERSDAC), and by the National Aeronautics and Space Administration (NASA) primarily through its Goddard Space Flight Center (GSFC) and Land Processes (LP) Distributed Active Archive Center (DAAC). ASTER is a moderate-resolution land remote sensing system onboard the Earth Observing System (EOS) Terra spacecraft. ASTER-acquired data are received at the White Sands, New Mexico, ground receiving station, and then transmitted via land network to the EOS Data and Operations System (EDOS) within the Goddard DAAC, located at the GSFC. EDOS pre-processes raw ASTER data to Level-0 (L0) data, and sends them via the high-speed Asia-Pacific Advanced Network (APAN) to the ASTER Ground Data System (GDS) in Japan. ASTER GDS processes the L0 data to level-1 (L1) datasets; they distribute these data to users, and also use them to generate higher-level products for their user community. ASTER GDS sends a copy of all L1A data they produce to NASA’s LP DAAC, located at the U.S. Geological Survey’s Center for Earth Resources Observation and Science (EROS) near Sioux Falls, South Dakota. All L1 data received from Japan are ingested, archived, and available for users at LP DAAC. The LP DAAC also generates and distributes higher-level products from L1 data based on requests from users. To meet time-critical needs related to sensor health and performance, natural disasters, national emergencies, and certain field campaigns, the ASTER Expedited Data System (EDS) was developed, and is operated jointly by U.S. and Japanese partners.

Book chapter↗
USGS2019

S2HM of buildings in USA

The evolution of seismic structural-health monitoring (S 2 HM) of buildings in the USA is described in this chapter, emphasizing real-time monitoring. Rapid and accurate assessment of post-earthquake building damage is of paramount importance to stakeholders (including owners, occupants, city officials, and rescue teams). Relying merely on rapid visual inspection could result in serious damage being missed because it is hidden by building finishes and fireproofing. Absent visible damage to a building’s frame, most steel or reinforced-concrete moment-frame buildings will be green-tagged based on limited visual indications of deformation, such as damage to partitions or glazing. Contrary, uncertainty in judging extent of structural damage may lead an inspector toward a relatively conservative tag, such as a red tag. In such cases, expensive, intrusive, and time-consuming inspections may be recommended to building owners (e.g., following the M w 6.7 1994 Northridge, Calif., earthquake, approximately 300 buildings were subjected to costly inspection of connections (FEMA 352)). Using real-time data-driven computation of drift ratios as the parametric indicator of structural deformation and damage to a structure could be of great value to minimize potential judgmental errors in such assessments. Recorded sensor data are an indication of performance, and performance-based design standards stipulate that the amplitude of relative displacement of a building’s roof (with respect to its base) indicates performance. Establishing sound criteria for performance is the most important issue for S 2 HM process, and since 2000 (in the USA), using real-time computed drift ratios and acceptable threshold criteria form the basis for almost all applications in S 2 HM.

Book chapter↗
USGS2020

Passive seismic survey of sediment thickness, Dasht-e-Nawar basin, eastern Afghanistan

Exploration of water resources is needed for public supply, extraction of mineral resources, and economic development in Afghanistan. Remotely-sensed data are useful for identifying the general nature of surface sediments, however, “boots on the ground” geophysical surveys or drilling programs are needed to quantify the thickness of sediments or aquifers. The nature of such investigations presents a risk to field crews that may prohibit exploration of potentially valuable aquifers or mineral resources. The Dasht-e-Nawar basin, in east-central Afghanistan, contains a 400 km2 playa that includes evaporative mineral deposits, particularly lithium, which has been of interest since the 1970s. However, exploration of the basin, as with many areas of Afghanistan, has been hampered by decades of conflict. In 2014, an investigation of the basin was conducted by the U.S. Department of Defense Task Force for Business and Stability Operations (TFBSO), and their contractor, in cooperation with the U.S. Geological Survey (USGS). For this investigation the USGS compared the results of a rapid passive seismic survey of basin sediment thickness to the results of an independently conducted gravity survey of the same area. Each point measurement for the passive seismic method requires less than 30 minutes in the field by one person. The technique utilizes ambient seismic noise without an external sound source such as required by traditional seismic surveys. Additionally, the technique does not require external sensor arrays, which can be kilometers long for some geophysical techniques. The passive seismic equipment used in this study weighs approximately 1 kilogram and is about 10 cm3 in size. Although relatively new for assessment of sediment thickness, several investigations have found this method to be capable of estimating sediment thicknesses, in the 10’s to 1000 meter range, in settings with unconsolidated sediment over bedrock and a contrast in acoustic impedance. In this investigation, the gravity survey was conducted during a period of 3 weeks by an experienced field crew; required a detailed, centimeter-scale land elevation survey; and required laboratory analyses of sediment and rock densities to interpret the gravity data. In contrast, the passive seismic survey was collected by two inexperienced operators over a period of 8 days and required no additional data to interpret. Due to security restrictions, USGS personnel could not visit the site and the seismic operator was trained immediately prior to the field work. Although the quality of the seismic survey was affected by strong afternoon winds, and by the inexperience of the field operator, the results were fairly comparable to the gravity survey. Similar basin sediment thicknesses and patterns in sediment thickness were identified by both surveys in the basin with an estimated maximum thickness of approximately 170 m. The passive seismic technique required substantially less field resources and time than would be required by other geophysical surveys. Although this method will not be effective in all geologic settings, it may be a valuable assessment tool for use before conducting other, more intensive, geophysical efforts or drilling programs, especially in regions with elevated security risks such as Afghanistan.

