Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Earth in Space”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19Linked to original sources
USGS2010

Structural analysis of three extensional detachment faults with data from the 2000 Space-Shuttle Radar Topography Mission

The Space-Shuttle Radar Topography Mission provided geologists with a detailed digital elevation model of most of Earth's land surface. This new database is used here for structural analysis of grooved surfaces interpreted to be the exhumed footwalls of three active or recently active extensional detachment faults. Exhumed fault footwalls, each with an areal extent of one hundred to several hundred square kilometers, make up much of Dayman dome in eastern Papua New Guinea, the western Gurla Mandhata massif in the central Himalaya, and the northern Tokorondo Mountains in central Sulawesi, Indonesia. Footwall curvature in profile varies from planar to slightly convex upward at Gurla Mandhata to strongly convex upward at northwestern Dayman dome. Fault curvature decreases away from the trace of the bounding detachment fault in western Dayman dome and in the Tokorondo massif, suggesting footwall flattening (reduction in curvature) following exhumation. Grooves of highly variable wavelength and amplitude reveal extension direction, although structural processes of groove genesis may be diverse.

GSA Today↗
USGS2008

State of Florida 1:24,000- and 1:100,000-scale quadrangle index map - Highlighting low-lying areas derived from USGS Digital Elevation Models

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts 1:24,000- and 1:100,000-scale quadrangle footprints over a color shaded relief representation of the State of Florida. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED source data for this map consists of a mixture of 30-meter- and 10-meter-resolution DEMs. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Figure 1 shows a similar representation for the entire U.S. Gulf Coast, using coarsened 30-meter NED data. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. Quadrangle names, dated April, 2006, were obtained from the Federal Geographic Names Information System. The NED data were downloaded in 2004.

Scientific Investigations Map↗
USGS2008

Gulf of Mexico region — Highlighting low-lying areas derived from USGS Digital Elevation Data

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation of the area surrounding the Gulf of Mexico. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s data) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Approximately one-half of the area shown on this map has DEM source data at a 30-meter resolution, with the remaining half consisting of 10-meter contour-derived DEM data or higher-resolution LIDAR data. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2005.

Alabama, Florida, Louisiana, Mississippi, Texas↗
USGS2008

State of Louisiana - Highlighting low-lying areas derived from USGS Digital Elevation Data

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation highlighting the State of Louisiana and depicts the surrounding areas using muted elevation colors. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data are a mixture of data and were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. Approximately one-half of the area shown on this map has DEM source data at a 30-meter resolution, with the remaining half consisting of mostly 10-meter contour-derived DEM data and some small areas of higher-resolution LIght Detection And Ranging (LIDAR) data along parts of the coastline. Areas below sea level typically are surrounded by levees or some other type of flood-control structures. State and parish boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2007.

Scientific Investigations Map↗
USGS2008

State of Texas - Highlighting low-lying areas derived from USGS Digital Elevation Data

In support of U.S. Geological Survey (USGS) disaster preparedness efforts, this map depicts a color shaded relief representation of Texas and a grayscale relief of the surrounding areas. The first 30 feet of relief above mean sea level are displayed as brightly colored 5-foot elevation bands, which highlight low-elevation areas at a coarse spatial resolution. Standard USGS National Elevation Dataset (NED) 1 arc-second (nominally 30-meter) digital elevation model (DEM) data are the basis for the map, which is designed to be used at a broad scale and for informational purposes only. The NED data were derived from the original 1:24,000-scale USGS topographic map bare-earth contours, which were converted into gridded quadrangle-based DEM tiles at a constant post spacing (grid cell size) of either 30 meters (data before the mid-1990s) or 10 meters (mid-1990s and later data). These individual-quadrangle DEMs were then converted to spherical coordinates (latitude/longitude decimal degrees) and edge-matched to ensure seamlessness. The NED source data for this map consists of a mixture of 30-meter- and 10-meter-resolution DEMs. State and county boundary, hydrography, city, and road layers were modified from USGS National Atlas data downloaded in 2003. The NED data were downloaded in 2002. Shaded relief over Mexico was obtained from the USGS National Atlas.

