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At least 181 records · Page 10Linked to original sources
USGS2008

Towards monitoring land-cover and land-use changes at a global scale: the global land survey 2005

Land cover is a critical component of the Earth system, infl uencing land-atmosphere interactions, greenhouse gas fl uxes, ecosystem health, and availability of food, fi ber, and energy for human populations. The recent Integrated Global Observations of Land (IGOL) report calls for the generation of maps documenting global land cover at resolutions between 10m and 30m at least every fi ve years (Townshend et al., in press). Moreover, despite 35 years of Landsat observations, there has not been a unifi ed global analysis of land-cover trends nor has there been a global assessment of land-cover change at Landsat-like resolution. Since the 1990s, the National Aeronautics and Space Administration (NASA) and the U.S. Geological Survey (USGS) have supported development of data sets based on global Landsat observations (Tucker et al., 2004). These land survey data sets, usually referred to as GeoCover ™, provide global, orthorectifi ed, typically cloud-free Landsat imagery centered on the years 1975, 1990, and 2000, with a preference for leaf-on conditions. Collectively, these data sets provided a consistent set of observations to assess land-cover changes at a decadal scale. These data are freely available via the Internet from the USGS Center for Earth Resources Observation and Science (EROS) (see http://earthexplorer.usgs.gov or http://glovis.usgs.gov). This has resulted in unprecedented downloads of data, which are widely used in scientifi c studies of land-cover change (e.g., Boone et al., 2007; Harris et al., 2005; Hilbert, 2006; Huang et al. 2007; Jantz et al., 2005, Kim et al., 2007; Leimgruber, 2005; Masek et al., 2006). NASA and USGS are continuing to support land-cover change research through the development of GLS2005 - an additional global Landsat assessment circa 20051 . Going beyond the earlier initiatives, this data set will establish a baseline for monitoring changes on a 5-year interval and will pave the way toward continuous global land-cover monitoring at Landsat-like resolution in the next decade.

Photogrammetric Engineering and Remote Sensing↗
USGS2004

An evaluation of gap-filled Landsat SLC-off imagery for wildland fire burn severity mapping

n May 31, 2003 unusual artifacts appeared within image data collected by the Enhanced Thematic Mapper plus (ETM+) instrument on-board the Landsat 7 spacecraft. The U.S. Geological Survey (USGS), with the support of NASA, has been working to find a means of compensating for the data gaps that result from a failure of the instrument’s scan line corrector (SLC). The SLC is an electromechanical device that compensates for the forward motion of the spacecraft by modifying the instrument’s optical path. The problem is likely due to a mechanical failure of the device for which there is no redundancy and that cannot be repaired or coaxed back into service. Further information regarding Landsat 7 and the SLC failure can be found at the Landsat Project home page (http://landsat7.usgs.gov).

Photogrammetric Engineering and Remote Sensing↗
USGS2007

Interferometric synthetic aperture radar (InSAR)—its past, present and future

Very simply, interferometric synthetic aperture radar (InSAR) involves the use of two or more synthetic aperture radar (SAR) images of the same area to extract landscape topography and its deformation patterns. A SAR system transmits electromagnetic waves at a wavelength that can range from a few millimeters to tens of centimeters and therefore can operate during day and night under all-weather conditions. Using SAR processing technique (Curlander and McDonough, 1991), both the intensity and phase of the reflected (or backscattered) radar signal of each ground resolution element (a few meters to tens of meters) can be calculated in the form of a complex-valued SAR image that represents the reflectivity of the ground surface. The amplitude or intensity of the SAR image is determined primarily by terrain slope, surface roughness, and dielectric constants, whereas the phase of the SAR image is determined primarily by the distance between the satellite antenna and the ground targets. InSAR imaging utilizes the interaction of electromagnetic waves, referred to as interference, to measure precise distances between the satellite antenna and ground resolution elements to derive landscape topography and its subtle change in elevation.

