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At least 1,081 records · Page 60Linked to original sources
USGS2018

Remote measurement of canopy water content in giant sequoias (Sequoiadendron giganteum) during drought

California experienced severe drought from 2012 to 2016, and there were visible changes in the forest canopy throughout the State. In 2014, unprecedented foliage dieback was recorded in giant sequoia ( Sequoiadendron giganteum ) trees in Sequoia National Park, in the southern California Sierra Nevada mountains. Although visible changes in sequoia canopies can be recorded, biochemical and physiological responses to drought stress in giant sequoia canopies are not well understood. Ground-based measurements provide insight into the mechanisms of drought responses in trees, but are often limited to few individuals, especially in trees of tall stature such as giant sequoia. Recent studies demonstrate that remotely measured forest canopy water content (CWC) is a general indicator of canopy response to drought, but the underpinning leaf- to canopy-level causes of observed variation in CWC remain poorly understood. We combined field and airborne remote sensing measurements taken in 2015 and 2016 to assess the biophysical responses of giant sequoias to drought. In 49 study trees, CWC was related to leaf water potential, but not to the other foliar traits, suggesting that changes in CWC were made at whole-canopy rather than leaf scales. We found a non-random, spatially varying pattern in mapped CWC, with lower CWC values at lower elevation and along the outer edges of the groves. This pattern was also observed in empirical measurements of foliage dieback from the ground, and in mapped CWC across multiple sequoia groves in this region, supporting the hypothesis that drought stress is expressed in canopy-level changes in giant sequoias. The fact that we can clearly detect a relationship between CWC and foliage dieback, even without taking into account prior variability or new leaf growth, strongly suggests that remotely sensed CWC, and changes in CWC, are a useful measure of water stress in giant sequoia, and valuable for assessing and managing these iconic forests in drought.

California↗
USGS2016

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH 4 ) emissions in China is important for improving our knowledge on CH 4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH 4 model to quantify the human and climate change induced contributions to natural wetland CH 4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH 4 emissions reduction (0.92 TgCH 4 ), and climate change contributed 20.4% to the CH 4 emissions increase (0.31 TgCH 4 ), suggesting that decreasing CH 4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH 4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH 4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH 4 emissions estimation.

Scientific Reports↗
USGS2021

Testing a generalizable machine learning workflow for aquatic invasive species on Rainbow Trout (Oncorhynchus mykiss) in northwest Montana

Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

Alberta, British Columbia, Montana↗
USGS2019

Validating a time series of annual grass percent cover in the sagebrush ecosystem

We mapped yearly (2000–2016) estimates of annual grass percent cover for much of the sagebrush ecosystem of the western United States using remotely sensed, climate, and geophysical data in regression-tree models. Annual grasses senesce and cure by early summer and then become beds of fine fuel that easily ignite and spread fire through rangeland systems. Our annual maps estimate the extent of these fuels and can serve as a tool to assist land managers and scientists in understanding the ecosystem’s response to weather variations, disturbances, and management. Validating the time series of annual maps is important for determining the usefulness of the data. To validate these maps, we compare Bureau of Land Management Assessment Inventory and Monitoring (AIM) data to mapped estimates and use a leave-one-out spatial assessment technique that is effective for validating maps that cover broad geographical extents. We hypothesize that the time series of annual maps exhibits high spatiotemporal variability because precipitation is highly variable in arid and semiarid environments where sagebrush is native, and invasive annual grasses respond to precipitation. The remotely sensed data that help drive our regression-tree model effectively measures annual grasses’ response to precipitation. The mean absolute error (MAE) rate varied depending on the validation data and technique used for comparison. The AIM plot data and our maps had substantial spatial incongruence, but despite this, the MAE rate for the assessment equaled 12.62%. The leave-one-out accuracy assessment had an MAE of 8.43%. We quantified bias, and bias was more substantial at higher percent cover. These annual maps can help management identify actions that may alleviate the current cycle of invasive grasses because it enables the assessment of the variability of annual grass − percent cover distribution through space and time, as part of dynamic systems rather than static systems.

