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At least 109 records · Page 6Linked to original sources
USGS2014

Examining change detection approaches for tropical mangrove monitoring

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Comparison of simulated HyspIRI with two multispectral sensors for invasive species mapping

This paper assesses the potential of a single HYSPIRI scene to estimate cover of the non-native invasive buffelgrass (Pennisetum ciliare) in a heterogeneous Sonoran Desert scrub ecosystem. We simulated HYSPIRI (60 m) along with two multispectral sensors, Thematic Mapper (TM; 30 m) and Advanced Space-borne Thermal Emission and Reflection Spectrometer (ASTER; 15 m), from high-resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; 3.2 m) imagery in an area infested by buffelgrass near Tucson, Arizona. We compared classification accuracies of all simulated sensors at spatial resolutions of 15 m, 30 m, and 60 m to evaluate tradeoffs of spectral and spatial resolution across the sensors. Although spectroscopically superior to Landsat TM and ASTER, ASTER easily outperformed HYSPIRI for small infestations (225 m 2 ) on account of its spatial resolution. Shortwave-infrared bands near 2.2 µm were key indicators for both HYSPIRI and ASTER, highlighting the benefit of narrow-wave SWIR for mapping invasive species in arid ecosystems.

Photogrammetric Engineering and Remote Sensing↗
USGS2014

Annual crop type classification of the U.S. Great Plains for 2000 to 2011

The purpose of this study was to increase the spatial and temporal availability of crop classification data. In this study, nearly 16.2 million crop observation points were used in the training of the US Great Plains classification tree crop type model (CTM). Each observation point was further defined by weekly Normalized Difference Vegetation Index, annual climate, and a number of other biogeophysical environmental characteristics. This study accounted for the most prevalent crop types in the region, including, corn, soybeans, winter wheat, spring wheat, cotton, sorghum, and alfalfa. Annual CTM crop maps of the US Great Plains were created for 2000 to 2011 at a spatial resolution of 250 meters. The CTM achieved an 87 percent classification success rate on 1.8 million observation points that were withheld from model training. Product validation was performed on greater than 15,000 county records with a coefficient of determination of R 2 = 0.76.

Great Plains↗
USGS2014

Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission

New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

Validation of geometric accuracy of Global Land Survey (GLS) 2000 data

The Global Land Survey (GLS) 2000 data were generated from Geocover™ 2000 data with the aim of producing a global data set of accuracy better than 25 m Root Mean Square Error (RMSE). An assessment and validation of accuracy of GLS 2000 data set, and its co-registration with Geocover™ 2000 data set is presented here. Since the availability of global data sets that have higher nominal accuracy than the GLS 2000 is a concern, the data sets were assessed in three tiers. In the first tier, the data were compared with the Geocover™ 2000 data. This comparison provided a means of localizing regions of higher differences. In the second tier, the GLS 2000 data were compared with systematically corrected Landsat-7 scenes that were obtained in a time period when the spacecraft pointing information was extremely accurate. These comparisons localize regions where the data are consistently off, which may indicate regions of higher errors. The third tier consisted of comparing the GLS 2000 data against higher accuracy reference data. The reference data were the Digital Ortho Quads over the United States, orthorectified SPOT data over Australia, and high accuracy check points obtained using triangulation bundle adjustment of Landsat-7 images over selected sites around the world. The study reveals that the geometric errors in Geocover™ 2000 data have been rectified in GLS 2000 data, and that the accuracy of GLS 2000 data can be expected to be better than 25 m RMSE for most of its constituent scenes.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

A landsat data tiling and compositing approach optimized for change detection in the conterminous United States

Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.

Photogrammetric Engineering and Remote Sensing↗
USGS2016

Status and trends of land change in selected U.S. ecoregions - 2000 to 2011

U.S. Geological Survey scientists developed a dataset of 2006 and 2011 land-use and land-cover ( LULC ) information for selected 100-km 2 sample blocks within 29 U.S. Environmental Protection Agency ( EPA ) Level III ecoregions across the conterminous United States. The data can be used with the previously published Land Cover Trends Dataset: 1973 to 2000 to assess landuse/land-cover change across a 37-year study period. Results from analysis of these data include ecoregion-based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km 2 in the Northern Basin and Range Ecoregion to a high of 78,782 km 2 in the Southeastern Plains Ecoregion. The Southeastern Plains continues to encompass one of the most intense forest harvesting and regrowth regions in the country, with 16.6 percent of the ecoregion changing between 2001 and 2011. These LULC change statistics provide a new, valuable resource that complements other reference data and field-verified LULC data. Researchers can use this resource to independently validate other land change products or to conduct regional land change assessments.

