James R. Irons

Landsat sensor performance: history and current status

The current Thematic Mapper (TM) class of Landsat sensors began with Landsat-4, which was launched in 1982. This series continued with the nearly identical sensor on Landsat-5, launched in 1984. The final sensor in the series was the Landsat-7 Enhanced Thematic Mapper Plus (ETM+), which was carried into orbit in 1999. Varying degrees of effort have been devoted to the characterization of these instruments and data over the past 22 years. Extensive short-lived efforts early in the history, very limited efforts in the middle years, and now a systematic program for continuing characterization of all three systems are apparent. Currently, both the Landsat-5 TM and the Landsat-7 ETM+ are operational and providing data. Despite 20+ years of operation, the TM on Landsat-5 is fully functional, although downlinks for the data are limited. Landsat-7 ETM+ experienced a failure of its Scan Line Corrector mechanism in May 2003. Although there are gaps in the data coverage, the data remain of equivalent quality to prefailure data. Data products have been developed to fill these gaps using other ETM+ scenes.

An off-nadir-pointing imaging spectroradiometer for terrestrial ecosystem studies

The Advanced Solid-state Array Spectroradiometer (ASAS), an airborne, off-nadir-pointing imaging spectroradiometer used to acquire bidirectional radiance data for terrestrial targets, is described. As its platform aircraft flies over a target, the sensor can image the target through a sequence of at least seven fore-to-aft view directions ranging up to 45 degrees on either side of nadir. ASAS acquires data for 29 spectral bands in the visible and near-infrared portions of the spectrum (465 to 871 nm) with a resolution of 15 nm. The basic ASAS data product is a sequence of digital images acquired from multiple view directions and consisting of calibrated spectral radiance values. Examples of ASAS data from field experiments are presented. The data demonstrate the combined effects of reflectance anisotropy and increased atmospheric path length on off-nadir observations. One result of these effects is a variation in vegetation indices as a function of view direction. >

National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier

Abstract Decision tree classifiers have received much recent attention, particularly with regards to land cover classifications at continental to global scales. Despite their many benefits and general flexibility, the use of decision trees with high spatial resolution data has not yet been fully explored. In support of the National Park Service (NPS) Vegetation Mapping Program (VMP), we have examined the feasibility of using a commercially available decision tree classifier with multitemporal satellite data from the Enhanced Thematic Mapper-Plus (ETM+) instrument to map 11 land cover types at the Delaware Water Gap National Recreation Area near Milford, PA. Ensemble techniques such as boosting and consensus filtering of the training data were used to improve both the quality of the input training data as well as the final products. Using land cover classes as specified by the National Vegetation Classification Standard at the Formation level, the final land cover map has an overall accuracy of 82% (κ=0.80) when tested against a validation data set acquired on the ground (n=195). This same accuracy is 99.5% when considering only forest vs. nonforest classes. Usage of ETM+ scenes acquired at multiple dates improves the accuracy over the use of a single date, particularly for the different forest types. These results demonstrate the potential applicability and usability of such an approach to the entire National Park system, and to high spatial resolution land cover and forest mapping applications in general.

The effects of spatial resolution on the classification of Thematic Mapper data

Abstract Actual and degraded LANDSAT-4 Thematic Mapper (TM) data were analysed to examine the effect of spatial resolution on the performance of a per pixel, maximum-likelihood classification algorithm. Analysis of variance (ANOVA) and a balanced, three-factor, eight-treatment, fixed-effects model were used to investigate the interactions between spatial resolution and two other TM refinements, spectral band configuration and data quantization. The goal was to evaluate quantitatively the effects of these attributes on classification accuracies obtained with all pixels (pure pixels plus mixed pixels) and on accuracies obtained with pure pixels alone. A comparison of results from these separate analyses supported previous explanations of the effects of increasing spatial resolution. First, the difficulty in classifying mixed pixels was demonstrated by an average 21 per cent decrease in percentage accuracy from the pure-pixel case to the pure-plus-mixed-pixel case for the eight ANOVA treatments. In the pure-...

Historical Record of Landsat Global Coverage: Mission Operations, NSLRSDA, and International Cooperator Stations

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.

The Landsat 7 mission: Terrestrial research and applications for the 21st century

The Landsat Earth observation approach introduced in 1972 created a new way of monitoring land cover and land use globally. The Landsat 7 mission, successfully launched on April 15, 1999, continues those observations and demonstrates significant progress in precise numerical radiometry, spectral differentiation, and seasonally repetitive monitoring. Substantial improvements in calibration procedures, both prior to launch and during normal operations, have also been made to ensure long-term stability in the acquired spectral radiometry. Landsat 7 data acquisitions are being driven by a long-term data acquisition plan that was designed to ensure that substantially cloud-free, seasonal coverage would be recorded and archived in the US for all land areas of the globe. NASA competitively selected a Landsat Science Team, consisting of representatives from US universities and government agencies, to exploit the Landsat 7 record for global change research. This team is addressing the technical and analytical means to process and analyze the core of this observation record, and for the first time in the history of the Landsat mission, the technical and operational aspects of the mission are being driven by the goals of the US science community. The expected outcome of these efforts is a rapid improvement in understanding the Earth system, as well as conceptual knowledge that will underpin significant advancements in the application of this technology for commercial, operational, educational, and research purposes. Pathways to achieve effective Landsat continuity in the early decades of the 21st century are also being given careful attention, and there is no question that the lessons learned from the Landsat 7 mission will strongly influence these next-generation sensor systems.

