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USGS2016

Dynamic reusable workflows for ocean science

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Journal of Marine Science and Engineering↗

USGS2013

Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+

The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed ( Arundo donax ), Lehmann lovegrass ( Eragrostis lehmanniana ), horehound ( Marrubium vulgare ) and buffelgrass ( Pennisteum ciliare ) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from <1.0 to 18%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with <50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land managers.

Texas↗

USGS2024

Remote sensing evapotranspiration in ensemble-based framework to enhance cascade routing and re-infiltration concept in integrated hydrological model applied to support decision making

Integrated hydrological models (IHMs) help characterize the complexity of surface–groundwater interactions. The cascade routing and re-infiltration (CRR) concept, recently applied to a MODFLOW 6 IHM, improved conceptualization and simulation of overland flow processes. The CRR controls the transfer of rejected infiltration and groundwater exfiltration from upslope areas to adjacent downslope areas where that water can be evaporated, re-infiltrated back to subsurface, or discharged to streams as direct runoff. The partitioning between these three components is controlled by uncertain parameters that must be estimated. Thus, by quantifying and reducing those uncertainties, next to uncertainties of the other model parameters (e.g. hydraulic and storage parameters), the reliability of the CRR is improved and the IHM is better suited for decision support modelling, the two key objectives of this work. To this end, the remotely sensed MODIS-ET product was incorporated into the calibration process for complementing traditional hydraulic head and streamflow observations. A total of approximately 150,000 observations guided the calibration of a 13-year MODFLOW 6 IHM simulation of the Sardon catchment (Spain) with daily stress periods. The model input uncertainty was represented by grid-cell-scale parameterization, yielding approximately 500,000 unknown input parameters to be conditioned. The calibration was carried out through an iterative ensemble smoother. Incorporating the MODIS-ET data improved the CRR implementation, and reduced uncertainties associated with other model parameters. Additionally, it significantly reduced the uncertainty associated with net recharge, a critical flux for water management that cannot be directly measured and rather is commonly estimated by IHM simulations.

Sardon catchment↗

USGS2021

Developing a strategy for the national coordinated soil moisture monitoring network

Soil moisture is a critical land surface variable, affecting a wide variety of climatological, agricultural, and hydrological processes. Determining the current soil moisture status is possible via a variety of methods, including in situ monitoring, remote sensing, and numerical modeling. Although all of these approaches are rapidly evolving, there is no cohesive strategy or framework to integrate these diverse information sources to develop and disseminate coordinated national soil moisture products that will improve our ability to understand climate variability. The National Coordinated Soil Moisture Monitoring Network initiative has developed a national strategy for network coordination with NOAA's National Integrated Drought Information System. The strategy is currently in review within NOAA, and work is underway to implement the initial milestones of the strategy. This update reviews the goals and steps being taken to establish this national-scale coordination for soil moisture monitoring in the United States.

Vadose Zone Journal↗

USGS2018

Estimating post-fire debris-flow hazards prior to wildfire using a statistical analysis of historical distributions of fire severity from remote sensing data

Following wildfire, mountainous areas of the western United States are susceptible to debris flow during intense rainfall. Convective storms that can generate debris flows in recently burned areas may occur during or immediately after the wildfire, leaving insufficient time for development and implementation of risk mitigation strategies. We present a method for estimating post-fire debris-flow hazards prior to wildfire using historical data to define the range of potential fire severities for a given location based on the statistical distribution of severity metrics obtained from remote sensing. Estimates of debris-flow likelihood, magnitude, and triggering rainfall threshold based upon the statistically simulated fire severity data provide hazard predictions consistent with those calculated from fire severity data collected after wildfire. Simulated fire severity data also produce hazard estimates that replicate observed debris-flow occurrence, rainfall conditions, and magnitude at a monitored site in the San Gabriel Mountains of southern California. Future applications of this method should rely upon a range of potential fire severity scenarios for improved pre-fire estimates of debris-flow hazard. The method presented here is also applicable to modeling other post-fire hazards, such as flooding and erosion risk, and for quantifying trends in observed fire severity in a changing climate.

