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At least 397 records · Page 22Linked to original sources
USGS2010

Hyperspectral analysis of columbia spotted frog habitat

Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96 (44 wetlands) correctly classified with the 2002 data and 89 (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74 (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72 (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats. ?? 2010 The Wildlife Society.

Journal of Wildlife Management↗
USGS2011

An acarologic survey and Amblyomma americanum distribution map with implications for tularemia risk in Missouri

In the United States, tickborne diseases occur focally. Missouri represents a major focus of several tickborne diseases that includes spotted fever rickettsiosis, tularemia, and ehrlichiosis. Our study sought to determine the potential risk of human exposure to human-biting vector ticks in this area. We collected ticks in 79 sites in southern Missouri during June 7–10, 2009, which yielded 1,047 adult and 3,585 nymphal Amblyomma americanum , 5 adult Amblyomma maculatum , 19 adult Dermacentor variabilis , and 5 nymphal Ixodes brunneus . Logistic regression analysis showed that areas posing an elevated risk of exposure to A. americanum nymphs or adults were more likely to be classified as forested than grassland, and the probability of being classified as elevated risk increased with increasing relative humidity during the month of June (30-year average). Overall accuracy of each of the two models was greater than 70% and showed that 20% and 30% of the state were classified as elevated risk for human exposure to nymphs and adults, respectively. We also found a significant positive association between heightened acarologic risk and counties reporting tularemia cases. Our study provides an updated distribution map for A. americanum in Missouri and suggests a wide-spread risk of human exposure to A. americanum and their associated pathogens in this region.

Missouri↗
USGS2008

Soil sail content estimation in the Yellow River delta with satellite hyperspectral data

Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl 2 . Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed large absolute values of regression coefficients, with the largest coefficient at 2203 nm. We obtained a root mean squared error (RMSE) for calibration (with 61 samples) of RMSE C = 0.753 ( R 2 = 0.893) and a root mean squared error for validation (with 30 samples) of RMSE V = 0.574. The prediction model was applied on a pixel-by-pixel basis to a Hyperion reflectance image to yield a quantitative surface distribution map of soil salt content. The result was validated successfully from 38 sampling points. We obtained an RMSE estimate of 1.037 ( R 2 = 0.784) for the soil salt content map derived by the PLSR model. The salinity map derived from the SWR model shows that the predicted value is higher than the true value. These results demonstrate that the PLSR method is a more suitable technique than stepwise regression for quantitative estimation of soil salt content in a large area.

Canadian Journal of Remote Sensing↗
USGS2026

A strategic method for expanding the Pacific Flyway Motus network

Understanding the movement and survival of birds of conservation concern during migration is critical for guiding conservation action. Unfortunately, the migratory life stage is understudied in many bird species because of the challenges of following such species over large distances, particularly those with small body sizes. One established method for tracking movement of small avian species is the Motus Wildlife Tracking System. This international collaborative network provides a powerful tool for tracking survival and movement, enabling estimation of parameters such as stopover duration, migratory connectivity, dispersal, and survival that are otherwise difficult to measure. While the Motus network is well established in eastern North America, coverage in the western part of the continent is limited, constraining sample sizes needed for robust demographic modeling. Expanding the Motus network is therefore a high priority for Pacific Flyway agencies. To date, methods for determining priority locations for new Motus receiving stations do not explicitly incorporate species occurrence. In collaboration with partners across the western United States and the Canadian Province of British Columbia, we used eBird data to prioritize strategic locations for new Motus receiving stations. We modeled multi-species co-occurrence, defined by abundance thresholds met in multiple weeks, using environmental and survey covariates while accounting for imperfect detection. Using occupancy model predictions, we identify high-use areas where new Motus stations are most likely to detect multiple species of interest. While the predictive maps provide a framework for strategically expanding the Motus network and supporting conservation of migratory stopover and wintering habitats in the Pacific Flyway, we also provide the analysis code and prediction output to support the expansion of Motus in additional regions.

