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USGS2011

Monitoring of urban subsidence with SAR interferometric point target analysis: A case study in Suzhou, China

Ground subsidence, mainly caused by over exploitation of groundwater and other underground resources, such as oil, gas and coal, occurs in many cities in China. The annual direct loss associated with subsidence across the country is estimated to exceed 100 million US dollar. Interferometric SAR (InSAR) is a powerful tool to map ground deformation at an unprecedented level of spatial detail. It has been widely used to investigate the deformation resulting from earthquakes, volcanoes and subsidence. Repeat-pass InSAR, however, may fail due to impacts of spatial decorrelation, temporal decorrelation and heterogeneous refractivity of atmosphere. In urban areas, a large amount of natural stable radar reflectors exists, such as buildings and engineering structures, at which radar signals can remain coherent during a long time interval. Interferometric point target analysis (IPTA) technique, also known as persistent scatterers (PS) InSAR is based on these reflectors. It overcomes the shortfalls in conventional InSAR. This paper presents a procedure for urban subsidence monitoring with IPTA. Calculation of linear deformation rate and height residual, and the non-linear deformation estimate, respectively, are discussed in detail. Especially, the former is highlighted by a novel and easily implemented 2-dimensional spatial search algorithm. Practically useful solutions that can significantly improve the robustness of IPTA, are recommended. Finally, the proposed procedure is applied to mapping the ground subsidence in Suzhou city, Jiangsu province, China. Thirty-four ERS-1/2 SAR scenes are analyzed, and the deformation information over 38,881 point targets between 1992 and 2000 are generated. The IPTA-derived deformation estimates correspond well with leveling measurements, demonstrating the potential of the proposed subsidence monitoring procedure based on IPTA technique. Two shortcomings of the IPTA-based procedure, e.g., the requirement of large number of SAR images and assumed linear plus non-linear deformation model, are discussed as the topics of further research.

Jaingsu Province↗

USGS2013

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

Maine↗

USGS2014

Performance and effects of land cover type on synthetic surface reflectance data and NDVI estimates for assessment and monitoring of semi-arid rangeland

Federal land management agencies provide stewardship over much of the rangelands in the arid andsemi-arid western United States, but they often lack data of the proper spatiotemporal resolution andextent needed to assess range conditions and monitor trends. Recent advances in the blending of com-plementary, remotely sensed data could provide public lands managers with the needed information.We applied the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) to five Landsat TMand concurrent Terra MODIS scenes, and used pixel-based regression and difference image analyses toevaluate the quality of synthetic reflectance and NDVI products associated with semi-arid rangeland. Pre-dicted red reflectance data consistently demonstrated higher accuracy, less bias, and stronger correlationwith observed data than did analogous near-infrared (NIR) data. The accuracy of both bands tended todecline as the lag between base and prediction dates increased; however, mean absolute errors (MAE)were typically ≤10%. The quality of area-wide NDVI estimates was less consistent than either spectra lband, although the MAE of estimates predicted using early season base pairs were ≤10% throughout the growing season. Correlation between known and predicted NDVI values and agreement with the 1:1regression line tended to decline as the prediction lag increased. Further analyses of NDVI predictions,based on a 22 June base pair and stratified by land cover/land use (LCLU), revealed accurate estimates through the growing season; however, inter-class performance varied. This work demonstrates the successful application of the STARFM algorithm to semi-arid rangeland; however, we encourage evaluation of STARFM’s performance on a per product basis, stratified by LCLU, with attention given to the influence of base pair selection and the impact of the time lag.

Idaho, Utah, Wyoming↗

USGS2019

Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30-m data using Random Forest classifier on Google Earth Engine

Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\or small farms with mixed signatures from different crop types and\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R 2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R 2 = 0.95 for South Korea and R 2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281 .