California, Colorado, Washington↗

USGS1991

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

International Journal of Geographical Information ↗

USGS2013

Possible return of Acropora cervicornis at Pulaski Shoal, Dry Tortugas National Park, Florida

Seabed classification is essential to assessing environmental associations and physical status in coral reef ecosystems. At Pulaski Shoal in Dry Tortugas National Park, Florida, nearly continuous underwater-image coverage was acquired in 15.5 hours in 2009 along 70.2 km of transect lines spanning ~0.2 km 2 . The Along-Track Reef-Imaging System (ATRIS), a boat-based, high-speed, digital imaging system, was used. ATRIS-derived benthic classes were merged with a QuickBird satellite image to create a habitat map that defines areas of senile coral reef, carbonate sand, seagrasses, and coral rubble. This atypical approach of starting with extensive, high-resolution in situ imagery and extrapolating between transect lines using satellite imagery leverages the strengths of each remote-sensing modality. The ATRIS images also captured the spatial distribution of two species once common on now-degraded Florida-Caribbean coral reefs: the stony staghorn coral Acropora cervicornis , a designated threatened species, and the long-spined urchin Diadema antillarum . This article documents the utility of ATRIS imagery for quantifying number and estimating age of A. cervicornis colonies ( n = 400, age range, 5–11 y) since the severe hypothermic die-off in the Dry Tortugas in 1976–77. This study is also the first to document the largest number of new colonies of A. cervicornis tabulated in an area of the park where coral-monitoring stations maintained by the Fish and Wildlife Research Institute have not been established. The elevated numbers provide an updated baseline for tracking revival of this species at Pulaski Shoal.

Florida↗

USGS2016

A synthesis of the basal thermal state of the Greenland Ice Sheet

The basal thermal state of an ice sheet (frozen or thawed) is an important control upon its evolution, dynamics and response to external forcings. However, this state can only be observed directly within sparse boreholes or inferred conclusively from the presence of subglacial lakes. Here we synthesize spatially extensive inferences of the basal thermal state of the Greenland Ice Sheet to better constrain this state. Existing inferences include outputs from the eight thermomechanical ice-flow models included in the SeaRISE effort. New remote-sensing inferences of the basal thermal state are derived from Holocene radiostratigraphy, modern surface velocity and MODIS imagery. Both thermomechanical modeling and remote inferences generally agree that the Northeast Greenland Ice Stream and large portions of the southwestern ice-drainage systems are thawed at the bed, whereas the bed beneath the central ice divides, particularly their west-facing slopes, is frozen. Elsewhere, there is poor agreement regarding the basal thermal state. Both models and remote inferences rarely represent the borehole-observed basal thermal state accurately near NorthGRIP and DYE-3. This synthesis identifies a large portion of the Greenland Ice Sheet (about one third by area) where additional observations would most improve knowledge of its overall basal thermal state.

Journal of Geophysical Research F: Earth Surface↗

USGS2019

Confronting uncertainty: Contributions of the wildlife profession to the broader scientific community

Most wildlife professionals are engaged in 1 or both of 2 basic endeavors: science and management. These endeavors are a focus of many other disciplines, leading to widespread sharing of general methodologies. Wildlife professionals have appropriately borrowed and assimilated many methods developed primarily in other disciplines but have also led the development of one class of quantitative methods, those that confront and incorporate uncertainty. Uncertainty arises in counts of focal entities, for which wildlife professionals have developed effective methods to deal with the common problems of nondetection and misclassification. These methods have been borrowed by disciplines as varied as paleobiology, medicine, human epidemiology, industrial quality control, military target acquisition, remote sensing, and human census. Uncertainty also arises in the modeling of those counts, specifically the observation and ecological processes that generated them. Wildlife professionals recognized the fundamental importance of model selection and rapidly assimilated methods for selecting the most appropriate model for a given data set. These methods for dealing with uncertainty inherent to counting and modeling are critical to the conduct of science and management. Wildlife professionals have developed additional methods for incorporating uncertainty in the accumulation of knowledge and the development of optimal decisions in an environment of learning. In some cases, professionals in other disciplines are using methods developed and popularized in the wildlife profession, but there is much potential for greater use. In this essay, I describe these areas of wildlife leadership, document their assimilation by other disciplines, and emphasize the potential for more interdisciplinary use of these methods.

