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Results for “Journal of Remote Sensing”

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At least 379 records · Page 21Linked to original sources
USGS2015

Calculating crop water requirement satisfaction in the West Africa Sahel with remotely sensed soil moisture

The Soil Moisture Active Passive (SMAP) mission will provide soil moisture data with unprecedented accuracy, resolution, and coverage, enabling models to better track agricultural drought and estimate yields. In turn, this information can be used to shape policy related to food and water from commodity markets to humanitarian relief efforts. New data alone, however, do not translate to improvements in drought and yield forecasts. New tools will be needed to transform SMAP data into agriculturally meaningful products. The objective of this study is to evaluate the possibility and efficiency of replacing the rainfall-derived soil moisture component of a crop water stress index with SMAP data. The approach is demonstrated with 0.1°-resolution, ~10-day microwave soil moisture from the European Space Agency and simulated soil moisture from the Famine Early Warning Systems Network Land Data Assimilation System. Over a West Africa domain, the approach is evaluated by comparing the different soil moisture estimates and their resulting Water Requirement Satisfaction Index values from 2000 to 2010. This study highlights how the ensemble of indices performs during wet versus dry years, over different land-cover types, and the correlation with national-level millet yields. The new approach is a feasible and useful way to quantitatively assess how satellite-derived rainfall and soil moisture track agricultural water deficits. Given the importance of soil moisture in many applications, ranging from agriculture to public health to fire, this study should inspire other modeling communities to reformulate existing tools to take advantage of SMAP data.

Journal of Hydrometeorology↗
USGS2023

Identifying building locations in the wildland–urban interface before and after fires with convolutional neural networks

Background: Wildland–urban interface (WUI) maps identify areas with wildfire risk, but they are often outdated owing to the lack of building data. Convolutional neural networks (CNNs) can extract building locations from remote sensing data, but their accuracy in WUI areas is unknown. Additionally, CNNs are computationally intensive and technically complex, making them challenging for end-users, such as those who use or create WUI maps, to apply. Aims: We identified buildings pre- and post-wildfire and estimated building destruction for three California wildfires: Camp, Tubbs and Woolsey. Methods: We evaluated a CNN-based building dataset and a CNN model from a separate commercial vendor to detect buildings from high-resolution imagery. This dataset and model represent to end-users the state of the art of what is readily available for potential WUI mapping. Key results: We found moderate accuracies for the building dataset and the CNN model and a severe underestimation of buildings and their destruction rates where trees occluded buildings. The CNN model performed best post-fire with accuracies ≥73%. Conclusions: Existing CNNs may be used with moderate accuracy for identifying individual buildings post-fire and mapping the extent of the WUI. The implications are, however, that CNNs are too inaccurate for post-fire damage assessments or building counts in the WUI.

International Journal of Wildland Fire↗
USGS2008

First in situ investigation of a dark wind streak on Mars

Prominent low‐albedo wind streaks issue from embayments at the north end of Victoria crater in Meridiani Planum, the site of surface investigations by the Mars Exploration Rover Opportunity. Opportunity was sent to examine the darkest of these wind streaks and compare it to the adjacent soil in the brighter corridor between the streaks. Two nearby sites inside and outside of the streak were selected for visible and infrared remote sensing and for elemental abundance measurements and images of the microscopic soil morphology. The rover was next sent to study the interaction of the wind streak with an obstacle, a small rock near the source of the streak. Opportunity's observations suggest that the Victoria wind streaks are deposits of basaltic sand blown out of the crater from the dark dunes nestled below the crater rim, particularly at the base of the alcove leading up to the darkest streak. No local sources of sand have been identified within the Victoria crater wall rock, suggesting that the sand is supplied from outside the crater and is presently escaping its temporary topographic trap. This process presents a possible explanation for the serrated margin of Victoria crater through abrasion of the soft rock as trapped sands are blown out of the crater and carve alcoves under various seasonal winds. Some indication of the rate of activity of these wind streaks will be provided by continued monitoring of the erasure of the rover's tracks.

