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Results for “Developments in Earth Surface Processes”

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At least 253 records · Page 14Linked to original sources
USGS2016

Debris flow runup on vertical barriers and adverse slopes

Runup of debris flows against obstacles in their paths is a complex process that involves profound flow deceleration and redirection. We investigate the dynamics and predictability of runup by comparing results from large-scale laboratory experiments, four simple analytical models, and a depth-integrated numerical model (D-Claw). The experiments and numerical simulations reveal the important influence of unsteady, multidimensional flow on runup, and the analytical models highlight key aspects of the underlying physics. Runup against a vertical barrier normal to the flow path is dominated by rapid development of a shock, or jump in flow height, associated with abrupt deceleration of the flow front. By contrast, runup on sloping obstacles is initially dominated by a smooth flux of mass and momentum from the flow body to the flow front, which precedes shock development and commonly increases the runup height. D-Claw simulations that account for the emergence of shocks show that predicted runup heights vary systematically with the adverse slope angle and also with the Froude number and degree of liquefaction (or effective basal friction) of incoming flows. They additionally clarify the strengths and limitations of simplified analytical models. Numerical simulations based on a priori knowledge of the evolving dynamics of incoming flows yield quite accurate runup predictions. Less predictive accuracy is attained in ab initio simulations that compute runup based solely on knowledge of static debris properties in a distant debris flow source area. Nevertheless, the paucity of inputs required in ab initio simulations enhances their prospective value in runup forecasting.

Journal of Geophysical Research F: Earth Surface↗
USGS2014

Modeling erosion of ice-rich permafrost bluffs along the Alaskan Beaufort Sea coast

The Arctic climate is changing, inducing accelerating retreat of ice-rich permafrost coastal bluffs. Along Alaska’s Beaufort Sea coast, erosion rates have increased roughly threefold from 6.8 to 19 m yr −1 since 1955 while the sea ice-free season has increased roughly twofold from 45 to 100 days since 1979. We develop a numerical model of bluff retreat to assess the relative roles of the length of sea ice-free season, sea level, water temperature, nearshore wavefield, and permafrost temperature in controlling erosion rates in this setting. The model captures the processes of erosion observed in short-term monitoring experiments along the Beaufort Sea coast, including evolution of melt notches, topple of ice wedge-bounded blocks, and degradation of these blocks. Model results agree with time-lapse imagery of bluff evolution and time series of ocean-based instrumentation. Erosion is highly episodic with 40% of erosion is accomplished during less than 5% of the sea ice-free season. Among the formulations of the submarine erosion rate we assessed, we advocate those that employ both water temperature and nearshore wavefield. As high water levels are a prerequisite for erosion, any future changes that increase the frequency with which water levels exceed the base of the bluffs will increase rates of coastal erosion. The certain increases in sea level and potential changes in storminess will both contribute to this effect. As water temperature also influences erosion rates, any further expansion of the sea ice-free season into the midsummer period of greatest insolation is likely to result in an additional increase in coastal retreat rates.

