Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Geospatial raster data and vector data created in the frame of the study "Mapping Arctic Lake Ice Backscatter Anomalies using Sentinel-1 Time Series on Google Earth Engine" submitted to the journal "Remote Sensing" and Python code to reproduce the results.

In addition to the full repository (Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies.zip), two reduced alternatives of this repository are available due to large file size of the full repository:

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_without_IW_result_data.zip contains the same data and Python scripts as the full repository, but results based on IW data and tiled EW delta sigma0 images directly exported from Google Earth Engine have been removed. The merged data (from tiled EW delta sigma0 images) and all other results deduced thereof are included.

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_scripts_and_reference_data_only.zip contains only the Python scripts and reference data. The directory structure was retained for better reproducibility.

Please see the associated README-files for details.

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Find alternative fueling stations near an address or ZIP code or along a route in the United States. Enter a state to see a station count. ## Data Collection Methods ## The data in the Alternative Fueling Station Locator are gathered and verified through a variety of methods. The National Renewable Energy Laboratory (NREL) obtains information about new stations from trade media, Clean Cities coordinators, an Add a Station form on the Alternative Fuels Data Center (AFDC) website, and through collaborating with infrastructure equipment and fuel providers. NREL regularly compares its station data with those of other relevant trade organizations and websites. Differences in methodologies and inclusion criteria may result in slight differences between NREL's database and those maintained by other organizations. NREL also collaborates with alternative fuel industry groups to maintain the data. NREL and its data collection subcontractor are currently collaborating with natural gas, electric drive, biodiesel, ethanol, and propane industry groups to establish best practices for identifying new stations in the most-timely manner possible and to develop a more rigorous network for the future. ## Station Update Schedule ## Existing stations in the database are contacted at least once a year on an established schedule to verify they are still operational and dispensing the fuel specified. Based on an established data collection schedule, the database is updated once a month with the exception of electric vehicle supply equipment (EVSE) data, which are updated twice a month. Stations that are no longer operational or no longer provide alternative fuel are removed from the database on a monthly basis or as they are identified. ## Mapping and Counting Methods ## Each point on the map is counted as one station in the station count. A station appears as one point on the map, regardless of the number of fuel dispensers or charging outlets at that location. Station addresses are geocoded and mapped using an automatic geocoding application. The geocoding application returns the most accurate location based on the provided address. Station locations may also be provided by external sources (e.g., station operators) and/or verified in a geographic information system (GIS) tool like Google Earth, Google Maps, or Google StreetView. This information is considered highly accurate, and these coordinates override any information generated using the geocoding application. ## Notes about Specific Station Types ## ### Private Stations ### Stations with an Access of "Private - Fleet customers only" may allow other entities to fuel through a business-to-business arrangement. For more information, fleet customers should refer to the information listed in the details section for that station to contact the station directly. ### Biodiesel Stations ### The Alternative Fueling Station Locator only includes stations offering biodiesel blends of 20% (B20) and above. ### Electric Vehicle Supply Equipment (EVSE) ### An electric charging station, or EVSE, appears as one point on the map, regardless of the number of charging outlets at that location. The number and type of charging outlets available are displayed as additional details when the station location is selected. Each point on the map is counted as one station in the station count. To see a total count of EVSE for all outlets available, go to the Alternative Fueling Station Counts by State table. Residential EVSE locations are not included in the Alternative Fueling Station Locator. ## Liquefied Petroleum Gas (Propane) Stations ### Because many propane stations serve customers other than drivers and fleets, NREL collaborated with the industry to effectively represent the differences. Each propane station is designated as a 'primary' or 'secondary' service type. Both types are able to fuel vehicles. However, locations with a 'primary' designation offer vehicle services and fuel priced specifically for use in vehicles. The details page for each station lists its service designation.

Blue economies are measured by mining national statistics or economic modeling, requiring substantial capability and quality data, both of which are not universally available. The lack of harmonized methods hampers international comparisons and results are usually only attributable at the national scale. An alternative method is described here that leverages an open computing environment and data to quantify blue economies using marine night light producing measurements that are intercomparable and scalable from national to regional to global.

