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USGS2019

India National Gas Hydrate Program Expedition-02: Operational and technical summary

The India National Gas Hydrate Program is being steered by the government of India's Ministry of Petroleum and Natural Gas (MoPNG) with participation of Directorate General of Hydrocarbons (DGH), Oil and Natural Gas Corporation Limited (ONGC), and the National Oil Companies and Research Institutes of India. The India National Gas Hydrate Program Expedition 01 (NGHP-01) established the presence of gas hydrate in the Krishna Godavari (KG) and Mahanadi Basins and in the offshore area of the Andaman Sea Basin. However, the gas hydrates discovered during NGHP-01 were mainly distributed as fracture-filling material in fine-grained clay-rich sediments. The India National Gas Hydrate Program Expedition 02 (NGHP-02) was carried out with an objective to discover gas hydrate in sand-rich sediment along the eastern offshore margin of India. ONGC planned and executed NGHP-02 on the behalf of the MoPNG. NGHP-02 started on March 3, 2015 and was completed on July 28, 2015 (total 147 days) using the Japanese scientific Drilling Vessel Chikyu (D/V Chikyu). During NGHP-02, 42 holes at 25 sites were drilled, cored, and/or surveyed with downhole logging tools. These sites were located in four areas along the eastern margin of India and formally named Area A (Mahanadi Basin, three sites), Area B (northern part of the KG-Basin, twelve sites), Area C (central part of the KG-Basin, six sites), and Area E (southern part to the KG-Basin, four sites). All 25 sites established during NGHP-02 were first drilled and logged with logging-while-drilling (LWD) tools and an additional 17 holes were then drilled and/or cored with conventional coring tools (HPCS/ESCS) or pressure coring tools (PCTB). Wireline logging was conducted in 10 holes and formation tests using a dual packer Modular Formation Dynamics Tester (MDT) tool were carried out in two holes. The onboard science team used the laboratory facilities on the D/V Chikyu to examine and analyse the physical properties, geochemistry, and sedimentology of all the cores collected during the expedition. Core samples were also analysed in additional post-expedition shore-based studies conducted in numerous domestic and international gas hydrate research laboratories. The NGHP-02 sediment cores were archived at the National Gas Hydrate Core Repository in Mumbai (India), which is associated with the ONGC Gas Hydrate Research and Technology Centre (GHRTC). The necessary data for characterizing the occurrence of gas hydrate, such as interstitial water chlorinities, core-derived gas chemistry, physical and sedimentological properties, thermal images of the recovered cores, pressure core and downhole measured logging data (LWD and/or conventional wireline log data), were obtained from most of the drill sites established during NGHP-02. Almost all the drill sites yielded evidence for the occurrence of gas hydrate; however, the inferred in situ concentration of gas hydrate varied substantially from site to site. For the most part, the interpretation of downhole logging data, core thermal images, interstitial water analyses, and pressure core images from the sites established during NGHP-02 indicate that the occurrence of concentrated gas hydrate is mostly associated with coarser grained (sand-rich) sediments. This paper presents the operational and technical summary of NGHP-02. NGHP-02 started on March 3, 2015 and was completed on July 28, 2015 (total 147 days) using the Japanese scientific Drilling Vessel Chikyu (D/V Chikyu). During NGHP-02, 42 holes at 25 sites were drilled, cored, and/or surveyed with downhole logging tools. These sites were located in four areas along the eastern margin of India and formally named Area A (Mahanadi Basin, three sites), Area B (northern part of the KG-Basin, twelve sites), Area C (central part of the KG-Basin, six sites), and Area E (southern part to the KG-Basin, four sites). All 25 sites established during NGHP-02 were first drilled and logged with logging-while-drilling (LWD) to

Journal of Marine and Petroleum Geology↗

USGS2014

Analysis of the impact of spatial resolution on land/water classifications using high-resolution aerial imagery

Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m 2 ). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.

