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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Long-term trends in water quality, land cover, and pesticide use in watersheds of the Southern Great Plains and their association with Prymnesium parvum
No full textPrymnesium parvum is a harmful alga responsible for numerous fish-kill events worldwide and in the United States. Different biotic and abiotic factors may influence P. parvum distribution, growth, and toxicity. Several studies have been conducted on the association of water quality, lake inflow, and some biotic variables with P. parvum presence and bloom formation; however, the combination of water quality, weather and climate, hydrologic, land cover, and pesticide variables has not been applied before. In Texas, two of the most impacted river basins since 2001 are the Colorado River and Brazos River basins. This study examined one reservoir impacted by P. parvum and one unimpacted (reference) reservoir from each of these basins to address the following objectives at the watershed-scale: 1) compare and contrast environmental conditions in impacted reservoirs in the periods before and after the onset of toxic blooms in 2001, 2) determine environmental conditions that are unique to impacted reservoirs in the two study basins, and 3) identify potential environmental drivers of toxic blooms. The temporal scale of the variables was annual (average or cumulative as appropriate). Principal component analysis (PCA) was performed to address objectives 1 and 2. The period of record (POR) for objective 1 was 1992-2017 and only data from P. parvum-impacted reservoirs were used. Results showed that the use of most pesticides generally declined since 2001, coincidentally with the onset of toxic blooms, while the use of the herbicide Glyphosate and atmospheric CO2 concentration increased. The POR for objective 2 was limited to 2001-2017 and data from all four reservoirs were included. Results showed that P. parvum-impacted reservoirs have higher specific conductance, lower percent wetland area, and lower use of certain pesticides. A classification and regression tree (CART) analysis was used to address objective 3. All reservoirs were included in this analysis, the POR was 1992-2017, the dependent variable was toxic bloom occurrence (YES or NO at annual scale), and independent variables included non-redundant variables selected from a correlation matrix. Salinity (specific conductance) was the primary split, where the risk of toxic bloom increased greatly at levels ≥ 1613 µS/cm. The second split was the insecticide Terbufos, where the risk of toxic bloom increased at relatively low use levels when combined with high salinity. To avoid collinearity, percent wetland area was not included in the original CART model because it was highly correlated with salinity, but a new model was rebuilt replacing salinity with percent wetland area. The first split was percent wetland area, where the risk of toxic bloom increased with values below ≤ 0.43%. Overall, these results suggest that 1) the reduced use of potentially growth inhibitory pesticides and the increased use of Glyphosate – a known stimulant of P. parvum growth – coupled with the rising levels of air CO2 – also a known stimulant of growth – may have contributed to the onset of toxic blooms in the early 2000s; and 2) high levels of salinity, lower percentages of wetland area, and lower use of certain pesticides are unique traits of P. parvum-impacted reservoirs and are also associated with increased risk of toxic blooms
Estimating cotton yield in breeder plots using unmanned aerial vehical (UAV) imagery
Get PDFTraditionally, cotton breeding programs have depended on mechanical harvest of small breeding plots to evaluate cultivars for lint yield. The primary limiting factor in testing of breeding lines is the time and equipment costs associated with harvest; large increases in a testing programs plot-load are not possible without additional investment. UAVs may provide a method to evaluate early-generation progeny rows and increase the overall volume of material evaluated without additional investment in harvest equipment. Unmanned Aerial Vehicles (UAVs) are commonly used for high-resolution imagery and have become popular for cotton (Gossypium hirsutum L.) phenotyping. Advancements in UAV imagery and image analysis may enable cotton researchers to advance cultivars based on UAV imagery yield estimates. This study was designed to evaluate methods of cotton lint yield estimation using UAV imagery collected before mechanical harvest. This study was conducted using data from three cotton breeding regions, Coastal Bend, High Plains, and Rolling Plains. Images were classified for pixel counts of lint, and boll counts were acquired by counting each contiguous group of lint pixels. Boll counts were found to be more closely correlated with harvested yield and visual ratings, suggesting that UAV boll counting methods may be appropriate for large-scale field breeding trials. UAV yield estimates were further enhanced when data was limited to analysis within pedigrees, suggesting that aerial imagery can be useful for advancement of early-generation cultivars as an alternative to traditional mechanical plot harvest