Texture transforms of remote sensing data

Abstract The application of image texture to the digital analysis of remotely sensed data requires a quantitative characterization of texture. Several methods reported in the literature characterize the spatial distribution of gray levels across an image segment as texture measures. These texture measures represent all the pixels within an image segment as samples from the same population. Another approach to texture analysis is the construction of texture transforms consisting of discrete measures for each pixel. This technique describes the gray-level distributions around each pixel. This paper reports the analysis of texture transforms of multispectral digital imagery. The transforms were derived from local properties computed within three-by-three pixel windows surrounding each pixel. The local properties were computed using a combination of available channels. The properties included: the mean, variance, skewness, and kurtosis of gray levels; mean and maximum gray-level differences; and mean and maximum Euclidean distances between gray level vectors. Logarithmic transformations of the local properties were used to expand the contrast of the texture transforms. Texture transforms of Landsat-2 MSS data were generated. The transforms were useful for edge detection and image enhancement, but did not prove useful as features for the thematic mapping of land cover. These results are compared to a previous investigation for which texture transforms of digitized aerial photography were effectively employed as features for the thematic mapping of terrain categories.

Prairie grassland bidirectional reflectances measured by different instruments at the FIFE site

Land surface reflectance measurements were acquired during the First ISLSCP Field Experiment (FIFE) field campaigns using a variety of ground-based and airborne spectral radiometers. To examine the validity of the assumption that the values acquired by the several different instruments and teams were interchangeable, the surface radiation measurement teams converged on a common site for 1 day during the fifth intensive field campaign (IFC 5) in 1989. The instruments compared for near-surface measurements included two ground-based Spectron Engineering SE59Os, one helicopter-mounted SE590, one ground-based and one helicopter-mounted Barnes modular multiband radiometer (MMR), and the portable apparatus for rapid acquisitions of bidirectional observations of land and atmosphere (PARABOLA) field radiometer. Comparisons were made for nadir measurements over a range of solar zenith angles and a range of off-nadir viewing angles. The bidirectional reflectances from the different instruments were generally found to be quite comparable. For example, for a 52° solar zenith angle, the nadir red and near-infrared spectral reflectance factors ranged from 3.5 to 4.5% and 28.2 to 31.9%, respectively. At the smaller solar zenith angles, however, the differences were somewhat greater (red, 4.5–6.1%; near-infrared (NIR), 25.0–28.9%), and the coefficients of variation for the samples taken by all of the instruments increased. Off-nadir viewing caused major departures from nadir bidirectional reflectances (30% reflectance at nadir compared with 55% at 60° off nadir in the NIR, for example), but all of the instruments captured the effects reasonably well. Spectral vegetation indices were found to have a considerable dependence on both solar zenith angle and sensor viewing angle. In spite of the general agreement between most instruments and teams, the lack of a more consistent band-to-band agreement resulted in appreciable differences in the spectral vegetation index values, which could potentially affect the accuracy and precision of remote sensing assessments of biophysical parameters.

Prediction and measurement of soil bidirectional reflectance

A model for soil bidirectional reflectance distribution functions in visible and reflective infrared wavelengths is introduced and compared to data acquired in the field. The model is based on the representation of soil surfaces by a collection of opaque spheres sitting on a Lambertian horizontal surface. The model is not sensitive to increases in the sphere area index beyond a value of 0.4. For comparison, soil reflectance factor data were acquired on a tilled field from many view directions and for a range of solar directions. The observed reflectance factor distributions were consistent with those predicted by the function; maximum reflectance occurred in the antisolar direction and reflectance decreased with increasing phase angle. Increasing the surface roughness by different tillage methods did not substantially alter the directional anisotropy of the soil reflectance factors. The model was fit to the data by a nonlinear least-squares procedure. >

Surface albedo from bidirectional reflectance

Abstract Total hemispherical shortwave reflectance (albedo) is a major parameter of interest for studies of land surface climatology and global change. Efforts to estimate albedo from remote sensing data have been constrained by the available instrumentation that typically provide observations of reflected radiance from a single view direction in narrow spectral bands. However, the capability to obtain multiple angle observations over the shortwave region is planned for Earth Observing System sensors. In this paper, methods for estimating albedo from multiple angle, discrete wavelength band radiometer measurements are examined. The methods include a numerical integration technique and the integration of an empirically derived equation for bidirectional reflectance. The validity of the described techniques is examined by comparing albedo computed from multiband radiometer data with simultaneously acquired pyranometer data from vegetated and bare soil surfaces. Shortwave albedo estimated from both techniques agree favorably with the independent pyranometer measurements. Absolute root mean square errors were 0.5% or less for both grass sod and bare soil surfaces.