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At least 199 records · Page 11Linked to original sources
USGS2012

Limitations and potential of satellite imagery to monitor environmental response to coastal flooding

Storm-surge flooding and marsh response throughout the coastal wetlands of Louisiana were mapped using several types of remote sensing data collected before and after Hurricanes Gustav and Ike in 2008. These included synthetic aperture radar (SAR) data obtained from the (1) C-band advance SAR (ASAR) aboard the Environmental Satellite, (2) phased-array type L-band SAR (PALSAR) aboard the Advanced Land Observing Satellite, and (3) optical data obtained from Thematic Mapper (TM) sensor aboard the Land Satellite (Landsat). In estuarine marshes, L-band SAR and C-band ASAR provided accurate flood extent information when depths averaged at least 80 cm, but only L-band SAR provided consistent subcanopy detection when depths averaged 50 cm or less. Low performance of inundation mapping based on C-band ASAR was attributed to an apparent inundation detection limit (>30 cm deep) in tall Spartina alterniflora marshes, a possible canopy collapse of shoreline fresh marsh exposed to repeated storm-surge inundations, wind-roughened water surfaces where water levels reached marsh canopy heights, and relatively high backscatter in the near-range portion of the SAR imagery. A TM-based vegetation index of live biomass indicated that the severity of marsh dieback was linked to differences in dominant species. The severest impacts were not necessarily caused by longer inundation but rather could be caused by repeated exposure of the palustrine marsh to elevated salinity floodwaters. Differential impacts occurred in estuarine marshes. The more brackish marshes on average suffered higher impacts than the more saline marshes, particularly the nearshore coastal marshes occupied by S. alterniflora.

Journal of Coastal Research↗
USGS2010

Thematic accuracy of the NLCD 2001 land cover for the conterminous United States

The land-cover thematic accuracy of NLCD 2001 was assessed from a probability-sample of 15,000 pixels. Nationwide, NLCD 2001 overall Anderson Level II and Level I accuracies were 78.7% and 85.3%, respectively. By comparison, overall accuracies at Level II and Level I for the NLCD 1992 were 58% and 80%. Forest and cropland were two classes showing substantial improvements in accuracy in NLCD 2001 relative to NLCD 1992. NLCD 2001 forest and cropland user's accuracies were 87% and 82%, respectively, compared to 80% and 43% for NLCD 1992. Accuracy results are reported for 10 geographic regions of the United States, with regional overall accuracies ranging from 68% to 86% for Level II and from 79% to 91% at Level I. Geographic variation in class-specific accuracy was strongly associated with the phenomenon that regionally more abundant land-cover classes had higher accuracy. Accuracy estimates based on several definitions of agreement are reported to provide an indication of the potential impact of reference data error on accuracy. Drawing on our experience from two NLCD national accuracy assessments, we discuss the use of designs incorporating auxiliary data to more seamlessly quantify reference data quality as a means to further advance thematic map accuracy assessment.

Remote Sensing of Environment↗
USGS2025

Historical coast snaps: Using centennial imagery to track shoreline change

Understanding long-term coastal evolution requires historical data, yet accessing reliable information becomes increasingly challenging for extended periods. While vertical aerial imagery has been extensively used in coastal studies since the mid-20th century, and satellite-derived shoreline measurements are now revolutionizing shoreline change studies, ground-based images, such as historical photographs and picture postcards, provide an alternative source of shoreline data for earlier periods when other datasets are scarce. Despite their frequent use for documenting qualitative morphological changes, these valuable historical data sources have rarely supported quantitative assessments of coastal evolution. This study demonstrates the potential of historical ground-oblique images for quantitatively assessing shoreline position and long-term change. Using Conceição-Duquesa Beach (Cascais, Portugal) as a case study, we analyze shoreline evolution over 92 years by applying a novel methodology to historical photographs and postcards. The approach combines image registration, shoreline detection, coordinate transformation, and rectification while accounting for positional uncertainty. Results reveal a significant counterclockwise rotation of the shoreline between the 20th and 21st centuries, exceeding estimated uncertainty thresholds. This study highlights the feasibility of using historical ground-based imagery to reconstruct shoreline positions and quantify long-term coastal change. The methodology is straightforward, adaptable, and offers a promising avenue for extending the temporal range of shoreline datasets, advancing our understanding of coastal evolution.