Alaska↗
USGS2017

Rare-earth elements

The rare-earth elements (REEs) are 15 elements that range in atomic number from 57 (lanthanum) to 71 (lutetium); they are commonly referred to as the “lanthanides.” Yttrium (atomic number 39) is also commonly regarded as an REE because it shares chemical and physical similarities and has affinities with the lanthanides. Although REEs are not rare in terms of average crustal abundance, the concentrated deposits of REEs are limited in number. Because of their unusual physical and chemical properties, the REEs have diverse defense, energy, industrial, and military technology applications. The glass industry is the leading consumer of REE raw materials, which are used for glass polishing and as additives that provide color and special optical properties to the glass. Lanthanum-based catalysts are used in petroleum refining, and cerium-based catalysts are used in automotive catalytic converters. The use of REEs in magnets is a rapidly increasing application. Neodymium-iron-boron magnets, which are the strongest known type of magnets, are used when space and weight are restrictions. Nickel-metal hydride batteries use anodes made of a lanthanum-based alloys. China, which has led the world production of REEs for decades, accounted for more than 90 percent of global production and supply, on average, during the past decade. Citing a need to retain its limited REE resources to meet domestic requirements as well as concerns about the environmental effects of mining, China began placing restrictions on the supply of REEs in 2010 through the imposition of quotas, licenses, and taxes. As a result, the global rare-earth industry has increased its stockpiling of REEs; explored for deposits outside of China; and promoted new efforts to conserve, recycle, and substitute for REEs. New mine production began at Mount Weld in Western Australia, and numerous other exploration and development projects noted in this chapter are ongoing throughout the world. The REE-bearing minerals are diverse and often complex in composition. At least 245 individual REE-bearing minerals are recognized; they are mainly carbonates, fluorocarbonates, and hydroxylcarbonates (n = 42); oxides (n = 59); silicates (n = 85); and phosphates (n = 26). Many of the world’s significant REE deposits occur in carbonatites, which are carbonate igneous rocks. The REEs also have a strong genetic association with alkaline magmatism. The systematic geologic and chemical processes that explain these observations are not well understood. Economic or potentially economic REE deposits have been found in (a) carbonatites, (b) peralkaline igneous systems, (c) magmatic magnetite-hematite bodies, (d) iron oxide-copper-gold (IOCG) deposits, (e) xenotime-monazite accumulations in mafic gneiss, (f) ion-absorption clay deposits, and (g) monazite-xenotime-bearing placer deposits. Carbonatites have been the world’s main source for the light REEs since the 1960s. Ion-adsorption clay deposits in southern China are the world’s primary source of the heavy REEs. Monazite-bearing placer deposits were important sources of REEs before the mid-1960s and may be again in the future. In recent years, REEs have been produced from large carbonatite bodies mined at the Mountain Pass deposit in California and, in China, at the Bayan Obo deposit in Nei Mongol Autonomous Region, the Maoniuping deposit in Sichuan Province, the Daluxiang deposit in Sichuan Province, and the Weishan deposit in Anhui Province. Alkaline igneous complexes have recently been targeted for exploration because of their enrichments in the heavy REEs. Information relevant to the environmental aspects of REE mining is limited. Little is known about the aquatic toxicity of REEs. The United States lacks drinking water standards for REEs. The concentrations of REEs in environmental media are influenced by their low abundances in crustal rocks and their limited solubility in most groundwaters and surface waters. The scarcity of sulfide minerals, including pyrite, minimizes or eliminates concerns about acid-mine drainage for carbonatite-hosted deposits and alkaline-intrusion-related REE deposits. For now, insights into environmental responses of REE mine wastes must rely on predictive models.

Professional Paper↗
USGS2020

Snow processes in mountain forests: Interception modeling for coarse-scale applications

Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.

Utah↗
USGS2009

Earth's magnetic field complex: U.S. National activities during the Decade of Geopotential Field Research

The US geomagnetism community is supported by NASA, NOAA, USGS, NSF, DOD, and US universities. During the Decade of Geopotential Field Research, inaugurated in 1999 with the launch of the Danish satellite Ørsted on a US rocket, the US community has been involved in satellite mission development and analysis, instrument development, model development, and in the discovery and understanding of new processes with satellite magnetic signatures. The ESA Swarm mission has been a primary focus of the US community, with three US scientists on Swarm's Mission Advisory Group. Swarm will measure, for the first time, the E-W gradient of the magnetic field. One of us (T. Sabaka) is involved with the development of a Comprehensive inversion scheme as part of the SMART consortium. This effort is an outgrowth of the Comprehensive Model [1]. Swarm will also provide valuable observations for ionospheric specification and forecast. The geomagnetism group at NOAA (S. Maus, P. Alken and C. Manoj) has developed algorithms to estimate the strength of the eastward electric field (EEF). As the driver of the equatorial plasma fountain, the EEF is an important space weather parameter. ESA is considering the implementation of the EEF as a dedicated inversion chain in the Level-2 Facility. In 2006, NASA launched a minisatellite magnetometer constellation mission (ST-5) to test technologies and software. The ST-5 constellation featured the first along-track gradient measurements. NASA has also initiated efforts to study geomagnetism mission concepts after Swarm. One of the ideas under consideration is the systematic measurement of radial field gradients. Instrument development, and geomagnetic observatories, are also an integral part of the US effort. The past decade has seen significant advances in the development of a self-calibrating vector helium magnetometer, and in the automation of the US observatory network. Working in coordination with Intermagnet, the USGS Geomagnetism Program has made operational 1-second data acquisition at 13 of its magnetic observatories. The Program is also developing a realtime 1-minute and 1-hour Dst service. Within the past decade, US scientists have been leaders in the development of models that describe the global geomagnetic environment, including comprehensive models (the CM series), maps of the lithospheric field from satellite (MF-series), near surface maps of the lithospheric field (WDMAM-series), models of the thickness of the magnetic crust, the IGRF and World Magnetic Model series, ionospheric models such as the EEJM1, JVDM1, and the IRI, and data assimilation-based models (MoSST-series) that predict the future state of the geomagneic field.