Pacific flyway↗
USGS2026

Dismembering the Lava Creek Tuff, Yellowstone volcano

The youngest large explosive eruption of Yellowstone volcano generated the 0.63 Ma Lava Creek Tuff (LCT) ignimbrite and a voluminous, widespread fall deposit accompanying formation of Yellowstone Caldera. The tuff is conventionally divided into members A (LCT-A) and B (LCT-B), defined by a reversal in density/welding profiles and the presence of phenocrystic amphibole in LCT-A. Remapping within the northeastern caldera sector has, however, documented multiple ignimbrites that yield LCT eruption ages but are distinct from documented members A and B. Thus, the framework by which the LCT was originally subdivided is re-examined here using field observations, petrologic analyses, and sanidine geochemistry from LCT-A and LCT-B ignimbrite (as previously mapped) at reference localities around the caldera. Mineral modal abundances in both LCT-A and LCT-B outcrops are dominated by quartz, followed by sanidine, with sizes and abundances that generally increase up-section, as previously documented. Amphibole is found by us, however, only in western and southern sectors, in deposits mapped as both LCT-A and LCT-B, and only as a trace phase. Further, a locality reported as LCT-A with amphibole present yields a sanidine 40 Ar/ 39 Ar date of 0.775 ± 0.002 Ma, precluding any LCT correlation. Ignimbrite samples from mapped LCT-A and LCT-B exhibit variable crystal sizes, sanidine cathodoluminescence image populations and Ba concentrations, with no clear distinction between the mapped members. Collectively this research found no evidence for a definable LCT-A versus LCT-B, meaning that the traditional A and B subdivision does not accurately represent the eruptive architecture of the Lava Creek Tuff and warrants revision.

Idaho, Montana, Wyoming↗
USGS2012

Seismic refraction measurements within the Peninsular terrane, south central Alaska

We present an interpretation of crustal seismic refraction data from the Peninsular terrane, one of the many exotic terranes that have been accreted to the continental margin of southern Alaska in the past 200 m.y. A seismic refraction line was collected along the Glenn Highway in the Copper River Basin of south central Alaska in 1984 and 1985, as part of the U.S. Geological Survey Trans‐Alaska Crustal Transect (TACT) program. P wave velocities of 2.7–3.5 km/s and thicknesses of 1–2 km characterize post‐Lower Jurassic sedimentary rocks that underlie most of the seismic refraction line. An average crustal velocity structure includes the following five velocity divisions. Beneath the sedimentary rocks lie 1–2 km of 4.0–4.6 km/s materials, correlating with andesitic volcaniclastic sedimentary rocks and lava flows of the Lower Jurassic Talkeetna Formation. Below these rocks, seismic velocity increases rapidly, from 5.0 to 6.1 km/s, in 2–3 km. At 7–8 km depth, velocity jumps to 6.3 km/s and increments to 6.6 km/s by 10–12 km depth. Velocities increase from 6.8 to 7.0 km/s between 12 to 20 km depth. At about 22 km depth, a jump in velocity from 7.0 to 7.4 km/s is inferred but is poorly resolved. Depth to the Moho discontinuity could not be determined from our data. The absence of clear PmP reflections may indicate that Moho is deeper than 40 km. Data from two offset shotpoints northeast of the line and within the Wrangellia terrane constrain the deep structure transition between Peninsular and Wrangellia terranes. The 6.3–6.6 km/s material thickens to the northeast, toward the suture between Peninsular and Wrangellia terranes, but southwest of its mapped trace at the West Fork fault. Peninsular terrane crustal structure appears dissimilar to that of continental interiors. It is similar to velocity structures determined for accreted island arc fragments in California, such as the basement of the Great Valley and the Klamath Mountains.

Alaska↗
USGS2012

Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on-time time-domain system that uses a continuous triangle-wave excitation to measure the target-step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre-processed and then used in a hybrid discrimination scheme involving both data-driven and numerical classification techniques. The data-driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self-organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self-organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

Geophysical Journal International↗
USGS2006

A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

Aim Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project. Location The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east. Methods The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’. Results The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics. Main conclusions The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub-tropical Asia, and it delivers reasonable thematic detail and quantitative estimates of the main land-cover proportions. The map may therefore serve for regional stratification or modelling of vegetation cover, but could also support the implementation of forest policies, watershed management or conservation strategies at regional scales.