Cascais↗
USGS2015

Classification of rocky headlands in California with relevance to littoral cell boundary delineation

Despite extensive studies of hydrodynamics and sediment flux along beaches, there is little information on the processes, pathways and timing of water and sediment transport around rocky headlands. In this study, headlands along the California coast are classified to advance understanding of headland dynamics and littoral cell boundaries in support of improved coastal management decisions. Geomorphological parameters for 78 headlands were quantified from geological maps, remote-sensing imagery, navigational charts, and shoreline geospatial databases. K -means cluster analysis grouped the headlands into eight distinct classes based on headland perimeter, bathymetric slope ratio, and the headland apex angle. Wave data were used to investigate the potential for sediment transport around the headland types and determine the efficacy of the headland as a littoral cell boundary. Four classes of headland appear to function well as littoral cell boundaries, with headland size (e.g., perimeter or area) and a marked change in nearshore bathymetry across the headland being relevant attributes. About half of the traditional California littoral cell boundaries align with headland classes that are expected to perform poorly in blocking alongshore sediment transport, calling into question these boundaries. Better definition of these littoral cell boundaries is important for regional sediment management decisions.

California↗
USGS2026

Sentinel-2 for chlorophyll-a water quality monitoring: A review of validation evidence and application potential

Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.

International Journal of Remote Sensing↗
USGS2002

Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat: Tucson

The research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat . By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data , effective models of habitat preference were created. Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition. Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data . Such relationships, once verified and established, can be used to help indirectly monitor the population. Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat . Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations. The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape.

Arizona↗
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↗
USGS2016

Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing

Climate change coupled with an intensifying wildfire regime is becoming an important driver of permafrost loss and ecosystem change in the northern boreal forest. There is a growing need to understand the effects of fire on the spatial distribution of permafrost and its associated ecological consequences. We focus on the effects of fire a decade after disturbance in a rocky upland landscape in the interior Alaskan boreal forest. Our main objectives were to (1) map near-surface permafrost distribution and drainage classes and (2) analyze the controls over landscape-scale patterns of post-fire permafrost degradation. Relationships among remote sensing variables and field-based data on soil properties (temperature, moisture, organic layer thickness) and vegetation (plant community composition) were analyzed using correlation, regression, and ordination analyses. The remote sensing data we considered included spectral indices from optical datasets (Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)), the principal components of a time series of radar backscatter (Advanced Land Observing Satellite—Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR)), and topographic variables from a Light Detection and Ranging (LiDAR)-derived digital elevation model (DEM). We found strong empirical relationships between the normalized difference infrared index (NDII) and post-fire vegetation, soil moisture, and soil temperature, enabling us to indirectly map permafrost status and drainage class using regression-based models. The thickness of the insulating surface organic layer after fire, a measure of burn severity, was an important control over the extent of permafrost degradation. According to our classifications, 90% of the area considered to have experienced high severity burn (using the difference normalized burn ratio (dNBR)) lacked permafrost after fire. Permafrost thaw, in turn, likely increased drainage and resulted in drier surface soils. Burn severity also influenced plant community composition, which was tightly linked to soil temperature and moisture. Overall, interactions between burn severity, topography, and vegetation appear to control the distribution of near-surface permafrost and associated drainage conditions after disturbance.

Alaska↗
USGS2024

An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape

Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.

Baja California Sur↗
USGS2023

Sediment sources and connectivity linked to hydrologic pathways and geomorphic processes: A conceptual model to specify sediment sources and pathways through space and time