Journal of Geophysical Research E: Planets↗
USGS2022

Animal tracing with sulfur isotopes: Spatial segregation and climate variability in Africa likely contribute to population trends of a migratory songbird

Climatic conditions affect animals but range-wide impacts at the population level remain largely unknown, especially in migratory species. However, studying climate–population relationships is still challenging in small migrants due to a lack of efficient and cost-effective geographic tracking method. Spatial distribution patterns of environmental stable isotopes (so called ‘isoscapes’) generally overcome these limitations but none of the currently available isoscapes provide a substantial longitudinal gradient in species-rich sub-Saharan Africa. In this region, sulphur ( δ 34 S) has not been sufficiently explored on a larger scale. We developed a δ 34 S isoscape to trace animal origins in sub-Saharan Africa by coupling known-origin samples from tracked migratory birds with continental remotely sensed environmental data building on environment– δ 34 S relationships using a flexible machine learning technique. Furthermore, we link population-specific nonbreeding grounds with interannual climatic variation that might translate to breeding population trends. The predicted δ 34 S isotopic map featured east–west and coast-to-inland isotopic gradients and was applied to predict nonbreeding grounds of three breeding populations of Eurasian Reed Warblers Acrocephalus scirpaceus with two distinct migratory phenotypes. Breeding populations as well as migratory phenotypes exhibited large-scale segregation within the African nonbreeding range. These regions also differed substantially in the interannual climatic variation, with higher interannual variability in the eastern part of the range during 2001–2012. Over the same period, the eastern European breeding population seemed to have experienced a more steep decline in population size. The link between migratory patterns and large-scale climatic variability appears important to better understand population trajectories in many declining migratory animals. We believe animal tracing using sulphur isotopes will facilitate these efforts and offers manifold ecological and forensic applications in the biodiversity hotspot of sub-Saharan Africa.

Journal of Animal Ecology↗
USGS2024

Before the fire: Predicting burn severity and potential post-fire debris-flow hazards to Colorado River Cutthroat Trout (Oncorhynchus clarkii pleuriticus) conservation populations

Background Colorado River Cutthroat Trout (CRCT; Oncorhynchus clarkii pleuriticus ) conservation populations may be at risk from wildfire and post-fire debris flows hazards. Aim To predict burn severity and potential post-fire debris flow hazard classifications to CRCT conservation populations before wildfires occur. Methods We used remote sensing, spatial analyses, and machine learning to model 28 wildfire incidents (2016–2020) and spatially predict burn severity from pre-wildfire environmental factors to evaluate the likelihood (%) and volume (m 3 ) hazard classification of post-fire debris flow. Key results Burn severity was best predicted by fuels, followed by topography, physical ecosystem conditions, and weather (mean adjusted R 2 = 0.54). Predictions of high or moderate burn severity covered 1.1 (15% of study area) and 1.5 (19% of study area) million ha, respectively, and varied by watershed. Combined high or moderate debris flow hazard classification included 80% of stream reaches with conservation populations and 97% of conservation population point nodes. Conclusions Predicted burn severity and potential post-fire debris flow indicated moderate to high hazard for CRCT conservation populations native to the Green and Yampa rivers of the Upper Colorado River Basin. Implications Future management actions can incorporate predicted burn severity and potential post-fire debris flow to mitigate impacts to CRCT and other at-risk resource values before a wildfire occurs.

Colorado, Utah, Wyoming↗
USGS2007

Evaluation of Light Detection and Ranging (LIDAR) for measuring river corridor topography

LIDAR is relatively new in the commercial market for remote sensing of topography and it is difficult to find objective reporting on the accuracy of LIDAR measurements in an applied context. Accuracy specifications for LIDAR data in published evaluations range from 1 to 2 m root mean square error (RMSEx,y) and 15 to 20 cm RMSEz. Most of these estimates are based on measurements over relatively flat, homogeneous terrain. This study evaluated the accuracy of one LIDAR data set over a range of terrain types in a western river corridor. Elevation errors based on measurements over all terrain types were larger (RMSEz equals 43 cm) than values typically reported. This result is largely attributable to horizontal positioning limitations (1 to 2 m RMSEx,y) in areas with variable terrain and large topographic relief. Cross-sectional profiles indicated algorithms that were effective for removing vegetation in relatively flat terrain were less effective near the active channel where dense vegetation was found in a narrow band along a low terrace. LIDAR provides relatively accurate data at densities (50,000 to 100,000 points per km 2 ) not feasible with other survey technologies. Other options for projects requiring higher accuracy include low-altitude aerial photography and intensive ground surveying.