International Journal of Applied Earth Observation↗

USGS2020

Surface soil temperature seasonal variation estimation in a forested area using combined satellite observations and in-situ measurements

Surface soil temperature is the soil temperature from the surface to 10 cm in depth. Surface soil temperature plays a significant role in agricultural drought monitoring, ecosystem energy transfer modeling, and global carbon cycle evaluation. Studies have been proposed to estimate surface soil temperature, but surface soil temperature monitoring within forested areas still poses a significant challenge. In this study, we proposed a surface soil temperature retrieval method using combined satellite observations and in-situ measurements for the Great Dismal Swamp (GDS). The GDS is a U.S. protected area managed and protected by the U.S. Fish and Wildlife Service. It is located along the boundary of Virginia and North Carolina, with maple gum, Atlantic white cedar, and pine pocosin as the main forest cover types. Ground-based surface soil temperature measurements were collected for these forest types from May 2015 to April 2017. Both the Land Remote Sensing Satellite (Landsat) Thermal Infrared Sensor (TIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) carry two thermal infrared (TIR) channels. The TIR channels with similar corresponding wavelengths were first fused using an improved fusing model to generate high resolution TIR measurements. Then the enterprise algorithm was applied to calculate land surface temperature (LST) from the fused TIR bands. An improved soil temperature retrieval method was applied to generate surface soil temperature based on LST and vegetation index (VI) within the study area for the three forest types. In-situ measurements were used to build the surface soil temperature retrieval method, and results were then validated. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were integrated separately as VIs in the model to monitor surface soil temperature. The R 2 for retrieved surface soil temperature through satellite observations was 0.76, and the RMSE was 1.96 ℃ ">℃ ℃ when NDVI was integrated in the model; the R 2 was 0.78, and the RMSE was 1.85 ℃ ">℃ ℃ when EVI was used.

North Carolina, Virginia↗

USGS2021

Remote sensing-based actual evapotranspiration assessment in a data-scarce area of Brazil: A case study of the Urucuia Aquifer System

The large groundwater reserves of the Urucuia Aquifer System (UAS) enabled agricultural development and economic growth in the western Bahia State, in northeastern Brazil. Over the last several years, concern has grown around the aquifer’s diminishing water levels, and water balance (WB) studies are in demand. Considering the lack of measured actual evapotranspiration (ET a ), a major component of the water cycle, this work uses the Operational Simplified Surface Energy Balance (SSEBop) model to estimate ET a , and compares it to basin-scale estimates from the Soil Moisture Accounting Procedure (SMAP) monthly model and from an annual WB closure method, based on gridded meteorological data and the Gravity Recovery and Climate Experiment (GRACE) product. Additionally, a comparative assessment of different versions of the SSEBop parameterization was performed. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery was used to implement eight different versions of the SSEBop algorithm over the UAS between 2000 and 2013. SSEBop and SMAP ET a yielded similar seasonal patterns, with correlation coefficient (r) up to 0.65, mean difference (MD) of 0.8 mm/month and mean absolute difference (MAD) of 18.5 mm/month. Comparison of SSEBop annual ET a estimates to annual SMAP and WB closure estimates yielded low MD (12.1 and −7.3 mm/year, respectively) and MAD (82.5 and 82.8 mm/year, respectively), but also low r values (0.00 and 0.37, respectively). The comparison of the different SSEBop versions indicated the need to incorporate a calibration step of the aerodynamic heat resistance (r ah ) parameter. SSEBop results were also used for land cover and drought monitoring. Analysis indicates that agriculture, associated with an increasing trend of atmospheric evaporative demand, is responsible for the decrease in groundwater levels and streamflow in the studied time period.

Urucuia Aquifer System↗

USGS2022

Bathymetry retrieval from CubeSat image sequences with short time lags

The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R 2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.