Sediment connectivity is a conceptualization for the transfer and storage of sediment among different geomorphic compartments across upland landscapes and channel networks. Sediment connectivity and dysconnectivity are linked to the water cycle and hydrologic systems with the associated multiscale interactions with climate, soil, topography, ecology, and landuse/landcover under natural variability and human intervention. We review current sediment connectivity and modeling approaches evaluating and quantifying water and sediment transfer in catchment systems. Many studies highlight the interaction between sediment and water in defining landscape connectivity, but many efforts to quantify and/or simulate sediment connectivity rely on the topographic/structural controls on sediment erosion and delivery. More recent modeling efforts integrate functional and structural connectivity to capture hydrologic properties influencing sediment delivery. Though the recent modeling development is encouraging, a comprehensive sediment connectivity framework, which integrates geomorphic and hydrologic processes across spatiotemporal scales, has not yet been accomplished. Such an effort requires understanding the hydrologic and geomorphic processes that control sediment source, storage, and transport at different spatiotemporal scales and across various geophysical conditions. We propose a path for developing this new understanding through an integrated hydrologic and sediment connectivity conceptual model that broadly categorizes dominant processes and patterns relevant to understanding sediment flux dynamics. The conceptual model describes hydrologic–sediment connectivity regimes through spatial-temporal feedback between hydrologic processes and geomorphic drivers. We propose that in combining hydrologic and sediment connectivity into a single conceptual model, patterns emerge such that catchments will exist in a single characteristic behavior at a particular instance, which would shift with space and time, and with landscape disturbances. Using the conceptual model as a “thinking” tool, we extract case studies from a multidisciplinary literature review—from hydrology, geomorphology, biogeochemistry, and watershed modeling to remote-sensing technology—that correspond to each of the dominant hydrologic–sediment connectivity regimes. Sediment and water interactions in real-world examples through various observational and modeling techniques illustrate the advancements in the spatial and temporal scales of landscape connectivity observations and simulations. The conceptual model and case studies provide a foundation for advancing the understanding and predictive capability of watershed sediment processes at multiple spatiotemporal scales. Plain language summary: Soil erosion and movement across the landscape are closely linked to rain events and flow pathways. Landscape connectivity is a way to consider how soil erosion from different parts of the landscape is connected to the streams. We explore where soil erosion occurs and how eroded soil moves across the landscape through the interaction with rainfall and drainage. The comprehensive understanding of sediment connectivity and its dependence on rainfall characteristics and watershed hydrology may help to inform the effective distribution of conservation funds and management actions to address water pollution from excess sediment.

Frontiers in Water↗
USGS1996

Film cameras or digital sensors? The challenge ahead for aerial imaging

Cartographic aerial cameras continue to play the key role in producing quality products for the aerial photography business, and specifically for the National Aerial Photography Program (NAPP). One NAPP photograph taken with cameras capable of 39 lp/mm system resolution can contain the equivalent of 432 million pixels at 11 ??m spot size, and the cost is less than $75 per photograph to scan and output the pixels on a magnetic storage medium. On the digital side, solid state charge coupled device linear and area arrays can yield quality resolution (7 to 12 ??m detector size) and a broader dynamic range. If linear arrays are to compete with film cameras, they will require precise attitude and positioning of the aircraft so that the lines of pixels can be unscrambled and put into a suitable homogeneous scene that is acceptable to an interpreter. Area arrays need to be much larger than currently available to image scenes competitive in size with film cameras. Analysis of the relative advantages and disadvantages of the two systems show that the analog approach is more economical at present. However, as arrays become larger, attitude sensors become more refined, global positioning system coordinate readouts become commonplace, and storage capacity becomes more affordable, the digital camera may emerge as the imaging system for the future. Several technical challenges must be overcome if digital sensors are to advance to where they can support mapping, charting, and geographic information system applications.

Photogrammetric Engineering and Remote Sensing↗
USGS2023

Transferring deep learning models for hydrographic feature extraction from IfSAR data in Alaska