Conference Paper↗
USGS2025

Global methane budget 2000-2020

Understanding and quantifying the global methane (CH 4 ) budget is important for assessing realistic pathways to mitigate climate change. CH 4 is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO 2 ), and both emissions and atmospheric concentrations of CH 4 have continued to increase since 2007 after a temporary pause. The relative importance of CH 4 emissions compared to those of CO 2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in quantifying the factors responsible for the observed atmospheric growth rate arise from diverse, geographically overlapping CH 4 sources and from the uncertain magnitude and temporal change in the destruction of CH 4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise, and update the global CH 4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH 4 budget, integrating results of top-down CH 4 emission estimates (based on in situ and Greenhouse Gases Observing SATellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full data sets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this 2025 edition benefits from important progress in estimating inland freshwater emissions, with better counting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double counting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double counting that may exist (average of 23 Tg CH 4 yr −1 ). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH 4 yr −1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH 4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH 4 yr −1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH 4 yr −1 or ∼ 65 % is attributed to direct anthropogenic sources in the fossil, agriculture, and waste and anthropogenic biomass burning (range 350–391 Tg CH 4 yr −1 or 63 %–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH 4 yr −1 (range 9–40). The 2020 emission rate is the highest of the period and reaches 608 Tg CH 4 yr −1 (range 581–627), which is 12 % higher than the average emissions in the 2000s. Since 2012, global direct anthropogenic CH 4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH 4 yr −1 ) larger global emissions (669 Tg CH 4 yr −1 , range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH 4 yr −1 in Saunois et al. (2016, 2020) respectively), and for the first time uncertainties in bottom-up and top-down budgets overlap. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH 4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. The tropospheric loss of methane, as the main contributor to methane lifetime, has been estimated at 563 [510–663] Tg CH 4 yr −1 based on chemistry–climate models. These values are slightly larger than for 2000–2009 due to the impact of the rise in atmospheric methane and remaining large uncertainty ( ∼ 25 %). The total sink of CH 4 is estimated at 633 [507–796] Tg CH 4 yr −1 by the bottom-up approaches and at 554 [550–567] Tg CH 4 yr −1 by top-down approaches. However, most of the top-down models use the same OH distribution, which introduces less uncertainty to the global budget than is likely justified. For 2010–2019, agriculture and waste contributed an estimated 228 [213–242] Tg CH 4 yr −1 in the top-down budget and 211 [195–231] Tg CH 4 yr −1 in the bottom-up budget. Fossil fuel emissions contributed 115 [100–124] Tg CH 4 yr −1 in the top-down budget and 120 [117–125] Tg CH 4 yr −1 in the bottom-up budget. Biomass and biofuel burning contributed 27 [26–27] Tg CH 4 yr −1 in the top-down budget and 28 [21–39] Tg CH 4 yr −1 in the bottom-up budget. We identify five major priorities for improving the CH 4 budget: (i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH 4 based on a robust classification of different types of emitting ecosystems; (ii) further development of process-based models for inland-water emissions; (iii) intensification of CH 4 observations at local (e.g. FLUXNET-CH 4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture, and landfills) to improve source partitioning.

Earth System Science Data↗
USGS2011

EAARL Coastal Topography--Cape Canaveral, Florida, 2009: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Kennedy Space Center, FL. This project provides highly detailed and accurate datasets of a portion of the eastern Florida coastline beachface, acquired on May 28, 2009. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine aircraft, but the instrument was deployed on a Pilatus PC-6. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations.