Rangeland Ecology and Management↗
USGS2012

Crustal magnetic anomalies

This Quadrennial Report departs in format from its predecessors. Earlier reports provided brief descriptions of the entire range of topics on “crustal magnetic anomalies.” Following guidelines established by the AGU, we instead focus our discussion on only a few selected topics. Our abbreviated list of references reflects this restricted focus. A more comprehensive bibliography can be obtained from the authors as noted subsequently. The past quadrennium was distinguished by a significant change in the scale and scope of crustal magnetic anomaly research. Advances in instrumentation, navigation, and data processing techniques resulted in increasing use of two-dimensional anomaly maps rather than profile data. Although maps are commonly used in aeromagnetic studies of continental areas, their use in the ocean basins has been limited until recently. The other important change has been a trend towards increased integration of magnetic anomaly data with gravity, seismic, geochemical, remote sensing, and geologic data to enhance the interpretation and reduce the ambiguity of magnetic field data.

Reviews of Geophysics↗
USGS2021

Estimating the influence of oyster reef chains on freshwater detention at the estuary scale using Landsat-8 imagery

Oyster reef chains grow in response to local hydrodynamics and can redirect flows, particularly when reef chains grow perpendicular to freshwater flow paths. Singularly, oyster reef chains can act as porous dams that may facilitate nearshore accumulation of fresh or low-salinity water, in turn creating intermediate salinities that support oyster growth and estuarine conditions. However, oyster-driven freshwater detention has only been confirmed by limited, point-scale observational data, and simplified models. Oyster reef-driven freshwater detention in real ecosystems at the estuary scale remains largely unexplored. In this study, we analyzed the visible bands in 30-m resolution remote sensing (RS) images recorded by the Operational Land Imager aboard Landsat-8 to characterize the freshwater detention effect of oyster reef chains across a set of hydrologic conditions. Our results support prior findings indicating that 30-m resolution RS images recorded by the Operational Land Imager aboard Landsat-8 are useful for analyzing coastal dynamics after atmospheric correction, despite having been originally designed for terrestrial studies. Statistical models of water-leaving reflectance revealed that freshwater detention by oyster reefs was evident across the estuary, with the greatest effect occurring in the region closest to shore. Additionally, statistical modeling results and spatial patterns apparent in the satellite images suggested that reef-driven freshwater detention occurred under high riverine discharge conditions, but was less evident when flow was low. Beyond offering insight on the potential role of oyster reefs as mediators of estuarine hydrology, this study presents a transferable methodological framework for exploring estuarine biophysical feedbacks in blackwater river estuaries using satellite remote sensing.

Florida↗
USGS2010

Volcanic-ash dispersion modeling of the 2006 eruption of Augustine Volcano using the Puff model

Volcanic ash is one of the major potential hazards from volcanic eruptions. It can have both short-range effects from proximal ashfall and long range impacts from volcanic ash clouds. The timely tracking and understanding of recently emitted volcanic ash clouds is important, because they can cause severe damage to jet aircraft engines and shut down major airports. Dispersion models play an important role in forecasting the movement of volcanic ash clouds by being the only means to predict a clouds' trajectory. Where available, comparisons are possible to both remote-sensing data and observations from the ground and aircraft. This was demonstrated in January 2006, when Augustine Volcano erupted after about a 20-year hiatus. From January 11 to 28, 2006, there were 13 explosive events, with some lasting as long as 11 minutes and producing ash clouds as high as 10-12 km (33,000-39,000 ft) above mean sea level (a.m.s.l). From January 28 to February 4, 2006, there was a more continuous phase, with ash clouds reaching 4-5 km a.m.s.l (13,000-16,000 ft). During the eruption, the Puff dispersion model was used by the Alaska Volcano Observatory for trajectory forecasting of the associated volcanic ash eruption clouds. The six explosive events on January 13 and 14, 2006, were the first time the 'multiple eruptions' capability of the Puff model was used during an eruption response. Here we show the Puff model predictions made during the 2006 Augustine eruption and compare these predictions to satellite remote-sensing data, Next Generation Radar (NEXRAD) radar, and ashfall measurements. In addition, we discuss how automated predictions for volcanoes at elevated alert status provide a quicker assessment of the risk from the potential ash clouds.