Deep Learning of Geospatial Patterns for Remote Sensing Image Downscaling
No full textRemote sensing image downscaling can be regarded as an inverse problem that each coarse pixel is corresponding to infinite combinations of fine pixels. With the increasing demanding of fine imagery in various fields, many researchers have devoted themselves to the studies of efficient downscaling algorithms in the past few decades. Geostatistical methods, particularly the kriging family of methods, have been extensively used in remote sensing image downscaling to account for the geospatial patterns of imagery. These geostatistical methods enjoy the flexibility of integrating point spread function and the possibility of preserving spectral information of original imagery [e.g., area-to-point kriging (ATPK)]. As one of state-of-the-art geostatistical methods, ATPK, which relies on two-point statistics, is incapable of modeling complex geospatial patterns in heterogeneous area. In the past few years, deep learning-based algorithms have shown great potential in learning complex spatial patters for various computer vision applications, and many researchers have successfully adopted these methods in different remote sensing applications including image downscaling. The application of deep learning in remote sensing, however, is largely limited by the requirement of massive training datasets which are extremely difficult to come by. Recently, an unsupervised deep learning method, namely Deep Image Prior (DIP), was proposed for single image super-resolution without any training data. In this study, we explore the performance of DIP in modeling complex geospatial patterns for remote sensing image downscaling and integrate it into a regression framework to include the statistical relationships with available ancillary datasets. In this thesis, the methodological details of DIP and the DIP-based regression framework are first discussed. We then apply the deep learning-based framework into real case studies (with Landsat and MODIS) and highlight the advantages with a performance comparison with ATPK-based framework
Near real-time monitoring of tropical dry forests in North and Central America
Get PDFTropical Dry Forests (TDF) represent one of the most preferred habitats in the tropics for human settlements and exploitation, and directly and indirectly provide vital natural resources, such as food, water, wood products, minerals, medicines, etc., to support the lives and livelihoods of approximately 90 million people in Latin America. Unfortunately, the rate of TDF deforestation in Latin America, as well as globally, has had an increasing trend for the last several decades. Deforestation monitoring, using time-series of Landsat imagery, has is becoming a reality with the advent of cloud computing, open-source programming platforms, and near real time distribution of imagery data, but little has been done to implement these systems in TDF landscapes. The general objective for my research was to evaluate the feasibility and efficiency of automated time-series analysis tools (e.g. BFAST in the R statistical analysis programming language) for detecting and monitoring deforestation in TDF landscapes using satellite imagery. Results show that BFAST time-series analysis tools were effective in accurately determining deforestation events. Vegetation indices that utilize the shortwave infrared bands prove to be more sensitive to forest disturbance than other indices using the red and near infrared bands. Moderate to extreme negative magnitude values proved to be the determining products that indicated a deforestation event, with value ranges varying widely between study sites/regions. However, the application of BFAST for shorter time frames in near real-time (weeks to 3 months) will only be possible through the use of combined, multi-sensor data to handle gaps due to poor quality images and cloud cover, as well as external data to eliminate commission errors. The methods discussed in this study could provide near real-time and eventually true real-time capabilities that provide a better understanding of land-cover change dynamics, which would assist in conservation efforts to help protect biodiversity around the world
Petropolis: A Study of Urban Growth Patterns with reference to Hydraulic Fracturing in the Greater Midland-Odessa Region