Data Series↗
USGS2014

EAARL-B coastal topography: eastern New Jersey, Hurricane Sandy, 2012: first surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida. This project provides highly detailed and accurate datasets for a portion of the New Jersey coastline beachface, acquired pre-Hurricane Sandy on October 26, and post-Hurricane Sandy on November 1 and November 5, 2012. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar system, known as the second-generation Experimental Advanced Airborne Research Lidar (EAARL-B), was used during data acquisition. The EAARL-B system is a raster-scanning, waveform-resolving, green-wavelength (532-nm) lidar designed to map nearshore bathymetry, topography, and vegetation structure simultaneously. The EAARL-B sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, down-looking red-green-blue (RGB) and infrared (IR) digital cameras, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL-B platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL-B system. The resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Web site.

New Jersey↗
USGS2019

Remote sensing of dryland ecosystem structure and function: Progress, challenges, and opportunities

Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.

Remote Sensing of Environment↗
USGS1981

Significant results from using earth observation satellites for mineral and energy resource exploration

A large number of Earth-observation satellites orbit our world several times each day, providing new information about the land and sea surfaces and the overlying thin layer of atmosphere that makes our planet unique. Meteorological satellites have had the longest history of experimental use and most are now considered operational. The geologic information collected by the Landsat, Polar Orbiting Geophysical Observatory (POGO), Magsat, Heat Capacity Mapping Mission (HCMM) and Seasat land and ocean observation systems is being thoroughly tested, and some of these systems are now approaching operational use. Landsat multispectral images provide views of large areas of the Earth under uniform lighting conditions and can be obtained at a variety of scales and formats. Not only do the Landsat data provide highly useful images showing surficial materials and structures such as folds and faults, but also measurements and computer-derived ratios of the brightness of different rock types, alteration zones, and mineral associations. These data have led to the finding of a variety of new ore deposits. In addition, the combination of Landsat digital data and aeromagnetic data has extended the use of Landsat as an exploration tool which can be used to readily relate surface features to subsurface anomalies. Magsat data, now being collected, are helping refine information on major crustal anomalies that were first recognized during the analysis of POGO data. The more nearly circular orbit, lower altitude, and increased sophistication of its vector magnetometer enable Magsat to provide more precise information than POGO. Information of this type is required to develop crustal models. Although Magsat is designed to operate for only 4–8 months, the number of orbits that it should be able to make will be sufficient to accomplish its mission and to record a major magnetic storm expected in 1980. HCMM is a two-band visible to near-IR (0.55–1.1 μm) and thermal infrared (10.2–12.5 μm) system designed to measure reflected solar energy, determine the heat capacity of rocks and to monitor soil moisture, thermal effluents, plant canopy temperatures and snow cover. Launched in April 1978, it is in sun-synchronous, circular orbit at an altitude of 620 km. It is a relatively low-resolution system with an instantaneous field of view (IFOV) of 500–600 m and a swath width of 716 km. However, the system is designed to detect objects in the range of 260°–340° K with a sensitivity (NEδT) of 0.4°K at 280°. Recording the thermal radiation of urban heat islands and high thermal inertia of quartzite strata in the Appalachian region are two examples of its land applications. Launched in June 1978, Seasat operated for only 100 days, but successfully acquired much information over both sea and land. The collection of synthetic aperture radar (SAR) imagery and radar altimetry was particularly important to geologists. Although there are difficulties in processing and distributing these data in a timely manner, initial evaluations indicate that the radar imagery supplements Landsat data by increasing the spectral range and offering a different look angle. The radar altimeter provides accurate profiles over narrow strips of land (1 km wide) and has demonstrated usefulness in measuring icecap surfaces (Greenland, Iceland, and Antarctica). The Salar of Uyuni in southern Bolivia served as a calibration site for the altimeter and has enabled investigators to develop a land-based smoothing algorithm that is believed to increase the accuracy of the system to 10 cm. Data from the altimeter are currently being used to measure subsidence resulting from ground water withdrawal in the Phoenix-Tucson area.