International Journal of Applied Earth Observation↗

USGS2013

Reevaluation of lake trout and lake whitefish bioenergetics models

Using a corrected algorithm for balancing the energy budget, we reevaluated the Wisconsin bioenergetics model for lake trout (Salvelinus namaycush) in the laboratory and for lake whitefish (Coregonus clupeaformis) in the laboratory and in the field. For lake trout, results showed that the bioenergetics model slightly overestimated food consumption by the lake trout when they were fed low and intermediate rations, whereas the model predicted food consumption by lake trout fed ad libitum without any detectable bias. The slight bias in model predictions for lake trout on restricted rations may have been an artifact of the feeding schedule for these fish, and we would therefore recommend application of the Wisconsin lake trout bioenergetics model to lake trout populations in the field without any revisions to the model. Use of the Wisconsin bioenergetics model for coregonids resulted in overestimation of food consumption by lake whitefish both in the laboratory and in the field by between 20 and 30%, on average. This overestimation of food consumption was most likely due to overestimation of respiration rate. We therefore adjusted the respiration component of the bioenergetics model to obtain a good fit to the observed consumption in our laboratory tanks. The adjusted model predicted the consumption in the laboratory and the field without any detectable bias. Until a detailed lake whitefish respiration study can be conducted, we recommend application of our adjusted version of the Wisconsin generalized coregonid bioenergetics model to lake whitefish populations in the field.

Journal of Great Lakes Research↗

USGS2019

A comparison of chlorophyll a values obtained from an autonomous underwater vehicle to satellite-based measures for Lake Michigan

Accurate methods to track changes in lake productivity through time and space are critical to fisheries management. Chlorophyll a is the most widely studied proxy for ecosystem primary production, and has been the topic of many studies. The main sources of chlorophyll a measurements are ship-based measures or multi-spectral satellite data. Autonomous underwater vehicles can survey large spatial extents approaching the scale of satellite data, but with the accuracy of ship-based water sampling methods. We use several statistical measures to compare measures of chlorophyll a collected in Lake Michigan with spatiotemporally matched satellite-derived measures of chlorophyll a from the MODIS Aqua multi-spectral sensor using NASA’s OC3 and the Great Lakes Fit algorithms. Our findings show a near one to one relationship between AUV data and both satellite-derived data sets when the AUV data are coarsened to the resolution of the satellite data. A comparison of satellite-based chlorophyll a to AUV-derived chlorophyll summarized in discrete water depth bins suggested that, based on decreasing coefficients of determination, satellite estimates of chlorophyll accounted for the most variability in chlorophyll a concentrations in the upper 10 m of the water column, even though satellite sensors may detect past this depth.

Lake Michigan↗

USGS2016

Uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model at multiple flux tower sites

Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET ( ET o ); and parameters, differential temperature ( dT ), and maximum ET scalar ( K max ), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables ( ET o and LST) and two key parameters ( K max and dT ).

Journal of Hydrology↗

USGS2016

An overview of current applications, challenges, and future trends in distributed process-based models in hydrology

Process-based hydrological models have a long history dating back to the 1960s. Criticized by some as over-parameterized, overly complex, and difficult to use, a more nuanced view is that these tools are necessary in many situations and, in a certain class of problems, they are the most appropriate type of hydrological model. This is especially the case in situations where knowledge of flow paths or distributed state variables and/or preservation of physical constraints is important. Examples of this include: spatiotemporal variability of soil moisture, groundwater flow and runoff generation, sediment and contaminant transport, or when feedbacks among various Earth’s system processes or understanding the impacts of climate non-stationarity are of primary concern. These are situations where process-based models excel and other models are unverifiable. This article presents this pragmatic view in the context of existing literature to justify the approach where applicable and necessary. We review how improvements in data availability, computational resources and algorithms have made detailed hydrological simulations a reality. Avenues for the future of process-based hydrological models are presented suggesting their use as virtual laboratories, for design purposes, and with a powerful treatment of uncertainty.

