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NASA NTRS1985

Evaluation of space SAR as a land-cover classification

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Brisco, B.↗

NASA NTRS1974

The ERTS-1 investigation (ER-600). Volume 1: ERTS-1 agricultural analysis

The Agriculture Analysis Team of the Johnson Space Center conducted a 1-year-long investigation of ERTS-1 multispectral data to evaluate how well features of agricultural importance could be detected, identified, and located; and their areal extent measured. Six study areas were selected in cooperation with the U.S. Department of Agriculture. Two basic analytical approaches were used to meet the objectives. The conventional image interpretation technique revealed that a particular color was an indication of the density of vegetative cover, not an indication of crop classification. Computer-aided techniques were used to classify crop types (i.e., small grains, truck farm crops, grasses, summer fallow) to accuracies as high as 95 percent on large (12 hectares or more) well-defined fields. A further breakdown into crop species (wheat, barley, soybeans, oats, corn) reduced the accuracy to 70 to 80 percent for single-date observations.

Erb, R. B.↗

NASA NTRS1977

Pattern recognition of Landsat data based upon temporal trend analysis

The Delta Classifier defined as an agricultural crop classification scheme employing a temporal trend procedure is applied to more than 100 different Landsat data sets collected during the 1974-1975 growing season throughout the major wheat-producing regions of the United States. The classification approach stresses examination of temporal trends of the Landsat mean vectors of crops in the absence of corresponding ground truth information. It is shown that the resulting classifications compare favorably to ground truth estimates for wheat proportion in those cases where ground truth is available, and that the temporal trend procedure yields estimates of the wheat proportion that are comparable to the best results from maximum likelihood classification with photointerpreter-defined training fields.

Engvall, J. L.↗

NASA NTRS1983

Development of visible/infrared/microwave agriculture classification and biomass estimation algorithms

This paper describes the results of a study to determine if crop acreage and biomass estimates could be improved by using visible IR and microwave data. The objectives were to (1) develop and test agricultural crop classification models using two or more spectral regions (visible through microwave), and (2) estimate biomass by including microwave with visible and infrared data. Aircraft multispectral data collected during the study included visible and infrared data (multiband data from 0.5 m - 12 m), and active microwave data K band (2 cm), C band (6 cm), L band (20 cm), and P band (75 cm) HH and HV polarizations. Ground truth data from each field consisted of soil moisture and biomass measurements. Results indicated that C, L, and P band active microwave data combined with visible and infrared data improved crop discrimination and biomass estimates compared to results using only visible and infrared data. The active microwave frequencies were sensitive to different biomass levels; K and C being sensitive to differences at low biomass levels, while P band was sensitive to differences at high biomass levels.

Rosenthal, W. D.↗

NASA NTRS1976

Investigation of LANDSAT follow-on thematic mapper spatial, radiometric and spectral resolution

The author has identified the following significant results. Fine resolution M7 multispectral scanner data collected during the Corn Blight Watch Experiment in 1971 served as the basis for this study. Different locations and times of year were studied. Definite improvement using 30-40 meter spatial resolution over present LANDSAT 1 resolution and over 50-60 meter resolution was observed, using crop area mensuration as the measure. Simulation studies carried out to extrapolate the empirical results to a range of field size distributions confirmed this effect, showing the improvement to be most pronounced for field sizes of 1-4 hectares. Radiometric sensitivity study showed significant degradation of crop classification accuracy immediately upon relaxation from the nominally specified values of 0.5% noise equivalent reflectance. This was especially the case for data which were spectrally similar such as that collected early in the growing season and also when attempting to accomplish crop stress detection.

Nalepka, R. F.↗

NASA NTRS1974

Remote sensing in Iowa agriculture

The author has identified the following significant results. Analysis of 1972 single-date coverage indicated that a complete crop classification was not attainable at the test sites. Good multi-date coverage during 1973 indicates that many of the problems encountered in 1972 will be minimized. In addition, the compilation of springtime imagery covering the entire state of Iowa has added a new dimension to interpretation of Iowa's natural resources. ERTS-1 has provided data necessary to achieve the broad synoptic view not attainable through other means. This should provide soils and crop researchers and land use planners a base map of Iowa. Granted and due to the resolution of ERTS-1, not all details are observable for many land use planning needs, but this gives a general and current view of Iowa.