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Associated publication forthcoming: Fox, L., Peter, B. G., Frake, A. N., and Messina, J. P. (Forthcoming). A Bayesian maximum entropy model for predicting tsetse ecological distributions. Journal TBD. Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (Forthcoming): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (Forthcoming) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (Forthcoming) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/... Visit https://dataone.org/datasets/sha256%3A3695619598269618f05611b802adc5f0e04bc7317cfecc7fcd6bc2536f881776 for complete metadata about this dataset.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. This dataset includes all 50 variables as available on CDS. ERA5-Land data is available from 1950 to three months from real-time. Please consult the ERA5-Land "Known Issues" section. In particular, note that three components of the total evapotranspiration have values swapped as follows: variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)), variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)), variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)). The asset is a daily aggregate of ECMWF ERA5 Land hourly assets which includes both flow and non-flow bands. Flow bands are formed by collecting the first hour's data of the following day which holds aggregated sum of previous day and while the non-flow bands are created by averaging all hourly data of the day. The flow bands are labeled with the "_sum" identifier, which approach is different from the daily data produced by Copernicus Climate Data Store, where flow bands are averaged too. Daily aggregates have been pre-calculated to facilitate many applications requiring easy and fast access to the data. Precipitation and other flow (accumulated) bands might occasionally have negative values, which doesn't make physical sense. At other times their values might be excessively high. This problem is due to how the GRIB format saves data: it simplifies or "packs" the data into smaller, less precise numbers, which can introduce errors. These errors get worse when the data varies a lot. Because of this, when we look at the data for a whole day to compute daily totals, sometimes the highest amount of rainfall recorded at one time can seem larger than the total rainfall measured for the entire day. To learn more, Please see: "Why are there sometimes small negative precipitation accumulations"

Abstract: We present a detailed geomorphological map (1:5000-scale) of a middle mountainous area in Jena, Germany. To overcome limitations associated with traditional field-based approaches and to extend the possibility of manually digital mapping in a structural way, we propose an approach using geographic information systems (GIS) and high-resolution digital data. The geomorphological map features were extracted by manually interpreting and analyzing the combination of different data sources using light detection and ranging (LiDAR) data. A combination of topographic and geological maps, digital orthophotos (DOPs), Google Earth images, field investigations, and derivatives from digital terrain models (DTMs) revealed that it is possible to generate and present the geomorphologic features involved in classical mapping approaches. We found that LiDAR-DTM and land surface parameters (LSPs) can provide better results when incorporating the visual interpretation of multidirectional hillshade and LSP composite maps. The genesis of landforms can be readily identified, and findings enabled us to systematically delineate landforms and geomorphological process domains. Although our approach provides a cost effective, objective, and reproducible alternative for the classical approach, we suggest that further use of digital data should be undertaken to support analysis and applications.

JavaScript code to be implemented in Google Earth Engine(c). The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.

Abstract This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow.

The National Census of Ferry Operators (NCFO) Terminals dataset was collected through December 31, 2020 and compiled on October 16, 2024 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Ferry Terminals dataset represents all ferry terminals from operators that provided responses to the 2020 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the United States Virgin Island, and American Samoa. The terminals represent departure and arrival locations for ferry segments in the NCFO. As part of the NCFO questionnaire, respondents were asked terminal name, city, and state. From this information, longitude and latitudes were obtained through open, online searches that include operator websites and map platforms such as Google Maps and Open Street Maps. As a result, termina sometimes do not represent the exact location where ferry vessels dock but may represent other locations such as the operator’s business location or alternative docking locations. However, whenever possible, the terminal locations represent departure and arrival points for that ferry segment. Each terminal contains information about its operation and ownership status, in addition to geographic location and transportation connections, whenever operators provided this information.

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

ArcGIS geological map of Heard Island created using legacy field and sample data together with satellite imagery and published in Fox, Jodi M., et al. "Construction of an intraplate island volcano: The volcanic history of Heard Island." Bulletin of Volcanology 83.5 (2021): 37. The geological map was created in ArcMap 10.0 using satellite imagery, aerial photography, and historical maps and data. An initial map was generated by outlining geological features observed in the remote sensing images and the aerial photographs. This map was then cross-referenced with all available published and unpublished data to verify rock type, stratigraphic unit, and contact relationships. Where uncertainty in rock type or composition existed, the feature has been assigned to the stratigraphic unit without using a rock type label. In addition to published data, we collated and reviewed legacy unpublished maps, rock collections and unpublished data including hand-drawn sketches and notebooks.

Criteria for allocation of rocks to formations were not changed from previous work (Barling 1990; Barling 1994; Lambeth 1948; Lambeth 1952; Stephenson 1964).