Journal of Hydrology↗

USGS2021

Machine learning predictions of mean ages of shallow well samples in the Great Lakes Basin, USA

The travel time or “age” of groundwater affects catchment responses to hydrologic changes , geochemical reactions, and time lags between management actions and responses at down-gradient streams and wells. Use of atmospheric tracers has facilitated the characterization of groundwater ages, but most wells lack such measurements. This study applied machine learning to predict ages in wells across a large region around the Great Lakes Basin using well, chemistry, and landscape characteristics. For a dataset of age tracers in 961 samples, the travel time from the land surface to the sample location was estimated for each sample using parametric functions. The mean travel times were then modeled using a gradient boosting machine (GBM) algorithm with cross validation tuning of model metaparameters. The GBM approach was able to closely match estimated ages for the training data (RMSE = 0.26 natural-log scale years) and provided a reasonable match to testing data (RMSE = 0.84). Of the variables tested, well characteristics (e.g. depth), land use, hydrologic indicators (e.g. topographic wetness index), and water chemistry (e.g. nitrate, fluoride, and pH), substantially affected the predictions of age. GBM prediction was applied to 14,335 groundwater samples with median sample depth of 5.4 m, indicating for the Great Lakes Basin a broad distribution of ages among wells with a median of 32.9 years. Lag times of decades are likely for these wells to respond to changing solute fluxes near land surface. While depth variables most strongly affected predicted mean ages, chemical constituents exhibited smooth trends with age, consistent with prevailing conceptual models of evolving sources and geochemistry flowpaths. The results provide proof of concept for use of readily available variables of well, landscape, and chemical characteristics to improve groundwater age estimates across large regions.

Great Lakes basin↗

USGS2025

Multi-lithofacies alluvial characterization via airborne electromagnetic-borehole fusion using ordinary interval kriging and geologic constraints

Airborne electromagnetic (AEM) survey provides extensive spatial coverage and detailed resolution at the near surface and can be used to develop hydrogeological models. However, utilization of AEM data is not straightforward because AEM resistivity is an indirect measurement for inferring sediment types. This study develops an ordinary interval kriging (OIK) algorithm and a resistivity-to-multi-lithofacies (R2ML) data fusion workflow for multi-lithofacies alluvial characterization. OIK utilizes irregular interval data to construct three-dimensional (3D) resistivity fields from one-dimensional inverted AEM resistivity models. The R2ML workflow maps the resistivity field generated from OIK into a multi-facies lithological model, incorporating geologic constraints derived from well logs and geological observations. The numerical and real-world cases demonstrate that OIK is computationally efficient, accounts for 3D anisotropy, and minimizes the smoothing effect, thereby preserving resistivity contrasts and reducing interpolation uncertainty. The methodology is applied to lithologic characterization of the Mississippi River Valley alluvial aquifer (MRVA) in the Shellmound area, Mississippi, U.S. A frequency-domain AEM survey was conducted to support groundwater studies for the managed aquifer recharge (MAR) to the MRVA. The resulting lithological model, including four types of lithofacies—clay, very fine sand, fine-medium sands, and graveliferous sands, illustrates the geomorphological processes of the MRVA and implies potential MAR. The alignment between the lithological model and existing geological and hydrogeological investigations demonstrates that OIK and R2ML workflow effectively capture the subsurface architecture of the MRVA. The methods have broad applicability for characterizing alluvial aquifers through AEM-borehole data fusion, supporting sustainable groundwater management.

