1,720,976 research outputs found
Seagrass meadows derived from field to spaceborne earth observation at Midge Point, a coastal habitat in the central section of the Great Barrier Reef, September/October 2017
No full textSeagrass meadow extent and meadow-scape was mapped using three alternative approaches at Midge Point, a coastal turbid water habitat, in the central section of the Great Barrier Reef, in September/October 2017. Approach 1 included mapping meadow boundaries and meadow-scape (including patches and scars) during low spring tides on foot within two sites (MP2 andMP3, each 5.5 hectare in area) using a handheld Garmin GPSMap 64s (accuracy ±1.5–3 m) on the 17 September 2017. Approach 2 was where the meadows were surveyed at low tide with observations from a helicopter (Robinson R44) on the 17 October 2017. The boundaries of the meadows were delineated by on-board observers and tracked by helicopter (at 25 ±5 m altitude) using the tracks setting on a handheld Garmin GPSMap 64s. Within these meadows, observational spot-check data was collected at an altitude of 1–2 m above the substrate, from three haphazard placements of a 0.25 m2 quadrat out the side of the helicopter at a number haphazardly scattered points (10 m2). Approach 3 used PlanetScope Dove imagery captured on 09 October 2017 coinciding as close as possible to the field-surveys, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive.
For Approach 1, fine-scale meadow-scape boundaries (patches or scars within 5.5 hectare area) were mapped for each site using the imported GPS track to create a polyline which was then smoothed using the B-spline algorithm and saved as a polygon. In Approach 2, meso-scale seagrass meadow boundaries were mapped from the GPS tracks and by on-screen interpolation based on geolocated spot-checks, field notes, and geotagged oblique aerial photographs acquired from the helicopter. For Approach 3, we created spatially explicit seagrass maps from PlanetScope Dove imagery, and conducted the classification using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). The final model predictions were then gathered into separate rasters, based on Bootstrap Probability thresholds of 60% and 100%. The final rasters were cleaned using a majority filter algorithm, to eliminate stray pixel predictions using a moving window between 3 and 9 pixels depending on the size of the imagery
Seagrass meadows derived from field to spaceborne earth observation at Yule Point, a coastal habitat in the Cairns section of the Great Barrier Reef, October 2017 to July 2020
No full textSeagrass meadow extent and meadow-scape was mapped using four alternative approaches at Yule Point, a coastal clear water habitat, in the Cairns section of the Great Barrier Reef, between October 2017 and July 2020. Approach 1 included mapping meadow boundaries and meadow-scape (including patches and scars) during low spring tides on foot using a handheld Garmin GPSMap 64s (accuracy ±1.5–3 m) on the 15 October 2017. Approach 2 was where the meadows were surveyed at low tide with observations from a helicopter (Robinson R44/R66) on the 05 September 2017. The boundaries of the meadows were delineated by on-board observers and tracked by helicopter (at 25 ±5 m altitude) using the tracks setting on a handheld Garmin GPSMap 64s. Within these meadows, observational spot-check data was collected at an altitude of 1–2 m above the substrate, from three haphazard placements of a 0.25 m2 quadrat out the side of the helicopter at a number haphazardly scattered points (10 m2). Approach 3 used imagery collected during low spring tides with a DJI Mavic 2 Pro UAV at an altitude of 30 m (85% sidelap and frontlap) on the 20 July 2020. The orthomosaic of the captured images was created in PIX4D and the resolution was 0.2cm/pixel. Approach 4 used PlanetScope Dove imagery captured on 05 September 2017 and 09 August 2019 coinciding as close as possible to the field-surveys in 2017 and 2019, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive.
For Approach 1, fine-scale meadow-scape boundaries (patches or scars within 5.5 hectare area) were mapped using the imported GPS track to create a polyline which was then smoothed using the B-spline algorithm and saved as a polygon. In Approach 2, meso-scale seagrass meadow boundaries were mapped from the GPS tracks and by on-screen interpolation based on geolocated spot-checks, field notes, and geotagged oblique aerial photographs acquired from the helicopter. For Approach 3, spatially explicit seagrass maps were created, including seagrass cover, from the UAV nadir imagery using deep-learning techniques. Due to the lower seagrass density and occurrence of morphologically smaller species, the classes used were: (1) bare sediment, (2) low seagrass cover (>0 ≤25%), (3) high seagrass cover (>25%), (4) rubble/algae. For Approach 4, we created spatially explicit seagrass maps from PlanetScope Dove imagery, and conducted the classification using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). The final model predictions were then gathered into separate rasters, based on Bootstrap Probability thresholds of 60% and 100%. The final rasters were cleaned using a majority filter algorithm, to eliminate stray pixel predictions using a moving window between 3 and 9 pixels depending on the size of the imagery
Seagrass meadows derived from field to spaceborne earth observation at Green Island (Wunyami), a reef habitat in the Cairns section of the Great Barrier Reef, November 2020
No full textSeagrass meadow extent and meadow-scape was mapped using two alternative approaches at Green Island, a reef clear water habitat, in the Cairns section of the Great Barrier Reef, in November 2020. Approach 1 included mapping seagrass meadow-scape (including patches and scars) using imagery captured during low spring tides with a DJI Mavic 2 Pro UAV at an altitude of 100 m (85% sidelap and frontlap) on the 25 November 2020. The orthomosaic of the captured images was created in PIX4D and the resolution was 2.45cm/pixel. Approach 2 used PlanetScope Dove imagery captured on 05 November 2020 coinciding as close as possible to the field-surveys from 25 to 27 November 2020, with 3.7 m x 3.7 m pixels (nadir viewing) acquired from the PlanetScope archive.
For Approach 1, spatially explicit seagrass maps were created, including seagrass cover, from the UAV nadir imagery using deep-learning techniques. The abundance classes used were: (1) absence of seagrass (0%), (2) low seagrass cover (1-15%), (3) medium seagrass cover (>15-50%), and (4) high seagrass cover (>50%). For Approach 2, we created spatially explicit seagrass maps from PlanetScope Dove imagery, and conducted the classification using a machine-learning model (Random Forest) coupled with a Boot-strapping process (100 iterations). The final model predictions were then gathered into separate rasters, based on Bootstrap Probability thresholds of 60% and 100%. The final rasters were cleaned using a majority filter algorithm, to eliminate stray pixel predictions using a moving window between 3 and 9 pixels depending on the size of the imagery
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
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
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
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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