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Multispectral sensing techniques to detect ponderosa pine trees under stress from insect or diseases
Remote multispectral sensing of radiation characteristics of important agricultural features over widespread areas
Airborne multispectral sensing, describing multichannel scanners, pattern recognition techniques and equipment, etc
Remote multispectral sensing and data processing techniques for agricultural applications
A system of processing remotely sensed multispectral scanner data by computer programs to produce color-coded land use maps for large areas is described. The procedure is explained, the software and the hardware are described, and an analogous example of the procedure is presented. Detailed descriptions of the multispectral scanners currently in use are provided together with a summary of the background of current land use mapping techniques. The data analysis system used in the procedure and the pattern recognition software used are functionally described. Current efforts by the NASA Earth Resources Laboratory to evaluate operationally a less complex and less costly system are discussed in a separate section.
Multispectral remote sensing for detection and mapping of ecological objects and energy budget parameters measurement
Atmospheric effects on infrared multispectral sensing of sea-surface temperature from space
Multispectral sensing techniques for ground and airborne detection of Ponderosa pine trees under stress from insect or pathogenic organisms
A literature survey and analysis of the use of neural networks for the classification of remotely sensed multispectral imagery is presented. As part of a brief mathematical review, the backpropagation algorithm, which is the most common method of training multi-layer networks, is discussed with an emphasis on its application to pattern recognition. The analysis is divided into five aspects of neural network classification: (1) input data preprocessing, structure, and encoding; (2) output encoding and extraction of classes; (3) network architecture, (4) training algorithms; and (5) comparisons to conventional classifiers. The advantages of the neural network method over traditional classifiers are its non-parametric nature, arbitrary decision boundary capabilities, easy adaptation to different types of data and input structures, fuzzy output values that can enhance classification, and good generalization for use with multiple images. The disadvantages of the method are slow training time, inconsistent results due to random initial weights, and the requirement of obscure initialization values (e.g., learning rate and hidden layer size). Possible techniques for ameliorating these problems are discussed. It is concluded that, although the neural network method has several unique capabilities, it will become a useful tool in remote sensing only if it is made faster, more predictable, and easier to use.
Constrained-least-squares and weighted-least-squares mixing models for generating fraction images derived from remote sensing multispectral data are presented. An experiment considering three components within the pixels-eucalyptus, soil (understory), and shade-was performed. The generated fraction images for shade (shade image) derived from these two methods were compared by considering the performance and computer time. The derived shade images are related to the observed variation in forest structure, i.e., the fraction of inferred shade in the pixel is related to different eucalyptus ages.
The new unsupervised classification technique for classifying multispectral remote sensing data which can be either from the multispectral scanner or digitized color-separation aerial photographs consists of two parts: (a) a sequential statistical clustering which is a one-pass sequential variance analysis and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. Applications of the technique using an IBM-7094 computer on multispectral data sets over Purdue's Flight Line C-1 and the Yellowstone National Park test site have been accomplished. Comparisons between the classification maps by the unsupervised technique and the supervised maximum liklihood technique indicate that the classification accuracies are in agreement.
An application development system (ADS) is examined for remotely sensed, multispectral data at the Earth Observations Division (EOD) at Johnson Space Center. Design goals are detailed, along with design objectives that an ideal system should contain. The design objectives were arranged according to the priorities of EOD's program objectives. Four systems available to EOD were then measured against the ideal ADS as defined by the design objectives and their associated priorities. This was accomplished by rating each of the systems on each of the design objectives. Utilizing the established priorities, it was determined how each system stood up as an ADS. Recommendations were made as to possible courses of action for EOD to pursue to obtain a more efficient ADS.
Suspended sediment is an important environmental parameter for monitoring water quality, water movement, and land use. Quantitative suspended sediment determinations were made from analysis of aircraft remotely sensed multispectral digital data. A statistical analysis and derived regression equation were used to determine and plot quantitative suspended sediment concentration contours in the tidal James River, Virginia, on May 28, 1974. From the analysis, a single band, Band 8 (0.70-0.74 microns), was adequate for determining suspended sediment concentrations. A correlation coefficient of 0.89 was obtained with a mean inaccuracy of 23.5 percent for suspended sediment concentrations up to about 50 mg/l. Other water quality parameters - secchi disc depth and chlorophyll - also had high correlations with the remotely sensed data. Particle size distribution had only a fair correlation with the remotely sensed data.
Laboratory reflectance data, and field tests with multispectral remote sensors provide support for this hypotheses that differences in moisture content and water deficits are closely related to foliar reflectance from woody plants. When these relationships are taken into account, automatic recognition techniques become more powerful than when they are ignored. Evidence is increasing that moisture relationships inside plant foliage are much more closely related to foliar reflectance characteristics than are external variables such as soil moisture, wind, and air temperature. Short term changes in water deficits seem to have little influence on foliar reflectance, however. This is in distinct contrast to significant short-term changes in foliar emittance from the same plants with changing wind, air temperature, incident radiation, or water deficit conditions.
Application of multispectral sensors to detect insect and disease infestation of ponderosa pine trees
The detection of stress induced by bark beetles in conifers is reviewed in two sections: (1) the analysis of very small scale aerial photographs taken by NASA's RB-57F aircraft on August 10, 1969, and (2) the analysis of multispectral imagery obtained by the optical-mechanical line scanner. Underexposure of all films taken from the RB-57 aircraft and inadequate flight coverage prevented drawing definitive conclusions regarding optimum scales and film combinations to detect the discolored infestations. Preprocessing of the scanner signals by both analog and digital computers improved the accuracy of target recognition. Selection and ranking of the best channels for signature recognition was the greatest contribution of digital processing. Improvements were made in separating hardwoods from conifers and old-kill pine trees from recent discolored trees and from healthy trees, but accuracy of detecting the green infested trees is still not acceptable on either the SPARC or thermal-contouring processor. From six years of experience in processing line scan data it is clear that the greatest gain in previsual detection of stress will occur when registered multispectral data from a single aperture or common instantaneous field of view scanner system can be collected and processed.
Ground and aerial imaging techniques to detect tree damage caused by bark beetles in forested areas
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