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Cicone, R. C.

Publications and source records attributed to Cicone, R. C..

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Transformation Aids Crop Analysis From Spectral Data

Crop analysis aided by mathematical transformation that optimizes perspective of six-dimensional, six-band, spectral data taken from spacecraft or aircraft. Transformation applied to any temperatureclimate vegetated scene, providing direct view of regions of data concentration resulting from band correlations and fundamental reflectance properties of scene classes. Almost all of data variability captured in three spectral features, thus reducing by factor of 2 number of spectral features carried, incurring minimal loss of important information. Three-dimensional representation with two principal planes retains about 95 percent of six-dimensional spectral data used to distinguish among scenes containing green plants and bare soil with varying degrees of moisture.

Crist, E. P.↗
NASA NTRS1985

Thematic Mapper Spectral Dimensionality and Data Structure

A simulated LANDSAT 4 TM and MSS data set, representing three crops over three growing seasons and a wide variety of soil types, was used to evaluate the structure of TM data and to compare its characteristics to those of MSS data. TM bands 2, 3, and 4, transformed to tasseled cap-like coordinates, provide an equivalent data space to MSS tasseled cap data, with greater dynamic range and no apparent loss of information resulting from the exclusion of the 0.9 to 1.1 micron region. Data from the six reflective TM bands (excluding the thermal band) primarily occupy two planes and a transition zone between them. The plane of vegetation is comparable to the MSS tasseled cap plane, while the plane of soils and transition zone provide a new dimension of information unavailable from the MSS. This added dimension offers promise of improved ability to determine the relative mix of vegetation and soil in the sensor field of view and to estimate soil moisture status. The improvement in spectral characteristics of the TM over the MSS, not to mention the greater spatial resolution, have resulted in a significant increase in the information content of the data.

Crist, E. P.↗
NASA NTRS1984

Thematic Mapper Spectral Dimensionality and Data Structure

Thematic Mapper data, simulated from field and laboratory spectrometer measurements of a variety of agricultural crops and a wide range of soils, are analyzed to determine their dispersion in the six space defined by the reflective TM bands (i.e., excluding the thermal band). While similar analyses of MSS data from agricultural scenes show that the vast majority of the MSS data occupy a single plane, the simulated TM data primarily occupy three dimensions, defining two intersecting planes and a zone of transition between the two. Viewing the plane of Vegetation head on provides a projection comparable to the single plane of MSS data. The Plane of Soils and transition zone represent new information made available largely as a result of the longer infrared bands included in the Thematic Mapper. A transformation, named the Thematic Mapper Tasseled Cap, is presented which rotates the TM data such that the described data structure is most readily accessible to view.

Crist, E. P.↗
NASA NTRS1984

A physically-based transformation of Thematic Mapper data The TM tasseled cap

In an extension of previous simulation studies, a transformation of actual TM data in the six reflective bands is described which achieves three objectives: a fundamental view of TM data structures is presented, the vast majority of data variability is concentrated in a few (three) features, and the defined features can be directly associated with physical scene characteristics. The underlying TM data structure, based on three TM scenes as well as simulated data, is described, as are the general spectral characteristics of agricultural crops and other scene classes in the transformed data space.

Crist, E. P.↗
NASA NTRS1984

Application of the Tasseled Cap concept to simulated thematic mapper data

Thematic Mapper signal counts in the six reflective bands (i.e., excluding the thermal band) are simulated using field and laboratory spectrometer measurements of a variety of crops, crop conditions, and soil types. The Dave atmospheric model and prelaunch sensor characteristics comprise the other components of the simulation. The simulated data are found to occupy essentially three dimensions, two of which are equivalent to the MSS Tasseled Cap Greennes and Brightness features, and a third which is substantially influenced by the mid-infrared bands of the TM. This new dimension is primarily related to soil characteristics, including soil moisture. The nature and characteristics of each dimension are discussed, as are some of the expected information gains (over MSS data) resulting from the additional dimensionality of the data.