Scientific Investigations Map↗
USGS2023

Calibration and validation for the Surface Biology and Geology (SBG) mission concept: Recommendations for a multi-sensor system for imaging spectroscopy and thermal imagery

The primary objective of the National Aeronautics and Space Administration (NASA) Surface Biology and Geology (SBG) mission is to measure biological, physical, chemical, and mineralogical features of the Earth's surface, realizing a key conceptual component of the envisioned NASA Earth System Observatory (ESO). SBG is planned to launch as a two-platform mission in the late 2020s, the first of the ESO satellites. Targeted science and applications objectives based on observations of the Earth's SBG helped to define the mission architecture and instrument capabilities for the SBG mission concept. These objectives further drove the need for enabling change detection and trending of surface biological and geological features. These needs implied fundamental calibration goals to achieve the necessary science data quality characteristics. To meet those goals, calibration and validation pre-launch and on-orbit methods formed a basis of the calibration and validation concept, including the combined use of on-board references, vicarious techniques, and routine lunar imaging. International collaboration with space agencies in other countries, an important feature of the recommended SBG mission architecture, uncovered and emphasized the need for inter-calibration techniques that underscored the importance of collaborative instrument characterization data sharing and the use of common calibration references that are International System of Units (SI) traceable in pre-launch and post-launch on orbit calibration mission phases. International collaboration through the use of terrestrial and aquatic networks on six continents for vicarious calibration and validation activities will further assure necessary science data quality while in orbit.

JGR Biogeosciences↗
USGS2010

An overview of the web-based Google Earth coincident imaging tool

The Committee on Earth Observing Satellites (CEOS) Visualization Environment (COVE) tool is a browser-based application that leverages Google Earth web to display satellite sensor coverage areas. The analysis tool can also be used to identify near simultaneous surface observation locations for two or more satellites. The National Aeronautics and Space Administration (NASA) CEOS System Engineering Office (SEO) worked with the CEOS Working Group on Calibration and Validation (WGCV) to develop the COVE tool. The CEOS member organizations are currently operating and planning hundreds of Earth Observation (EO) satellites. Standard cross-comparison exercises between multiple sensors to compare near-simultaneous surface observations and to identify corresponding image pairs are time-consuming and labor-intensive. COVE is a suite of tools that have been developed to make such tasks easier.

Conference Paper↗
USGS1991

Volcanic eruptions; energy and size

The Earth is a dynamic planet. Many different processes are continuously developing, creating a delicate balance between the energy stored and generated in its interior and the heat lost into space. The heat in continuously transferred through complex self-regulating convection mechanisms on a planetary scale. The distribution of terrestrial heat flow reveals some of the fine structure of the energy transport mechanisms in the outer layers of the Earth. Of these mechanisms in the outer layers of the Earth. Of these mechanisms, volcanism is indeed the most remarkable, for it allows energy to be transported in rapid bursts to the surface. In order to maintain the subtle balance of the terrestrial heat machine, one may expect that some law or principle restricts the ways in which these volcanic bursts affect the overall energy transfer of the Earth. For instance, we know that the geothermal flux of the planet amounts to 10 28 erg/year. On the other hand, a single large event like the Lava Creek Tuff eruption that formed Yellowstone caldera over half a million years ago may release the same amount of energy in a very small area, over a short period of time.

Earthquakes & Volcanoes (USGS)↗
USGS2016

How well are you teaching one of the most important biological concepts for humankind? A call to action

We represent several generations of biology educators – with teaching experiences beginning in the 1940s and continuing to the present, from elementary school to graduate-level programs. We find the vast array of subjects that biology teachers can now cover both thrilling and mind-boggling. Depending on the grade level, units exist that focus on neurobiology, forensics, DNA analysis, biotechnology, marine biology, and a host of other topics. Although science teachers cover a potpourri of advanced topics, we must ask ourselves – no matter our biology-teaching responsibilities – how well we are teaching carrying capacity , one of the most fundamental biological concepts for our society, knowledge of which becomes more important every day. As biology teachers, most of you know that carrying capacity is defined as the maximum population an environment can sustain, given the amounts of food, habitat, and other resources available. Every environment – from your goldfish bowl to the local forest to planet Earth – can only sustain a set number (weight) of a particular species, based on available resources and space. Currently, most science classes teach …