Journal of the American Water Resources Associatio↗
USGS2012

A simple method for estimating basin-scale groundwater discharge by vegetation in the basin and range province of Arizona using remote sensing information and geographic information systems

Groundwater is a vital water resource in the arid to semi-arid southwestern United States. Accurate accounting of inflows to and outflows from the groundwater system is necessary to effectively manage this shared resource, including the important outflow component of groundwater discharge by vegetation. A simple method for estimating basin-scale groundwater discharge by vegetation is presented that uses remote sensing data from satellites, geographic information systems (GIS) land cover and stream location information, and a regression equation developed within the Southern Arizona study area relating the Enhanced Vegetation Index from the MODIS sensors on the Terra satellite to measured evapotranspiration. Results computed for 16-day composited satellite passes over the study area during the 2000 through 2007 time period demonstrate a sinusoidal pattern of annual groundwater discharge by vegetation with median values ranging from around 0.3 mm per day in the cooler winter months to around 1.5 mm per day during summer. Maximum estimated annual volume of groundwater discharge by vegetation was between 1.4 and 1.9 billion m 3 per year with an annual average of 1.6 billion m 3 . A simplified accounting of the contribution of precipitation to vegetation greenness was developed whereby monthly precipitation data were subtracted from computed vegetation discharge values, resulting in estimates of minimum groundwater discharge by vegetation. Basin-scale estimates of minimum and maximum groundwater discharge by vegetation produced by this simple method are useful bounding values for groundwater budgets and groundwater flow models, and the method may be applicable to other areas with similar vegetation types.

Arizona↗
USGS2006

Remote sensing studies of the Dionysius region of the Moon

The Dionysius region is located near the western edge of Mare Tranquillitatis and is centered on Dionysius crater, which exhibits a well-developed dark ray system. Proposed origins for these dark rays included impact melt deposits and dark primary ejecta. The region also contains extensive deposits of Cayley-type light plains. Clementine multispectral images and a variety of spacecraft photography were utilized to investigate the composition and origin of geologic units in the Dionysius region. The portions of the dark rays for which spectral and chemical data were obtained are composed of mare debris contaminated with minor amounts of highland material. Both five-point spectra and values of the optical maturity (OMAT) parameter indicate that the dark rays are dominated by mare basalts, not glassy impact melts. The high-albedo rays associated with Dionysius exhibit FeO and TiO2 values that are lower than those of the adjacent dark ray surfaces and OMAT values that indicate that bright ray surfaces are not fully mature. The high-albedo rays are bright largely because of the contrast in albedo between ray material containing highlands-rich ejecta and the adjacent mare-rich surfaces. The mafic debris ejected by Dionysius was derived from a dark, iron-rich unit exposed high on the inner wall of the crater. This layer probably represents a mare deposit that was present at the surface of the preimpact target site. With one possible exception, there is no evidence for buried mare basalts associated with Cayley plains in the region.

Journal of Geophysical Research E: Planets↗
USGS2017

Estimating floodwater depths from flood inundation maps and topography

Information on flood inundation extent is important for understanding societal exposure, water storage volumes, flood wave attenuation, future flood hazard, and other variables. A number of organizations now provide flood inundation maps based on satellite remote sensing. These data products can efficiently and accurately provide the areal extent of a flood event, but do not provide floodwater depth, an important attribute for first responders and damage assessment. Here we present a new methodology and a GIS-based tool, the Floodwater Depth Estimation Tool (FwDET), for estimating floodwater depth based solely on an inundation map and a digital elevation model (DEM). We compare the FwDET results against water depth maps derived from hydraulic simulation of two flood events, a large-scale event for which we use medium resolution input layer (10 m) and a small-scale event for which we use a high-resolution (LiDAR; 1 m) input. Further testing is performed for two inundation maps with a number of challenging features that include a narrow valley, a large reservoir, and an urban setting. The results show FwDET can accurately calculate floodwater depth for diverse flooding scenarios but also leads to considerable bias in locations where the inundation extent does not align well with the DEM. In these locations, manual adjustment or higher spatial resolution input is required.