Mahlstede, J. P.↗

NASA NTRS1973

Agriculture, Forestry, Range Resources

Significant results obtained from ERTS-1 observations of agriculture, forestry, and range resources are summarized. Four major parts are covered: (1) crop classification and mensuration; (2) timber and range resources survey and classification; (3) soil survey and mapping; and (4) subdiscipline areas.

Crea, W. J., Jr.↗

NASA NTRS1974

Application of ERTS-1 imagery to land use, forest density and soil investigations in Greece

Photographic and digital imagery received from ERTS-1 was analyzed and evaluated as to its usefulness for the assessment of agricultural and forest land resources. Black and white, and color composite imagery provided spectral and spatial data, which, when matched with temporal land information, provided the basis for a semidetailed land use and forest site evaluation cartography. Color composite photographs have provided some information on the status of irrigation of agricultural lands. Computer processed digital imagery was successfully used for detailed crop classification and semidetailed soil evaluation. The results and techniques of this investigation are applicable to ecological and geological conditions similar to those prevailing in the Eastern Mediterranean.

Yassoglou, N. J.↗

NASA NTRS1975

Statistical theory and methodology for remote sensing data analysis with special emphasis on LACIE

Crop proportion estimators for determining crop acreage through the use of remote sensing were evaluated. Several studies of these estimators were conducted, including an empirical comparison of the different estimators (using actual data) and an empirical study of the sensitivity (robustness) of the class of mixture estimators. The effect of missing data upon crop classification procedures is discussed in detail including a simulation of the missing data effect. The final problem addressed is that of taking yield data (bushels per acre) gathered at several yield stations and extrapolating these values over some specified large region. Computer programs developed in support of some of these activities are described.

Odell, P. L.↗

NASA NTRS1974

International Symposium on Remote Sensing of Environment, 9th, University of Michigan, Ann Arbor, Mich., April 15-19, 1974, Proceedings. Volumes 1, 2 & 3

The present work gathers together numerous papers describing the use of remote sensing technology for mapping, monitoring, and management of earth resources and man's environment. Studies using various types of sensing equipment are described, including multispectral scanners, radar imagery, spectrometers, lidar, and aerial photography, and both manual and computer-aided data processing techniques are described. Some of the topics covered include: estimation of population density in Tokyo districts from ERTS-1 data, a clustering algorithm for unsupervised crop classification, passive microwave sensing of moist soils, interactive computer processing for land use planning, the use of remote sensing to delineate floodplains, moisture detection from Skylab, scanning thermal plumes, electrically scanning microwave radiometers, oil slick detection by X-band synthetic aperture radar, and the use of space photos for search of oil and gas fields. Individual items are announced in this issue.

Source record↗

NASA NTRS1976

Benefits to world agriculture through remote sensing

Remote sensing of agricultural land permits crop classification and mensuration which can lead to improved forecasts of production. This technique is particularly important for nations which do not already have an accurate agricultural reporting system. Better forecasts have important economic effects. International grain traders can make better decisions about when to store, buy, and sell. Farmers can make better planting decisions by taking advantage of production estimates for areas out of phase with their own agricultural calendar. World economic benefits will accrue to both buyers and sellers because of increased food supply and price stabilization. This paper reviews the econometric models used to establish this scenario and estimates the dollar value of benefits for world wheat as 200 million dollars annually for the United States and 300 to 400 million dollars annually for the rest of the world.

Buffalano, A. C.↗

NASA NTRS1976

Radar system specifications for hydrology and agriculture

Using truck-mounted radar spectrometers, an experimental program was conducted from 1971 to 1976 to define radar system specifications for hydrological and agricultural applications. Data were acquired over the 1-18 GHz region, 0 deg (nadir) to 70 deg range and for all linear polarization combinations. The discussion here concerns radar parameters for crop classification and for soil moisture determination.

Ulaby, F. T.↗

NASA NTRS1979

Remote-sensing applications to geology

Results of two day workshop on applications of remote sensing to geology are summarized in report. Topics discussed are environmental analysis, crop classification, plant epidemics and diseases, irrigation reform, and soil surveys.