Crist, E. P.↗
NASA NTRS1984

Comparisons of the dimensionality and features of simulated Landsat-4 MSS and TM data

The spectral characteristics of the Landsat-4 MSS and TM are compared using signal counts simulated from field-measured reflectance spectra. The Dave atmospheric model and prelaunch sensor characteristics are also used in the simulation. Comparison is made based on individual bands and Tasseled Cap transformed data. TM bands 2, 3, and 4 are shown to contain virtually all the information contained in the four MSS bands. Tasseled Cap transformed data from the six reflective bands of the TM are shown to contain at least one and possibly two additional dimensions of information beyond the two dimensions in MSS data, while still providing a direct association with the MSS Tasseled Cap features.

Crist, E. P.↗
NASA NTRS1984

Assessment of technologies for classification of mixed pixels

A new method of directly classifying mixed pixels is described. This method and four frequently used indirect mixed pixel classification techniques are evaluated on Landsat MSS data from the U.S. Corn Belt using an automatic corn and soybean labeling technique. The results indicate that while more sophisticated, physically-based techniques for classsifying mixed pixels may yield a higher Percent Correct Classification (PCC) for those pixels, the net effect on a crop area proportion estimation procedure may be negative.

Metzler, M. D.↗
NASA NTRS1984

Investigations of Thematic Mapper data dimensionality and features using field spectrometer data

Landsat-4 TM and MSS data, simulated from field reflectance spectra, are used to determine the dimensionality and structure of TM data (excluding the thermal band), demonstrate the relationships between the two sensors, and derive a Tasseled Cap Transformation for TM data. The simulated TM data are found to primarily occupy three dimensions, and to be concentrated into two adjoining and orthogonal planes and a transition zone between the two. The 'Plane of Vegetation' is shown to be comparable to the MSS Tasseled Cap plane, while the 'Plane of Soils' represents new information. The potential for improved spectral estimation of the relative mix of vegegation and soil in the field of view, and for improved monitoring of soil moisture status, is demonstrated.

Crist, E. P.↗
NASA NTRS1983

Understanding and utilization of Thematic Mapper and other remotely sensed data for vegetation monitoring

The TM Tasseled Cap transformation, which provides both a 50% reduction in data volume with little or no loss of important information and spectral features with direct physical association, is presented and discussed. Using both simulated and actual TM data, some important characteristics of vegetation and soils in this feature space are described, as are the effects of solar elevation angle and atmospheric haze. A preliminary spectral haze diagnostic feature, based on only simulated data, is also examined. The characteristics of the TM thermal band are discussed, as is a demonstration of the use of TM data in energy balance studies. Some characteristics of AVHRR data are described, as are the sensitivities to scene content of several LANDSAT-MSS preprocessing techniques.

Crist, E. P.↗
NASA NTRS1983

Effects of preprocessing Landsat MSS data on derived features

Important to the use of multitemporal Landsat MSS data for earth resources monitoring, such as agricultural inventories, is the ability to minimize the effects of varying atmospheric and satellite viewing conditions, while extracting physically meaningful features from the data. In general, the approaches to the preprocessing problem have been derived from either physical or statistical models. This paper compares three proposed algorithms; XSTAR haze correction, Color Normalization, and Multiple Acquisition Mean Level Adjustment. These techniques represent physical, statistical, and hybrid physical-statistical models, respectively. The comparisons are made in the context of three feature extraction techniques; the Tasseled Cap, the Cate Color Cube. and Normalized Difference.

Parris, T. M.↗
NASA NTRS1982

Development, implementation and evaluation of satellite-aided agricultural monitoring systems

Research activities in support of AgRISTARS Inventory Technology Development Project in the use of aerospace remote sensing for agricultural inventory described include: (1) corn and soybean crop spectral temporal signature characterization; (2) efficient area estimation techniques development; and (3) advanced satellite and sensor system definition. Studies include a statistical evaluation of the impact of cultural and environmental factors on crop spectral profiles, the development and evaluation of an automatic crop area estimation procedure, and the joint use of SEASAT-SAR and LANDSAT MSS for crop inventory.

