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At least 55 records · Page 3Linked to original sources
USGS1982

Geometry of a mapping satellite.

The proposed mapping satellite Mapsat is to consist of fixed fore, vertical, and aft linear detector arrays, any two of which may be used simultaneously to obtain digital images for one- dimensional stereo correlation. The satellite attitude may be varied according to Fourier series to enable a given detector on one array to follow closely the groundtrack sensed by the corresponding detector on another array throughout the orbit. These tracking errors are negligible for a satellite stable within anticipated ranges. The required computations have been programmed in FORTRAN IV. -Author

Photogrammetric Engineering and Remote Sensing↗
USGS1999

The way forward

This paper focuses on the lessons hearned in the conduct of the lnternational Geosphere Biosphere Program's Data and Information System (rcnr-nts), global 1-km Land-Cover Mapping Project (n$cover). There is stiLL considerable fundamental research to be conducted dealing with the development and validation of thematic geospatial products derived from a combination of remotely sensed and ancillary data. Issues include database and data product development, classification legend definitions, processing and analysis techniques, and sampling strategies. A significant infrastructure is required to support an effort such as DISCover. The infrastructure put in place under the auspices of the IGBP-DIS serves as a model, and must be put in place to enable replication and development of projects such as Discover.

Photogrammetric Engineering and Remote Sensing↗
USGS1983

The role of remotely sensed and other special data for predictive modeling: the Umatilla, Oregon example

Landsat data and 1:24 000-scale aerial photographs were initially used to map the expansion of irrigation from 1973 to 1979 and to identify crops under irrigation in 1979. The crop data were then used with historical water requirement figures and digital topographic and hydrographic data to estimate water and power use for the 1979 irrigation season. The final project task involved production of a composite map of land suitability for irrigation development based on land cover (from Landsat), landownership, soil irrigability, slope gradient, and potential energy costs.

Oregon↗
USGS1999

Change analysis in the United Arab Emirates: An investigation of techniques

Much of the landscape of the United Arab Emirates has been transformed over the past 15 years by massive afforestation, beautification, and agricultural programs. The "greening" of the United Arab Emirates has had environmental consequences, however, including degraded groundwater quality and possible damage to natural regional ecosystems. Personnel from the Ground- Water Research project, a joint effort between the National Drilling Company of the Abu Dhabi Emirate and the U.S. Geological Survey, were interested in studying landscape change in the Abu Dhabi Emirate using Landsat thematic mapper (TM) data. The EROs Data Center in Sioux Falls, South Dakota was asked to investigate land-cover change techniques that (1) provided locational, quantitative, and qualitative information on landcover change within the Abu Dhabi Emirate; and (2) could be easily implemented by project personnel who were relatively inexperienced in remote sensing. A number of products were created with 1987 and 1996 Landsat TM data using change-detection techniques, including univariate image differencing, an "enhanced" image differencing, vegetation index differencing, post-classification differencing, and changevector analysis. The different techniques provided products that varied in levels of adequacy according to the specific application and the ease of implementation and interpretation. Specific quantitative values of change were most accurately and easily provided by the enhanced image-differencing technique, while the change-vector analysis excelled at providing rich qualitative detail about the nature of a change.

Photogrammetric Engineering and Remote Sensing↗
USGS1986

Processing techniques for digital sonar images from GLORIA.

Image processing techniques have been developed to handle data from one of the newest members of the remote sensing family of digital imaging systems. This paper discusses software to process data collected by the GLORIA (Geological Long Range Inclined Asdic) sonar imaging system, designed and built by the Institute of Oceanographic Sciences (IOS) in England, to correct for both geometric and radiometric distortions that exist in the original 'raw' data. Preprocessing algorithms that are GLORIA-specific include corrections for slant-range geometry, water column offset, aspect ratio distortion, changes in the ship's velocity, speckle noise, and shading problems caused by the power drop-off which occurs as a function of range.-from Author

Photogrammetric Engineering and Remote Sensing↗
USGS1991

Integration of remote sensing and GIS: Data and data access

CT: Theintegration of remote sensing tools and technology with the spatial analysis orientation of geographic information systems is a complex task. In this paper, we focus on the issues of making data available and useful to the user. In part, this involves a set of problems which reflect on the physical and logical structures used to encode the data. At the same time, however, the mechanisms and protocols which provide information about the data, and which maintain the data through time, have become increasingly important. We discuss these latter issues from the viewpoint of the functions which must be provided by archives of spatial data.