Source record↗

NASA NTRS1981

A temporal/spectral analysis of small grain crops and confusion crops

Spectral data from the LANDSAT-2 satellite were used to study the growth cycles of fields of wheat, barley, alfalfa, corn, sunflowers, soybeans, rye, flax, oats, millet, grass, and hay. Signatures of pastures, trees, and idle fallow were also studied. The growth cycles were portrayed in the form of temporal plots of the greeness-brightness transformation vector applied to average channel pixel values within the fields, all of which were in three counties in North Dakota. The plots of each crop reveal characteristics which can be used in crop classification procedures.

Johnson, W. R.↗

NASA NTRS1985

Vegetation canopy discrimination and biomass assessment using multipolarized airborne SAR

Multipolarized airborne Synthetic Aperture Radar (SAR) data were acquired over a largely agricultural test site near Macomb, Illinois, in conjunction with the Shuttle Imaging Radar (SIR-B) experiment in October 1984. The NASA/JPL L-band SAR operating at 1.225 GHz made a series of daily overflights with azimuth view angles both parallel and orthogonal to those of SIR-B. The SAR data was digitally recorded in the quadpolarization configuration. An extensive set of ground measurements were obtained throughout the test site and include biophysical and soil measurements of approximately 400 agricultural fields. Preliminary evaluation of some of the airborne SAR imagery indicates a great potential for crop discrimination and assessment of canopy condition. False color composites constructed from the combination of three linear polarizations (HH, VV, and HV) were found to be clearly superior to any single polarization for purposes of crop classification. In addition, an image constructed using the HH return to modulate intensity and the phase difference between HH and VV returns to modulate chroma indicates a clear capability for assessment of canopy height and/or biomass. In particular, corn fields heavily damaged by infestations of corn borer are readily distinguished from noninfested fields.

Ulaby, F. T.↗

NASA NTRSDate not supplied

Identifying and Mapping Agricultural Areas Using Synthetic Aperture Radar Time Series

This study uses Synthetic Aperture Radar (SAR) data to identify active agricultural extent in Mato Grosso, Brazil. Coefficient of variation (CoVar) calculations were applied to year-long time series stacks to identify areas of crop growth. A CoVar threshold value was determined using crop reference layers, then applied to create a crop/non-crop binary map. In the Mato Grosso region, CoVar-based processing resulted in high performance crop classification and allowed for the monitoring of dynamic cropland expansion between 2016 and 2021.

Jordan Bell↗

NASA NTRS1973

Remote sensing in Iowa agriculture: Identification and classification of Iowa crop lands using ERTS-1 and complimentary underflight imagery

The author has identified the following significant results. Results of the continuing analysis of ERTS-1 imagery covering Iowa during 1972 and periods during 1973 are covered. Emphasis is placed on the identification and classification of major crop types at two test sites in Iowa. Standard photointerpretive methods were used in this analysis including the direct enlargement of black and white single-band products and additive color multi-band procedures using a miniadcol system. The use of sequential coverage during the crop growing season is emphasized as a means to improve the effectiveness of ERTS-1 photointerpretations of crop land acreage estimates in Iowa. Illustrative black and white and color prints of both ERTS-1 and underflight imagery are included. In addition, forest land inventories at one test site are reported. A new method for the inventory of forest lands using ERTS-1 imagery is reported and compared with estimates obtained using earlier underflight imagery.

Mahlstede, J. P.↗

NASA NTRS1979

Identification and area estimation of agricultural crops by computer classification of Landsat MSS data

Landsat Multispectral Scanner (MSS) data covering a three-county area in northern Illinois were classified using computer-aided techniques as corn, soybeans, or 'other.' Recognition of test fields was 80% accurate. County estimates of the area of corn and soybeans agreed closely with those made by the USDA. Results of the use of a priori information in classification, techniques to produce unbiased area estimates, and the use of temporal and spatial features for classification are discussed. The extendability, variability, and size of training sets, wavelength band selection, and spectral characteristics of crops were also investigated.

Bauer, M. E.↗