Cicone, R. C.↗
NASA NTRS1982

Research and development of LANDSAT-based crop inventory techniques

A wide spectrum of technology pertaining to the inventory of crops using LANDSAT without in situ training data is addressed. Methods considered include Bayesian based through-the-season methods, estimation technology based on analytical profile fitting methods, and expert-based computer aided methods. Although the research was conducted using U.S. data, the adaptation of the technology to the Southern Hemisphere, especially Argentina was considered.

Horvath, R.↗
NASA NTRS1982

The evaluation of a semi-automated procedure for classifying corn and soybeans without ground data

Since the launch of Landsat 1 in 1973, research has been conducted with the objective to develop technology which would make it possible to achieve large area crop estimates on the basis of Landsat Multispectral Sensor (MSS) data without the benefit of ground observed training data. The present investigation is concerned with the evaluation of a technology which was developed to produce estimates of corn and soybean acreage in the central U.S. Corn Belt (Iowa, Illinois, and Indiana). A description of the employed technique is provided and details regarding the test of the developed technology are discussed. The obtained results show that considerable progress has been made toward creating an automatic, self-adapting procedure which has favorable bias and variance characteristics.

Metzler, M. D.↗
NASA NTRS1982

Comparison of Landsat MSS, Nimbus 7 CZCS, and NOAA 6/7 AVHRR features for land use analysis

Spectral characteristics of the Coastal Zone Color Scanner (CZCS) on board Nimbus 7, the Advanced Very High Resolution Radiometer (AVHRR) on NOAA 6 and 7 and the Multispectral Scanner (MSS) on Landsat 1-3 are analyzed to comparatively assess their utility for land use analysis through remote sensing. The examination of simulated in-band radiances suggests that each sensor would respond to incident radiation reflected from a typical agricultural scene in a highly comparable manner, with most of the variation captured in two physically related variables. Several measures of green vegetation are examined and features are proposed for crop condition assessment with consideration of the course resolution characteristics of AVHRR and CZCS.

Cicone, R. C.↗
NASA NTRS1981

Analysis of scanner data for crop inventories

Progress and technical issues are reported in the development of corn/soybeans area estimation procedures for use on data from South America, with particular emphasis on Argentina. Aspects related to the supporting research section of the AgRISTARS Project discussed include: (1) multisegment corn/soybean estimation; (2) through the season separability of corn and soybeans within the U.S. corn belt; (3) TTS estimation; (4) insights derived from the baseline corn and soybean procedure; (5) small fields research; and (6) simulating the spectral appearance of wheat as a function of its growth and development. To assist the foreign commodity production forecasting, the performance of the baseline corn/soybean procedure was analyzed and the procedure modified. Fundamental limitations were found in the existing guidelines for discriminating these two crops. The temporal and spectral characteristics of corn and soybeans must be determined because other crops grow with them in Argentina. The state of software technology is assessed and the use of profile techniques for estimation is considered.

Horvath, R.↗
NASA NTRS1981

Analysis of scanner data for crop inventories

Accomplishments for a machine-oriented small grains labeler T&E, and for Argentina ground data collection are reported. Features of the small grains labeler include temporal-spectral profiles, which characterize continuous patterns of crop spectral development, and crop calendar shift estimation, which adjusts for planting date differences of fields within a crop type. Corn and soybean classification technology development for area estimation for foreign commodity production forecasting is reported. Presentations supporting quarterly project management reviews and a quarterly technical interchange meeting are also included.

Horvath, R.↗
NASA NTRS1980

Procedure M - A framework for stratified area estimation

This paper describes Procedure M, a systematic approach to processing multispectral scanner data for classification and acreage estimation. A general discussion of the rationale and development of the procedure is given in the context of large-area agricultural applications. Specific examples are given in the form of test results on acreage estimation of spring small grains.

Kauth, R. J.↗