1548-1603
Published by: Taylor & Francis
http://www.tandfonline.com/action/journalInformation?journalCode=tgrs20
| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Environmental Sciences | 133 | 80 | 115 | 31 |
The aim of Giscience & Remote Sensing is to expand the discussion of research in Remote sensing, Remote sensing (archaeology), Land cover, Artificial intelligence and Cartography. Giscience & Remote Sensing explores topics in Remote sensing which can be helpful for research in disciplines like Vegetation and Normalized Difference Vegetation Index. The journal focuses on Vegetation research which is adjacent to topics in Physical geography.
The research on Normalized Difference Vegetation Index discussed in the journal draws on the closely related field of Climatology. The study on Land cover presented in the journal intersects with subjects under the field of Hydrology. While work presented in it provided substantial information on Artificial intelligence, it also covered topics in Machine learning, Computer vision and Pattern recognition.
The Lidar study tackled is a key component of adjacent topics in the area of Digital elevation model.
The journal articles investigate studies in Remote sensing, Cartography, Land cover, Remote sensing (archaeology) and Artificial intelligence. While work presented in the published articles provide substantial information on Remote sensing, it also covers topics in Pixel and Vegetation, Normalized Difference Vegetation Index. The published articles explore issues in Artificial intelligence which can be linked to other research areas like Machine learning, Data mining, Computer vision and Pattern recognition.
The journal focuses largely on the fields of Remote sensing, Remote sensing (archaeology), Climate change, Physical geography and Artificial intelligence. Some problems in Remote sensing that were presented in it overlapped with concepts under Cover (telecommunications) and Vegetation. While Remote sensing (archaeology) is the focus of the journal, it also provided insights into the studies of Ecosystem services, Land cover, Aboveground biomass, Deformation (meteorology) and Subsidence.
It addresses concerns in Climate change which are intertwined with other disciplines, such as Mainland China, Ecosystem, Normalized Difference Vegetation Index and Environmental resource management. Glacier research are fields of study within Physical geography but they also intertwine with concepts in Series (stratigraphy), Index method and Spatial extent. Topics in Artificial intelligence explored in Giscience & Remote Sensing were investigated in conjunction with research in Machine learning and Pattern recognition.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Giscience & Remote Sensing (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Giscience & Remote Sensing (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 4.62% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 33.87% were posted by at least one author from the top 10 institutions publishing in the journal. Another 4.84% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 19.35% of all publications and 41.94% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Masoud Mahdianpari;Hamid Jafarzadeh;Jean Elizabeth Granger;Fariba Mohammadimanesh
(2020)Murali Krishna Gumma;Prasad S. Thenkabail;Pardhasaradhi G. Teluguntla;Adam Oliphant
(2020)Hossein Shafizadeh-Moghadam;Morteza Khazaei;Seyed Kazem Alavipanah;Qihao Weng
(2021)Unknown
(2022)Minso Shin;Yoojin Kang;Seohui Park;Jungho Im
(2020)Exploring online education options in computer science offers flexibility and diverse pathways. Many students seek the easiest masters degree programs to balance study with work or personal commitments without compromising learning outcomes. These programs can be a practical step for career advancement or specialization.
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International Crops Research Institute for the Semi-Arid Tropics
Publications: 2