Professional Paper↗
USGS2026

Tracking baseflow supply dynamics using SWOT data from small groundwater-dominated lakes

In situ surface-water monitoring strategies are biased towards larger perennial streams and lakes and are generally not designed to track mechanisms of baseflow supply contributed by the dynamic storage of aquifers. Additionally, small (< 1 km 2 ) groundwater-influenced lakes and wetlands globally have little in situ monitoring infrastructure. We explored the utility of remotely sensed Surface Water Ocean Topography Satellite (SWOT) data, collected from 2023 onward, to characterise the seasonal and multi-year water-level trends of groundwater flow-through kettle lakes distributed across the permeable sediments of eastern Massachusetts, USA. This analysis indicated that water levels for kettle lakes with areas down to approximately 0.05 km 2 are resolvable in the study area. Our examination of 17 kettle lakes found that SWOT water-surface elevation data closely tracked groundwater levels in adjacent monitoring wells where available, including the timing of seasonal patterns (highest levels generally in late spring), although there was some variation between years and there was a substantial lag in the timing of high water levels for a lake located downgradient from a 30-m-thick vadose zone. Furthermore, SWOT-observed water-level increases in kettle lakes tracked with baseflow increases in two adjacent groundwater-dominated streams, as would be expected from increased hydraulic gradients. Unlike spectral remote sensing, SWOT data are generally not affected by cloud cover, resulting in a potential for groundwater-dominated lakes to be sentinels of dynamic storage patterns, including identification of baseflow drought lags, which are currently ill-defined hydrological processes. SWOT monitoring of groundwater-influenced surface waters shows potential for augmenting existing monitoring wells and streamgages as continuous monitors of groundwater levels and baseflow supply in permeable terrain.

Massachusetts↗
USGS2021

System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)

Executive Summary This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999. The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “ 2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium ” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.

Open-File Report↗
USGS2018

Climate-related variation in plant peak biomass and growth phenology across Pacific Northwest tidal marshes

The interannual variability of tidal marsh plant phenology is largely unknown and may have important ecological consequences. Marsh plants are critical to the biogeomorphic feedback processes that build estuarine soils, maintain marsh elevation relative to sea level, and sequester carbon. We calculated Tasseled Cap Greenness, a metric of plant biomass, using remotely sensed data available in the Landsat archive to assess how recent climate variation has affected biomass production and plant phenology across three maritime tidal marshes in the Pacific Northwest of the United States. First, we used clipped vegetation plots at one of our sites to confirm that tasseled cap greenness provided a useful measure of aboveground biomass (r 2 = 0.72). We then used multiple measures of biomass each growing season over 20–25 years per study site and developed models to test how peak biomass and the date of peak biomass varied with 94 climate and sea-level metrics using generalized linear models and Akaike Information Criterion (AIC) model selection. Peak biomass was positively related to total annual precipitation, while the best predictor for date of peak biomass was average growing season temperature, with the peak 7.2 days earlier per degree C. Our study provides insight into how plants in maritime tidal marshes respond to interannual climate variation and demonstrates the utility of time-series remote sensing data to assess ecological responses to climate stressors.