The National Hydrography Dataset (NHD) managed by the U.S. Geological Survey (USGS) is being updated with higher-quality feature representations through efforts that derive hydrography from 3DEP HR elevation datasets. Deriving hydrography from elevation through traditional flow routing and interactive methods is a complex, time-consuming process that must be tailored for different hydrogeomorphic conditions. The large volume of surface water features and HR remote sensing data make manual annotation of the water features over the entire nation infeasible. Furthermore, annual and seasonal variations of surface waters warrant some level of periodic updates to hydrography. Advances in deep learning technologies provide an opportunity to automate hydrography extraction and scale up the process to a nationwide level. One major challenge, however, is the effect of spatial heterogeneity due to the wide variety of hydrogeomorphic conditions in the United States. In other words, it is unclear how a deep learning model pre-trained in one set of hydrogeomorphic conditions can be effectively applied to other conditions for hydrographic feature extraction. This paper aims to provide some clarity in this regard by testing automated deep learning and its transferability to the extraction of hydrography from digital elevation model (DEM) data spanning a range of hydrogeomorphic conditions in Alaska. In transfer learning, the knowledge (e.g., neural network weights) from one domain is transferred to other domains and thereby decrease training requirements in the target domain.

Alaska↗
USGS2024

Advancing water security in Africa with new high-resolution discharge data

VegDischarge v1 is a comprehensive river discharge across Africa (2000–2021), produced by coupling the agro-hydrologic VegET model and the mizuRoute routing framework. Using remote sensing data and hydrological modeling, the 1-km runoff field simulated by VegET, and routed with mizuRoute, covers over 64,000 river segments in Africa. The VegET model simulates runoff based on vegetation and soil moisture dynamics, while mizuRoute processes this runoff through a detailed river network. Performance metrics show strong model reliability, with R² ranging from 0.5 to 0.9, NSE between 0.6 and 0.9, and KGE from 0.5 to 0.8. The total annual average discharge for Africa is quantified at 3238.1 km³ . year-1, with contributions to various oceanic basins: 989.9 km³ . year-1 to the North Atlantic, primarily from West African rivers like the Senegal, Gambia, Volta, and Niger; 1313.7 km³ . year-1 to the South Atlantic, largely from the Congo River; 212.5 km³ . year-1 to the Mediterranean Sea, predominantly from the Nile River; and 722.0 km³ . year-1 to the Indian Ocean, with substantial inputs from rivers such as the Zambezi. This VegDischarge v1 is valuable for policymakers, stakeholders, and researchers to better understand water availability, its temporal and spatial variations, that impact water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience mitigation strategies.

Scientific Data↗
USGS2007

Mapping raster imagery to the interrupted Goode Homolosine projection

Because of the increasing emphasis on global monitoring, processing remotely-sensed raster image data onto global map projections has become an important issue. One class of map projections, interrupted equal-area projections, is especially useful for this purpose. The use of the Interrupted Goode Homolosine map projection for the Global Land Advanced Very High Resolution Radiometer (AVHRR) 1 km project and the AVHRR Pathfinder project has produced a map that is both attractive to the viewer and useful for data analysis. This interrupted, composite, equal-area map projection uses the Sinusoidal projection for low latitudes and the Mollweide projection for high latitudes and is broken into 12 regions, each with its own central meridian. This combination of projections helps to preserve the shape of the land masses and results in a map that has less distortion than an uninterrupted global map. The use of the interrupted projection also simplifies the processing and management of larger data sets, because the data can be processed either separately in the component projections that make up the interrupted projection or directly onto the interrupted projection.

International Journal of Remote Sensing↗
USGS2025

The 3D National Topography Model Call for Action—Part 2: The Next Generation 3D Elevation Program

The three-dimensional (3D) National Topography Model initiative to integrate elevation and hydrography data includes the next generation of hydrography data from the 3D Hydrography Program and the next generation of elevation data from the 3D Elevation Program (3DEP). The first-ever collection of light detection and ranging (lidar) data for the nation (IfSAR for Alaska) provides a critical baseline reference, and the addition of multiple repeat elevation mapping projects as part of the next generation of 3DEP would substantially expand analysis capabilities. As the U.S. Geological Survey (USGS) is closing in on our goal of complete coverage with 3DEP data available or in progress for 98.3 percent of the Nation at the end of fiscal year 2024, the USGS is already transitioning to the next generation of 3DEP. Based on the 3D Nation Study results and input from a broad range of stakeholders, the USGS National Geospatial Program has finalized a new design for 3DEP that provides increased lidar quality levels and refresh rates. The new program is designed with more flexibility to meet changing user needs and take advantage of improvements in mapping technologies. The program will aim to expand the level of interagency coordination for topobathymetric lidar acquisition for inland rivers. The next generation of 3DEP will also aim to emphasize research, including advancing program design, products, and services and engaging and leveraging the evolving 3D industry. Research goals also include becoming more flexible in meeting user needs and taking advantage of evolving remote-sensing technologies. The program also plans to move from focusing on producing standard products to producing a concept of a 3D Nation Ecosystem with a variety of inputs, products, and services.