Journal of Biogeography↗
USGS2013

Identifying buried segments of active faults in the northern Rio Grande Rift using aeromagnetic, LiDAR,and gravity data, south-central Colorado, USA

Combined interpretation of aeromagnetic and LiDAR data builds on the strength of the aeromagnetic method to locate normal faults with significant offset under cover and the strength of LiDAR interpretation to identify the age and sense of motion of faults. Each data set helps resolve ambiguities in interpreting the other. In addition, gravity data can be used to infer the sense of motion for totally buried faults inferred solely from aeromagnetic data. Combined interpretation to identify active faults at the northern end of the San Luis Basin of the northern Rio Grande rift has confirmed general aspects of previous geologic mapping but has also provided significant improvements. The interpretation revises and extends mapped fault traces, confirms tectonic versus fluvial origins of steep stream banks, and gains additional information on the nature of active and potentially active partially and totally buried faults. Detailed morphology of surfaces mapped from the LiDAR data helps constrain ages of the faults that displace the deposits. The aeromagnetic data provide additional information about their extents in between discontinuous scarps and suggest that several totally buried, potentially active faults are present on both sides of the valley.

Colorado↗
USGS1998

Oyster resource zones of the Barataria and Terrebonne estuaries of Louisiana

A 1:100,000 scale map delineating the subtidal oyster resource zones within the Barataria and Terrebonne estuaries was developed. Strategies to accomplish the task included interviews with Louisiana oystermen and state biologists to develop a draft map, field sampling to document oyster (Crassostrea virginica), Dermo (Perkinsus marinus), and oyster drill (Stramonita haemastoma) abundances, use of historical salinity data to aid in map verification, and public meetings to allow comment on a draft before final map preparation. Four oyster resource zones were delineated on the final map: a dry zone where subtidal oysters may be found when salinities increase, a wet zone where subtidal oysters may be found when salinities are suppressed, a wet-dry zone where subtidal oysters may be consistently found due to favorable salinities, and a high-salinity zone where natural oyster populations are predominantly found in intertidal and shallow waters. The dry zone is largely coincident with the brackish-marsh habitat, with some intermediate-type marsh. The wet-dry zone is found at the interface of the brackish and saline marshes, but extends further seaward than up-estuary. The wet zone and the high salinity zones are areas of mostly open water fringed by salt marshes. The dry zone encompasses 91,775 hectares, of which 48,788 hectares are water (53%). The wet zone encompasses 83,525 hectares, of which 66,958 hectares are water (80%). The wet-dry zone encompasses 171,893 hectares, of which 104,733 hectares are water (61%). The high salinity zone encompasses 125,705 hectares, of which 113,369 hectares are water (90%). There is a clear trend of increasing water habitat in the four zones over the past 30 years, and oysters are now cultivated on bottoms that were once marsh. The map should be useful in managing the effects upon oysters of freshwater diversions into the estuaries. It provides a pre-diversion record of the location of oyster resource zones and should prove helpful in the seaward relocation of oysters leases.