Photogrammetric Engineering and Remote Sensing↗
USGS2004

Filling Landsat ETM+ SLC-off gaps using a segmentation model approach

The purpose of this article is to present a methodology for filling Landsat Scan Line Corrector (SLC)-off gaps with same-scene spectral data guided by a segmentation model. Failure of the SLC on the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) instrument resulted in a loss of approximately 25 percent of the spectral data. The missing data span across most of the image with scan gaps varying in size from two pixels near the center of the image to 14 pixels along the east and west edges. Even with the scan gaps, the radiometric and geometric qualities of the remaining portions of the image still meet design specifications and therefore contain useful information (see http:// landsat7.usgs.gov for additional information). The U.S. Geological Survey EROS Data Center (EDC) is evaluating several techniques to fill the gaps in SLC-off data to enhance the usability of the imagery (Howard and Lacasse 2004) (PE&RS, August 2004). The method presented here uses a segmentation model approach that allows for same-scene spectral data to be used to fill the gaps. The segment model is generated from a complete satellite image with no missing spectral data (e.g., Landsat 5, Landsat 7 SLCon, SPOT). The model is overlaid on the Landsat SLC-off image, and the missing data within the gaps are then estimated using SLC-off spectral data that intersect the segment boundary. A major advantage of this approach is that the gaps are filled using spectral data derived from the same SLC-off satellite image.

Photogrammetric Engineering and Remote Sensing↗
USGS2005

Recent U.S. Geological Survey applications of Lidar

As lidar (light detection and ranging) technology matures, more applications are being explored by U.S. Geological Survey (USGS) scientists throughout the Nation, both in collaboration with other Federal agencies and alone in support of USGS natural-hazards research (Crane et al., 2004). As the technology continues to improve and evolve, USGS scientists are finding new and unique methods to use and represent high-resolution lidar data, and new ways to make these data and derived information publicly available. Different lidar sensors and configurations have offered opportunities to use high-resolution elevation data for a variety of projects across all disciplines of the USGS. The following examples are just a few of the diverse projects in the USGS where lidar data is being used.

Photogrammetric Engineering and Remote Sensing↗
USGS2006

CLICK: The new USGS center for LIDAR information coordination and knowledge

Elevation data is rapidly becoming an important tool for the visualization and analysis of geographic information. The creation and display of three-dimensional models representing bare earth, vegetation, and structures have become major requirements for geographic research in the past few years. Light Detection and Ranging (lidar) has been increasingly accepted as an effective and accurate technology for acquiring high-resolution elevation data for bare earth, vegetation, and structures. Lidar is an active remote sensing system that records the distance, or range, of a laser fi red from an airborne or space borne platform such as an airplane, helicopter or satellite to objects or features on the Earth’s surface. By converting lidar data into bare ground topography and vegetation or structural morphologic information, extremely accurate, high-resolution elevation models can be derived to visualize and quantitatively represent scenes in three dimensions. In addition to high-resolution digital elevation models (Evans et al., 2001), other lidar-derived products include quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass (Lefsky et al., 2002), and models of urban areas such as building footprints and three-dimensional city models (Maas, 2001).

Photogrammetric Engineering and Remote Sensing↗
USGS2008

The need for a national LIDAR dataset

On May 21st and 22nd 2008, the U.S. Geological Survey (USGS), the National Aeronautics and Space Administration (NASA), and the Association of American State Geologists (AASG) hosted the Second National Light Detection and Ranging (Lidar) Initiative Strategy Meeting at USGS Headquarters in Reston, Virginia. The USGS is taking the lead in cooperation with many partners to design and implement a future high-resolution National Lidar Dataset. Initial work is focused on determining viability, developing requirements and specifi cations, establishing what types of information contained in a lidar signal are most important, and identifying key stakeholders and their respective roles. In February 2007, USGS hosted the fi rst National Lidar Initiative Strategy Meeting at USGS Headquarters in Virginia. The presentations and a published summary report from the fi rst meeting can be found on the Center for Lidar Information Coordination and Knowledge (CLICK) Website: http://lidar.cr.usgs.gov. The fi rst meeting demonstrated the public need for consistent lidar data at the national scale. The goals of the second meeting were to further expand on the ideas and information developed in the fi rst meeting, to bring more stakeholders together, to both refi ne and expand on the requirements and capabilities needed, and to discuss an organizational and funding approach for an initiative of this magnitude. The approximately 200 participants represented Federal, State, local, commercial and academic interests. The second meeting included a public solicitation for presentations and posters to better democratize the workshop. All of the oral presentation abstracts that were submitted were accepted, and the 25 poster submissions augmented and expanded upon the oral presentations. The presentations from this second meeting, including audio, can be found on CLICK at http://lidar.cr.usgs.gov/national_lidar_2008.php. Based on the presentations and the discussion sessions, the following points were emphasized throughout the meeting