Advances in Space Research↗
USGS2023

Debris-flow process controls on steepland morphology in the San Gabriel Mountains, California

Steep landscapes evolve largely by debris flows, in addition to fluvial and hillslope processes. Abundant field observations document that debris flows incise valley bottoms and transport substantial sediment volumes, yet their contributions to steepland morphology remain uncertain. This has, in turn, limited the development of debris-flow incision rate formulations that produce morphology consistent with natural landscapes. In many landscapes, including the San Gabriel Mountains (SGM), California, steady-state fluvial channel longitudinal profiles are concave-up and exhibit a power-law relationship between channel slope and drainage area. At low drainage areas, however, valley slopes become nearly constant. These topographic forms result in a characteristically curved slope-area signature in log-log space. Here, we use a one-dimensional landform evolution model that incorporates debris-flow erosion to reproduce the relationship between this curved slope-area signature and erosion rate in the SGM. Topographic analysis indicates that the drainage area at which steepland valleys transition to fluvial channels correlates with measured erosion rates in the SGM, and our model results reproduce these relationships. Further, the model only produces realistic valley profiles when parameters that dictate the relationship between debris-flow erosion, valley-bottom slope, and debris-flow depth are within a narrow range. This result helps place constraints on the mathematical form of a debris-flow incision law. Finally, modeled fluvial incision outpaces debris-flow erosion at drainage areas less than those at which valleys morphologically transition from near-invariant slopes to concave profiles. This result emphasizes the critical role of debris-flow incision for setting steepland form, even as fluvial incision becomes the dominant incisional process.

California↗
USGS2023

Documenting Arctic sea ice dynamics with Global Fiducials Program imagery

For more than 25 years, the U.S. Geological Survey (USGS) has used the remote-sensing capabilities of United States National Imagery Systems (USNIS) to obtain high-resolution electro-optical imagery to monitor Earth’s response to global environmental change. A major focus has been monitoring sea ice behavior in the Arctic Ocean and its marginal seas. In 1997 and 1998, under the direction of the Global Fiducials Program (GFP), USNIS imagery was collected during the Surface Heat Budget of the Arctic Ocean (SHEBA) Project. In 1999, collection of USNIS imagery of six static sea ice sites in the Arctic Ocean and its marginal seas began, and the imagery was archived in the USGS-hosted Global Fiducials Library (GFL). The static sites were imaged through 2014, creating time series of geographically referenced images which scientists have used to study seasonal changes in Arctic ice over the same locations for extended time periods. In early 2009, the Central Intelligence Agency’s MEDEA Program requested that the USGS use USNIS imagery to track movements of sea ice floes during an entire Arctic summer (April through September). The goal was to improve researchers’ understanding of seasonal changes in Arctic sea ice. In order to track and repeatedly capture imagery of the same ice as it drifted across the Arctic Ocean, the USGS developed a methodology and a series of protocols to use data from telemetering drift buoys deployed at locations across the Arctic Ocean by the International Arctic Buoy Programme (IABP) to track the drift of targeted ice masses for periods that exceeded a year. Resulting time series of sea ice imagery, captured while monitoring 38 individual buoys, were archived in the GFL. In 2013 and 2014, in support of the Seasonal Ice Zone Reconnaissance Surveys (SIZRS) Program led by the University of Washington, Seattle, Washington, the USGS requested the collection of USNIS imagery of selected sites in the Beaufort and Chukchi Seas located at every degree of latitude between 70º and 80º N. along a north-south transect. This was done to track and understand the interplay among the ice, atmosphere, and ocean and what it contributes to the rapid decline in summer ice extent that has occurred in recent years. Under the auspices of the GFP, thousands of sea ice images have been collected. Many of those that pass a quality and cloud-cover screening are archived in the GFL. Of these, more than 1,750 sea ice images have been publicly released, following an editing and processing procedure that produces high-resolution degraded images, known as “literal imagery-derived products” or LIDPs. These LIDPs have been approved for free, unrestricted public distribution and scientific analysis. The LIDPs can be downloaded from the USGS Global Fiducials Library Data Access Portal (USGS GFLDAP) at https://www.usgs.gov/global-fiducials-library-data-access-portal . In addition, nonliteral imagery-derived products (nonliteral IDPs), such as metadata, maps, charts, and graphs, have also been released.

