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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1980

The application of LANDSAT remote sensing technology to natural resources management. Section 1: Introduction to VICAR - Image classification module. Section 2: Forest resource assessment of Humboldt County.

A teaching module on image classification procedures using the VICAR computer software package was developed to optimize the training benefits for users of the VICAR programs. The field test of the module is discussed. An intensive forest land inventory strategy was developed for Humboldt County. The results indicate that LANDSAT data can be computer classified to yield site specific forest resource information with high accuracy (82%). The "Douglas-fir 80%" category was found to cover approximately 21% of the county and "Mixed Conifer 80%" covering about 13%. The "Redwood 80%" resource category, which represented dense old growth trees as well as large second growth, comprised 4.0% of the total vegetation mosaic. Furthermore, the "Brush" and "Brush-Regeneration" categories were found to be a significant part of the vegetative community, with area estimates of 9.4 and 10.0%.

Fox, L., III↗
NASA NTRS1980

Land-cover classification of California using mosaicking and high-speed processing

A land cover classification of Landsat MSS data has been made for the entire state of California. Digital mosaicking of 32 Landsat scenes with registered digital elevation, slope, and aspect was prepared. Stratified unsupervised clustering generated the spectral classes which were identified into 16 categories, mostly forest types, using U-2 CIR photos. A verification using 81 primary sample units resulted in a forest/nonforest accuracy of 80.2% and a commercial conifer and hardwood accuracy of 75.1%. The results are now being used in many projects throughout the state.

Peterson, D. L.↗
NASA NTRS1981

Landsat classification of coastal wetlands in Texas

Through a multiagency study of Landsat imagery applications, an analysis of Texas coastal wetlands shows that five Level III categories of wetlands can be delineated using image interpretation: topographically low marshes, topographically high marshes, tidal flats, sea grass and algal flats, and vegetated dredged material. Image interpretation involves optical enlargement of 1:1,000,000 scale, Landsat transparencies to a scale of 1:125,000 and mapping on a stable film base. Digital classification procedures, resulting in 1:24,000 scale line printer maps as output, require several iterations to display welands effectively. Accuracies of 65% were achieved for all wetland categories combined.

Finley, R. J.↗
NASA NTRS1981

Landsat hydrobiological classification for an inland fresh water marsh within Everglades National Park

The considered investigation is concerned with the application of Landsat Multispectral Scanner (MSS) data to the classification of vegetative communities and the establishment of flow vectors for the Shark River Slough in Everglades National Park, Florida. A systematic array of 'ground truth' was established utilizing comprehensive hydrologic field data and conventional high altitude infrared aerial photography. A control network was defined that represented all hydrobiological zones (those wetland vegetative communities that directly influence the rate of overland sheet flow) in the Shark River Slough. These data were then directly applied to the Landsat imagery utilizing an interactive multispectral processor which generated hydrographic maps of the slough and defined the surface radiance characteristics of each hydrobiological system. It was found that the application of Landsat imagery for hydrologic applications in a wetlands area, such as the Shark River Slough in Everglades National Park, is definitely a viable tool for resource management.

Rose, P. W.↗
NASA NTRS1980

Procedure 1 and forestland classification using Landsat data

Procedure 1 (P-1) was developed for the Large Area Crop Inventory Experiment (LACIE) and has been used extensively to develop land-use classification of agricultural areas. The P-1 approach requires that pixels (also called dots) of known identity must be located in the study scene. The entire area is clustered and the spectral classes formed are identified using the dots. The analyst need only locate and identify the dots. The rest of the work is done by the computer. The objective of the reported study was to evaluate the effectiveness of P-1's automated approach in a complex forest-land situation. The study site was located in the eastern half of the San Juan National Forest in southwestern Colorado. The study showed that P-1 performed as well as the Multicluster Blocks approach on the rugged study area.

Nelson, R. F.↗
NASA NTRS1981

Sampling Landsat classifications for crop area estimation

An investigation was conducted to evaluate the effect of several sampling alternatives on the accuracy of crop area estimates made from classification of Landsat Multispectral Scanner (MSS) data. The specific objective was to assess the precision and the bias associated with alternative sampling schemes involving different numbers of several sampling unit sizes. The estimates achieved using the 5 by 6 nm segments were found to have the least precision of any sampling scheme tested. The estimates become more precise as the segment size decreases and more segments are taken. The precision of the 5 by 6 nm segments was significantly less than that of the pixel samples. None of the sampling schemes was significantly biased on the average, and none of the average estimates differed significantly from the population parameter. The maximum absolute deviation, however, was directly related to sampling unit size and should be considered in selection of a sampling unit.

Hixson, M. M.↗
NASA NTRS1980

Taxonomic classification of world map units in crop producing areas of Argentina and Brazil with representative US soil series and major land resource areas in which they occur

The most probable current U.S. taxonomic classification of the soils estimated to dominate world soil map units (WSM)) in selected crop producing states of Argentina and Brazil are presented. Representative U.S. soil series the units are given. The map units occurring in each state are listed with areal extent and major U.S. land resource areas in which similar soils most probably occur. Soil series sampled in LARS Technical Report 111579 and major land resource areas in which they occur with corresponding similar WSM units at the taxonomic subgroup levels are given.