Journal of the American Water Resources Associatio↗
USGS2014

Time-averaged discharge rate of subaerial lava at Kīlauea Volcano, Hawai‘i, measured from TanDEM-X interferometry: Implications for magma supply and storage during 2011-2013

Differencing digital elevation models (DEMs) derived from TerraSAR add-on for Digital Elevation Measurements (TanDEM-X) synthetic aperture radar imagery provides a measurement of elevation change over time. On the East Rift Zone (EZR) of Kīlauea Volcano, Hawai‘i, the effusion of lava causes changes in topography. When these elevation changes are summed over the area of an active lava flow, it is possible to quantify the volume of lava emplaced at the surface during the time spanned by the TanDEM-X data—a parameter that can be difficult to measure across the entirety of an ~100 km 2 lava flow field using ground-based techniques or optical remote sensing data. Based on the differences between multiple TanDEM-X-derived DEMs collected days to weeks apart, the mean dense-rock equivalent time-averaged discharge rate of lava at Kīlauea between mid-2011 and mid-2013 was approximately 2 m 3 /s, which is about half the long-term average rate over the course of Kīlauea's 1983–present ERZ eruption. This result implies that there was an increase in the proportion of lava stored versus erupted, a decrease in the rate of magma supply to the volcano, or some combination of both during this time period. In addition to constraining the time-averaged discharge rate of lava and the rates of magma supply and storage, topographic change maps derived from space-based TanDEM-X data provide insights into the four-dimensional evolution of Kīlauea's ERZ lava flow field. TanDEM-X data are a valuable complement to other space-, air-, and ground-based observations of eruptive activity at Kīlauea and offer great promise at locations around the world for aiding with monitoring not just volcanic eruptions but any hazardous activity that results in surface change, including landslides, floods, earthquakes, and other natural and anthropogenic processes.

Hawai'i↗
USGS2004

Sequestration of carbon in soil organic matter in Senegal: an overview

Sequestration of Carbon in Soil Organic Matter ( SOCSOM ) in Senegal is a multi-disciplinary development project planned and refined through two international workshops. The project was implemented by integrating a core of international experts in remote sensing, biogeochemical modeling, community socio-economic assessments, and carbon measurements in a fully collaborative manner with Senegal organizations, national scientists, and local knowledge and expertise. The study addresses the potential role developing countries in semi-arid areas can play in climate mitigation activities. Multiple benefits to smallholders could accrue as a result of management practices to re-establish soil carbon content lost because of land use changes or management practices that are not sustainable. The specific importance for the Sahel is because of the high vulnerability to climate change in already impoverished rural societies. The project focuses on four objectives in specific locations across the agroecological zones of Senegal. These objectives are: use of soil sampling and biogeochemical modeling to quantify the biophysical potential for carbon sequestration and to determine the sensitivity of the carbon stocks to various management and climate scenarios, to evaluate the socio-economic and cultural requirements necessary for successful project implementation directed toward an aggregation of smallholders to sequester around 100,000 t carbon (C), to support capacity building to develop a Carbon Specialist Team, and to initiate extrapolation from site-specific project areas to the Sahel region and the national level.

Journal of Arid Environments↗
USGS2026

Estimating basal area change by tree size with Sentinel-2 imagery following four fires in California, USA

Background Failure to account for tree size when estimating burn severity may not accurately capture post-fire tree mortality and post-fire forest structure. Aims We explored whether basal area mortality by tree size class could be determined from remotely-sensed burn severity indices based solely on Sentinel-2 satellite imagery. Methods We used data collected in four large California wildfires to model the relationship between proportional basal area mortality and burn severity indices derived from Sentinel-2 imagery for three tree diameter class thresholds: small (15 to 30 cm), medium (30 to 50 cm) and large (>50 cm). Key results Our models showed that for a given burn severity index value, the proportion of mortality was greater overall in smaller trees, and that the proportion of mortality in large trees changed more slowly than that of smaller trees with changing burn severity index values. Conclusions We found that models that accounted for tree size can more precisely estimate changes in forest size structure than a similar model that did not account for tree size. Implications Explicitly accounting for tree size can improve estimates of post-fire forest structure, including for large trees which make up the bulk of stand biomass and post-fire seed sources.