Louisiana↗

USGS2013

Radiometric cross-calibration of EO-1 ALI with L7 ETM+ and Terra MODIS sensors using near-simultaneous desert observations

The Earth Observing-1 (EO-1) satellite was launched on November 21, 2000, as part of a one-year technology demonstration mission. The mission was extended because of the value it continued to add to the scientific community. EO-1 has now been operational for more than a decade, providing both multispectral and hyperspectral measurements. As part of the EO-1 mission, the Advanced Land Imager (ALI) sensor demonstrates a potential technological direction for the next generation of Landsat sensors. To evaluate the ALI sensor capabilities as a precursor to the Operational Land Imager (OLI) onboard the Landsat Data Continuity Mission (LDCM, or Landsat 8 after launch), its measured top-of-atmosphere (TOA) reflectances were compared to the well-calibrated Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors in the reflective solar bands (RSB). These three satellites operate in a near-polar, sun-synchronous orbit 705 km above the Earth's surface. EO-1 was designed to fly one minute behind L7 and approximately 30 minutes in front of Terra. In this configuration, all the three sensors can view near-identical ground targets with similar atmospheric, solar, and viewing conditions. However, because of the differences in the relative spectral response (RSR), the measured physical quantities can be significantly different while observing the same target. The cross-calibration of ALI with ETM+ and MODIS was performed using near-simultaneous surface observations based on image statistics from areas observed by these sensors over four desert sites (Libya 4, Mauritania 2, Arabia 1, and Sudan 1). The differences in the measured TOA reflectances due to RSR mismatches were compensated by using a spectral band adjustment factor (SBAF), which takes into account the spectral profile of the target and the RSR of each sensor. For this study, the spectral profile of the target comes from the near-simultaneous EO-1 Hyperion data over these sites. The results indicate that the TOA reflectance measurements for ALI agree with those of ETM+ and MODIS to within 5% after the application of SBAF.

Arabia↗

USGS2015

Intercomparison of SO 2 camera systems for imaging volcanic gas plumes

SO 2 camera systems are increasingly being used to image volcanic gas plumes. The ability to derive SO 2 emission rates directly from the acquired imagery at high time resolution allows volcanic process studies that incorporate other high time-resolution datasets. Though the general principles behind the SO 2 camera have remained the same for a number of years, recent advances in CCD technology and an improved understanding of the physics behind the measurements have driven a continuous evolution of the camera systems. Here we present an intercomparison of seven different SO 2 cameras. In the first part of the experiment, the various technical designs are compared and the advantages and drawbacks of individual design options are considered. Though the ideal design was found to be dependent on the specific application, a number of general recommendations are made. Next, a time series of images recorded by all instruments at Stromboli Volcano (Italy) is compared. All instruments were easily able to capture SO 2 clouds emitted from the summit vents. Quantitative comparison of the SO 2 load in an individual cloud yielded an intra-instrument precision of about 12%. From the imagery, emission rates were then derived according to each group's standard retrieval process. A daily average SO 2 emission rate of 61 ± 10 t/d was calculated. Due to differences in spatial integration methods and plume velocity determination, the time-dependent progression of SO 2 emissions varied significantly among the individual systems. However, integration over distinct degassing events yielded comparable SO 2 masses. Based on the intercomparison data, we find an approximate 1-sigma precision of 20% for the emission rates derived from the various SO 2 cameras. Though it may still be improved in the future, this is currently within the typical accuracy of the measurement and is considered sufficient for most applications.

Journal of Volcanology and Geothermal Research↗

USGS2002

U.S. Geological Survey spatial data access

The U.S. Geological Survey (USGS) has done a progress review on improving access to its spatial data holdings over the Web. The USGS EROS Data Center has created three major Web-based interfaces to deliver spatial data to the general public; they are Earth Explorer, the Seamless Data Distribution System (SDDS), and the USGS Web Mapping Portal. Lessons were learned in developing these systems, and various resources were needed for their implementation. The USGS serves as a fact-finding agency in the U.S. Government that collects, monitors, analyzes, and provides scientific information about natural resource conditions and issues. To carry out its mission, the USGS has created and managed spatial data since its inception. Originally relying on paper maps, the USGS now uses advanced technology to produce digital representations of the Earth’s features. The spatial products of the USGS include both source and derivative data. Derivative datasets include Digital Orthophoto Quadrangles (DOQ), Digital Elevation Models, Digital Line Graphs, land-cover Digital Raster Graphics, and the seamless National Elevation Dataset. These products, created with automated processes, use aerial photographs, satellite images, or other cartographic information such as scanned paper maps as source data. With Earth Explorer, users can search multiple inventories through metadata queries and can browse satellite and DOQ imagery. They can place orders and make payment through secure credit card transactions. Some USGS spatial data can be accessed with SDDS. The SDDS uses an ArcIMS map service interface to identify the user’s areas of interest and determine the output format; it allows the user to either download the actual spatial data directly for small areas or place orders for larger areas to be delivered on media. The USGS Web Mapping Portal provides views of national and international datasets through an ArcIMS map service interface. In addition, the map portal posts news about new map services available from the USGS, many simultaneously published on the Environmental Systems Research Institute Geography Network. These three information systems use new software tools and expanded hardware to meet the requirements of the users. The systems are designed to handle the required workload and are relatively easy to enhance and maintain. The software tools give users a high level of functionality and help the system conform to industry standards. The hardware and software architecture is designed to handle the large amounts of spatial data and Internet traffic required by the information systems. Last, customer support was needed to answer questions, monitor e-mail, and report customer problems.