Summary of the stratigraphy of Heard Island is as follows:

1. Unconsolidated Deposits (Recent) - Moraines, beach pebble, gravel and sand deposits.
2. Coastal Volcanic Cones (less than 15 ka) - Basaltic ash and scoria cones and associated small lavas.
3. Newer Lavas (~750 ka- Present) - Comprises the Laurens Peninsula Group and the Big Ben Group.
3a. Laurens Peninsula Group - Trachyte, tephrite, trachyandesite and basanite porphyritic lavas. Phenocrysts include clinopyroxene, olivine, plagioclase, kaersutite, magnetite, ilmenite and apatite. High TiO2 and P2O5 content.
3b. Big Ben Group - Basalt-trachybasalt and basanite porphyritic lavas. Basalt-trachybasalt phenocryts include olivine, clinopyroxene, plagioclase and Fe-Ti oxides. Basanite phenocrysts and megacrysts include olivine and clinopyroxene
4. Drygalski Formation (3.63-2.5 Ma) - Subhorizontal. Volcaniclastic breccia, conglomerate, sandstone and mudstone. Conglomerates are clast and matrix supported. Clasts are mainly basalt with minor trachyte, limestone and chert. Pillow lavas. Tillite. Microfossils include foraminifera and palynomorphs. Macrofossil - Austrochlamys heardensis
5. Laurens Peninsula Limestone (Middle Eocene-Middle Oligocene) - Thin, white, grey and blue styolitic carbonate interbedded with thin, soft tuffaceous shales. Lense of chert. Microfossils include foraminifera, coccoliths and palynomorphs. Intruded by trachybasalt and dolerite dykes (5 cm-2 m thick) and dolerite and gabbro sills. Folded and tilted.

For creation of the Heard Island geological map limestone and carbonate rocks were allocated to the Laurens Peninsula Limestones. Fresh, unaltered basalts were allocated to the Newer Lavas (Barling 1990). The Drygalski Formation includes all noncarbonate sedimentary rocks, clastic facies, and basalts between the Laurens Peninsula Limestones and the Newer Lavas (Barling 1990). Defining the boundary between the Drygalski Formation and the Newer Lavas is problematic, here we used the absence of chlorite as a criterion for allocating basalts to the Newer Lavas and the presence of basaltic pillows to allocate rocks to the Drygalski Formation consistent with Barling (1990). Although not ideal, these criteria were retained in the absence of more robust alternatives. Ridges of sediment in front of or adjacent to glaciers (current or since retreated) were mapped as moraines. Glacial retreat has been significant since the 1940s (~20 vol.% reduction), and locations where glaciers have been observed but have since retreated are relatively well known (Ruddell 2006). Ridges of unconsolidated sediment that have unclear relationships with glaciers and that could have been produced by aeolian and/or alluvial processes were mapped as unconsolidated sediment.

Remote Sensing Resources Utilised:
1. Mosaic of QuickBird satellite images of Heard Island (0.6m resolution) collected between 2006 and 2009 provided by the Australian Antarctic Division Data Centre (AADC).
2. Satellite imagery from Google™ Earth. Images collected 1984-2016.
3. Landsat 8 imagery from NASA via the USGS EarthExplorer online platform. Images collected 2013-2020.
4. Analogue aerial photographs collected in 1987 and held at the AADC