The American Biology Teacher↗
USGS2014

Topographic lidar survey of the Alabama, Mississippi, and Southeast Louisiana Barrier Islands, from September 5 to October 11, 2012

This Data Series Report contains lidar elevation data collected from September 5 to October 11, 2012, for the barrier islands of Alabama, Mississippi and southeast Louisiana, including the coast near Port Fourchon. Most of the data were collected September 5–10, 2012, with a reflight conducted on October 11, 2012, to increase point density in some areas. Point cloud data—data points described in three dimensions—in lidar data exchange format (LAS), and bare earth digital elevation models (DEMs) in ERDAS Imagine raster format (IMG), are available as downloadable files. The point cloud data were processed to extract bare earth data; therefore, the point cloud data are organized into four classes: 1-unclassified, 2-ground, 7-noise and 9-water. Aero-Metric, Inc., was contracted by the U.S. Geological Survey (USGS) to collect and process these data. The lidar data were acquired at a horizontal spacing (or nominal pulse spacing) of 1 meter (m) or less. The USGS conducted two ground surveys in a small area on Chandeleur Island on September 6, 2012, one on bare earth and the other in both bare earth and vegetated areas. The USGS calculated a vertical root mean square error (RMSEz) of 0.072 m and an offset of 0.007 m using interpolated 2-m by 2-m resolution grid surfaces made from the lidar bare-earth data and the combined USGS ground surveys. Aero-Metric, Inc., calculated an RMSEz of 0.025 m by comparing the USGS bare earth ground survey point data to the closest lidar points. The USGS also conducted a terrestrial lidar survey on Dauphin Island, Louisiana, on September 3, 2012. The USGS calculated a RMSEz of 0.32 m and an offset of 0.27 m, meaning the lidar data were 0.27 m higher than the ground truth (Guy and others, 2013), using interpolated 2-m by 2-m resolution grid surfaces from the airborne lidar bare-earth data and the terrestrial lidar survey. This lidar survey was acquired to document the changes of several different barrier island systems resulting from Hurricane Isaac (Guy and others, 2013). The survey supports detailed studies of Louisiana, Mississippi and Alabama barrier islands that resolve annual and episodic changes in beaches, berms and dunes associated with processes driven by storms, sea-level rise, and even human restoration activities. These lidar data are available to Federal, State and local governments, emergency-response officials, resource managers, and the general public.

Alabama; Louisiana; Mississippi↗
USGS2025

System characterization report on Resourcesat-2A Linear Imaging Self Scanning-4 sensor

Executive Summary This report documents the system characterization of the Indian Space Research Organisation Resourcesat-2A Linear Imaging Self Scanning-4 (LISS–4) sensor. It is part of a series of system characterization reports produced by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports describe the methodology and procedures used for characterization, present technical and operational information about the specific sensing system being evaluated, and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. Resourcesat-2A was launched in 2016 on the Polar Satellite Launch Vehicle-C36; it is identical to Resourcesat-2, and together, they decrease imaging revisit time from 5 days to 2–3 days, providing data continuity and improved temporal resolution. Resouresat-2 and 2A carry the Advanced Wide Field Sensor, Linear Imaging Self Scanning-3, and LISS–4 medium-resolution imaging sensors, continuing the legacy of the Indian Space Research Organisation’s Indian Remote Sensing-1C/1D/P3 satellite programs. More information about the Indian Space Research Organisation’s satellites and sensors is available through the Joint Agency Commercial Imagery Evaluation Earth Observing Satellites Online Compendium at https://calval.cr.usgs.gov/apps/compendium/ and from the manufacturer at https://www.isro.gov.in/ . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team assessed the geometric, radiometric, and spatial performances of the Resourcesat-2A LISS–4 sensor. Geometric performance is divided into the interior geometric performance of band-to-band registration and the exterior geometric performance of geolocation accuracy. The interior geometric performance had mean offsets in the range of −0.118 to 0.024 pixel in easting and −0.053 to 0.022 pixel in northing with root mean square error values from 0.067 to 0.230 pixel in easting and from 0.087 to 0.2 pixel in northing. The exterior geometric performance had offsets in the range of 2.55 to 7.85 meters (m) in easting and −6.15 to 11.15 m in northing with root mean square error values in the range of 2.6 to 8.2 m in easting and 6.35 to 11.8 m in northing compared to the U.S. Department of Agriculture National Agriculture Imagery Program and WorldView-3 orthoimages. The measured radiometric performance had offsets from 0.003 to 0.024 and slopes from 0.736 to 0.952, and spatial performance was in the range of 1.633 to 1.903 pixels for the full width at half maximum with a modulation transfer function at a Nyquist frequency in the range of 0.0529 to 0.0952.