Oregon, Washington↗
USGS2016

Forecasting climate change impacts on plant populations over large spatial extents

Plant population models are powerful tools for predicting climate change impacts in one location, but are difficult to apply at landscape scales. We overcome this limitation by taking advantage of two recent advances: remotely sensed, species-specific estimates of plant cover and statistical models developed for spatiotemporal dynamics of animal populations. Using computationally efficient model reparameterizations, we fit a spatiotemporal population model to a 28-year time series of sagebrush ( Artemisia spp.) percent cover over a 2.5 × 5 km landscape in southwestern Wyoming while formally accounting for spatial autocorrelation. We include interannual variation in precipitation and temperature as covariates in the model to investigate how climate affects the cover of sagebrush. We then use the model to forecast the future abundance of sagebrush at the landscape scale under projected climate change, generating spatially explicit estimates of sagebrush population trajectories that have, until now, been impossible to produce at this scale. Our broadscale and long-term predictions are rooted in small-scale and short-term population dynamics and provide an alternative to predictions offered by species distribution models that do not include population dynamics. Our approach, which combines several existing techniques in a novel way, demonstrates the use of remote sensing data to model population responses to environmental change that play out at spatial scales far greater than the traditional field study plot.

Wyoming↗
USGS2007

Color shaded-relief and surface-classification maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

The northeastern part of the National Petroleum Reserve in Alaska (NPRA) has become an area of active petroleum exploration during the past five years. Recent leasing and exploration drilling in the NPRA requires the U.S. Bureau of Land Management (BLM) to manage and monitor a variety of surface activities that include seismic surveying, exploration drilling, oil-field development drilling, construction of oil-production facilities, and construction of pipelines and access roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require rapid compilation and analysis of data pertaining to surface and subsurface geology, hydrology, and biology. In addition, BLM must monitor these activities and assess their impacts on the natural environment. Timely and accurate completion of these land-management tasks requires elevation, hydrologic, geologic, petroleum-activity, and cadastral data, all integrated in digital formats at a higher resolution than is currently available in nondigital (paper) formats. To support these land-management tasks, a series of maps was generated from remotely sensed data in an area of high petroleum-industry activity (fig. 1). The maps cover an area from approximately latitude 70°00' N. to 70°30' N. and from longitude 151°00' W. to 153°10' W. The area includes the Alpine oil field in the east, the Husky Inigok exploration well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a proposed pipeline to carry oil from discovery wells in NPRA to the Alpine oil field. This map area is referred to as the "Fish Creek area" after a creek that flows through the region. The map series includes (1) a color shaded-relief map based on 5-m-resolution data (sheet 1), (2) a surface-classification map based on 30-m-resolution data (sheet 2), and (3) a 5-m-resolution shaded relief-surface classification map that combines the shaded-relief and surface-classification data (sheet 3). Remote sensing datasets that were used to compile the maps include Landsat 7 Enhanced Thematic Mapper+ (ETM+), and interferometric synthetic aperture radar (IFSAR) data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 2005) was used in conjunction with ETM+ and IFSAR data.

Alaska↗
USGS2005

Planning for bird conservation: a tale of two models

Planning for bird conservation has become increasingly reliant on remote sensing, geographical information systems, and, especially, models used to predict the occurrence of bird species as well as their density and demographics. We address the role of such tools by contrasting two models used in bird conservation. One, the Mallard ( Anas platyrhynchos) productivity model, is very detailed, mechanistic, and based on an enormous body of research. The Mallard model has been extensively used with success to guide management efforts for Mallards and certain other species of ducks. The other model, the concept of Bird Conservation Areas, is more simple, less mechanistic, and less well-grounded in research. This concept proposes that large patches of suitable habitat in a proper landscape will be adequate to maintain populations of birds. The Bird Conservation Area concept recently has been evaluated in the northern tallgrass prairie, where its fundamental assumptions have been found not to hold consistently. We argue that a more comprehensive understanding of the biology of individual species, and how they respond to habitat features, will be essential before we can use remotely sensed information and geographic information system products with confidence.