Published Resources Utilised
1. Barling J (1990) The petrogenesis of the Newer Lavas on Heard Island unpublished thesis. Department of Earth Sciences, Monash University, Melbourne
2. Barling J (1994) Origin and evolution of a high-Ti ocean island basalt suite; the Laurens Peninsula Series. Heard Island, Indian Ocean Mineralogical Magazine 58A:49–50
3. Barling J, Goldstein SJ,Wheller GE, Nicholls IA (1988) Heard Island; an example of large isotopic variations on a small oceanic island. Chemical Geology 70:46–46
4. Barling J, Goldstein SL, Nicholls IA (1994) Geochemistry of Heard Island (southern Indian Ocean); characterization of an enriched mantle component and implications for enrichment of the sub-Indian Ocean mantle, Journal of Petrology. 35:1017–1053
5. Clarke I (1979) Petrogenesis of basic and ultrabasic lavas on Heard Island. J Geol Soc Aust 26:272–272
6. Clarke I, McDougall I, Whitford DJ (1983) Volcanic evolution of Heard and McDonald islands, southern Indian Ocean. In: Oliver RL, James PR, Jago JB (eds) Antarctic earth science. Cambridge University, Cambridge, United Kingdom (GBR), pp 631–635
7. Collerson KD, Regelous M, Frankland RA, Wendt JI, Wheller G, Anonymous (1998) 1997 eruption of McDonald Island (southern Indian Ocean); new trace element and Th-Sr-Pb-Nd isotopic constraints on Heard-McDonald island magmatism Abstracts. Geological Society of Australia 49:87
8. Duncan RA, Quilty PG, Barling J, Fox JM (2016b) Geological development of Heard Island. Central Kerguelen Plateau. Aust J Earth Sci 63:81–89
9. Fox JM (2014) Heard Island up-date LAVA news. Geological Society of Australia 25:6–7
10. JonkersHA (2003) Late Cenozoic - recent pectinidae (mollusca: bivalvia) of the Southern Ocean and neighbouring regions. Monographs of marine Mollusca no.5. Backhuys Publishers BV, Leiden
11. Kiernan K, McConnell A, Yates T (1998) Tube-fed pahoehoe lava-flow features of Azorella Peninsula, Heard Island, southern Indian Ocean. Polar Record 34:225–236
12. Lambeth AJ (1952) A geological account of Heard Island. Journal and Proceedings of the Royal Society of New South Wales 86 Part 1:14–19
13. Orth K, Carey RJ, Wright R (2013) Heard Island volcanic eruption. September-October, November 2012 LAVA News 24:3–4
14. Patrick M (2013) Heard (Australia): Satellite imagery reveals lava flows in December 2012 Bulletin of the Global Volcanism Network 38:1
15. Patrick MR, Smellie JL (2013) Synthesis: A spaceborne inventory of volcanic activity in Antarctica and southern oceans, 2000–10. Antarctic Science 25:475–500
16. Quilty PG, Wheller G (2000) Heard Island and the McDonald Islands; a window into the Kerguelen Plateau. Papers and Proceedings of the Royal Society of Tasmania 133 Part 2:1–12
17. Quilty PG, Shafik S, McMinn A, Brady H, Clarke I (1983) Microfossil evidence for the age and environment of deposition of sediments of Heard and McDonald Islands. In: Oliver RL, James PR, Jago JB (eds) Antarctic Earth Science. Cambridge University, Cambridge, pp 636–639
18. Quilty PG, Murray-Wallace CV, Whitehead JM (2004) Austrochlamys heardensis (Fleming, 1957) (bivalvia, pectinidae) from Central Kerguelen plateau, Indian Ocean; palaeontology and possible tectonic significance. Antarctic Science 16:329–338. https://doi.org/10.1017/S0954102004002160
19. Ruddell A (2006) An inventory of present glaciers on Heard Island and their historical variation. In: Green K, Woehler EJ (eds) Heard Island; Southern Ocean Sentinel. Surrey Beatty, Chipping Norton, New South Wales (AUS), pp 28–51
20. Stephenson PJ (1964) Some geological observations on Heard Island. In: Adie RJ (ed) Antarctic Geology - Proceedings of the first international symposium on Antarctic geology. North-Holland Publishing Company, Amsterdam, pp 14–24
21. Stephenson PJ (1972) Geochemistry of some Heard Island igneous rocks. In: Adie RJ (ed) Antarctic Geology and Geophysics. Scandinavian University Books, Oslo, pp 793–801
22. Stephenson PJ, Barling J, Wheller G, Clarke I (2006) The geology and volcanic geomorphology of Heard Island. In: Green K, Woehler EJ (eds) Heard Island; Southern Ocean Sentinel. Surrey Beatty, Chipping Norton, Australia, pp 10–27
23. Truswell EM, Quilty PG, McMinn A, MacPhail MK, Wheller GE (2005) Late Miocene vegetation and palaeoenvironments of the Drygalski Formation, Heard Island, Indian Ocean; evidence from palynology. Antarctic Science 17:427–442. https://doi.org/10.1017/S0954102005002865
24. Tyrrell GW (1937) The petrology of Heard Island BANZARE reports 2part 3:27-56

Unpublished Resources Utilised:

1. H.O. Fletcher, 1929 Rock Collection Australian Museum, Sydney.
2. A.J. Lambeth, 1948-1949 Rock collection, hand drawn outcrop sketches and maps, field notebooks, Australian Museum, Sydney
3. P. Blaxland, 1948 Rock Collection Australian Museum, Sydney.
4. G.C Compton, 1951 Personal letter outlining geological observations with sketches made during survey of Heard Island, Australian Museum Sydney.
5. P.G. Law and T. Burstall, 1953 ANARE Interim Report 7 Heard Island, Australian Antarctic Museum Library.
6. I. Clarke, 1982 Technical Report - Expedition to the Australian Territory of Heard Island and McDonald Island, Australian Antarctic Museum Library.
7. R. Vining, 1983 A report of activities by the Heard Island Expedition 1983, Australian Antarctic Division Library, Kingston Tasmania
8. H.R. Burton

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