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Results for “International Journal of Remote Sensing”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 55 records · Page 3Linked to original sources
USGS2007

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts.

Arizona↗
USGS2010

Capability of AVHRR data in discriminating rangeland cover mixtures

A combination of high temporal resolution Advanced Very High Resolution Radiometer (AVHRR) data and high spatial information Map Information Analysis and Display System (MIADS) landuse/landcover data from the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) were used to investigate the feasibility of using the combined dataset for regional evapotranspiration (ET) studies. It was shown that the biweekly maximum Normalized Difference Vegetation Index (NDVI) composite AVHRR data were capable of discriminating rangelands with different types of trees and shrubs species. AVHRR data also showed a potential to distinguish canopy cover differences within a mix of similar species. The combination of MIADS data and AVHRR data can be used to study temporal dynamics of various cover types for use in regional ET estimates.

International Journal of Remote Sensing↗
USGS2010

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

International Journal of Remote Sensing↗
USGS2010

An evaluation of the global 1-km AVHRR land dataset

This paper summarizes the steps taken in the generation of the global 1-km AVHRR land dataset, and it documents an evaluation of the data product with respect to the original specifications and its usefulness in research and applications to date. The evaluation addresses data characterization, processing, compositing and handling issues. Examples of the main scientific outputs are presented and options for improved processing are outlined and prioritized. The dataset has made a significant contribution, and a strong recommendation is made for its reprocessing and continuation to produce a long-term record for global change research.

International Journal of Remote Sensing↗
USGS2010

Study of high SAR backscattering caused by an increase of soil moisture over a sparsely vegetated area: Implications for characteristics of backscattering

We used interferometric methods on a pair of repeat-pass ERS-1 synthetic aperture radar (SAR) images to study soil moisture changes over sparsely vegetated targets. The intensity of the SAR image acquired at one time was higher than that of an image acquired at an earlier time. We used a correlation image computed from the SAR image pair to study the cause of the observed changes in SAR intensity. Because a reduction of correlation over areas with intensity changes was not observed, we interpreted the intensity changes as not being caused by changes in roughness/structure, but by a change in soil moisture owing to rainfall. An increase in soil moisture ranging from 5% to 20% is the most likely explanation for the increase of intensity. These analyses imply that both intensity and phase information should be used in SAR change detection applications.

International Journal of Remote Sensing↗
USGS2010

An assessment of support vector machines for land cover classification

The support vector machine (SVM) is a group of theoretically superior machine learning algorithms. It was found competitive with the best available machine learning algorithms in classifying high-dimensional data sets. This paper gives an introduction to the theoretical development of the SVM and an experimental evaluation of its accuracy, stability and training speed in deriving land cover classifications from satellite images. The SVM was compared to three other popular classifiers, including the maximum likelihood classifier (MLC), neural network classifiers (NNC) and decision tree classifiers (DTC). The impacts of kernel configuration on the performance of the SVM and of the selection of training data and input variables on the four classifiers were also evaluated in this experiment.

International Journal of Remote Sensing↗
USGS2010

Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance

A new tasselled cap transformation based on Landsat 7 at-satellite reflectance was developed. This transformation is most appropriate for regional applications where atmospheric correction is not feasible. The brightness, greenness and wetness of the derived transformation collectively explained over 97% of the spectral variance of the individual scenes used in this study.

International Journal of Remote Sensing↗
USGS2010

The utility of estimating net primary productivity over Alaska using baseline AVHRR data

Net primary productivity (NPP) is a fundamental ecological variable that provides information about the health and status of vegetation communities. The Normalized Difference Vegetation Index, or NDVI, derived from the Advanced Very High Resolution Radiometer (AVHRR) is increasingly being used to model or predict NPP, especially over large remote areas. In this article, seven seasonally based metrics calculated from a seven-year baseline NDVI dataset were used to model NPP over Alaska, USA. For each growing season, they included maximum, mean and summed NDVI, total days, product of total days and maximum NDVI, an integral estimate of NDVI and a summed product of NDVI and solar radiation. Field (plot) derived NPP estimates were assigned to 18 land cover classes from an Alaskan statewide land cover database. Linear relationships between NPP and each NDVI metric were analysed at four scales: plot, 1-km, 10-km and 20-km pixels. Results show moderate to poor relationship between any of the metrics and NPP estimates for all data sets and scales. Use of NDVI for estimating NPP may be possible, but caution is required due to data seasonality, the scaling process used and land surface heterogeneity.

Alaska↗
USGS2010

Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s

The interannual variability and trend of above-ground photosynthetic activity of Arctic tundra vegetation in the 1990s is examined for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage (LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data. Smaller SINDVI values occurred during the three years (1992-1994) following the volcanic eruption of Mt Pinatubo. Even after implementing corrections for this stratospheric aerosol effect and adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most pronounced increase was observed for the foothills physiographical province.

Alaska↗
USGS2010

Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.