Open-File Report↗
USGS2010

EAARL Coastal Topography-Eastern Florida, Post-Hurricane Jeanne, 2004: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the eastern Florida coastline beachface, acquired post-Hurricane Jeanne (September 2004 hurricane) on October 1, 2004. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2010

EAARL Coastal Topography-Eastern Louisiana Barrier Islands, Post-Hurricane Gustav, 2008: First Surface

These remotely sensed, geographically referenced elevation measurements of lidar-derived first-surface (FS) topography datasets were produced collaboratively by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the eastern Louisiana barrier islands, acquired post-Hurricane Gustav (September 2008 hurricane) on September 6 and 7, 2008. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Decision Support for Coastal Science and Management website.

Data Series↗
USGS2009

EAARL Coastal Topography - Northern Gulf of Mexico, 2007: First surface

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) elevation data were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the National Park Service (NPS), Gulf Coast Network, Lafayette, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. The project provides highly detailed and accurate datasets of select barrier islands and peninsular regions of Louisiana, Mississippi, Alabama, and Florida, acquired June 27-30, 2007. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of Lidar data in an interactive or batch mode. Modules for presurvey flight line definition, flight path plotting, Lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.

Alabama, Mississippi↗
USGS2013

EAARL coastal topography and imagery–Western Louisiana, post-Hurricane Rita, 2005: First surface

These remotely sensed, geographically referenced color-infrared (CIR) imagery and elevation measurements of lidar-derived first-surface (FS) topography datasets were produced by the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center, St. Petersburg, Florida, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, Virginia. This project provides highly detailed and accurate datasets of a portion of the Louisiana coastline beachface, acquired post-Hurricane Rita on September 27-28 and October 2, 2005. The datasets are made available for use as a management tool to research scientists and natural-resource managers. An innovative airborne lidar instrument originally developed at the National Aeronautics and Space Administration (NASA) Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multispectral color-infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for sub-meter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of lidar data in an interactive or batch mode. Modules for presurvey flight-line definition, flight-path plotting, lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or sub-aerial topography. Specialized filtering algorithms have been implemented to determine the "bare earth" under vegetation from a point cloud of last return elevations. For more information about similar projects, please visit the Lidar for Science and Resource Management Website.

Louisiana↗
USGS2017

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

Many approaches have been developed for measuring or estimating actual evapotranspiration ( ET a ), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ET a . Several remote sensing methods can be used to estimate ET a at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ET a estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ET a and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ET a that are currently applied successfully in the United States. The METRIC model can produce maps of ET a at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ET a against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ET a . The SSEBop model has been used to produce maps of ET a over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ET a estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ET a estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth Engine TM has been used to implement METRIC with automated calibration for regional-scale estimates of ET a using Landsat data. The U.S. Geological Survey also is using Google Earth Engine TM to implement SSEBop for estimating ET a in the United States at a continental scale using Landsat data.

Scientific Investigations Report↗
USGS2017

Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA

This study examined the effects of natural and anthropogenic changes in confining margin width by applying remote sensing techniques – fusing LiDAR topography with image-derived bathymetry – over a large spatial extent: 58 km of the Snake River, Wyoming, USA. Fused digital elevation models from 2007 and 2012 were differenced to quantify changes in the volume of stored sediment, develop morphological sediment budgets, and infer spatial gradients in bed material transport. Our study spanned two similar reaches that were subject to different controls on confining margin width: natural terraces versus artificial levees. Channel planform in reaches with similar slope and confining margin width differed depending on whether the margins were natural or anthropogenic. The effects of tributaries also differed between the two reaches. Generally, the natural reach featured greater confining margin widths and was depositional, whereas artificial lateral constriction in the leveed reach produced a sediment budget that was closer to balanced. Although our remote sensing methods provided topographic data over a large area, net volumetric changes were not statistically significant due to the uncertainty associated with bed elevation estimates. We therefore focused on along-channel spatial differences in bed material transport rather than absolute volumes of sediment. To complement indirect estimates of sediment transport derived by morphological sediment budgeting, we collected field data on bed mobility through a tracer study. Surface and subsurface grain size measurements were combined with bed mobility observations to calculate armoring and dimensionless sediment transport ratios, which indicated that sediment supply exceeded transport capacity in the natural reach and vice versa in the leveed reach. We hypothesize that constriction by levees induced an initial phase of incision and bed armoring. Because levees prevented bank erosion, the channel excavated sediment by migrating rapidly across the restricted braidplain and eroding bars and islands.

Wyoming↗