Circular↗
USGS2013

Data management challenges in species distribution modeling

An important component in the fields of ecology and conservation biology is understanding the environmental conditions and geographic areas that are suitable for a given species to inhabit. A common tool in determining such areas is species distribution modeling which uses computer algorithms to determine the spatial distribution of organisms. Most commonly the correlative relationships between the organism and environmental variables are the primary consideration. The data requirements for this type of modeling consist of known presence and possibly absence locations of the species as well as the values of environmental or climatic covariates thought to define the species habitat suitability at these locations. These covariate data are generally extracted from remotely sensed imagery, interpolated/gridded historical climate data, or downscaled climate model output. Traditionally, ecologists and biologists have constructed species distribution models using workflows and data that reside primarily on their local workstations or networks. This workflow is becoming challenging as scientists increasingly try to use these modeling techniques to inform management decisions under different climate change scenarios. This challenge stems from the fact that remote sensing products, gridded historical climate, and downscaled climate models are not only increasing in spatial and temporal resolution but proliferating as well. Any rigorous assessment of uncertainty requires a computationally intensive sensitivity analysis accounting for various sources of uncertainty. The scientists fitting these models generally do not have the background in computer science required to take advantage of recent advances in web-service based data acquisition, remote high-powered data processing, or scientific workflow systems. Ecologists in the field of modeling are in need of a tractable platform that abstracts the inherent computational complexity required to incorporate the burgeoning field of coupled climate and ecological response modeling. In this paper we describe the computational challenges in species distribution modeling and solutions using scientific workflow systems. We focus on the Software for Assisted Species Modeling (SAHM) a package within VisTrails, an open-source scientific workflow system.

Bulletin of the Technical Committee on Data Engine↗
USGS2006

Trend analysis of time-series phenology of North America derived from satellite data

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface since the 1980s. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land-surface processes over large areas. Phenologic metrics, including start of season, end of season, duration of season, and seasonally integrated greenness, were derived from 8 km advanced very high resolution radiometer (AVHRR) data over North America spanning the years 1982-2003. Trend analysis was performed on annual summaries of the metrics to determine areas with increasing or decreasing growing season trends for the time period under study. Results show a trend toward earlier starts of season in limited areas of the mixed boreal forest, and a trend toward later end of season in well-defined areas of New England and southeastern Canada. Results in Saskatchewan, Canada, include a trend toward longer duration of season over a well-defined area, principally as a result of regional changes in land use practices. Changing seasonality appears to be an integrated response to a complex of factors, including climate change, but also, in many places, changes in land use practices. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