Journal of Shellfish Research↗
USGS2023

A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome

Escalated wildfire activity within the western U.S. has widespread societal impacts and long-term consequences for the imperiled sagebrush ( Artemisia spp.) biome. Shifts from historical fire regimes and the interplay between frequent disturbance and invasive annual grasses may initiate permanent state transitions as wildfire frequency outpaces sagebrush communities’ innate capacity to recover. Therefore, wildfire management is at the core of conservation plans for sagebrush ecosystems, especially critical habitat for species of conservation concern such as the greater sage-grouse ( Centrocercus urophasianus ; hereafter sage-grouse). Fuel breaks help facilitate wildfire suppression by modifying behavior through fuels modification and allowing safe access points for containment by firefighters. The Bureau of Land Management has proposed to roughly double the existing fuel break network in the western U.S., centered on the Great Basin. To our knowledge, no broad-scale examination of fuel break effectiveness or the environmental conditions under which fuel breaks are expected to be most effective has been conducted. We performed a retrospective assessment of probability of fuel break contributing to wildfire containment on recorded wildfire and fuel break interactions from 1985 to 2018 within the western U.S. We characterized environmental, fuels, and weather conditions within 500 m of wildfire contact, and within 5 km of the approaching wildfire. We used a binomial mixed model within a Bayesian framework to identify relationships between these variables and fuel break success. Fuel breaks were least successful in areas classified as having low resilience to disturbance and low resistance to invasion, in areas composed of primarily woody fuels, and when operating in high temperature and low precipitation conditions. Fuel breaks were most effective in areas where fine fuels dominated and in areas that were readily accessible. Maintenance history and fuel break type also contributed to the probability of containment. Overall results indicate a complex and sometimes paradoxical relationship between landscape characteristics that promote wildfire spread and those that impact fuel break effectiveness. Finally, we developed predictive maps of fuel break effectiveness by fuel break type to further elucidate these complex relationships and to inform urgently needed fuel break placement and maintenance priorities across the sagebrush biome.

Journal of Environmental Management↗
USGS2017

Relative abundance of deformed wing virus, Varroa destructor virus 1, and their recombinants in honey bees (Apis mellifera) assessed by kmer analysis of public RNA-Seq data

Deformed wing virus (DWV) is a major pathogen of concern to apiculture, and recent reports have indicated the local predominance and potential virulence of recombinants between DWV and a related virus, Varroa destructor virus 1 (VDV). However, little is known about the frequency and titer of VDV and recombinants relative to DWV generally. In this study, I assessed the relative occurrence and titer of DWV and VDV in public RNA-seq accessions of honey bee using a rapid, kmer-based approach. Three recombinant types were detectable graphically and corroborated by de novo assembly. Recombination breakpoints did not disrupt the capsid-encoding region, consistent with previous reports, and both VDV- and DWV-derived capsids were observed in recombinant backgrounds. High abundance of VDV kmers was largely restricted to recombinant forms. Non-metric multidimensional scaling identified genotypic clusters among DWV isolates, which was corroborated by read mapping and consensus generation. The recently described DWV-C lineage was not detected in the searched accessions. The data further highlight the utility of high-throughput sequencing to monitor viral polymorphisms and statistically test biological predictors of titer, and point to the need for consistent methodologies and sampling schemes.

Journal of Invertebrate Pathology↗
USGS2012

Dark materials in Valles Marineris: Indications of the style of volcanism and magmatism on Mars

Rifting on the equatorial canyon system of Valles Marineris provides a unique view of the interior of the Martian crust to depths reaching 7 km, exposing several in situ bedrock units which testify to past volcanic and magmatic processes on Mars. A thick, regionally extensive deposit observed in Coprates and Juventae chasmata is interpreted on the basis of spectral reflectance, erosional morphology, and tendency for eolian mobilization to be composed of mafic glass, possibly an ancient Martian analogue of the lunar terra mantling deposits. Spectral mapping suggests that the dark floor‐covering materials in the lower canyons are derived from this unit. A series of cliffs in the Ophir Chasma wall rock is interpreted to be exposures of resistant bedrock; the spectral signature of this massive and uniform unit most closely resembles that of terrestrial mafic rocks altered to or coated by crystalline hematite. Application of computer mapping techniques to probable young volcanic materials in the central troughs yields an inferred distribution of volcanic activity consistent with the interpretation of extrusion along faults near the margins of the canyon floors. This result supports the hypothesis that the valles originated through tectonic extension.

