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Results for “Journal of Remote Sensing”

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At least 127 records · Page 7Linked to original sources
USGS2007

The use of a vegetation index for assessment of the urban heat island effect

A vegetation index and radiative surface temperature were derived from NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) data for the Seattle, WA region from 28 June through 4 July 1991. The vegetation index and surface temperature values were computed for locations of weather observation stations within the region and compared to observed minimum air temperatures. These comparisons were used to evaluate the use of AVHRR data to assess the influence of the urban environment on observed minimum air temperatures (the urban heat island effect). AVHRR derived normalized difference vegetation index (NDVI) and radiant surface temperature data from a one week composite product were both related significantly to observed minimum temperatures, however, the vegetation index accounted for a greater amount of the spatial variation observed in mean minimum temperatures. The difference in the NDVI between urban and rural regions appears to be an indicator of the difference in surface properties (i.e., evaporation and heat storage capacity) between the two environments that are responsible for differences in urban and rural minimum temperatures.

International Journal of Remote Sensing↗
USGS2007

The 1 km AVHRR global land data set: first stages in implementation

The global land 1 km data set project represents an international effort to acquire, archive, process, and distribute 1 km AVHRR data of the entire global land surface in order to meet the needs of the international science community. A network of 26 high resolution picture transmission (HRPT) stations, along with data recorded by the National Oceanic and Atmospheric Administration (NOAA), has been acquiring daily global land coverage since 1 April 1992. A data set of over 30000 AVHRR images has been archived and made available for distribution by the United States Geological Survey, EROS Data Center and the European Space Agency. Under the guidance of the International Geosphere Biosphere programme, processing standards for the AVHRR data have been developed for calibration, atmospheric correction, geometric registration, and the production of global 10-day maximum normalized difference vegetation index (NDVI) composites. The major uses of the composites are related to the study of surface vegetation cover. A prototype 10-day composite was produced for the period of 21–30 June 1992. Production of an 18-month time series of 10-day composites is underway.

International Journal of Remote Sensing↗
USGS2007

Survey of geographical information system and image processing software

The Global Resource Information Database—a part of the United Nations Environment Programme—conducts a bi-annual survey of geographical information system (GIS) and image processing (IP) software. This survey makes information about software products available in developing countries. The 1993 survey showed that the number of installations of GIS, IP, and related software products increased dramatically from 1991 to 1993, mostly in North America and Europe.

International Journal of Remote Sensing↗
USGS2007

Monitoring flooding in coastal wetlands by using radar imagery and ground-based measurements

Satellite radar was used in a Florida Juncus roemerianus marsh to map tidal flooding, a critical control of coastal vegetation distribution. Radar images taken during a time of near-continuous recordings of ground-based hydrology measurements directly linked marsh flooding to lowered radar returns and indicated a negative covariation between flood frequency and radar return. Flood-extent contours extracted from the radar images and calibrated with point depth measurements showed marsh elevation could be estimated to about 8 cm compared to the 150 cm topographic contours currently available.

International Journal of Remote Sensing↗
USGS2007

Characteristics of the 1 km AVHRR data set for North America

The North America portion of a new global 1 km AVHRR time-series dataset was produced recently by the U.S. Geological Survey, EROS Data Center. Characteristics of the dataset were evaluated for scan-angle distribution, image area distortion as the result of map projection, distribution of high solar zenith angle, and cloud presence in image composites produced using maximum values of normalized difference vegetation index (NDVI). The evaluation showed that the compositing procedure exhibits a bias favouring off-nadir pixels, particularly at post-nadir (forward scanning) positions in the winter months. Results for scan angle distribution and image area distortion provide a basis for calculating the data's effective minimum mapping area for various geographical locations. The amount of missing data due to large solar zenith angle effect varies from 42 per cent in January to 1 per cent in July. Cloud contaminated pixels estimated for the thirty-six 10-day composites range from 7??5 per cent in May to 1??6 per cent in November. Recompositing the North America data set from 10-day cycles to monthly cycles can effectively reduce the amount of cloudy pixels in the data.