Journal of Geospatial Engineering↗

USGS2005

The Circumpolar Arctic vegetation map

Question: What are the major vegetation units in the Arctic, what is their composition, and how are they distributed among major bioclimate subzones and countries? Location: The Arctic tundra region, north of the tree line. Methods: A photo-interpretive approach was used to delineate the vegetation onto an Advanced Very High Resolution Radiometer (AVHRR) base image. Mapping experts within nine Arctic regions prepared draft maps using geographic information technology (ArcInfo) of their portion of the Arctic, and these were later synthesized to make the final map. Area analysis of the map was done according to bioclimate subzones, and country. The integrated mapping procedures resulted in other maps of vegetation, topography, soils, landscapes, lake cover, substrate pH, and above-ground biomass. Results: The final map was published at 1:7 500 000 scale map. Within the Arctic (total area = 7.11 x 106 km 2), about 5.05 ?? 106 km2 is vegetated. The remainder is ice covered. The map legend generally portrays the zonal vegetation within each map polygon. About 26% of the vegetated area is erect shrublands, 18% peaty graminoid tundras, 13% mountain complexes, 12% barrens, 11% mineral graminoid tundras, 11% prostrate-shrub tundras, and 7% wetlands. Canada has by far the most terrain in the High Arctic mostly associated with abundant barren types and prostrate dwarf-shrub tundra, whereas Russia has the largest area in the Low Arctic, predominantly low-shrub tundra. Conclusions: The CAVM is the first vegetation map of an entire global biome at a comparable resolution. The consistent treatment of the vegetation across the circumpolar Arctic, abundant ancillary material, and digital database should promote the application to numerous land-use, and climate-change applications and will make updating the map relatively easy. ?? IAVS; Opulus Press.

Journal of Vegetation Science↗

USGS2015

Evaluating unsupervised methods to size and classify suspended particles using digital in-line holography

Substantial information can be gained from digital in-line holography of marine particles, eliminating depth-of-field and focusing errors associated with standard lens-based imaging methods. However, for the technique to reach its full potential in oceanographic research, fully unsupervised (automated) methods are required for focusing, segmentation, sizing and classification of particles. These computational challenges are the subject of this paper, in which we draw upon data collected using a variety of holographic systems developed at Plymouth University, UK, from a significant range of particle types, sizes and shapes. A new method for noise reduction in reconstructed planes is found to be successful in aiding particle segmentation and sizing. The performance of an automated routine for deriving particle characteristics (and subsequent size distributions) is evaluated against equivalent size metrics obtained by a trained operative measuring grain axes on screen. The unsupervised method is found to be reliable, despite some errors resulting from over-segmentation of particles. A simple unsupervised particle classification system is developed, and is capable of successfully differentiating sand grains, bubbles and diatoms from within the surf-zone. Avoiding miscounting bubbles and biological particles as sand grains enables more accurate estimates of sand concentrations, and is especially important in deployments of particle monitoring instrumentation in aerated water. Perhaps the greatest potential for further development in the computational aspects of particle holography is in the area of unsupervised particle classification. The simple method proposed here provides a foundation upon which further development could lead to reliable identification of more complex particle populations, such as those containing phytoplankton, zooplankton, flocculated cohesive sediments and oil droplets.