Journal of Geophysical Research B: Solid Earth↗
USGS2017

Annual estimates of recharge, quick-flow runoff, and ET for the contiguous U.S. using empirical regression equations

This study presents new data-driven, annual estimates of the division of precipitation into the recharge, quick-flow runoff, and evapotranspiration (ET) water budget components for 2000-2013 for the contiguous United States (CONUS). The algorithms used to produce these maps ensure water budget consistency over this broad spatial scale, with contributions from precipitation influx attributed to each component at 800 m resolution. The quick-flow runoff estimates for the contribution to the rapidly varying portion of the hydrograph are produced using data from 1,434 gaged watersheds, and depend on precipitation, soil saturated hydraulic conductivity, and surficial geology type. Evapotranspiration estimates are produced from a regression using water balance data from 679 gaged watersheds and depend on land cover, temperature, and precipitation. The quick-flow and ET estimates are combined to calculate recharge as the remainder of precipitation. The ET and recharge estimates are checked against independent field data, and the results show good agreement. Comparisons of recharge estimates with groundwater extraction data show that in 15% of the country, groundwater is being extracted at rates higher than the local recharge. These maps of the internally consistent water budget components of recharge, quick-flow runoff, and ET, being derived from and tested against data, are expected to provide reliable first-order estimates of these quantities across the CONUS, even where field measurements are sparse.

Journal of the American Water Resources Associatio↗
USGS2018

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

Mapping high resolution (30-m or better) cropland extent over very large areas such as continents or large countries or regions accurately, precisely, repeatedly, and rapidly is of great importance for addressing the global food and water security challenges. Such cropland extent products capture individual farm fields, small or large, and are crucial for developing accurate higher-level cropland products such as cropping intensities, crop types, crop watering methods (irrigated or rainfed), crop productivity, and crop water productivity. It also brings many challenges that include handling massively large data volumes, computing power, and collecting resource intensive reference training and validation data over complex geographic and political boundaries. Thereby, this study developed a precise and accurate Landsat 30-m derived cropland extent product for two very important, distinct, diverse, and large countries: Australia and China. The study used of eight bands (blue, green, red, NIR, SWIR1, SWIR2, TIR1, and NDVI) of Landsat-8 every 16-day Operational Land Imager (OLI) data for the years 2013–2015. The classification was performed by using a pixel-based supervised random forest (RF) machine learning algorithm (MLA) executed on the Google Earth Engine (GEE) cloud computing platform. Each band was time-composited over 4–6 time-periods over a year using median value for various agro-ecological zones (AEZs) of Australia and China. This resulted in a 32–48-layer mega-file data-cube (MFDC) for each of the AEZs. Reference training and validation data were gathered from: (a) field visits, (b) sub-meter to 5-m very high spatial resolution imagery (VHRI) data, and (c) ancillary sources such as from the National agriculture bureaus. Croplands versus non-croplands knowledge base for training the RF algorithm were derived from MFDC using 958 reference-training samples for Australia and 2130 reference-training samples for China. The resulting 30-m cropland extent product was assessed for accuracies using independent validation samples: 900 for Australia and 1972 for China. The 30-m cropland extent product of Australia showed an overall accuracy of 97.6% with a producer’s accuracy of 98.8% (errors of omissions = 1.2%), and user’s accuracy of 79% (errors of commissions = 21%) for the cropland class. For China, overall accuracies were 94% with a producer’s accuracy of 80% (errors of omissions = 20%), and user’s accuracy of 84.2% (errors of commissions = 15.8%) for cropland class. Total cropland areas of Australia were estimated as 35.1 million hectares and 165.2 million hectares for China. These estimates were higher by 8.6% for Australia and 3.9% for China when compared with the traditionally derived national statistics. The cropland extent product further demonstrated the ability to estimate sub-national cropland areas accurately by providing an R 2 value of 0.85 when compared with province-wise cropland areas of China. The study provides a paradigm-shift on how cropland maps are produced using multi-date remote sensing. These products can be browsed at www.croplands.org and made available for download at NASA’s Land Processes Distributed Active Archive Center (LP DAAC) https://www.lpdaac.usgs.gov/node/1282 .