International Journal of Remote Sensing↗
USGS2007

Compression of the Global Land 1-km AVHRR dataset

Large datasets, such as the Global Land 1-km Advanced Very High Resolution Radiometer (AVHRR) Data Set (Eidenshink and Faundeen 1994), require compression methods that provide efficient storage and quick access to portions of the data. A method of lossless compression is described that provides multiresolution decompression within geographic subwindows of multi-spectral, global, 1-km, AVHRR images. The compression algorithm segments each image into blocks and compresses each block in a hierarchical format. Users can access the data by specifying either a geographic subwindow or the whole image and a resolution (1,2,4, 8, or 16 km). The Global Land 1-km AVHRR data are presented in the Interrupted Goode's Homolosine map projection. These images contain masked regions for non-land areas which comprise 80 per cent of the image. A quadtree algorithm is used to compress the masked regions. The compressed region data are stored separately from the compressed land data. Results show that the masked regions compress to 0·143 per cent of the bytes they occupy in the test image and the land areas are compressed to 33·2 per cent of their original size. The entire image is compressed hierarchically to 6·72 per cent of the original image size, reducing the data from 9·05 gigabytes to 623 megabytes. These results are compared to the first order entropy of the residual image produced with lossless Joint Photographic Experts Group predictors. Compression results are also given for Lempel-Ziv-Welch (LZW) and LZ77, the algorithms used by UNIX compress and GZIP respectively. In addition to providing multiresolution decompression of geographic subwindows of the data, the hierarchical approach and the use of quadtrees for storing the masked regions gives a marked improvement over these popular methods.

International Journal of Remote Sensing↗
USGS2007

Estimating the effective spatial resolution of an AVHRR time series

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

International Journal of Remote Sensing↗
USGS2010

Estimating maize production in Kenya using NDVI: Some statistical considerations

A regression model approach using a normalized difference vegetation index (NDVI) has the potential for estimating crop production in East Africa. However, before production estimation can become a reality, the underlying model assumptions and statistical nature of the sample data (NDVI and crop production) must be examined rigorously. Annual maize production statistics from 1982-90 for 36 agricultural districts within Kenya were used as the dependent variable; median area NDVI (independent variable) values from each agricultural district and year were extracted from the annual maximum NDVI data set. The input data and the statistical association of NDVI with maize production for Kenya were tested systematically for the following items: (1) homogeneity of the data when pooling the sample, (2) gross data errors and influence points, (3) serial (time) correlation, (4) spatial autocorrelation and (5) stability of the regression coefficients. The results of using a simple regression model with NDVI as the only independent variable are encouraging (r 0.75, p 0.05) and illustrate that NDVI can be a responsive indicator of maize production, especially in areas of high NDVI spatial variability, which coincide with areas of production variability in Kenya.

International Journal of Remote Sensing↗
USGS2010

Technical note assessing the accuracy of Landsat Thematic Mapper classification using double sampling

Double sampling was used to provide a cost efficient estimate of the accuracy of a Landsat Thematic Mapper (TM) classification map of a scene located in the Rocky Mountain National Park, Colorado. In the first phase, 200 sample points were randomly selected to assess the accuracy between Landsat TM data and aerial photography. The overall accuracy and Kappa statistic were 49.5 per cent and 32.5 per cent, respectively. In the second phase, 25 sample points identified in the first phase were selected using stratified random sampling and located in the field. This information was used to correct for misclassification errors associated with the first phase samples. The overall accuracy and Kappa statistic increased to 59.6 per cent and 45.6 per cent, respectively.

International Journal of Remote Sensing↗
USGS2010

Surface phenology and satellite sensor-derived onset of greenness: An initial comparison

The objective of this work was to document the utility of phenological data derived from satellite sensors by comparing them with modelled phenology. Surface phenological model outputs (first leaf and first bloom dates) were correlated positively with satellite sensor-derived start of season (SOS) dates for 1991-1995 across the eastern United States. The correlation was highest for forest (r 0.62 for deciduous trees and 0.64 for mixed woodland) and tall grass (r 0.46) and lowest for short grass (r 0.37). The average correlation over all land cover types was 0.61. Average SOS dates were consistently earlier than Spring Index dates across all land cover types. This finding and limited native tree phenology data suggest that the SOS technique detects understorey green-up in the forest rather than overstorey species. The biweekly temporal resolution of the satellite sensor data placed an upper limit on prediction accuracy; thus, year-to-year variations at individual sites were typically small. Nevertheless, the correct biweek SOS could be identified from the surface models 61% of the time, and 1 biweek 96% of the time. Further temporal refinement of the satellite sensor measurements is necessary in order to connect them with surface phenology adequately and to develop links among 'green wave' components in selected biomes.