Journal of Wildlife Management↗

USGS2019

Monitoring, forecasting collapse events, and mapping pyroclastic deposits at Sinabung volcano with satellite imagery

During the ongoing (2013–present) eruption of Sinabung volcano, north Sumatra, we have routinely used a variety of satellite remote sensing data to observe and forecast lava dome and lava flow collapse events, to map the resulting pyroclastic deposits, and to estimate effusion rates. In this paper, we focus on the first two years of the current eruption (September 2013–December 2015), and we summarize major events in 2016. We divide the eruption into 5 major phases: 1) phreatomagmatic (July 2013–18 December 2013), 2) first dome growth and collapse (18 December 2013–10 January 2014), 3) lava-flow (10 January 2014–mid-September 2014), 4) second lava dome and collapse (mid-September 2014–July 2015), 5) lava dome collapse and ash explosion phase (August 2015–present). Throughout the eruption, remotely sensed information has been instrumental in assessing the stability of the lava dome and flow and to forecast collapse events that produce pyroclastic density currents (PDCs: block-and-ash flows, co-ignimbrite surges, and blasts). Forecasts based on remote sensing data in combination with seismic, geodetic and gas-monitoring data have also helped inform decisions related to alert levels and evacuations. Relatively unusual aspects of the Sinabung eruption include the transition from dome to flow morphology (phase 2 to phase 3 transition) and the frequent occurrence during phase 3 of collapses from the lava flow-front and flow-margins—collapses that produced extensive pyroclastic density currents. By analogy to the well-known “Merapi type” collapses and pyroclastic deposits, we propose that lava flow-front and flow-margin collapses with associated PDCs be known as “Sinabung type.” Although detailed study of deposits has not been possible due to continuing hazards, our observations suggest that the transition from lava dome to lava flow and the occurrence of flow-front and flow-margin collapses reflect a particular combination of lava viscosity and steepness of slope. Our observations also show clear evidence of at least one slope-parallel high-velocity and dilute PDC (a “blast”) that emanated from a lava-margin collapse site 500 m downslope from the vent. This 1 February 2014 blast downed and singed a forest out to at least 3.9 km from the collapse site and killed 16 people. We also use a combination of field and remotely sensed data to map the distribution of Sinabung deposits. We estimate eruptive volumes and extrusion rates by combining sequential measurements of lava surface and pyroclastic flow areas with thickness estimates derived from simple geometric assumptions, oblique photographs and Digital Elevation Models (DEMs) derived from remotely sensed data. Our estimates of short-term effusion rates vary widely on a daily to weekly basis, from <1 to >20 m 3 s −1 . In a few cases, periods of increased extrusion precede lava flow-front collapses by a few days to a week, suggesting delays in transmittance of effusion pulses as lava moves from vent to flow front. We find that, as of 1 January 2016, the total area of deposits is 10 7 m 2 , and their approximate deposit volume is about 0.3 km 3 , equivalent to 0.2 km 3 Dense Rock Equivalent (DRE). We anticipate that our deposit maps will be valuable in the future as a framework for the study of the magmatic and textural evolution of eruptive products through time.

Sinabung volcano↗

USGS1987

The Badain Jaran Desert: Remote sensing investigations.

Approximately half the Badain Jaran Desert in the north-western Alashan Plain of northern China is a sand sea. The remainder is gravel or bedrock. The north-western border of the desert is a playa. The desert has been imaged by both Landsat and the Shuttle Imaging Radar (SIR-A). Landsat analysis indicates there are two dune patterns in the sand sea, north-east oriented crescentic dunes along the northern and western borders, and complex star dunes in the central and eastern desert. Although the orientation and morphology of the dunes are easily visible on Landsat, they cannot be determined with the radar image obtained from the aspect angle used during the SIR-A mission. An abrupt change in wavelength of the dune pattern near the Badain Jaran Playa is mappable on Landsat, but not seen on the radar image. The playa appears to be considerably larger on radar than on the Landsat, and we may be seeing subsurface penetration of dry surficial sands with the radar. Archaeological evidence suggests the playa was the location of prehistoric and historic human activity. SIR-A data indicate the playa was formerly a considerably larger inland lake.

Badain Jaran Desert↗

USGS2000

Characteristic length scale of input data in distributed models: implications for modeling grain size

The appropriate spatial scale for a distributed energy balance model was investigated by: (a) determining the scale of variability associated with the remotely sensed and GIS-generated model input data; and (b) examining the effects of input data spatial aggregation on model response. The semi-variogram and the characteristic length calculated from the spatial autocorrelation were used to determine the scale of variability of the remotely sensed and GIS-generated model input data. The data were collected from two hillsides at Upper Sheep Creek, a sub-basin of the Reynolds Creek Experimental Watershed, in southwest Idaho. The data were analyzed in terms of the semivariance and the integral of the autocorrelation. The minimum characteristic length associated with the variability of the data used in the analysis was 15 m. Simulated and observed radiometric surface temperature fields at different spatial resolutions were compared. The correlation between agreement simulated and observed fields sharply declined after a 10×10 m 2 modeling grid size. A modeling grid size of about 10×10 m 2 was deemed to be the best compromise to achieve: (a) reduction of computation time and the size of the support data; and (b) a reproduction of the observed radiometric surface temperature.