California↗
USGS2019

Overview of the eruptions of Sinabung Volcano, 2010 and 2013-present, and details of the 2013 phreatomagmatic phase

A small phreatic eruption of Sinabung Volcano, North Sumatra on August 28, 2010, at 18:30 local time marked the first eruption in the past ~1200 years. The eruption took place from two small vents in the south crater area. Explosions and ash emissions from these vents generated multiple ash plumes that reached altitudes of up to 5 km during early- to mid-September. By the end of September, only low level steam plumes were visible and the alert level was reduced from Level 4 (highest) to Level 3. The 2010 eruption effectively ended at this time. Beginning two days after the initial 2010 eruption, activity of the eruption has been monitoring continuously by a telemetered seismic network surrounding the volcano and by remotely sensed observations. This monitoring system was supplemented with a near-field continuous GPS network, beginning in February 2011. Persistent fumarolic emissions continued for almost 3 years following the 2010 eruption, before a new eruption began on 15 September 2013. This eruption continues to the present. The ongoing eruption is divided into 5 major phases: 1) phreatomagmatic phase (July 2013 - 18 December 2013); 2) first dome and collapse phase with pyroclastic density currents (PDCs; block-and-ash flows and related surges) to south (18 December 2013 - 10 January 2014); 3) lava-flow and collapse phase (10 January 2014 - mid-September 2014); 4) second lava dome and collapse phase with PDCs to south (mid-September 2014 - July 2015); 5) lava dome collapse and ash explosion phase with PDCs to southeast and east (August 2015 - present). The volcano erupted intermittently during the early phreatomagmatic phase with small vertical ash explosions. Then the eruption became increasingly vigorous with more repetitive and intense vertical ash explosions during late October through November. The first small pyroclastic density currents (PDCs) began on November 1. These pyroclastic flows descended the southeastern flank to a distance of 2 km.

Journal of Volcanology and Geothermal Research↗
USGS2019

Evaluating the mid-infrared bi-spectral index for improved assessment of low-severity fire effects in a conifer forest

Remote sensing products provide a vital understanding of wildfire effects across a landscape, but detection and delineation of low- and mixed-severity fire remains difficult. While data provided by the Monitoring Trends in Burn Severity project (MTBS) are frequently used to assess severity in the United States, alternative indices can offer improvement to the measurement of low-severity fire effects and should be considered for future product development and adoption. This research note evaluated one such alternative, the mid-infrared bi-spectral index (MIRBI), which was developed in savannah ecosystems to isolate spectral changes caused by burning and reduce noise from other factors. MIRBI, differenced MIRBI (dMIRBI) and burn severity indices used by MTBS were assessed for spectral optimality at distinguishing severity and the ability to separate unburned and burned canopy in a conifer forest. The MIRBI indices were better at isolating changes caused by burning and demonstrated higher spectral separability, particularly at low severity. These findings suggest that MIRBI indices can provide an enhanced alternative or compliment to current MTBS products in high canopy cover forests for applications such as discernment of fire perimeters and unburned islands, as well as identification of low-severity fire effects.

International Journal of Wildland Fire↗
USGS2010

Lake temperature and ice cover regimes in the Alaskan Subarctic and Arctic: Integrated monitoring, remote sensing, and modeling