International Journal of Remote Sensing↗
USGS2010

Exploring the correlation between Southern Africa NDVI and Pacific sea surface temperatures: Results for the 1998 maize growing season

Several studies have identified statistically significant correlations between Pacific sea surface temperature anomalies and NDVI anomalies in Southern Africa. The potential predictive value of the relationship was explored for the 1998 maize growing season. Cross-validation techniques suggested a more useful relationship for regions of wet anomaly than for regions of dry anomaly. Observed 1998 NDVI anomaly patterns were consistent with this result. Wet anomalies were observed as expected, but wide areas of expected dry anomalies exhibited average or above-average greeness.

International Journal of Remote Sensing↗
USGS2007

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

To maximize the spectral distinctiveness (information) of the canopy reflectance, an atmospheric correction strategy was implemented to provide accurate estimates of the intrinsic reflectance from the Earth Observing 1 (EO1) satellite Hyperion sensor signal. In rendering the canopy reflectance, an estimate of optical depth derived from a measurement of downwelling irradiance was used to drive a radiative transfer simulation of atmospheric scattering and attenuation. During the atmospheric model simulation, the input whole-terrain background reflectance estimate was changed to minimize the differences between the model predicted and the observed canopy reflectance spectra at 34 sites. Lacking appropriate spectrally invariant scene targets, inclusion of the field and predicted comparison maximized the model accuracy and, thereby, the detail and precision in the canopy reflectance necessary to detect low percentage occurrences of invasive plants. After accounting for artifacts surrounding prominent absorption features from about 400nm to 1000nm, the atmospheric adjustment strategy correctly explained 99% of the observed canopy reflectance spectra variance. Separately, model simulation explained an average of 88%??9% of the observed variance in the visible and 98% ?? 1% in the near-infrared wavelengths. In the 34 model simulations, maximum differences between the observed and predicted reflectances were typically less than ?? 1% in the visible; however, maximum reflectance differences higher than ?? 1.6% (<??2.3%) at more than a few wavelengths were observed at three sites. In the near-infrared wavelengths, maximum reflectance differences remained less than ??3% for 68% of the comparisons (??1 standard deviation) and less than ??6% for 95% of the comparisons (??2 standard deviation). Higher reflectance differences in the visible and near-infrared wavelengths were most likely associated with problems in the comparison, not in the model generation. ?? 2005 US Government.

International Journal of Remote Sensing↗
USGS1992

Land cover mapping of the Upper Kuskokwim Resource Management Area, Alaska, using Landsat and a digital data base approach

Digital land cover and terrain data for the Upper Kuskokwim Resource Management Area in south-central Alaska were produced by the U.S. Geological Survey for the U.S. Bureau of Land Management. These and other environmental data were incorporated into a digital data base to assist in the production of a land cover map and to facilitate in the management and planning of the area. The digital data base is registered to a 50 m Universal Transverse Mercator grid and is stored on computer-compatible tape to facilitate transfer between different processing systems. The data base includes land cover classifications, terrain models (elevation, slope, aspect, solar illumination), autumn and winter Landsat multi-spectral scanner data, soils and surficial geology data, and area boundary. The spectral classification of Landsat multi-spectral scanner data resulted in seven major classes and 24 subclasses. Major classes were forest, shrubland, dwarf scrub, herbaceous, barren, water, and other.

Canadian Journal of Remote Sensing↗
USGS2014

Landsat TM memory effect characterization and correction

Before radiometric calibration of Landsat Thematic Mapper (TM) data can be done accurately, it is necessary to minimize the effects of artifacts present in the data that originate in the instrument's signal processing path. These artifacts have been observed in downlinked image data since shortly after launch of Landsat 4 and 5. However, no comprehensive work has been done to characterize all the artifacts and develop methods for their correction. In this paper, the most problematic artifact is discussed: memory effect (ME). Characterization of this artifact is presented, including the parameters necessary for its correction. In addition, a correction algorithm is described that removes the artifact from TM imagery. It will be shown that this artifact causes significant radiometry errors, but the effect can be removed in a straightforward manner.

Canadian Journal of Remote Sensing↗
USGS2014

Landsat-7 Enhanced Thematic Mapper plus radiometric calibration

Landsat-7 is currently being built and tested for launch in 1998. The Enhanced Thematic Mapper Plus (ETM+) sensor for Landsat-7, a derivative of the highly successful Thematic Mapper (TM) sensors on Landsats 4 and 5, and the Landsat-7 ground system are being built to provide enhanced radiometric calibration performance. In addition, regular vicarious calibration campaigns are being planned to provide additional information for calibration of the ETM+ instrument. The primary upgrades to the instrument include the addition of two solar calibrators: the full aperture solar calibrator, a deployable diffuser, and the partial aperture solar calibrator, a passive device that allows the ETM+ to image the sun. The ground processing incorporates for the first time an off-line facility, the Image Assessment System (IAS), to perform calibration, evaluation and analysis. Within the IAS, processing capabilities include radiometric artifact characterization and correction, radiometric calibration from the multiple calibrator sources, inclusion of results from vicarious calibration and statistical trending of calibration data to improve calibration estimation. The Landsat Product Generation System, the portion of the ground system responsible for producing calibrated products, will incorporate the radiometric artifact correction algorithms and will use the calibration information generated by the IAS. This calibration information will also be supplied to ground processing systems throughout the world.