GIScience and Remote Sensing↗
USGS2009

Optical satellite data volcano monitoring: a multi-sensor rapid response system

In this chapter, the use of satellite remote sensing to monitor active geological processes is described. Specifically, threats posed by volcanic eruptions are briefly outlined, and essential monitoring requirements are discussed. As an application example, a collaborative, multi-agency operational volcano monitoring system in the north Pacific is highlighted with a focus on the 2007 eruption of Kliuchevskoi volcano, Russia. The data from this system have been used since 2004 to detect the onset of volcanic activity, support the emergency response to large eruptions, and assess the volcanic products produced following the eruption. The overall utility of such integrative assessments is also summarized. The work described in this chapter was originally funded through two National Aeronautics and Space Administration (NASA) Earth System Science research grants that focused on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument. A skilled team of volcanologists, geologists, satellite tasking experts, satellite ground system experts, system engineers and software developers collaborated to accomplish the objectives. The first project, Automation of the ASTER Emergency Data Acquisition Protocol for Scientific Analysis, Disaster Monitoring, and Preparedness , established the original collaborative research and monitoring program between the University of Pittsburgh (UP), the Alaska Volcano Observatory (AVO), the NASA Land Processes Distributed Active Archive Center (LP DAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, and affiliates on the ASTER Science Team at the Jet Propulsion Laboratory (JPL) as well as associates at the Earth Remote Sensing Data Analysis Center (ERSDAC) in Japan. This grant, completed in 2008, also allowed for detailed volcanic analyses and data validation during three separate summer field campaigns to Kamchatka Russia. The second project, Expansion and synergistic use of the ASTER Urgent Request Protocol (URP) for natural disaster monitoring and scientific analysis , has expanded the project to other volcanoes around the world and is in progress through 2011. The focus on ASTER data is due to the suitability of the sensor for natural disaster monitoring and the availability of data. The instrument has several unique facets that make it especially attractive for volcanic observations (Ramsey and Dehn, 2004). Specifically, ASTER routinely collects data at night, it has the ability to generate digital elevation models using stereo imaging, it can collect data in various gain states to minimize data saturation, it has a cross-track pointing capability for faster targeting, and it collects data up to ±85° latitude for better global coverage. As with any optical imaging-based remote sensing, the viewing conditions can negatively impact the data quality. This impact varies across the optical and thermal infrared wavelengths as well as being a function of the specific atmospheric window within a given wavelength region. Water vapor and cloud formation can obscure surface data in the visible and near infrared (VNIR)/shortwave infrared (SWIR) region due mainly to non-selective scattering of the incident photons. In the longer wavelengths of the thermal infrared (TIR), scattering is less of an issue, but heavy cloud cover can still obscure the ground due to atmospheric absorption. Thin clouds can be optically-transparent in the VNIR and TIR regions, but can cause errors in the extracted surface reflectance or derived surface temperatures. In regions prone to heavy cloud cover, optical remote sensing can be improved through increased temporal resolution. As more images are acquired in a given time period the chances of a clear image improve dramatically. The Advanced Very High Resolution Radiometer (AVHRR) routine monitoring, which commonly collects 4-6 images per day of any north Pacific volcano, takes advantage of this fact. The rapid response program described in this chapter also improves the temporal resolution of the ASTER instrument. ASTER has been acquiring images of volcanic eruptions since soon after its launch in December 1999. An early example included the observations of the large pyroclastic flow deposit emplaced at Bezymianny volcano in Kamchatka, Russia. The first images in March 2000, just weeks after the eruption, revealed the extent, composition, and cooling history of this large deposit and of the active lava dome (Ramsey and Dehn, 2004). The initial results from these early datasets spurred interest in using ASTER data for expanded volcano monitoring in the north Pacific. It also gave rise to the multi-year NASA-funded programs of rapid response scheduling and imaging throughout the Aleutian, Kamchatka and Kurile arcs. Since the formal establishment of the programs, the data have provided detailed descriptions of the eruptions of Augustine, Bezymianny, Kliuchevskoi and Sheveluch volcanoes over the past nine years (Wessels et al., in press; Carter et al., 2007, 2008; Ramsey et al., 2008; Rose and Ramsey, 2009). The initial research focus of this rapid response program was specifically on automating the ASTER sensor’s ability for targeted observational scheduling using the expedited data system. This urgent request protocol is one of the unique characteristics of ASTER. It provides a limited number of emergency observations, typically at a much-improved temporal resolution and quicker turnaround with data processing in the United States rather than in Japan. This can speed the reception of the processed data by several days to a week. The ongoing multi-agency research and operational collaboration has been highly successful. AVO serves as the primary source for status information on volcanic activity, working closely with the National Weather Service (NWS), Federal Aviation Administration (FAA), military and other state and federal emergency services. Collaboration with the Russian Institute of Volcanology and Seismology (IVS)/Kamchatka Volcanic Eruption Response Team (KVERT) is also maintained. Once a volcano is identified as having increased thermal output, ASTER is automatically tasked and the volcano is targeted at the next available opportunity. After the data are acquired, scientists at all the agencies have access to the images, with the primary science analysis carried out at the University of Pittsburgh and AVO. Results are disseminated to the responsible monitoring agencies and the global community through e-mail mailing lists.

Book chapter↗