Mississippi↗

USGS2005

Seismicity and tilt associated with the 2003 Anatahan eruption sequence

On May 10, 2003, the first historical eruption of Anatahan volcano in the western Pacific Mariana Islands was fortuitously recorded by a broadband seismograph installed on the island only 4 days prior to the eruption. This station, located 7 km WNW of the active crater, together with another broadband seismograph on Sarigan Island 45 km to the north, continued to operate throughout the 2-month period of major eruptive activity in May and June and throughout the majority of the following year. In June 2003, the Saipan Emergency Management Office and the US Geological Survey installed two telemetered high-gain short-period seismic stations to monitor the activity in real-time. The only earthquakes detected in the 4-day period from the initial seismograph installation until 6 h prior to the eruption occurred approximately 20 km to the northeast of the island on May 8. The first volcano-tectonic (VT) event located near the volcano occurred at 01:53 GMT on May 10. The number of events per hour then increased dramatically and a period of about 80 discrete earthquakes per hour commenced at about 06:20 GMT, immediately prior to the estimated eruption time of 07:30 from the Volcanic Ash Advisory Center. A long-period tilt signal recorded on the horizontal components of the broadband seismograph, indicating upward movement of the crater region, also commenced at about 06:20. Inflation continued until 09:30, when the direction of tilt reversed. Deflation continued until 17:50, coinciding with a reduction in the number of VT events. The larger VT events were located with a linearized least-squares location algorithm. Magnitudes of located VT events on May 10 ranged from 2.0 to 3.2, but a period of larger VT events were recorded on May 11, with the largest M 4.2. After about 36 h of intense earthquake activity, the number of discrete VT events declined and was replaced by nearly continuous volcanic tremor for the next 6 weeks. Differing types of very long-period events may suggest complex non-destructive magmatic source mechanisms, persisting dominantly throughout the first 10 days of the initial eruption. Visual reports indicate that a small craggy dome extruded sometime between May 20 and June 5. From analogy with other volcanic dome extrusions, we believe the dome probably extruded just as the tremor amplitude decreased dramatically about May 24. This dome was then destroyed between June 13 and 14. Reduced displacement of the co-eruption tremor is estimated as approximately 40-80 cm2, suggesting an eruption with a Volcanic Explosivity Index of about 3. 2005 Elsevier B.V. All rights reserved.

Northern Mariana Islands↗

USGS2015

An automated SO2 camera system for continuous, real-time monitoring of gas emissions from Kīlauea Volcano's summit Overlook Crater

SO 2 camera systems allow rapid two-dimensional imaging of sulfur dioxide (SO 2 ) emitted from volcanic vents. Here, we describe the development of an SO 2 camera system specifically designed for semi-permanent field installation and continuous use. The integration of innovative but largely “off-the-shelf” components allowed us to assemble a robust and highly customizable instrument capable of continuous, long-term deployment at Kīlauea Volcano's summit Overlook Crater. Recorded imagery is telemetered to the USGS Hawaiian Volcano Observatory (HVO) where a novel automatic retrieval algorithm derives SO 2 column densities and emission rates in real-time. Imagery and corresponding emission rates displayed in the HVO operations center and on the internal observatory website provide HVO staff with useful information for assessing the volcano's current activity. The ever-growing archive of continuous imagery and high-resolution emission rates in combination with continuous data from other monitoring techniques provides insight into shallow volcanic processes occurring at the Overlook Crater. An exemplary dataset from September 2013 is discussed in which a variation in the efficiency of shallow circulation and convection, the processes that transport volatile-rich magma to the surface of the summit lava lake, appears to have caused two distinctly different phases of lake activity and degassing. This first successful deployment of an SO 2 camera for continuous, real-time volcano monitoring shows how this versatile technique might soon be adapted and applied to monitor SO 2 degassing at other volcanoes around the world.

Hawai'i↗

USGS2018

A retrospective look at the February 1993 east rift zone intrusion at Kīlauea volcano, Hawaii

The February 1993 dike intrusion in the East Rift Zone (ERZ) of Kīlauea Volcano, Hawai'i, was recognized from tilt and seismic data, but ground-based geodetic data were too sparse to constrain the characteristics of the intrusion. Analysis of Interferometric Synthetic Aperture Radar (InSAR) from the Japan Aerospace Exploration Agency (JAXA) JERS-1 satellite reveals a maximum of ~30 cm of line-of-sight (LOS) displacement occurring near Makaopuhi Crater in the middle ERZ of Kīlauea. We model this deformation signal as a subvertical dike using a 3D-Mixed Boundary Element Method (3D-MBEM) paired with a nonlinear inversion algorithm to find the best-fit model. The best-fit dike is located just to the west of Makaopuhi Crater striking N50°W, extends to within 100 m of the surface, is ~1.3 km in length by ~4.2 km in width along strike, and has a total volume of ~7.4 × 10 6 m 3 . In addition, a post-intrusion interferogram from JERS-1 spanning 1993–1997 was analyzed. Guided by previous results, our model for the 4-year period consists of opening of the deep rift zones by about 0.5 m at 3–8.5 km depth beneath the Southwest Rift Zone (SWRZ), ERZ and the summit. A sub-horizontal detachment fault is connected to the seaward side of the vertical dike-like source to mimic the décollement known to exist beneath the volcano. We classify the 1993 dike intrusion as a passive intrusion similar to those that occurred in 1997 and 1999. Passive intrusions lack precursory inflation at Kīlauea's summit, and the likely triggering mechanism is persistent deep rift opening combined with seaward motion of the south flank along the basal décollement. Passive intrusions make forecasting and hazard assessment difficult since they are not preceded by inflation nor by large increases in seismicity.