Canadian Journal of Remote Sensing↗
USGS2013

Classification of freshwater ice conditions on the Alaskan Arctic Coastal Plain using ground penetrating radar and TerraSAR-X satellite data

Arctic freshwater ecosystems have responded rapidly to climatic changes over the last half century. Lakes and rivers are experiencing a thinning of the seasonal ice cover, which may increase potential over-wintering freshwater habitat, winter water supply for industrial withdrawal, and permafrost degradation. Here, we combined the use of ground penetrating radar (GPR) and high-resolution (HR) spotlight TerraSAR-X (TSX) satellite data (1.25 m resolution) to identify and characterize floating ice and grounded ice conditions in lakes, ponds, beaded stream pools, and an alluvial river channel. Classified ice conditions from the GPR and the TSX data showed excellent agreement: 90.6% for a predominantly floating ice lake, 99.7% for a grounded ice lake, 79.0% for a beaded stream course, and 92.1% for the alluvial river channel. A GIS-based analysis of 890 surface water features larger than 0.01 ha showed that 42% of the total surface water area potentially provided over-wintering habitat during the 2012/2013 winter. Lakes accounted for 89% of this area, whereas the alluvial river channel accounted for 10% and ponds and beaded stream pools each accounted for <1%. Identification of smaller landscape features such as beaded stream pools may be important because of their distribution and role in connecting other water bodies on the landscape. These findings advance techniques for detecting and knowledge associated with potential winter habitat distribution for fish and invertebrates at the local scale in a region of the Arctic with increasing stressors related to climate and land use change.

Alaska↗
USGS2018

Sentinel-2A MSI and Landsat-8 OLI radiometric cross comparison over desert sites

The Sentinel-2A and Landsat-8 satellites carry on-board moderate resolution multispectral imagers for the purpose of documenting the Earth’s changing surface. Though they are independently built and managed, users will certainly take advantage of the opportunity to have higher temporal coverage by combining the datasets. Thus it is important for the radiometric and geometric calibration of the MultiSpectral Instrument (MSI) and the Operational Land Imager (OLI) to be compatible. Cross-calibration of MSI to OLI has been accomplished using multiple techniques involving the use of pseudo-invariant calibration sites (PICS) using direct comparisons as well as through use of PICS models predicting top-of-atmosphere reflectance. A team from the University of Arizona is acquiring field data under both instruments for vicarious calibration of the sensors. This paper shows that the work done to date by the Landsat and Sentinel-2 calibration teams has resulted in stable radiometric calibration for each instrument and consistency to ~2.5% between the instruments for all the spectral bands that the instruments have in common.

European Journal of Remote Sensing↗
USGS2009

An automated imagery orthorectification pilot

Automated orthorectification of raw image products is now possible based on the comprehensive metadata collected by Global Positioning Systems and Inertial Measurement Unit technology aboard aircraft and satellite digital imaging systems, and based on emerging pattern-matching and automated image-to-image and control point selection capabilities in many advanced image processing systems. Automated orthorectification of standard aerial photography is also possible if a camera calibration report and sufficient metadata is available. Orthorectification of historical imagery, for which only limited metadata was available, was also attempted and found to require some user input, creating a semi-automated process that still has significant potential to reduce processing time and expense for the conversion of archival historical imagery into geospatially enabled, digital formats, facilitating preservation and utilization of a vast archive of historical imagery. Over 90 percent of the frames of historical aerial photos used in this experiment were successfully orthorectified to the accuracy of the USGS 100K base map series utilized for the geospatial reference of the archive. The accuracy standard for the 100K series maps is approximately 167 feet (51 meters). The main problems associated with orthorectification failure were cloud cover, shadow and historical landscape change which confused automated image-to-image matching processes. Further research is recommended to optimize automated orthorectification methods and enable broad operational use, especially as related to historical imagery archives.

Journal of Remote Sensing↗