Idaho↗

USGS2014

Spatial variability and landscape controls of near-surface permafrost within the Alaskan Yukon River Basin

The distribution of permafrost is important to understand because of permafrost's influence on high-latitude ecosystem structure and functions. Moreover, near-surface (defined here as within 1 m of the Earth's surface) permafrost is particularly susceptible to a warming climate and is generally poorly mapped at regional scales. Subsequently, our objectives were to (1) develop the first-known binary and probabilistic maps of near-surface permafrost distributions at a 30 m resolution in the Alaskan Yukon River Basin by employing decision tree models, field measurements, and remotely sensed and mapped biophysical data; (2) evaluate the relative contribution of 39 biophysical variables used in the models; and (3) assess the landscape-scale factors controlling spatial variations in permafrost extent. Areas estimated to be present and absent of near-surface permafrost occupy approximately 46% and 45% of the Alaskan Yukon River Basin, respectively; masked areas (e.g., water and developed) account for the remaining 9% of the landscape. Strong predictors of near-surface permafrost include climatic indices, land cover, topography, and Landsat 7 Enhanced Thematic Mapper Plus spectral information. Our quantitative modeling approach enabled us to generate regional near-surface permafrost maps and provide essential information for resource managers and modelers to better understand near-surface permafrost distribution and how it relates to environmental factors and conditions.

Alaska↗

USGS2023

A river basin spatial model to quantitively advance understanding of riverine tree response dynamics to water availability and hydrological management

Ecological condition continues to decline in arid and semi-arid river basins globally due to hydrological over-abstraction combined with changing climatic conditions. Whilst provision of water for the environment has been a primary approach to alleviate ecological decline, how to accurately monitor changes in riverine trees at fine spatial and temporal scales, remains a substantial challenge. This is further complicated by constantly changing water availability across expansive river basins with varying climatic zones. Within, we combine rare, fine-scale, high frequency temporal in-situ field collected data with machine learning and remote sensing, to provide a robust model that enables broadscale monitoring of physiological tree water stress response to environmental changes via actual evapotranspiration (ET). Physiological variation of Eucalyptus camaldulensis (River Red Gum) and E. largiflorens (Black Box) trees across 10 study locations in the southern Murray-Darling Basin, Australia, was captured instantaneously using sap flow sensors, substantially reducing tree response lags encountered by monitoring visual canopy changes. Actual ET measurement of both species was used to bias correct a national spatial ET product where a Random Forest model was trained using continuous timeseries of in-situ data of up to four years. Precise monthly AMLETT ( A ustralia-wide M achine L earning ET for T rees) ET outputs in 30 m pixel resolution from 2012 to 2021, were derived by incorporating additional remote sensing layers such as soil moisture, land surface temperature, radiation and EVI and NDVI in the Random Forest model. Landsat and Sentinal-2 correlation results between in-situ ET and AMLETT ET returned R 2 of 0.94 (RMSE 6.63 mm period −1 ) and 0.92 (RMSE 6.89 mm period −1 ), respectively. In comparison, correlation between in-situ ET and a national ET product returned R 2 of 0.44 (RMSE 34.08 mm period −1 ) highlighting the need for bias correction to generate accurate absolute ET values. The AMLETT method presented here, enhances environmental management in river basins worldwide. Such robust broadscale monitoring can inform water accounting and importantly, assist decisions on where to prioritize water for the environment to restore and protect key ecological assets and preserve floodplain and riparian ecological function.

The Journal of Environmental Management↗

USGS2020

Integrating perspectives to understand lake ice dynamics in a changing world

Ice cover plays a critical role in physical, biogeochemical, and ecological processes in lakes. Despite its importance, winter limnology remains relatively understudied. Here, we provide a primer on the predominant drivers of freshwater lake ice cover and the current methodologies used to study lake ice, including in situ and remote sensing observations, physical based models, and experiments. We highlight opportunities for future research by integrating these four disciplines to address key knowledge gaps in our understanding of lake ice dynamics in changing winters. Advances in technology, data integration, and interdisciplinary collaboration will allow the field to move toward developing global forecasts of lake ice cover for small to large lakes across broad spatial and temporal scales, quantifying ice quality and ice thickness, moving from binary to continuous ice records, and determining how winter ice conditions and quality impact ecosystem processes in lakes over winter. Ultimately, integrating disciplines will improve our ability to understand the impacts of changing winters on lake ice.