Lake surface regimes are fundamental attributes of lake ecosystems and their interaction with the land and atmosphere. High latitudes may be particularly sensitive to climate change, however, adequate baselines for these lakes are often lacking. In this study, we couple monitoring, remote sensing, and modeling techniques to generate baseline datasets of lake surface temperature and ice cover in the Alaskan Subarctic and Arctic. No detectable trends were observed during this study period, but a number of interesting patterns were noted among lakes and between regions. The largest Arctic lake was relatively unresponsive to air temperature, while the largest Subarctic lake was very responsive likely because it is fed by glacial runoff. Mean late summer water temperatures were higher than air temperatures with differences ranging from 1.7 to 5.4°C in Subarctic lakes and from 2.4 to 3.2°C in Arctic lakes. The warmest mean summer water temperature in both regions was in 2004, with the exception of Subarctic glacially fed lake that was highest in 2005. Ice-out timing had high coherence within regions and years, typically occurring in late May in Subarctic and in early-July in Arctic lakes. Ice-on timing was more dependent on lake size and depth, often varying among lakes within a region. Such analyses provide an important baseline of lake surface regimes at a time when there is increasing interest in high-latitude water ecosystems and resources during an uncertain climate future.

Journal of the American Water Resources Associatio↗
USGS2015

Evaluating a satellite-based seasonal evapotranspiration product and identifying its relationship with other satellite-derived products and crop yield: A case study for Ethiopia

Satellite-derived evapotranspiration anomalies and normalized difference vegetation index (NDVI) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data are currently used for African agricultural drought monitoring and food security status assessment. In this study, a process to evaluate satellite-derived evapotranspiration (ETa) products with a geospatial statistical exploratory technique that uses NDVI, satellite-derived rainfall estimate (RFE), and crop yield data has been developed. The main goal of this study was to evaluate the ETa using the NDVI and RFE, and identify a relationship between the ETa and Ethiopia’s cereal crop (i.e., teff, sorghum, corn/maize, barley, and wheat) yields during the main rainy season. Since crop production is one of the main factors affecting food security, the evaluation of remote sensing-based seasonal ETa was done to identify the appropriateness of this tool as a proxy for monitoring vegetation condition in drought vulnerable and food insecure areas to support decision makers. The results of this study showed that the comparison between seasonal ETa and RFE produced strong correlation ( R 2 > 0.99) for all 41 crop growing zones in Ethiopia. The results of the spatial regression analyses of seasonal ETa and NDVI using Ordinary Least Squares and Geographically Weighted Regression showed relatively weak yearly spatial relationships ( R 2 < 0.7) for all cropping zones. However, for each individual crop zones, the correlation between NDVI and ETa ranged between 0.3 and 0.84 for about 44% of the cropping zones. Similarly, for each individual crop zones, the correlation ( R 2 ) between the seasonal ETa anomaly and de-trended cereal crop yield was between 0.4 and 0.82 for 76% (31 out of 41) of the crop growing zones. The preliminary results indicated that the ETa products have a good predictive potential for these 31 identified zones in Ethiopia. Decision makers may potentially use ETa products for monitoring cereal crop yields and early warning of food insecurity during drought years for these identified zones.

International Journal of Applied Earth Observation↗
USGS2018

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Colorado Plateau↗
USGS2019

Thermal, deformation, and degassing remote sensing time-series (A.D. 2000-2017) at the 47 most active volcanoes in Latin America: Implications for volcanic systems

Volcanoes are hazardous to local and global populations, but only a fraction are continuously monitored by ground-based sensors. For example, in Latin America, more than 60% of Holocene volcanoes are unmonitored, meaning long-term multi-parameter datasets of volcanic activity are rare and sparse. We use satellite observations of degassing, thermal anomalies, and surface deformation spanning 17 years at 47 of the most active volcanoes in Latin America, and compare these datasets to ground-based observations archived by the Global Volcanism Program (GVP). This first comparison of multi-satellite time-series on a regional scale provides information regarding volcanic behavior during, non-, pre-, syn- and post-eruptive periods. For example, at Copahue volcano, deviations from background activity in all three types of satellite measurements were manifested months to years in advance of renewed eruptive activity in 2012. By quantifying the amount of degassing, thermal output, and deformation measured at each of these volcanoes, we test the classification of these volcanoes as open or closed volcanic systems. We find that ~28% of the volcanoes do not fall into either classification and the rest show elements of both, demonstrating a dynamic range of behavior that can change over time. Finally, we recommend how volcano monitoring could be improved through better coordination of available satellite-based capabilities and new instruments.

Journal of Geophysical Research↗