Hawaii↗

USGS2022

Rock alteration mapping in and around fossil shallow intrusions at Mt. Ruapehu New Zealand with laboratory and aerial hyperspectral imaging

Diagnostic absorption features in hyperspectral data can be used to identify a specific mineral or mineral associations. However, it is unknown how accurate hyperspectral mapping can be for identifying alteration mineral compositions at the resolution required to describe structures such as fossil intrusions, or whether it can accurately quantify the alteration present. This study compared petrographic observation with visible, near-infrared (VNIR), and shortwave infrared (SWIR) hyperspectral remote sensing at laboratory- (centimetre-scale) and aerial- (metre-scale) scales to characterise the abundance of surface hydrothermal rock alteration in and around a shallow fossil intrusion on Pinnacle Ridge, Mt. Ruapehu, New Zealand. We classified a high-resolution aerial hyperspectral image to develop a new surface alteration map using Spectral Angle Mapper (SAM) algorithm. The petrographic thin-section and the laboratory and aerial hyperspectral imaging revealed a spectrum of hydrous alteration phases as indicated by the presence of an absorption feature at 2207 nm. Moderate correlation exists between the depth of the absorption feature at 2207 nm and the point counting-derived alteration percent values, indicating reliability of laboratory-based hyperspectral analytical methods. In contrast, aerial hyperspectral data failed to provide any clear correlations to field-mapped alteration using a band-depth approach, and we interpret this due to ‘oversampling’ of surface (supergene) alteration, spectral mixing, and sensor limitations (e.g., bandwidth, signal-to-noise ratio). The hyperspectral image-derived alteration map, created using supervised image classification, can loosely be translated to a geotechnical map where porosity and permeability play a major role in localizing hydrothermal fluid flow and the formation of alteration mineral associations.

Mt. Ruapehu↗

USGS2012

Determination of sediment provenance by unmixing the mineralogy of source-area sediments: The "SedUnMix" program

Along the margins of areas such as Greenland and Baffin Bay, sediment composition reflects a complex mixture of sources associated with the transport of sediment in sea ice, icebergs, melt-water and turbidite plumes. Similar situations arise in many contexts associated with sediment transport and with the mixing of sediments from different source areas. The question is: can contributions from discrete sediment (bedrock) sources be distinguished in a mixed sediment by using mineralogy, and, if so, how accurately? To solve this problem, four end-member source sediments were mixed in various proportions to form eleven artificial mixtures. Two of the end-member sediments are felsic, and the other two have more mafic compositions. End member and mixed sediment mineralogies were measured for the < 2 mm sediment fractions by quantitative X-ray diffraction (qXRD). The proportions of source sediments in the mixtures then were calculated using an Excel macro program named SedUnMix, and the results were evaluated to determine the robustness of the algorithm. The program permits the unmixing of up to six end members, each of which can be represented by up to 5 alternative compositions, so as to better simulate variability within each source region. The results indicate that we can track the relative percentages of the four end members in the mixtures. We recommend, prior to applying the technique to down-core or to other provenance problems, that a suite of known, artificial mixtures of sediments from probable source areas be prepared, scanned, analyzed for quantitative mineralogy, and then analyzed by SedUnMix to check the sensitivity of the method for each specific unmixing problem.

Baffin Bay↗