No full textEnergy sprawl is the most significant driver of land use change in the United States. US energy production is predicted to rise 27% by 2040, directly impacting over 200,000 square km of additional land. Presently, the majority of US oil and gas comes from unconventional drilling. Unlike other energy sources, extractive energy must continually be drilled and mined to sustain production, significantly impacting land use land cover (LULC). Hydraulic fracturing uniquely affects urban growth due to its rapid production and distribution process. Being at the forefront of domestic production, the Permian Basin played an essential role in the economic development of cities such as Midland and Odessa. Due to the advancement of seismic imaging and other technologies, producers widely started using hydraulic fracturing around 2010. Hydraulic Fracturing or Fracking is the controversial technological process of petroleum extraction that involves injecting a high-pressure liquid mixture to open up the subterranean rock formation. Although debated for its negative environmental consequences, fracking is commonly considered an agent of accelerated economic growth. This rapid expansion, combined with the environmental impacts, becomes an actor of change in urban fabric. Although many researchers have studied the significant habitat loss and fragmentation directly associated with energy sprawls, the land use implications, especially the urban growth patterns, need to be better understood. The urbanizing impacts of hydraulic fracturing are both spatial and temporal; this study aims to understand how oil and gas extraction has influenced the urban growth in the Greater Midland-Odessa region before and after the implementation of widespread fracking within the timeframe 2001-2011 and 2011-2021. NLCD land use data and other relevant data sets are acquired, and urban sprawl's extent and spatial characteristics are analyzed. In the later part of the study, the growth characteristics are explained by re-conceptualizing urban spatial elasticity. The analysis reveals a ‘ripple effect’ of upward and downward curve of growth rings with approximately 10-mile radius with peak density in the mid-zones. This research also identifies multiple types of sprawls and more satellite growth after widespread fracking. The key land use change patterns are identified along with a trend that indicates the fusion of both Midland and Odessa into a larger metropolitan area. An explanation is provided for the mechanism of how the extraction activities attract urban growth as ‘pull-factors’. Based on this analysis, a set of guidelines are suggested for planning and policy, addressing strategies for adaptation as well as future growth. This study is one of the earliest to examine the urbanization driven by oil and gas production in the US, and recommendations are also made in the end for much-needed future research
Variations on the Author
Get PDF“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
Avian Community Responses and Vegetation Structure Changes Following Prescribed Thinning in Pinyon-Juniper Woodlands: A Seven-Year Study in South-Central New Mexico
Get PDFPinyon-juniper woodlands occupy approximately 40 million hectares throughout the western United States and have expanded in range and density over the past 150 years. This is primarily due to fire suppression, overgrazing, and climate change. Management goals in these ecosystems focus on reducing hazardous fuel loads via mechanical thinning to enhance grass and forb cover for ungulate habitat and rangeland health. This seven-year study examined the avian community response and vegetation structure changes following mechanical thinning in pinyon-juniper woodlands of the Fort Stanton-Snowy River Cave NCA in southern New Mexico. We conducted three rounds of standardized avian point count surveys at 220 sites, 97 Control (un-thinned) and 123 Treatment (thinned) each summer from 2018 to 2024. Results revealed subtle but significant differences in avian community composition. While species richness and diversity remained similar between Managements, community analyses demonstrated that woodland species (e.g., Black-throated Gray Warbler and Mountain Chickadee) showed a preference for Control sites. In contrast, grassland species (e.g., Chihuahuan Meadowlark and Lark Sparrow) were strongly associated with Treatment sites. Distance sampling analysis found that seven woodland species showed significant density reductions in thinned sites. However, occupancy modeling results found that from 29 species, all responses to thinning were positive or neutral, suggesting current management practices effectively support a broad array of species. We also assessed multiple vegetation metrics transects at 65 sites (25 Control, 40 Treatment) during the study period. Vegetation monitoring revealed that mechanical thinning effectively altered woodland structure while promoting understory development. Treatment sites exhibited lower bare ground cover and higher foliar cover than Control sites, and canopy and basal cover exhibited a moderately negative correlation. Woody stem distribution shifted from smaller seedlings in Control sites toward medium and larger shrubs in treatment areas, with selective retention of mature trees maintaining structural diversity. These findings suggest that mechanical thinning, when implemented with habitat retention in mind, can effectively balance management objectives with conservation goals
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe 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
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