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By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
High-resolution digital elevation data acquired by airborne laser scanning (ALS) for the Denton Hills, along the coastal foothills of the Royal Society Range, Transantarctic Mountains, are examined for applications to bedrock and glacial geomorphic mapping. Digital elevation models (DEMs), displayed as shaded-relief images and slope maps, portray geomorphic landscape features in unprecedented detail across the region. Structures of both ductile and brittle origin, ranging in age from the Paleozoic to the Quaternary, can be mapped from the DEMs. Glacial features, providing a record of the limits of grounded ice, of lake paleoshorelines, and of proglacial lake-ice conveyor deposits, are also prominent on the DEMs. The ALS-derived topographic data have great potential for a range of mapping applications in regions of ice-free terrain in Antarctica
Thick accumulations of ice, called “aufeis,” form during winter along stream and river valleys in arctic and subarctic regions. In high-gradient alpine streams, aufeis forms mostly as a result of ground-water discharge into the stream channel. The ice occludes this discharge, perturbing the steady-state condition, and causing an incremental rise in the local water table until discharge occurs higher on the stream bank above the previously formed ice. Successive freezing of onlapping ice layers can lead to aufeis accumulations several meters thick. The location and extent of aufeis in high-gradient streams may be useful to relate local hydrology to geologic structure. In the Goodpaster River basin study area, mineral deposits are known to occur, the location of which may be structurally controlled. Therefore, a more thorough understanding of regional geologic structures may facilitate a more detailed understanding of the genesis of the mineral deposits. Extensive aufeis was observed during visits to the Goodpaster River basin in east-central Alaska during 1999, 2001, and 2002. Seeps from the sides of the valleys caused ice to build up, giving the ice surface a concave-upward shape perpendicular to the stream direction. This concavity is evidence for ground-water discharge along the length of the aufeis, as opposed to discharge from a single upstream point. During thaw, streamflow is commonly observed out of the normal channel, evidence that occlusion of the channel (and shallow sediments) by ice is a viable mechanism for causing the water table to rise. The thickest (>3 meters) and most extensive aufeis (100’s of meters to kilometers along valleys) coincided with locations of laterally extensive (>5 kilometers) mapped high-angle brittle fault zones, suggesting that the fault zones are hydraulically conductive. Additional evidence of water flow is provided by observed changes in stream-water chemistry in reaches in which aufeis forms, despite a lack of surface tributaries. Minor or no aufeis was observed in many other drainage valleys where no laterally extensive structures have been mapped, implying that aufeis formation results from more than a topographic effect or discharge from bank storage. Thus, the presence of thick, laterally extensive aufeis in highgradient streams may be a useful aid to geologic structural mapping in arctic and subarctic climates.
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This report consists of a large map sheet and a pamphlet. The map shows the geology, some photographs, description of map units, and correlation of map units. The pamphlet gives the full text about the geologic map. The area covered by this map includes parts of four U.S. Geological Survey 7.5' topographic quadrangles (Kilauea Crater, Volcano, Ka`u Desert, and Makaopuhi). It encompasses the summit, upper rift zones, and Koa`e Fault System of Kilauea Volcano and a part of the adjacent, southeast flank of Mauna Loa Volcano. The map is dominated by products of eruptions from Kilauea Volcano, the southernmost of the five volcanoes on the Island of Hawai`i and one of the world's most active volcanoes. At its summit (1,243 m) is Kilauea Crater, a 3 km-by-5 km collapse caldera that formed, possibly over several centuries, between about 200 and 500 years ago. Radiating away from the summit caldera are two linear zones of intrusion and eruption, the east and the southwest rift zones. Repeated subaerial eruptions from the summit and rift zones have built a gently sloping, elongate shield volcano covering approximately 1,500 km2. Much of the volcano lies under water; the east rift zone extends 110 km from the summit to a depth of more than 5,000 m below sea level; whereas the southwest rift zone has a more limited submarine continuation. South of the summit caldera, mostly north-facing normal faults and open fractures of the Koa`e Fault System extend between the two rift zones. The Koa`e Fault System is interpreted as a tear-away structure that accommodates southward movement of Kilauea's flank in response to distension of the volcano perpendicular to the rift zones.
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The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
By selecting one of the four series options shown below, namely, -A, -B, -C, and -D for the geologic, topographic, Landsat natural-color, and Landsat false-color maps, respectively, the user will be taken to that map.