Photogrammetric Engineering and Remote Sensing↗
USGS2008

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Photogrammetric Engineering and Remote Sensing↗
USGS2009

Volumetric visualization of multiple-return LIDAR data: Using voxels

Elevation data are an important component in the visualization and analysis of geographic information. The creation and display of 3D models representing bare earth, vegetation, and surface structures have become a major focus of light detection and ranging (lidar) remote sensing research in the past few years. Lidar is an active sensor that records the distance, or range, of a laser usually fi red from an airplane, helicopter, or satellite. By converting the millions of 3D lidar returns from a system into bare ground, vegetation, or structural elevation information, extremely accurate, high-resolution elevation models can be derived and produced to visualize and quantify scenes in three dimensions. These data can be used to produce high-resolution bare-earth digital elevation models; quantitative estimates of vegetative features such as canopy height, canopy closure, and biomass; and models of urban areas such as building footprints and 3D city models.

Photogrammetric Engineering and Remote Sensing↗
USGS2006

Historical record of Landsat global coverage

The long-term, 34+ year record of global Landsat remote sensing data is a critical resource to study the Earth system and human impacts on this system. The National Satellite Land Remote Sensing Data Archive (NSLRSDA) is charged by public law to: “maintain a permanent, comprehensive Government archive of global Landsat and other land remote sensing data for long-term monitoring and study of the changing global environment” (U.S. Congress, 1992). The advisory committee for NSLRSDA requested a detailed analysis of observation coverage within the U.S. Landsat holdings, as well as that acquired and held by International Cooperator (IC) stations. Our analyses, to date, have found gaps of varying magnitude in U.S. holdings of Landsat global coverage data, which appear to reflect technical or administrative variations in mission operations. In many cases it may be possible to partially fill these gaps in U.S. holdings through observations that were acquired and are now being held at International Cooperator stations.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Testing a small UAS for mapping artisanal diamond mining sites in Africa

Remote sensing technology is advancing at an unprecedented rate. At the forefront of the new technological developments are unmanned aircraft systems (UAS). The advent of small, lightweight, low-cost, and user-friendly UAS is greatly expanding the potential applications of remote sensing technology and improving the set of tools available to researchers seeking to map and monitor terrain from above. In this article, we explore the applications of a small UAS for mapping informal diamond mining sites in Africa. We found that this technology provides aerial imagery of unparalleled resolution in a data-sparse, difficult to access, and remote terrain.

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

Quantifying multi-temporal urban development characteristics in Las Vegas from Landsat and ASTER data

Urban development has expanded rapidly in Las Vegas, Nevada of the United States, over the last fifty years. A major environmental change associated with this urbanization trend is the transformation of the landscape from natural cover types to increasingly anthropogenic impervious surface. This research utilizes remote sensing data from both the Landsat and Terra-Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instruments in conjunction with digital orthophotography to estimate urban extent and its temporal changes by determining sub-pixel impervious surfaces. Percent impervious surface area has shown encouraging agreement with urban land extent and development density. Results indicate that total urban land-use increases approximately 110 percent from 1984 to 2002. Most of the increases are associated with medium-to high-density urban development. Places having significant increases in impervious surfaces are in the northwestern and southeastern parts of Las Vegas. Most high-density urban development, however, appears in central Las Vegas. Impervious surface conditions for 2002 measured from Landsat and ASTER satellite data are compared in terms of their accuracy.