Photogrammetric Engineering and Remote Sensing↗
USGS2017

Operational shoreline mapping with high spatial resolution radar and geographic processing

A comprehensive mapping technology was developed utilizing standard image processing and available GIS procedures to automate shoreline identification and mapping from 2 m synthetic aperture radar ( SAR ) HH amplitude data. The development used four NASA Uninhabited Aerial Vehicle SAR ( UAVSAR ) data collections between summer 2009 and 2012 and a fall 2012 collection of wetlands dominantly fronted by vegetated shorelines along the Mississippi River Delta that are beset by severe storms, toxic releases, and relative sea-level rise. In comparison to shorelines interpreted from 0.3 m and 1 m orthophotography, the automated GIS 10 m alongshore sampling found SAR shoreline mapping accuracy to be ±2 m, well within the lower range of reported shoreline mapping accuracies. The high comparability was obtained even though water levels differed between the SAR and photography image pairs and included all shorelines regardless of complexity. The SAR mapping technology is highly repeatable and extendable to other SAR instruments with similar operational functionality.

Photogrammetric Engineering and Remote Sensing↗
USGS2018

Removing rural roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011

Quantifying change in urban land provides important information to create empirical models examining the effects of human land use. Maps of developed land from the National Land Cover Database ( NLCD ) of the conterminous United States include rural roads in the developed land class and therefore overestimate the amount of urban land. To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7 percent) than the NLCD maps (96.2 percent). More importantly, the urban maps resulted in lower commission error of the urban class (23 percent versus 57 percent for the NLCD in 2006) with the trade-off of slightly inflated omission error (20 percent for the urban map, 16 percent for NLCD in 2006). The removal of approximately 230,000 km 2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands.

Photogrammetric Engineering and Remote Sensing↗
USGS2021

Semi-centennial of Landsat observations and pending Landsat 9 launch

The first Landsat was placed in orbit on 23 July 1972, followed by a series of missions that have provided nearly continuous, two-satellite 8-day repeat image coverage of the Earth’s land areas for the last half-century. These observations have substantially enhanced our understanding of the Earth’s terrestrial dynamics, both as a major element of the Earth’s physical system, the primary home of humans, and the major source of natural resources that support them. The history of Landsat is complex, reflective of the human systems that sustain it. Despite the conflicted perspectives surrounding the continuation of the program, Landsat has survived based on worldwide recognition of its critical contributions to understanding land dynamics, management of natural resources and Earth system science. Launch of Landsat 9 is anticipated in Fall 2021, and current planning for the next generation, Landsat Next is well underway. The community of Landsat data users is looking forward to another 50 years of the Landsat program.

Photogrammetric Engineering and Remote Sensing (PE↗
USGS1980

Enumeration of prairie wetlands with Landsat and aircraft data

A method is described for making an estimate of wetland numbers in the glaciated prairie region. A double-phase sampling approach is used which consists of first making a total census of wetlands using Landsat data, and then adjusting the Landsat results on the basis of samples derived from high resolution aircraft data. The method is relatively simple to use and has general applicability for estimating habitat features not consistently detectable or resolvable on Landsat imagery because their size range includes features less than the resolution capability of the satellite's sensor.

Photogrammetric Engineering and Remote Sensing↗
USGS1981

Use of 35-mm color aerial photography to acquire mallard sex ratio data

A conventional 35-mm camera equipped with an f2.8 135-mm lens and ASA 64 color film was used to acquire sex ratio data on mallards ( Anas platyrhynchos ) wintering in the Platte River Valley of south-central Nebraska. Prelight focusing for a distance of 30.5 metres and setting of shutter speed at 1/2000 of a second eliminated focusing and reduced image motion problems and resulted in high-resolution, large-scale aerial photography of small targets. This technique has broad application to the problem of determining sex ratios of various species of waterfowl concentrated on wintering and staging areas. The aerial photographic method was cheaper than the ground ocular method when costs were compared on a per-100 bird basis.

Nebraska↗
USGS1976

Utilization of satellite data for inventorying prairie ponds and lakes

By using data acquired by LANDSAT-1 (formerly ERTS- 1), studies were conducted in extracting information necessary for formulating management decisions relating to migratory waterfowl. Management decisions are based in part on an assessment ofhabitat characteristics, specifically numbers, distribution, and quality of ponds and lakes in the prime breeding range. This paper reports on a study concerned with mapping open surface water features in the glaciated prairies. Emphasis was placed on the recognition of these features based upon water's uniquely low radiance in a single nearinfrared waveband. The results of this recognition were thematic maps and statistics relating to open surface water. In a related effort, the added information content of multiple spectral wavebands was used for discriminating surface water at a level of detail finer than the virtual resolution of the data. The basic theory of this technique and some preliminary results are described.