Journal of Atmospheric and Oceanic Technology↗

USGS2022

NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

Bats play crucial ecological roles and provide valuable ecosystem services, yet many populations face serious threats from various ecological disturbances. The North American Bat Monitoring Program (NABat) aims to use its technology infrastructure to assess status and trends of bat populations, while developing innovative and community-driven conservation solutions. Here, we present NABat ML , an automated machine-learning algorithm that improves the scalability and scientific transparency of NABat acoustic monitoring. This model combines signal processing techniques and convolutional neural networks (CNNs) to detect and classify recorded bat echolocation calls. We developed our CNN model with internet-based computing resources (‘cloud environment’), and trained it on >600,000 spectrogram images. We also incorporated species range maps to improve the robustness and accuracy of the model for future ‘unseen’ data. We evaluated model performance using a comprehensive, independent, holdout dataset. NABat ML successfully distinguished 31 classes (30 species and a noise class) with overall weighted-average accuracy and precision rates of 92%, and ≥90% classification accuracy for 19 of the bat species. Using a single cloud-environment computing instance, the entire model training process took <16 h. Synthesis and applications . Our convolutional neural network (CNN)-based model, NABat ML , classifies 30 North American bat species using their recorded echolocation calls with an overall accuracy of 92%. In addition to providing highly accurate species-level classification, NABat ML and its outputs are compatible with Bayesian and other statistical techniques for measuring uncertainty in classification. Our model is open-source and reproducible, enabling future implementations as software on end-user devices and cloud-based web applications. These qualities make NABat ML highly suitable for applications ranging from grassroots community science initiatives to big-data methods developed and implemented by researchers and professional practitioners. We believe the transparency and accessibility of NABat ML will encourage broad-scale participation in bat monitoring, and enable development of innovative solutions needed to conserve North American bat species.

Journal of Applied Ecology↗

USGS1982

Recommended procedures and techniques for the petrographic description of bituminous coals

Modern coal petrology requires rapid and precise description of great numbers of coal core or bench samples in order to acquire the information required to understand and predict vertical and lateral variation of coal quality for correlation with coal-bed thickness, depositional environment, suitability for technological uses, etc. Procedures for coal description vary in accordance with the objectives of the description. To achieve our aim of acquiring the maximum amount of quantitative information within the shortest period of time, we have adopted a combined megascopic-microscopic procedure. Megascopic analysis is used to identify the distinctive lithologies present, and microscopic analysis is required only to describe representative examples of the mixed lithologies observed. This procedure greatly decreases the number of microscopic analyses needed for adequate description of a sample. For quantitative megascopic description of coal microlithotypes, microlithotype assemblages, and lithotypes, we use (V) for vitrite or vitrain, (E) for liptite, (I) for inertite or fusain, (M) for mineral layers or lenses other than iron sulfide, (S) for iron sulfide, and (X 1 ), (X 2 ), etc. for mixed lithologies. Microscopic description is expressed in terms of V representing the vitrinite maceral group, E the exinite group, I the inertinite group, and M mineral components. volume percentages are expressed as subscripts. Thus (V) 20 (V 80 E 10 I 5 M 5 ) 80 indicates a lithotype or assemblage of microlithotypes consisting of 20 vol. % vitrite and 80% of a mixed lithology having a modal maceral composition V 80 E 10 I 5 M 5 . This bulk composition can alternatively be recalculated and described as V 84 E 8 I 4 M 4 . To generate these quantitative data rapidly and accurately, we utilize an automated image analysis system (AIAS). Plots of VEIM data on easily constructed ternary diagrams provide readily comprehended illustrations of the range of modal composition of the lithologic units making up a given coal bed. The use of bulk-specific-gravity determinations is alo recommended for identification and characterization of the distinctive lithologic units. The availability of an AIAS also enhances the capability to acquire textural information. Ranges of size of maceral and mineral grains can be quickly and precisely determined by use of an AIAS. We assume that shape characteristics of coal particles can also be readily evaluated by automated image analysis, although this evaluation has not yet been attempted in our laboratory. Definitive data on the particulate mineral content of coal constitute another important segment of petrographic description. Characterization of mineral content may be accomplished by optical identification, electron microprobe analysis, X-ray diffraction, and scanning and transmission electron microscopy. Individual mineral grains in place in polished blocks or polished this sections, or separated from the coal matrix by sink-float methods are studied by analytical techniques appropriate to the conditions of sampling.