Dasht-e-Nawar basin↗
USGS2024

Remote sensing of volcano deformation and surface change

Volcanic unrest and eruptions are associated with surface deformation and landscape change that can be detected, characterized, and tracked via remote sensing measurements. Subsurface processes, including magma accumulation, withdrawal, and transport, can cause displacements at the surface that are best tracked at subaerial volcanoes with interferometric synthetic aperture radar (InSAR) and Global Navigation Satellite System (GNSS) measurements, although non-volcanic activity, like hydrothermal and tectonic sources, can complicate interpretations. Surface change is often associated with the emplacement of volcanic deposits, which modify the landscape and can experience post-emplacement deformation or morphological changes over time. Measurement of surface topography at volcanoes via remote means is a particularly important capability, given the control that topography exerts on many volcanic hazards and the potential for topographic change measurements to provide information about eruption rates. A much broader set of tools is available to investigate surface change at volcanoes, including not only InSAR and GNSS, but also synthetic aperture radar amplitude data, visible imagery, and lidar, acquired from airborne, ground-based, and satellite platforms. These data can also be used to identify instability of volcanic flanks and even have potential for use in detecting airborne ash plumes. Although hidden from traditional airborne and space-based remote sensing, deformation and surface change associated with submarine volcanism can be investigated with pressure sensors and bathymetric measurements—the below-water remote sensing analogs of GNSS and InSAR, respectively.

Book chapter↗
USGS2025

Monitoring lightning and electrification in volcanic plumes

Detection of the electrical discharges and, more fundamentally, the electrification from volcanic plumes is an emerging capability for volcano monitoring. In addition to being able to detect an explosive eruption, such measurements also provide insight into volcanic processes. Volcanic lightning monitoring can be achieved through varying levels of complexity. At the lowest complexity, an observatory can use low-cost global lightning data sets for eruption detection and characterization, while at the highest complexity local lightning sensors can be installed to improve the level of observational detail and response times. This chapter provides background about volcanic lightning and plume electrification, information concerning the relevant instrumentation to detect lightning and electrified plumes, examples of how lightning can be used to understand eruptive processes, and a guide to implementing volcanic lightning detection into a monitoring scheme.

Book chapter↗
USGS2017

Monitoring protocols: Options, approaches, implementation, benefits

Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses—typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today’s monitoring needs are more complex (or multidimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, conceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management’s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.

Book chapter↗
USGS2018

GNIS-LD: Serving and visualizing the Geographic Names Information System Gazetteer as linked data

In this dataset description paper we introduce the GNIS-LD, an authoritative and public domain Linked Dataset derived from the Geographic Names Information System (GNIS) which was developed by the U.S. Geological Survey (USGS) and the U.S. Board on Geographic Names. GNIS provides data about current, as well as historical, physical, and cultural geographic features in the United States. We describe the dataset, introduce an ontology for geographic feature types, and demonstrate the utility of recent linked geographic data contributions made in conjunction with the development of this resource. Co-reference resolution links to GeoNames.org and DBpedia are provided in the form of owl:sameAs relations. Finally, we point out how the adapted workflow is foundational for complex Digital Line Graph (DLG) data from the USGS National Map and how the GNIS-LD data can be integrated with DLG and other data sources such as sensor observations.

Conference Paper↗
USGS2019

Volcano deformation: Insights into magmatic systems

Volcano geodesy is the branch of geodetic science that deals with the changing shapes of volcanoes, whether large or small, deep-seated or surficial. Together with seismicity and volcanic gas flux, deformation of the ground surface can be a key indicator of subsurface conditions and processes at volcanoes—information that not only improves scientific understanding of magmatic systems but also is useful for assessing volcano hazards and mitigating their potential consequences. To take full advantage of such information requires detailed characterization of the deformation field in space and time. Currently, no single geodetic technique is capable of providing both the high spatial and temporal resolution required. However, important advances have been made recently by combining information from real-time in situ sensors such as continuous GPS, strainmeters, and tiltmeters with repeated campaign-style GPS, microgravity, interferometric synthetic aperture radar (InSAR), lidar, and photogrammetric observations. Continuous real-time data constrain the timing but not necessarily the spatial extent and pattern of deformation, whereas InSAR provides detailed spatial information but only at intervals of several days to weeks. Repeated microgravity surveys, when combined with independent measurements of surface height, are uniquely sensitive to changes in subsurface mass distribution and therefore can be used to distinguish among processes driven by magma, hydrous fluids, or gas. Simultaneous analysis of multiple geodetic datasets, especially when guided by information from global volcano databases and numerical simulations of volcanic processes and products, can provide a statistical basis for outcome prediction and serve as a guide for additional observations. In the foreseeable future, interactions among magmatic, tectonic, and hydrothermal systems will be monitored and modeled at regional scale in real time, enabling new insights into Earth’s subsurface environment.

Book chapter↗