General Technical Report↗
USGS2026

Cyanobacterial bloom occurrence and emergency department visits for asthma or wheeze, Wisconsin, 2017–2019

Background: Cyanobacterial harmful algal blooms (cyanoHABs) pose risks to human and animal health. Methods: We investigated the relationship between cyanoHABs and asthma or wheeze-related emergency department (ED) visits near three Wisconsin cities (Green Bay, Madison, and Oshkosh) during 2017–2019. CyanoHAB exposure was approximated using the Cyanobacterial Assessment Network remotely sensed satellite indicator of cyanobacterial biomass, a chlorophyl algorithm (Chl BS ) aggregated by water-adjacent ZIP Code Tabulation Areas (ZCTA), and distance weighted from the nearest waterbody. Weekly counts of ED visits for asthma or wheeze were aggregated by ZCTA. Poisson generalized linear models estimated the association between the weekly number of ED visits and weekly Chl BS, adjusting for maximum temperature, dewpoint, fine particulate matter (PM 2.5 ), month, and correlation within ZCTA. Results: During 2017–2019, 7,057 ED visits for asthma or wheeze occurred in the study area (42 ZCTAs). Peaks in Chl BS occurred between June and October, with higher values in Lake Winnebago and Lake Mendota compared to Green Bay. Chl BS was not associated with ED visits for asthma or wheeze (adjusted rate ratio = 1.00, 95% confidence interval = 0.99, 1.00), and the presence of onshore winds did not change this result. Monthly aggregations of ED visits and Chl BS showed a monotonic trend between increasing Chl BS and ED visits during July–September. Conclusion: This study demonstrates the utility of remote sensing data in environmental health research. Future studies could explore individual-level exposure and outcomes to refine health risks associated with cyanoHABs.

Wisconsin↗
USGS2017

Velocities of the Pine Island, Thwaites, and smaller glaciers along the Marie Byrd Land coast, West Antarctica

Average velocities for time intervals ranging from < 1 to 15 years were measured by tracking ice-surface patterns on sequential Landsat and European Remote-sensing Satellite synthetic aperture radar images. Velocities ofThwaites Glacier range from 2.2 km a −1 above the grounding line to 3.4 km a −1 at the limit of measurements onThwaites Glacier ice tongue. The glacier increases in velocity by about 1 km a −1 where it crosses the grounding line. Over the period 1984-93, Thwaites Glacier ice tongue accelerated by about 0.6 kin a .Velocities of the floating part of several minor glaciers and some ice shelves are also determined: Land Glacier, 17—1.9 km a −1 ; DeVicq Glacier, 0.7-1.1 km a −1 ; Dotson Ice Shelf 0.2-0.5 km a −1 ; Gctz Ice Shelf, 0.2-0.8 km a −1 ; and Sulzberger Ice Shelf, 0.01 -0.02 km a −1 . The high velocities along the Marie Byrd Land coast are consistent with the high precipitation rates over West Antarctica and, for some of the glaciers, the lack of buttressing ice shelves.

Annals of Glaciology↗
USGS2016

Vegetative response to water availability on the San Carlos Apache Reservation

On the San Carlos Apache Reservation in east-central Arizona, U.S.A., vegetation types such as ponderosa pine forests, pinyon-juniper woodlands, and grasslands have significant ecological, cultural, and economic value for the Tribe. This value extends beyond the tribal lands and across the Western United States. Vegetation across the Southwestern United States is susceptible to drought conditions and fluctuating water availability. Remotely sensed vegetation indices can be used to measure and monitor spatial and temporal vegetative response to fluctuating water availability conditions. We used the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Modified Soil Adjusted Vegetation Index II (MSAVI 2 ) to measure the condition of three dominant vegetation types (ponderosa pine forest, woodland, and grassland) in response to two fluctuating environmental variables: precipitation and the Standardized Precipitation Evapotranspiration Index (SPEI). The study period covered 2002 through 2014 and focused on a region within the San Carlos Apache Reservation. We determined that grassland and woodland had a similar moderate to strong, year-round, positive relationship with precipitation as well as with summer SPEI. This suggests that these vegetation types respond negatively to drought conditions and are more susceptible to initial precipitation deficits. Ponderosa pine forest had a comparatively weaker relationship with monthly precipitation and summer SPEI, indicating that it is more buffered against short-term drought conditions. This research highlights the response of multiple, dominant vegetation types to seasonal and inter-annual water availability. This research demonstrates that multi-temporal remote sensing imagery can be an effective tool for the large scale detection of vegetation response to adverse impacts from climate change and support potential management practices such as increased monitoring and management of drought-affected areas. Different vegetation types displayed various responses to water availability, further highlighting the need for individual management plans for forest and woodland, especially considering the projected drier conditions in the Southwest U.S. and other arid or semi-arid regions around the world.