International Journal of Coal Geology↗

USGS2013

Merapi 2010 eruption—Chronology and extrusion rates monitored with satellite radar and used in eruption forecasting

Despite dense cloud cover, satellite-borne commercial Synthetic Aperture Radar (SAR) enabled frequent monitoring of Merapi volcano's 2010 eruption. Near-real-time interpretation of images derived from the amplitude of the SAR signals and timely delivery of these interpretations to those responsible for warnings, allowed satellite remote sensing for the first time to play an equal role with in situ seismic, geodetic and gas monitoring in guiding life-saving decisions during a major volcanic crisis. Our remotely sensed data provide an observational chronology for the main phase of the 2010 eruption, which lasted 12 days (26 October–7 November, 2010). Unlike the prolonged low-rate and relatively low explosivity dome-forming and collapse eruptions of recent decades at Merapi, the eruption began with an explosive eruption that produced a new summit crater on 26 October and was accompanied by an ash column and pyroclastic flows that extended 8 km down the flanks. This initial explosive event was followed by smaller explosive eruptions on 29 October–1 November, then by a period of rapid dome growth on 1–4 November, which produced a summit lava dome with a volume of ~ 5 × 10 6 m 3 . A paroxysmal VEI 4 magmatic eruption (with ash column to 17 km altitude) destroyed this dome, greatly enlarged the new summit crater and produced extensive pyroclastic flows (to ~ 16 km radial distance in the Gendol drainage) and surges during the night of 4–5 November. The paroxysmal eruption was followed by a period of jetting of gas and tephra and by a second short period (12 h) of rapid dome growth on 6 November. The eruption ended with low-level ash and steam emissions that buried the 6 November dome with tephra and continued at low levels until seismicity decreased to background levels by about 23 November. Our near-real-time commercial SAR documented the explosive events on 26 October and 4–5 November and high rates of dome growth (> 25 m 3 s − 1 ). An event tree analysis for the previous 2006 Merapi eruption indicated that for lava dome extrusion rates > 1.2 m 3 s − 1 , the probability of a large (1872-scale) eruption was ~ 10%. Consequently, the order-of-magnitude greater rates in 2010, along with the explosive start of the eruption on 26 October, the large volume of lava accumulating at the summit by 4 November, and the rapid and large increases in seismic energy release, deformation and gas emissions were the basis for warnings of an unusually large eruption by the Indonesian Geological Agency's Center for Volcanology and Geologic Hazard Mitigation (CVGHM) and their Volcano Research and Technology Development Center (BPPTK) in Yogyakarta — warnings that saved thousands of lives.

Merapi Volcano↗

USGS1999

Overview of the Mars Pathfinder Mission: Launch through landing, surface operations, data sets, and science results

Mars Pathfinder successfully landed at Ares Vallis on July 4, 1997, deployed and navigated a small rover about 100 m clockwise around the lander, and collected data from three science instruments and ten technology experiments. The mission operated for three months and returned 2.3 Gbits of data, including over 16,500 lander and 550 rover images, 16 chemical analyses of rocks and soil, and 8.5 million individual temperature, pressure and wind measurements. Path‐finder is the best known location on Mars, having been clearly identified with respect to other features on the surface by correlating five prominent horizon features and two small craters in lander images with those in high‐resolution orbiter images and in inertial space from two‐way ranging and Doppler tracking. Tracking of the lander has fixed the spin pole of Mars, determined the precession rate since Viking 20 years ago, and indicates a polar moment of inertia, which constrains a central metallic core to be between 1300 and ∼2000 km in radius. Dark rocks appear to be high in silica and geochemically similar to anorogenic andesites; lighter rocks are richer in sulfur and lower in silica, consistent with being coated with various amounts of dust. Rover and lander images show rocks with a variety of morphologies, fabrics and textures, suggesting a variety of rock types are present. Rounded pebbles and cobbles on the surface as well as rounded bumps and pits on some rocks indicate these rocks may be conglomerates (although other explanations are also possible), which almost definitely require liquid water to form and a warmer and wetter past. Air‐borne dust is composed of composite silicate particles with a small fraction of a highly magnetic mineral, interpreted to be most likely maghemite; explanations suggest iron was dissolved from crustal materials during an active hydrologic cycle with maghemite freeze dried onto silicate dust grains. Remote sensing data at a scale of a kilometer or greater and an Earth analog correctly predicted a rocky plain safe for landing and roving with a variety of rocks deposited by catstrophic floods, which are relatively dust free. The surface appears to have changed little since it formed billions of years ago, with the exception that eolian activity may have deflated the surface by ∼3–7 cm, sculpted wind tails, collected sand into dunes, and eroded ventifacts (fluted and grooved rocks). Pathfinder found a dusty lower atmosphere, early morning water ice clouds, and morning near‐surface air temperatures that changed abruptly with time and height. Small scale vortices, interpreted to be dust devils, were observed repeatedly in the afternoon by the meteorology instruments and have been imaged.

Journal of Geophysical Research E: Planets↗