Photogrammetric Engineering and Remote Sensing↗
USGS2025

Artificial neural network multilayer perceptron models to classify California’s crops using Harmonized Landsat Sentinel (HLS) data

Advances in remote sensing and machine learning are enhancing cropland classification, vital for global food and water security. We used multispectral Harmonized Landsat 8 Sentinel-2 (HLS) 30-m data in an artificial neural network (ANN) multi-layer perceptron (MLP) model to classify five crop classes (cotton, alfalfa, tree crops, grapes, and others) in California's Central Valley. The ANN MLP model, trained on 2021 data from the United States Department of Agriculture's Cropland Data Layer, was validated by classifying crops for an independent year, 2022. Across the five crop classes, the overall accuracy was 74%. Producer's and user's accuracies ranged from 65% to 87%, with cotton achieving the highest accuracies. The study highlights the potential of using deep learning with HLS time series data for accurate global crop classification.

California↗
USGS2003

Use of multispectral Ikonos imagery for discriminating between conventional and conservation agricultural tillage practices

There is a global concern about the increase in atmospheric concentrations of greenhouse gases. One method being discussed to encourage greenhouse gas mitigation efforts is based on a trading system whereby carbon emitters can buy effective mitigation efforts from farmers implementing conservation tillage practices. These practices sequester carbon from the atmosphere, and such a trading system would require a low-cost and accurate method of verification. Remote sensing technology can offer such a verification technique. This paper is focused on the use of standard image processing procedures applied to a multispectral Ikonos image, to determine whether it is possible to validate that farmers have complied with agreements to implement conservation tillage practices. A principal component analysis (PCA) was performed in order to isolate image variance in cropped fields. Analyses of variance (ANOVA) statistical procedures were used to evaluate the capability of each Ikonos band and each principal component to discriminate between conventional and conservation tillage practices. A logistic regression model was implemented on the principal component most effective in discriminating between conventional and conservation tillage, in order to produce a map of the probability of conventional tillage. The Ikonos imagery, in combination with ground-reference information, proved to be a useful tool for verification of conservation tillage practices.

Photogrammetric Engineering and Remote Sensing↗
USGS2011

USGS remote sensing coordination for the 2010 Haiti earthquake

In response to the devastating 12 January 2010, earthquake in Haiti, the US Geological Survey (USGS) provided essential coordinating services for remote sensing activities. Communication was rapidly established between the widely distributed response teams and data providers to define imaging requirements and sensor tasking opportunities. Data acquired from a variety of sources were received and archived by the USGS, and these products were subsequently distributed using the Hazards Data Distribution System (HDDS) and other mechanisms. Within six weeks after the earthquake, over 600,000 files representing 54 terabytes of data were provided to the response community. The USGS directly supported a wide variety of groups in their use of these data to characterize post-earthquake conditions and to make comparisons with pre-event imagery. The rapid and continuing response achieved was enabled by existing imaging and ground systems, and skilled personnel adept in all aspects of satellite data acquisition, processing, distribution and analysis. The information derived from image interpretation assisted senior planners and on-site teams to direct assistance where it was most needed.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Remote sensing of tamarisk biomass, insect herbivory, and defoliation: Novel methods in the Grand Canyon Region, Arizona

Tamarisk is an invasive, riparian shrub species in the southwestern USA. The northern tamarisk beetle (Diorhabda carinulata) has been introduced to several states to control tamarisk. We classified tamarisk distribution in the Glen Canyon National Recreation Area, Arizona using a 0.2 m resolution, airborne multispectral data and estimated tamarisk beetle effects (overall accuracy of 86 percent) leading to leaf defoliation in a 49,408 m2 area. We also estimated individual tamarisk tree biomass and their uncertainties using airbonre liday data (100 points/m2). On average, total above ground tamarisk biomass was 8.67 kg/m2 (SD=17.6). The tamarisk beetle defoliation resulted in a mean leaf biomass loss of 0.52 kg/m2 and an equivalent of 25,692 kg across the entire study area. Our defoliated tamarisk map and biomass estimates can help inform restoration treatments to reduce tamarisk. Continued monitoring of tamarisk and tamarisk beetle effects are recommended to understand the currently-unknown eventual equilibrium between the two species and the cascading effects on ecosystem processes.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Estimating forest and woodland aboveground biomass using active and passive remote sensing

Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha –1 across all woodland and forest species. Landsat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha –1 . Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States.

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↗