Open-File Report↗
USGS2025

Airborne geophysics for geologic mapping of critical mineral systems in the United States southern midcontinent

The increased demand for clean energy technology and a significant reliance on foreign supply chains have given impetus to understanding critical mineral systems and locating potential resources within the United States. At least thirteen critical mineral-bearing systems have been identified throughout the U.S. southern Midcontinent (Hofstra and Kreiner, 2020) but much of the region’s geologic framework is concealed by vegetation and sedimentary cover that hinder traditional geologic mapping efforts. Airborne geophysical data provide an effective way to overcome these obstacles and to provide additional insight into the deeper structures that underlie shallow mineralization. However, legacy airborne magnetic and radiometric data were collected using now-outdated instruments and methods, inconsistent survey parameters, and large flight-line spacings resulting in low-resolution data that present challenges to regional-scale study and interpretation. Over the last decade, the U.S. Geological Survey Earth Mapping Resources Initiative (EMRI) and National Cooperative Geologic Mapping Program have conducted a series of high-resolution airborne magnetic and radiometric surveys across the southern Midcontinent (Fig. 1) as part of an effort to improve understanding of the geophysical framework and natural resource potential in the region. These surveys are designed using modern survey methods and instruments with consistent parameters for flight-line spacing and flight height relative to magnetic sources. The EMRI airborne surveys are planned in collaboration with State geological surveys based on focus areas (Dicken et al., 2022) according to the presence of or potential for critical mineral deposits. High-resolution airborne magnetic and radiometric data cover focus areas such as the southeast Missouri iron metallogenic province and South-Central iron-oxide-apatite (IOA) – iron-oxide-copper-gold (IOCG) province, the Magnet Cove alkaline-carbonatite complex, the Midwest Permian ultramafic dike district, the Illinois-Kentucky fluorspar district, and several Mississippi Valley-type lead-zinc deposits and districts (Fig. 1). These focus areas represent known deposits or prospective host systems of critical minerals including rare earth elements (REEs), platinum-group elements (PGEs), cobalt, lithium, fluorspar, niobium, titanium, vanadium, lead, zinc, gallium, germanium, and many more. Other significant geologic and geophysical features covered include the Reelfoot rift, the New Madrid seismic zone, the Illinois basin, the Arkoma basin, the South-Central magnetic lineament, and the Kentucky-Tennessee magnetic anomaly (Fig. 1). This presentation focuses on new airborne magnetic and radiometric data with continuous coverage across parts of six states, preliminary interpretations, examples of geologic mapping applications, and discussion of newly discovered magnetic anomalies and follow-up investigations.

Conference Paper↗
USGS1964

Geographic data from space

Space science has been called “the collection of scientific problems to which space vehicles can make some specific contributions not achievable by ground-based experiments.” Geography, the most spatial of the sciences, has now been marked as one of these “space sciences.” The National Aeronautics and Space Administration (NASA) is sponsoring an investigation to identify the Potential geographic benefits from the nation’s space program. This is part of NASA’s long-range inquiry to determine the kinds of scientific activities which might profitably be carried out on future space missions. Among such future activities which are now being planned by NASA are a series of manned earth orbital missions, many of which would be devoted to research. Experiments in physics, astronomy, geophysics, meteorology, and biology are being discussed for these long-range missions. The question which is being put to geographers is, essentially, what would it mean to geographic research to have an observation satellite (or many such satellites) orbiting the earth, gathering data about earth-surface features and environments?