Photogrammetric Engineering and Remote Sensing↗
USGS1997

Supervised classification of Landsat thematic mapper imagery in a semi-arid rangeland by nonparametric discriminant analysis

In this article the authors used a nonparametric discriminant function in a supervised classification of Landsat Thematic Mapper satellite imagery of a ~240,000-ha semi-arid region in the Snake River Plains, southwestern Idaho. First, agriculture pixels were classified by distance from the soil baseline and water pixels by the thermal band value. Next, successive nonparametric discriminant functions were used to separate grassland and shrubland categories with subsequent classifications of vegetation within major classes. Accuracy in separating grasslands and shrublands was 80 percent and remained consistent relative to different thresholds in minimum percent ground cover defining shrublands. Within major grassland shrubland groups, researchers achieved 64 percent accuracy in separating dominant vegetation classes.

Photogrammetric Engineering and Remote Sensing↗
USGS2000

Accuracy assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region

The U.S. Geological Survey, in cooperation with other government and private organizations, is producing a conterminous U.S. land-cover map using Landsat Thematic Mapper 30-meter data for the Federal regions designated by the U.S. Environmental Protection Agency. Accuracy assessment is to be conducted for each Federal region to estimate overall and class-specific accuracies. In Region 2, consisting of New York and New Jersey, the accuracy assessment was completed for 15 land-cover and land-use classes, using interpreted 1:40,000-scale aerial photographs as reference data. The methodology used for Region 2 features a two-stage, geographically stratified approach, with a general sample of all classes (1,033 sample sites), and a separate sample for rare classes (294 sample sites). A confidence index was recorded for each land-cover interpretation on the 1:40,000-scale aerial photography The estimated overall accuracy for Region 2 was 63 percent (standard error 1.4 percent) using all sample sites, and 75.2 percent (standard error 1.5 percent) using only reference sites with a high-confidence index. User's and producer's accuracies for the general sample and user's accuracy for the sample of rare classes, as well as variance for the estimated accuracy parameters, were also reported. Narrowly defined land-use classes and heterogeneous conditions of land cover are the major causes of misclassification errors. Recommendations for modifying the accuracy assessment methodology for use in the other nine Federal regions are provided.

Photogrammetric Engineering and Remote Sensing↗
USGS1983

Objective procedures for lineament enhancement and extraction

A longterm research goal at EROS Data Center is to develop automated, objective procedures for lineament mapping. In support of this goal, a five-step digital convolution procedure has been used to produce directionally enhanced images, which contain few artifacts and little noise. The main limitation of this procedure is that little enhancement of lineaments occurs in dissected terrain, in shadowed areas, and in flat areas with a uniform land cover. The directional enhancement procedure can be modified to extract edge and line segments from an image. Any of various decision rules can then be used to connect the line segments and to produce a final lineament map. The result is an interpretive map, but one that is based on an objective extraction of lineament components by digital processing. -from Authors

Photogrammetric Engineering and Remote Sensing↗
USGS1982

An automated mapping satellite system ( Mapsat).

The favorable environment of space permits a satellite to orbit the Earth with very high stability as long as no local perturbing forces are involved. Solid-state linear-array sensors have no moving parts and create no perturbing force on the satellite. Digital data from highly stabilized stereo linear arrays are amenable to simplified processing to produce both planimetric imagery and elevation data. A satellite imaging system, called Mapsat, including this concept has been proposed to produce data from which automated mapping in near real time can be accomplished. Image maps as large as 1:50 000 scale with contours as close as a 20-m interval may be produced from Mapsat data. -from Author

Photogrammetric Engineering and Remote Sensing↗
USGS1982

Computation with physical values from Landsat digital data

Landsat digital images are commonly analyzed by using the digital numbers for each pixel recorded on a computer-compatible magnetic tape. Although this procedure may be satisfactory when only a single, internally consistent image is used, the procedure may produce incorrect results if more than one image is used for analysis as in mosaics or temporal overlays. The digital numbers for each pixel should be converted to their dimensioned equivalents such as radiance, as measured at the satellite, in milliwatts per square centimetre per steradian, or reflectance.-from Author

Photogrammetric Engineering and Remote Sensing↗