International Journal of Remote Sensing↗
USGS2010

Water vapour correction of the daily 1 km AVHRR global land dataset: Part I validation and use of the Water Vapour input field

An atmospheric correction algorithm developed for the 1 km Advanced Very High Resolution Radiometer (AVHRR) global land dataset was modified to include a near real-time total column water vapour data input field to account for the natural variability of atmospheric water vapour. The real-time data input field used for this study is the Television and Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) Pathfinder A global total column water vapour dataset. It was validated prior to its use in the AVHRR atmospheric correction process using two North American AVHRR scenes, namely 13 June and 28 November 1996. The validation results are consistent with those reported by others and entail a comparison between TOVS, radiosonde, experimental sounding, microwave radiometer, and data from a hand-held sunphotometer. The use of this data layer as input to the AVHRR atmospheric correction process is discussed.

International Journal of Remote Sensing↗
USGS2010

The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

International Journal of Remote Sensing↗
USGS2010

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

International Journal of Remote Sensing↗
USGS2010

AVHRR channel selection for land cover classification

Mapping land cover of large regions often requires processing of satellite images collected from several time periods at many spectral wavelength channels. However, manipulating and processing large amounts of image data increases the complexity and time, and hence the cost, that it takes to produce a land cover map. Very few studies have evaluated the importance of individual Advanced Very High Resolution Radiometer (AVHRR) channels for discriminating cover types, especially the thermal channels (channels 3, 4 and 5). Studies rarely perform a multi-year analysis to determine the impact of inter-annual variability on the classification results. We evaluated 5 years of AVHRR data using combinations of the original AVHRR spectral channels (1-5) to determine which channels are most important for cover type discrimination, yet stabilize inter-annual variability. Particular attention was placed on the channels in the thermal portion of the spectrum. Fourteen cover types over the entire state of Colorado were evaluated using a supervised classification approach on all two-, three-, four- and five-channel combinations for seven AVHRR biweekly composite datasets covering the entire growing season for each of 5 years. Results show that all three of the major portions of the electromagnetic spectrum represented by the AVHRR sensor are required to discriminate cover types effectively and stabilize inter-annual variability. Of the two-channel combinations, channels 1 (red visible) and 2 (near-infrared) had, by far, the highest average overall accuracy (72.2%), yet the inter-annual classification accuracies were highly variable. Including a thermal channel (channel 4) significantly increased the average overall classification accuracy by 5.5% and stabilized interannual variability. Each of the thermal channels gave similar classification accuracies; however, because of the problems in consistently interpreting channel 3 data, either channel 4 or 5 was found to be a more appropriate choice. Substituting the thermal channel with a single elevation layer resulted in equivalent classification accuracies and inter-annual variability.

International Journal of Remote Sensing↗
USGS2010

On-orbit performance of the Landsat-7 ETM+ radiometric calibrators

The Landsat-7 Enhanced Thematic Mapper Plus (ETM+) incorporates two new devices to improve its absolute radiometric calibration: a Full Aperture Solar Calibrator (FASC) and a Partial Aperture Solar Calibrator (PASC). The FASC is a diffuser panel, typically deployed once per month. Initial FASC absolute calibration results were within 5% of the pre-launch calibrations. Over time, the responses of the ETM+ to the FASC have varied with the location viewed on the panel, suggesting a localized degradation or contamination of the panel. On the best part of the panel, the trends in response range from m 1.4% y m 1 (band 4) to +0.6% y m 1 (band 7), with band 5 showing the least change at m 0.4% y m 1 . Changes in the panel reflectance due to UV exposure are believed to be the origin of these trends. The PASC is a set of auxiliary optics that allows the ETM+ to image the Sun through reduced apertures. PASC data have normally been acquired on a daily basis. Unlike the FASC, the PASC has exhibited significant anomalies. During the first six months of operation, responses to the PASC increased up to 60%, sending bands 2, 3 and 8 into saturation (band 1 was saturated at launch). The short-wave infrared (SWIR) band individual detectors have shown variations up to - 20% in response to the PASC. The variation is different for each detector. After the first six months, the responses to the PASC have become more stable, with much of the variation related to the within-scan position of the solar image. Overall results to date for all calibrators and comparisons with vicarious calibrations indicate that most of the response variations have been due to the calibrators themselves and suggest that the instrument has been stable with changes in response of less than 0.5% y m 1 .

International Journal of Remote Sensing↗
USGS2010

A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.

International Journal of Remote Sensing↗
USGS2010

Detecting trends in landscape pattern metrics over a 20-year period using a sampling-based monitoring programme

Temporal trends in landscape pattern metrics describing texture, patch shape and patch size were evaluated in the US Middle Atlantic Coastal Plain Ecoregion. The landscape pattern metrics were calculated for a sample of land use/cover data obtained for four points in time from 1973-1992. The multiple sampling dates permit evaluation of trend, whereas availability of only two sampling dates allows only evaluation of change. Observed statistically significant trends in the landscape pattern metrics demonstrated that the sampling-based monitoring protocol was able to detect a trend toward a more fine-grained landscape in this ecoregion. This sampling and analysis protocol is being extended spatially to the remaining 83 ecoregions in the US and temporally to the year 2000 to provide a national and regional synthesis of the temporal and spatial dynamics of landscape pattern covering the period 1973-2000.

International Journal of Remote Sensing↗
USGS2010

A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.

International Journal of Remote Sensing↗