Flood‐inundation data are most useful for decision makers when presented in the context of maps of affected communities and (or) areas. But because the data are scarce and rarely cover the full extent of the flooding, interpolation and extrapolation of the information are needed. Many geographic information systems provide various interpolation tools, but these tools often ignore the effects of the topographic and hydraulic features that influence flooding. A barrier mapping method was developed to improve maps of storm tide produced by Hurricane Rita. Maps were developed for the maximum storm tide and at 3‐h intervals from midnight (00:00 hours) through noon (12:00 hours) on 24 September 2005. The improved maps depict storm‐tide elevations and the extent of flooding. The extent of storm‐tide inundation from the improved maximum storm‐tide map was compared with the extent of flood inundation from a map prepared by the Federal Emergency Management Agency (FEMA). The boundaries from these two maps generally compared quite well especially along the Calcasieu River. Also a cross‐section profile that parallels the Louisiana coast was developed from the maximum storm‐tide map and included FEMA high‐water marks.
Differential interferometric synthetic aperture radar (SAR) (DInSAR) has proven to be very useful in mapping and monitoring land subsidence in many regions of the world. Shanghai, China's largest city, is one of such areas suffering from land subsidence as a result of severe withdrawal of groundwater for different usages. DInSAR application in Shanghai with the C-band European Remote Sensing 1 & 2 (ERS-1/2) SAR data has been difficult mainly due to the problem of decorrelation of InSAR pairs with temporal baselines larger than 10 months. To overcome the coherence loss of C-band InSAR data, we used eight L-band Japanese Earth Resource Satellite (JERS-1) SAR data acquired during 2 October 1992 to 15 July 1998 to study land subsidence phenomenon in Shanghai. Three of the images were used to produce two separate digital elevation models (DEMs) of the study area to remove topographic fringes from the interferograms used for subsidence mapping. Six interferograms were used to generate 2 different time series of deformation maps over Shanghai. The cumulative subsidence map generated from each of the time series is in agreement with the land subsidence measurements of Shanghai city from 1990-1998, produced from other survey methods. ?? 2007 IEEE.
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The U.S. Geological Survey has generated a comprehensive data clearinghouse of digital vector cliff edges and associated rates of cliff retreat along the open-ocean California coast. These data, which are presented herein, were compiled as part of the U.S. Geological Survey's National Assessment of Shoreline Change Project. Cliff erosion is a chronic problem along many coastlines of the United States. As coastal populations continue to grow and community infrastructures are threatened by erosion, there is increased demand for accurate information including rates and trends of coastal cliff retreat. There is also a critical need for these data to be consistent from one region to another. One objective of this work is to a develop standard, repeatable methodology for mapping and analyzing cliff edge retreat so that periodic, systematic, and internally consistent updates of cliff edge position and associated rates of erosion can be made at a national scale. This data compilation for open-ocean cliff edges for the California coast is a separate, yet related study to Hapke and others, 2006 documenting shoreline change along sandy shorelines of the California coast, which is itself one in a series that includes the Gulf of Mexico and the Southeast Atlantic coast (Morton and others, 2004; Morton and Miller, 2005). Future reports and data compilations will include coverage of the Northeast U.S., the Great Lakes, Hawaii and Alaska. Cliff edge change is determined by comparing the positions of one historical cliff edge digitized from maps with a modern cliff edge derived from topographic LIDAR (light detection and ranging) surveys. Historical cliff edges for the California coast represent the 1920s-1930s time-period; the most recent cliff edge was delineated using data collected between 1998 and 2002. End-point rate calculations were used to evaluate rates of erosion between the two cliff edges. Please refer to our full report on cliff edge erosion along the California coastline at http://pubs.usgs.gov/of/2007/1133/ for additional information regarding methods and results (Hapke and others, 2007). Data in this report are organized into downloadable layers by region (Northern, Central and Southern California) and are provided as vector datasets with accompanying metadata. Vector cliff edges may represent a compilation of data from one or more sources and the sources used are included in the dataset metadata. This project employs the Environmental Systems Research Institute's (ESRI) ArcGIS as it's Geographic Information System (GIS) mapping tool and contains several data layers (shapefiles) that are used to create a geographic view of the California coast. The vector data form a basemap comprising polygon and line themes that include a U.S. coastline (1:80,000), U.S. cities, and state boundaries.