1,721,029 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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An evaluation of soil erosion hazard: A case study in Southern Africa using geomatics technologies
No full textAccelerated soil erosion in Malawi, Southern Africa, increasingly threatens agricultural productivity, given current and projected population growth trends. Previous attempts to document soil erosion potential have had limited success, lacking appropriate information and diagnostic tools. This study utilized geomatics technologies and the latest available information from topography, soils, climate, vegetation, and land use of a watershed in southern Malawi. The Soil Loss Estimation Model for Southern Africa (SLEMSA), developed for conditions in Zimbabwe, was evaluated and used to create a soil erosion hazard map for the watershed under Malawi conditions. The SLEMSA sub-models of cover, soil loss, and topography were computed from energy interception, rainfall energy, and soil erodibility, and slope length and steepness, respectively. Geomatics technologies including remote sensing and Geographic Information Systems (GIS) provided the tools with which land cover/land use, a digital elevation model, and slope length and steepness were extracted and integrated with rainfall and soils spatial information. Geomatics technologies enable rapid update of the model as new and better data sets become available. Sensitivity analyses of the SLEMSA model revealed that rainfall energy and slope steepness have the greatest influence on soil erosion hazard estimates in this watershed. Energy interception was intermediate in sensitivity level, whereas slope length and soil erodibility ranked lowest. Energy interception and soil erodibility were shown by parameter behavior analysis to behave in a linear fashion with respect to soil erosion hazard, whereas rainfall energy, slope steepness, and slope length exhibit non-linear behavior. When SLEMSA input parameters and results were compared to alternative methods of soil erosion assessment, such as drainage density and drainage texture, the model provided more spatially explicit information using 30 meter grid cells. Results of this study indicate that more accurate soil erosion estimates can be made when: (1) higher resolution digital elevation models are used; (2) data from improved precipitation station network are available, and; (3) greater investment in rainfall energy research
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Vegetation distributions in semi-arid environments: Spatial analysis for climate and landscape characterization
No full textSpatially explicit knowledge of land cover is increasingly important for environmental modeling and decision support for land managers. Such knowledge is often provided over large regions by thematic maps produced from remotely sensed satellite data. Remote sensing of vegetation in semi-arid areas is complicated, however, by high levels of landscape spatial heterogeneity, resulting in large part from spatially varying soils, topography, and microclimates. Increased understanding of spatial distributions of vegetation and the factors affecting them will enhance our ability to inventory and monitor natural resources, and to model potential consequences of land management alternatives and larger issues such as global climate change. In addition, the uncertainty in spatial knowledge must be made spatially explicit in order to determine where more information is needed and where predictions maybe less reliable. Geostatistical kriging and multiple linear regression interpolation were used to map climate spatial distributions over the San Pedro River watershed, southeastern Arizona. Both methods used climate station location and elevation and climate data. Although mean interpolation errors were similar, kriging climate with elevation as external drift was preferred due to the patterns of spatial bias in regression errors. Interpolation results provided a step toward understanding climate influence on vegetation in this area. Accuracies of four land cover maps covering the upper San Pedro watershed, mapped from remotely sensed data, were determined using aerial photography, digital orthophoto quadrangles, and airborne video data reference data sets as alternatives to contemporaneous ground-collected data. Overall map accuracies were 67--75%; class accuracies varied more for smaller classes than for larger ones. Finally, the uncertainty of occurrence of the low-accuracy Mesquite Woodland class was mapped using simple indicator kriging with locally varying means and data derived from accuracy assessment information. Enhanced class discrimination in an independent validation data set confirmed the utility of this procedure. The results of these analyses can provide direct input for use in environmental modeling and can inform land management decision making, and the methods can be employed in other settings where spatial variability and uncertainty play large roles in the landscape
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Extending a field-based Sonoran desert vegetation classification to a regional scale using optical and microwave satellite imagery
No full textVegetation mapping in and regions facilitates ecological studies, land management, and provides a record to which future land changes can be compared. Accurate and representative mapping of desert vegetation requires a sound field sampling program and a methodology to transform the data collected into a representative classification system. Time and cost constraints require that a remote sensing approach be used if such a classification system is to be applied on a regional scale. However, desert vegetation may be sparse and thus difficult to sense at typical satellite resolutions, especially given the problem of soil reflectance. This study was designed to address these concerns by conducting vegetation mapping research using field and satellite data from the US Army Yuma Proving Ground (USYPG) in Southwest Arizona. Line and belt transect data from the Army's Land Condition Trend Analysis (LCTA) Program were transformed into relative cover and relative density classification schemes using cluster analysis. Ordination analysis of the same data produced two and three-dimensional graphs on which the homogeneity of each vegetation class could be examined. It was found that the use of correspondence analysis (CA), detrended correspondence analysis (DCA), and non-metric multidimensional scaling (NMS) ordination methods was superior to the use of any single ordination method for helping to clarify between-class and within-class relationships in vegetation composition. Analysis of these between-class and within-class relationships were of key importance in examining how well relative cover and relative density schemes characterize the USYPG vegetation. Using these two classification schemes as reference data, maximum likelihood and artificial neural net classifications were then performed on a coregistered dataset consisting of a summer Landsat Thematic Mapper (TM) image, one spring and one summer ERS-1 microwave image, and elevation, slope, and aspect layers. Classifications using a combination of ERS-1 imagery and elevation, slope, and aspect data were superior to classifications carried out using Landsat TM data alone. In all classification iterations it was consistently found that the highest classification accuracy was obtained by using a combination of Landsat TM, ERS-1, and elevation, slope, and aspect data. Maximum likelihood classification accuracy was found to be higher than artificial neural net classification in all cases
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Extracting temporal and spatial information from remotely sensed data for mapping wildlife habitat
No full textThe research accomplished in this dissertation used both mathematical and statistical techniques to extract and evaluate measures of landscape temporal dynamics and spatial structure from remotely sensed data for the purpose of mapping wildlife habitat. By coupling the landscape measures gleaned from the remotely sensed data with various sets of animal sightings and population data, effective models of habitat preference were created. Measures of temporal dynamics of vegetation greenness as measured by National Oceanographic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite were used to effectively characterize and map season specific habitat of the Sonoran pronghorn antelope, as well as produce preliminary models of potential yellow-billed cuckoo habitat in Arizona. Various measures that capture different aspects of the temporal dynamics of the landscape were derived from AVHRR Normalized Difference Vegetation Index composite data using three main classes of calculations: basic statistics, standardized principal components analysis, and Fourier analysis. Pronghorn habitat models based on the AVHRR measures correspond visually and statistically to GIS-based models produced using data that represent detailed knowledge of ground-condition. Measures of temporal dynamics also revealed statistically significant correlations with annual estimates of elk population in selected Arizona Game Management Units, suggesting elk respond to regional environmental changes that can be measured using satellite data. Such relationships, once verified and established, can be used to help indirectly monitor the population. Measures of landscape spatial structure derived from IKONOS high spatial resolution (1-m) satellite data using geostatistics effectively map details of Sonoran pronghorn antelope habitat. Local estimates of the nugget, sill, and range variogram parameters calculated within 25 x 25-meter image windows describe the spatial autocorrelation of the image, permitting classification of all pixels into coherent units whose signature graphs exhibit a classic variogram shape. The variogram parameters captured in these signatures have been shown in previous studies to discriminate between different species-specific vegetation associations. The synoptic view of the landscape provided by satellite data can inform resource management efforts. The ability to characterize the spatial structure and temporal dynamics of habitat using repeatable remote sensing data allows closer monitoring of the relationship between a species and its landscape
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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