Photogrammetric Engineering and Remote Sensing↗
USGS2000

Use of Argon, Corona, and Landsat imagery to assess 30 years of land resource changes in west-central Senegal

Over the past 35 years, an agricultural area of west-central Senegal has experienced rapid population growth, fast expansion of agricultural lands, a decline in rainfall, and degradation of vegetative and soil resources. Although such changes have not escaped the attention of Senegal's people, its government, and the scientific community the ability to monitor and quantify land resource trends of recent decades has been difficult. Recently available high-resolution satellite photographs from the American Argon and Corona Programs provide coverage of Senegal back to 1963. The photographs make it possible to study and map land resources at the beginning of the Space Age. In this study, we characterize the changes that have occurred in the region from the early 1960s to the mid-1 990s. Early Argon and Corona photographs are used to reconstruct the historical land use and land cover; comparisons are made with assessments from recent Landsat images. Field studies and aerial surveys provide additional insight. The forces of change, driven primarily by population growth and unsustainable agricultural practices, are examined

Photogrammetric Engineering and Remote Sensing↗
USGS1995

Digital image comparison by subtracting contextual transformations—percentile rank order differentiation

The common method of digital image comparison by subtraction imposes various constraints on the image contents. Precise registration of images is required to assure proper evaluation of surface locations. The attribute being measured and the calibration and scaling of the sensor are also important to the validity and interpretability of the subtraction result. Influences of sensor gains and offsets complicate the subtraction process. The presence of any uniform systematic transformation component in one of two images to be compared distorts the subtraction results and requires analyst intervention to interpret or remove it. A new technique has been developed to overcome these constraints. Images to be compared are first transformed using the cumulative relative frequency as a transfer function. The transformed images represent the contextual relationship of each surface location with respect to all others within the image. The process of differentiating between the transformed images results in a percentile rank ordered difference. This process produces consistent terrain-change information even when the above requirements necessary for subtraction are relaxed. This technique may be valuable to an appropriately designed hierarchical terrain-monitoring methodology because it does not require human participation in the process.

Photogrammetric Engineering and Remote Sensing↗
USGS1995

Map projections for global and continental data sets and an analysis of pixel distortion caused by reprojection

In global change studies the effects of map projection properties on data quality are apparent, and the choice of projection is significant. To aid compilers of global and continental data sets, six equal-area projections were chosen: the interrupted Goode Homolosine, the interrupted Mollweide, the Wagner IV, and the Wagner VII for global maps; the Lambert Azimuthal Equal-Area for hemisphere maps; and the Oblated Equal-Area and the Lambert Azimuthal Equal-Area for continental maps. Distortions in small-scale maps caused by reprojection, and the additional distortions incurred when reprojecting raster images, were quantified and graphically depicted. For raster images, the errors caused by the usual resampling methods (pixel brightness level interpolation) were responsible for much of the additional error where the local resolution and scale change were the greatest.

Photogrammetric Engineering and Remote Sensing↗
USGS1992

A technique for the reduction of banding in Landsat Thematic Mapper Images

The radiometric difference between forward and reverse scans in Landsat thematic mapper (TM) images, referred to as "banding," can create problems when enhancing the image for interpretation or when performing quantitative studies. Recent research has led to the development of a method that reduces the banding in Landsat TM data sets. It involves passing a one-dimensional spatial kernel over the data set. This kernel is developed from the statistics of the banding pattern and is based on the Wiener filter. It has been implemented on both a DOS-based microcomputer and several UNIX-based computer systems. The algorithm has successfully reduced the banding in several test data sets.