Journal of Geophysical Research: Biogeosciences↗

USGS1978

Remote-sensing methods for monitoring surface coal mining in the northern Great Plains

Recent studies at a large surface coal mine in southern Montana confirm that remote sensing is both feasible and effective for gathering land-use and environmental data (spatial, dynamic, and seasonal) for large-scale surface mines in the northern Great Plains. The Western Energy Co.'s Rosebud mine near Colstrip, Mont., was selected as a test site because it typifies surface operations in the Powder River Basin of Montana and Wyoming and elsewhere in the northern Great Plains. Several basic interpretive and analytical remote-sensing techniques were used to identify and delineate various categories of surface-mining operations and concurrent stages of reclamation that characterize most, if not all, such mining operations. Color infrared and black-and-white aerial photographs and a black-and-white band 5 Landsat image were used to identify (1) high wall and bench areas, (2) ungraded spoils, (3) graded and recontoured areas, (4) revegetated recontoured areas, (5) natural and impounded surface water, and (6) miscellaneous areas. Over the lifespan of an extensive surface mine, cultural and natural processes and cumulative environmental effects can be monitored by capitalizing on the close correlation between enhanced satellite imagery, infrared and (or) black-and-white aerial photography, standard large-scale topographic maps (such as U.S. Geological Survey 7½-minute quadrangle maps), and results of onsite inspection of mining and reclamation by Federal or State agencies.

Montana↗

USGS2009

Thorium abundances on the Aristarchus plateau: Insights into the composition of the Aristarchus pyroclastic glass deposits

Thorium (Th) data from the Lunar Prospector gamma ray spectrometer (LP‐GRS) are used to constrain the composition of lunar pyroclastic glass deposits on top of the Aristarchus plateau. Our goal is to use forward modeling of LP‐GRS Th data to measure the Th abundances on the plateau and then to determine if the elevated Th abundances on the plateau are associated with the pyroclastic deposits or with thorium‐rich ejecta from Aristarchus crater. We use a variety of remote sensing data to show that there is a large, homogenous portion of the pyroclastics on the plateau that has seen little or no contamination from the Th‐rich ejecta of Aristarchus crater. Our results show that the uncontaminated pyroclastic glasses on Aristarchus plateau have an average Th content of 6.7 ppm and ∼7 wt % TiO 2 . These Th and Ti values are consistent with Th‐rich, intermediate‐Ti yellow glasses from the lunar sample suite. On the basis of this information, we use petrologic equations and interelement correlations for the Moon to estimate the composition of the source region from which the Aristarchus glasses were derived. We find that the source region for the Aristarchus glasses contained high abundances of heat‐producing elements, which most likely served as a thermal driver for the prolonged volcanic activity in this region of the Moon.

Journal of Geophysical Research E: Planets↗

USGS2007

Analysis of Alaskan burn severity patterns using remotely sensed data

Wildland fire is the dominant large-scale disturbance mechanism in the Alaskan boreal forest, and it strongly influences forest structure and function. In this research, patterns of burn severity in the Alaskan boreal forest are characterised using 24 fires. First, the relationship between burn severity and area burned is quantified using a linear regression. Second, the spatial correlation of burn severity as a function of topography is modelled using a variogram analysis. Finally, the relationship between vegetation type and spatial patterns of burn severity is quantified using linear models where variograms account for spatial correlation. These results show that: 1) average burn severity increases with the natural logarithm of the area of the wildfire, 2) burn severity is more variable in topographically complex landscapes than in flat landscapes, and 3) there is a significant relationship between burn severity and vegetation type in flat landscapes but not in topographically complex landscapes. These results strengthen the argument that differential flammability of vegetation exists in some boreal landscapes of Alaska. Additionally, these results suggest that through feedbacks between vegetation and burn severity, the distribution of forest vegetation through time is likely more stable in flat terrain than it is in areas with more complex topography. ?? IAWF 2007.

International Journal of Wildland Fire↗