Professional Geographer↗
USGS2017

Alaska and Yukon magnetic compilation, residual total magnetic field

This map is a compilation of aeromagnetic surveys over Yukon and eastern Alaska. Aeromagnetic surveys measure the total intensity of the earth's magnetic field. The field was measured by a magnetometer aboard an aircraft flown in parallel lines spaced at 200 m to 10000 m across the map area. The magnetic field reflects magnetic properties of bedrock and provides qualitative and quantitative information used in geological mapping. Understanding the geology will help geologists map the area, assist mineral/hydrocarbon exploration activities, and provide useful information necessary for communities, aboriginal associations, and government to make land use decisions. This survey was flown to improve our knowledge of the area. It will support ongoing geological mapping and resource assessment.

Alaska, Yukon↗
USGS1997

The Landsat program: Its origins, evolution, and impacts

Landsat 1 began an era of space-based resource data collection that changed the way science, industry, governments, and the general public view the Earth. For the last 25 years, the Landsat program - despite being hampered by institutional problems and budget uncertainties - has successfully provided a continuous supply of synoptic, repetitive, multi-spectral data of the Earth's land areas. These data have profoundly affected programs for mapping resources, monitoring environmental changes, and assessing global habitability. The societal applications this program generated are so compelling that international systems have proliferated to carry on the tasks initiated with Landsat data.

Photogrammetric Engineering and Remote Sensing↗
USGS2021

Earth Resources Observation and Science Center hosting services

The Earth Resources Observation and Science (EROS) Center has a long history of leveraging technology in support of Earth science and business applications including data management, processing, and virtualization and complex solutions to visualize and distribute data. It is the aim of EROS to offer operational excellence and service as a key component to the Federal Cloud-Smart directives and Department of the Interior data center consolidation goals. The EROS Center offers space, network, security, and environmental controls and information technology service management. Included with leveraging technology, the EROS Center has industry-leading staff to assist with supporting Earth science and business applications.

Fact Sheet↗
USGS2005

Blind comparisons of shear-wave velocities at closely-spaced sites in San Jose, California: Proceedings of a Workshop held at the US Geological Survey, Menlo Park, May 3, 2004

Shear-wave velocities within several hundred meters of Earth's surface are important in specifying earthquake ground motions for engineering design. Not only are the shearwave velocities used in classifying sites for use of modern building codes, but they are also used in site-specific studies of particularly significant structures. Many are the methods for estimating sub-surface shear-wave velocities, but few are the blind comparisons of a number of the methods at a single site. The word "blind" is important here and means that the measurements and interpretations are done completely independent of one another. Stephen Hartzell of the USGS office on Golden, Colorado realized that such an experiment would be very useful for assessing the strengths and weaknesses of the various methods, and he and Jack Boatwright of the USGS office in Menlo Park, California, in cooperation with Carl Wentworth of the Menlo Park USGS office found a convenient site in the city of San Jose, California. The site had good access and space for conducting experiments, and a borehole drilled to several hundred meters by the Santa Clara Valley Water District was made available for downhole logging. Jack Boatwright asked David Boore to coordinate the experiment. In turn, David Boore persuaded several teams to make measurements, helped with the local logistics, collected the results, and organized and conducted an International Workshop in May, 2004. At this meeting the participants in the experiment gathered in Menlo Park to describe their measurements and interpretations, and to see the results of the comparisons of the various methods for the first time. This Open-File Report describes the results of that workshop. One of the participants, Michael Asten, offered to help the coordinator prepare this report. Because of his lead role in pulling the report together, Dr. Asten is the lead author of the paper to follow and is also the lead Compiler for the Open-File Report. It is important to recognize that most of the participants in the experiments contributed at their own expense. It is gratifying that many people recognized the importance of the experiment and were willing to volunteer their time and resources. We thank them for this effort. This Report is organized in three parts: the first part is a paper summarizing the results of the Workshop, and presenting some conclusions regarding the various methods; the second part is a compilation of those documents describing the experiments that were presented at the meeting (a few of the reports have had minor post-meeting revisions, but with only one exception noted later, none of the models were changed). The final part of this Report is a compilation of the presentations from the meeting -- these are largely in the form of Powerpoint files. No attempt has been made by the compilers to edit the material in parts 2 and 3. It is included here as is for the benefit of the reader.

California↗
USGS2009

Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year-round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79-91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org). ?? 2009 Taylor & Francis.

International Journal of Remote Sensing↗