Photogrammetric Engineering and Remote Sensing↗
USGS1993

An earth remote sensing satellite- 1 Synthetic Aperture Radar Mosaic of the Tanana River Basin in Alaska

Because the pixel location in a line of Synthetic Aperture Radar (SAR) image data is directly related to the distance the pixel is from the radar, terrain elevations cause large displacement errors in the geo-referenced location of the pixel. This is especially true for radar systems with small angles between the nadir and look vectors. Thus, to geo-register a SAR image accurately, the terrain of the area must be taken into account. (Curlander et al., 1987; Kwok et al., 1987, Schreier et al., 1990; Wivell et al., 1992). As part of the 1992 National Aeronautics and Space Administration's Earth Observing System Version 0 activities, a prototype SAR geocod-. ing and terrain correction system was developed at the US. Geological Survey's (USGS) E~os Data Center (EDC) in Sioux Falls, South Dakota. Using this system with 3-arc-second digital elevation models (DEMs) mosaicked at the ED^ Alaska Field Office, 21 ERS-I s.4~ scenes acquired at the Alaska SAR Facility were automatically geocoded, terrain corrected, and mosaicked. The geo-registered scenes were mosaicked using a simple concatenation.

Photogrammetric Engineering and Remote Sensing↗
USGS1995

Effects of empirical versus model-based reflectance calibration on automated analysis of imaging spectrometer data: a case study from the Drum Mountains, Utah

Data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have been calibrated to surface reflectance using an empirical method and an atmospheric model-based method. Single spectra extracted from both calibrated data sets for locations with known mineralogy compared favorably with laboratory and field spectral measurements of samples from the same locations. Generally, spectral features were somewhat subdued in data calibrated using the model-based method when compared with those calibrated using the empirical method. Automated feature extraction and expert system analysis techniques have been successfully applied to both data sets to produce similar endmember probability images and spectral endmember libraries. Linear spectral unmixing procedures applied to both calibrated data sets produced similar image maps. These comparisons demonstrated the utility of the model-based approach for atmospherically correcting imaging spectrometer data prior to extraction of scientific information. The results indicated that imaging spectrometer data can be calibrated and analyzed without a priori knowledge of the remote target.

Photogrammetric Engineering and Remote Sensing↗
USGS1996

A digital system for surface reconstruction

A digital photogrammetric system, STEREO, was developed to determine three dimensional coordinates of points of interest (POIs) defined with a grid on a textureless and smooth-surfaced specimen. Two CCD cameras were set up with unknown orientation and recorded digital images of a reference model and a specimen. Points on the model were selected as control or check points for calibrating or assessing the system. A new algorithm for edge-detection called local maximum convolution (LMC) helped extract the POIs from the stereo image pairs. The system then matched the extracted POIs and used a least squares “bundle” adjustment procedure to solve for the camera orientation parameters and the coordinates of the POIs. An experiment with STEREO found that the standard deviation of the residuals at the check points was approximately 24%, 49% and 56% of the pixel size in the X, Y and Z directions, respectively. The average of the absolute values of the residuals at the check points was approximately 19%, 36% and 49% of the pixel size in the X, Y and Z directions, respectively. With the graphical user interface, STEREO demonstrated a high degree of automation and its operation does not require special knowledge of photogrammetry, computers or image processing.

Photogrammetric Engineering and Remote Sensing↗
USGS1999

The Global Land-Cover Characteristics Database: The users' perspective

A unique global land-cover characteristics database developed by the U.S. Geological Survey has been available to users since mid-1997. Access to the data is through the internet under the EROS (Earth Resources Observation Systems) Data Center's home page (http://edcwww.cr.usgs.gov/landdaac/glcc/glcc.html). Since the release of the database, the data have been incorporated into various environmental research and modeling applications, including mapping global biodiversity, mesoscale climate simulations, carbon cycle modeling, and estimating habitat destruction. Since the early stages of the project, user feedback has provided a means to understand data utility in applications, garner suggestions for data improvements, and gain insights into the technical challenges faced by users. Synthesis of user feedback provided a means to generate a user profile and derive a list of applications-critical criteria for land-cover data. User suggestions have lead to revisions in the database, including label changes, alternative classification schemes, and additional projections for the data.

Photogrammetric Engineering and Remote Sensing↗