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS1999

Early evolution of a stratospheric volcanic eruption cloud as observed with TOMS and AVHRR

This paper is a detailed study of remote sensing data from the total ozone mapping spectrometer (TOMS) and the advanced very high resolution radiometer (AVHRR) satellite detectors, of the 1982 eruption of El Chichón, Mexico. The volcanic cloud/atmosphere interactions in the first four days of this eruption were investigated by combining ultraviolet retrievals to estimate the mass of sulfur dioxide in the volcanic cloud [ Krueger et al. , 1995] with thermal infrared retrievals of the size, optical depth, and mass of fine-grained (1–10 μm radius) volcanic ash [ Wen and Rose , 1994]. Our study provides the first direct evidence of gravitational separation of ash from a stratospheric, gas-rich, plinian eruption column and documents the marked differences in residence times of volcanic ash and sulfur dioxide in volcanic clouds. The eruption column reached as high as 32 km [ Carey and Sigurdsson , 1986] and was injected into an atmosphere with a strong wind shear, which allowed for an observation of the separation of sulfur dioxide and volcanic ash. The upper, more sulfur dioxide-rich part of the cloud was transported to the west in the stratosphere, while the fine-grained ash traveled to the south in the troposphere. The mass of sulfur dioxide released was estimated at 7.1 × 10 9 kg with the mass decreasing by approximately 4% 1 day after the peak. The mass of fine-grained volcanic ash detected was estimated at 6.5 × 10 9 kg, amounting to about 0.7% of the estimated mass of the ash which fell out in the mapped ash blanket close to the volcano. Over the following days, 98% of this remaining fine ash was removed from the volcanic cloud, and the effective radius of ash in the volcanic cloud decreased from about 8 μm to about 4 μm.

Journal of Geophysical Research D: Atmospheres↗
USGS1995

Spatial trends in Pearson Type III statistical parameters

Spatial trends in the statistical parameters (mean, standard deviation, and skewness coefficient) of a Pearson Type III distribution of the logarithms of annual flood peaks for small rural basins (less than 90 km2) are delineated using a climate factor CT, (T=2-, 25-, and 100-yr recurrence intervals), which quantifies the effects of long-term climatic data (rainfall and pan evaporation) on observed T-yr floods. Maps showing trends in average parameter values demonstrate the geographically varying influence of climate on the magnitude of Pearson Type III statistical parameters. The spatial trends in variability of the parameter values characterize the sensitivity of statistical parameters to the interaction of basin-runoff characteristics (hydrology) and climate. -from Authors

Journal of Hydraulic Engineering↗
USGS2020

Stress gradients interact with disturbance to reveal alternative states in salt marsh: Multivariate resilience at the landscape scale

Stress gradients influence many ecosystem processes and properties, including ecosystem recovery from and resistance to disturbance. While recent analytical approaches have advanced multivariate metrics of ecosystem resilience that allow quantification of conceptual resilience models and identification of thresholds of state change, these approaches are not often translated to landscape scales. Using natural and restored salt marshes in Louisiana, USA, we quantified plant community recovery and resistance metrics along flooding stress gradients. n ‐dimensional hypervolumes of plant community biomass and structure were simulated using field data collected from disturbance‐recovery experiments. The relationships between multivariate resilience metrics and flooding stress gradients were then mapped at community‐ and landscape‐relevant scales by scaling with airborne‐derived data across the region. Greater pre‐disturbance abiotic stress decreased live belowground, but not aboveground, biomass, and ultimately led to lower post‐disturbance total recovery, recovery rates, and resistance of plant communities. Vegetated plots flooded >52% of the time transitioned to an alternative, unvegetated state after disturbance. Mapping revealed differences in spatial patterns of resilience‐ highlighting low, interior marsh edges as especially vulnerable to the combination of chronic flooding stress and acute disturbance. At the landscape scale, approximately half of the area (48%) is vulnerable to state change after pulse disturbances. Synthesis . Ultimately, we quantify the ball‐and‐cup conceptual model for a salt marsh ecosystem and its alternative state, mudflat. We find that increasing abiotic stress due to climate change diminishes ecosystem resilience, but the interaction with common episodic disturbances is necessary to reveal transitions to alternative states and quantify state change thresholds. Quantifying and mapping resilience and where alternative states may exist in this fashion improves ecologists’ ability to investigate the mechanisms of stress gradient control on emergent ecosystem properties, while providing spatially explicit resources for managing ecosystems according to their projected resilience.

Louisiana↗