Huckle, H. F.↗
NASA NTRS1981

An evaluation of ISOCLS and CLASSY clustering algorithms for forest classification in northern Idaho

Both the iterative self-organizing clustering system (ISOCLS) and the CLASSY algorithms were applied to forest and nonforest classes for one 1:24,000 quadrangle map of northern Idaho and the classification and mapping accuracies were evaluated with 1:30,000 color infrared aerial photography. Confusion matrices for the two clustering algorithms were generated and studied to determine which is most applicable to forest and rangeland inventories in future projects. In an unsupervised mode, ISOCLS requires many trial-and-error runs to find the proper parameters to separate desired information classes. CLASSY tells more in a single run concerning the classes that can be separated, shows more promise for forest stratification than ISOCLS, and shows more promise for consistency. One major drawback to CLASSY is that important forest and range classes that are smaller than a minimum cluster size will be combined with other classes. The algorithm requires so much computer storage that only data sets as small as a quadrangle can be used at one time.

Werth, L. F.↗
NASA NTRS1982

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

Agricultural crop classification models using two or more spectral regions (visible through microwave) are considered in an effort to estimate biomass at Guymon, Oklahoma Dalhart, Texas. Both grounds truth and aerial data were used. Results indicate that inclusion of C, L, and P band active microwave data, from look angles greater than 35 deg from nadir, with visible and infrared data improve crop discrimination and biomass estimates compared to results using only visible and infrared data. The microwave frequencies were sensitive to different biomass levels. The K and C band were sensitive to differences at low biomass levels, while P band was sensitive to differences at high biomass levels. Two indices, one using only active microwave data and the other using data from the middle and near infrared bands, were well correlated to total biomass. It is implied that inclusion of active microwave sensors with visible and infrared sensors on future satellites could aid in crop discrimination and biomass estimation.

Rosenthal, W. D.↗
NASA NTRS1982

Misregistration's effects on classification and proportion estimation accuracy

The estimates of crop type and acreage are undertaken in the AgRISTARS program by registering multiple date acquisitions of small subareas of LANDSAT scenes (termed segments), and applying multispectral analysis to them. An important contribution to errors in classification and acreage estimates is misregistration between multiple acquisitions. The formula used to express this relationship is given and the operations applied are so shown in diagrams. The taking of a LANDSAT feature vector and the derivation of the brightness and greeness are illustrated. It is shown that for any given sensor IFOV geometry, typical populations of fields can be derived and histograms can be plotted of the number of fields against field size according to ground truth. As a function of the resolution element, the IFOV of the sensor can draw the proportion of pure pixels in a given crop. Because the thematic mapper has a smaller resolution, the proportion of pixels that are pure in any given area will be larger.

Juday, R. D.↗
NASA NTRS1981

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Strahler, A. H.↗
NASA NTRS1982

Use of Landsat-derived temporal profiles for corn-soybean feature extraction and classification

A physical model is presented, which has been derived from multitemporal-multispectral data acquired by Landsat satellites to describe the behavior and new features that are crop specific. A feasibility study over 40 sites was performed to classify the segment pixels into those of corn, soybeans, and others using the new features and a linear classifier. Results agree well with other existing methods, and it is shown the multitemporal-multispectral scanner data can be transformed into two parameters that are closely related to the target of interest and thus can be used in classification. The approach is less time intensive than other techniques and requires labeling of only pure pixels.

Badhwar, G. D.↗
NASA NTRS1982

Classification of simulated and actual NOAA-6 AVHRR data for hydrologic land-surface feature definition

An examination of the possibilities of using Landsat data to simulate NOAA-6 Advanced Very High Resolution Radiometer (AVHRR) data on two channels, as well as using actual NOAA-6 imagery, for large-scale hydrological studies is presented. A running average was obtained of 18 consecutive pixels of 1 km resolution taken by the Landsat scanners were scaled up to 8-bit data and investigated for different gray levels. AVHRR data comprising five channels of 10-bit, band-interleaved information covering 10 deg latitude were analyzed and a suitable pixel grid was chosen for comparison with the Landsat data in a supervised classification format, an unsupervised mode, and with ground truth. Landcover delineation was explored by removing snow, water, and cloud features from the cluster analysis, and resulted in less than 10% difference. Low resolution large-scale data was determined useful for characterizing some landcover features if weekly and/or monthly updates are maintained.

Ormsby, J. P.↗
NASA NTRS1982

Two-dimensional spectral classifications for O stars in the southern Milky Way

Spectral classifications in the two-dimensional system are given for 53 additional O stars in the southern hemisphere. A number of these stars are members of the interesting clusters NGC 3324, Trumpler 14/16, NGC 3603, and NGC 6334, which are discussed in particular. A 4-m H-alpha photograph of the remarkable nebulosities associated with NGC 6334 is reproduced.

Walborn, N. R.↗
NASA NTRS1981

Contextual classification on a CDC Flexible Processor system

A potential hardware organization for the Flexible Processor Array is presented. An algorithm that implements a contextual classifier for remote sensing data analysis is given, along with uniprocessor classification algorithms. The Flexible Processor algorithm is provided, as are simulated timings for contextual classifiers run on the Flexible Processor Array and another system. The timings are analyzed for context neighborhoods of sizes three and nine.

Smith, B. W.↗
NASA NTRS1982

The UV stellar classification program

The UV stellar classification program based on IUE data, its rationale, and its status, are described. The program provides sequences of calibrated spectra covering all spectral types.

Heck, A.↗
NASA NTRS1981

Statewide lake classification utilizing LANDSAT imagery for the state of Wisconsin

A cooperative program between the Wisconsin Department of Natural Resources and the University of Wisconsin-Madison resulted in the assessment of the trophic condition of approximately 3,000 significant inland lakes in Wisconsin. The feasibility of using both photographic and digital representations of LANDSAT multispectral scanner data for lake classification was investigated. The result was the development of a nearly automated system which, with minimal human interaction, locates and extracts the lake data, then corrects the data for atmospheric effects, and finally classifies all the significant lakes in the state as to trophic condition.

Martin, R. H.↗