Arizona↗
USGS2018

An interferometric synthetic aperture radar (InSAR) habitat suitability model to identify overwinter conditions for coregonine whitefishes in Arctic lagoons

Lagoons provide critical habitats for many fishes, including coregonine whitefishes, which are a mainstay in many subsistence fisheries of rural communities in Arctic Alaska. Despite their importance, little is known about the overwintering habits of whitefishes in Arctic Alaska due to the challenges associated with sampling during winter. We developed a habitat suitability (HS) model to understand the potential range of physical conditions that whitefishes experience during the Arctic winter, using three indicator lagoons that represent a range of environmental characteristics. The HS model was built using a three-step approach. First, remote sensing that uses interferometric synthetic aperture radar (InSAR) identified areas of floating and bottomfast ice. Second, through in-field ground-truthing, we confirmed the presence and quality of liquid water (water depth, temperature, and dissolved oxygen) beneath the ice cover. Third, we assessed the suitability of that liquid water as habitat for whitefishes based on published literature and expert interpretation of water quality parameters. InSAR determined that 0, 65.4, and 88.2% of the three lagoons were composed of floating ice corresponding with areas of liquid water beneath a layer of ice. The HS model indicated that all three lagoons had reduced suitability as whitefish habitat in winter than in summer due to the loss of habitat because of the presence of bottomfast ice and a reduction in the quality of liquid water due to cold temperatures, high salinities, and low dissolved oxygen levels. However, only the shallowest lagoon had lethal conditions and zero suitability as whitefish habitat. The methods outlined here provide a simple, cost-effective method to identify habitats that consistently provide critical winter habitat and integrate remote sensing in a HS model framework.

Alaska↗
USGS2021

Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery

Earthquake-induced secondary ground failure hazards, such as liquefaction and landslides, result in catastrophic building and infrastructure damage as well as human fatalities. To facilitate emergency responses and mitigate losses, the U.S. Geological Survey provides a rapid hazard estimation system for earthquake-triggered landslides and liquefaction using geospatial susceptibility proxies and ShakeMap ground motion estimates. However, the resolution and accuracy of these models are often limited by coarse-granularity and large uncertainties of available geospatial features provided at a regional scale. Recently, with the advancement of remote sensing technologies, synthetic aperture radar (SAR) images are captured and analyzed to obtain a rapid estimate of earthquake-induced correlation changes between pre- and post-event images. These correlation changes indicate ground failures and building damage t, showing the potential to provide supplementary information for rapid hazard and loss estimation. However, the exact causes of changes in satellite images are not directly ascertained by the DPM alone. For example, changes could be due to building damage, landslides, liquefaction, noise or any combination thereof. More importantly, the occurrence and intensity of landslides, liquefaction, and building damages are spatially correlated, which makes it yet more challenging to distinguish the sources of any such changes. In this study, we develop a generalized causal graph-based Bayesian Network that models the physical interdependencies between geospatial features, seismic ground failures and building damage, as well as DPMs. Geospatial features provide physical insights for estimating ground failure occurrence while DPMs contain event-specific surface change observations. This physics-informed causal graph incorporate these variables with complex physical relationships in one holistic Bayesian updating scheme to effectively fuse information from both geospatial models and remote sensing data. This framework is scalable and flexible enough to deal with highly complex multi-hazard combinations. We then develop a stochastic variational inference algorithm to jointly update the intractable posterior probabilities of unobserved landslides, liquefaction, and building damage at different locations efficiently. In addition, a local graphical model pruning algorithm is presented to reduce the computational cost of large-scale seismic ground failure estimation. We apply this framework to September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January 2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our algorithm

Conference Paper↗