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Geocarto International
H-index 38

Geocarto International

1010-6049

Published by: Taylor & Francis

https://www.tandfonline.com/toc/tgei20/current

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Environmental Sciences 121 69 156 32
Earth Science 126 47 81 25
Ecology and Evolution 421 20 21 9

Additional Metrics

Number of Best Scientists*: 196
Documents by Best Scientists*: 303
Top 100 Ranked Scientists*: 8
SCIMAGO H-index: 66
SCIMAGO SJR: 0.824
Impact Factor: 3.5

Overview

Top Research Topics at Geocarto International?

Geocarto International primarily focuses on research topics in Remote sensing, Remote sensing (archaeology), Hydrology, Cartography and Land cover. Topics in Remote sensing explored in the journal were investigated in conjunction with research in Satellite and Vegetation, Normalized Difference Vegetation Index. It links adjacent topics like Remote sensing (archaeology) with Geographic information system.

Drainage basin is a major topic of Hydrology research presented in Geocarto International. Research in Land cover discussed is concerned with the study of Land use as a whole.

  • Remote sensing (38.45%)
  • Remote sensing (archaeology) (18.12%)
  • Hydrology (11.86%)

What are the most cited papers published in the journal?

  • Introduction to remote sensing (2245 citations)
  • Computer Processing of Remotely-Sensed Images: An Introduction (957 citations)
  • Remote sensing: Principles and interpretation (827 citations)

Research areas of the most cited articles at Geocarto International:

Remote sensing, Hydrology, Remote sensing (archaeology), Land cover and Vegetation are the main subjects of interest in the published articles. The studies on Remote sensing discussed at the journal articles can also contribute to research in the domains of Cartography and Normalized Difference Vegetation Index. The most cited articles with studies in Land cover featured incorporate elements of Contextual image classification and Pixel, Support vector machine, Artificial intelligence.

What topics the last edition of the journal is best known for?

  • Ecology
  • Artificial intelligence
  • Statistics

The previous edition focused in particular on these issues:

The topics of Remote sensing, Remote sensing (archaeology), Artificial intelligence, Environmental resource management and Physical geography are the focal point of discussions in the journal. Hyperspectral imaging is the primary subject of Remote sensing works presented in it. The in-depth study on Remote sensing (archaeology) also explores topics in the intersecting field of Land use.

In Geocarto International, Machine learning and Pattern recognition are investigated in conjunction with one another to address concerns in Artificial intelligence research. Decision tree and Support vector machine are all aspects of Machine learning research featured in Geocarto International.

The most cited articles from the last journal are:

  • Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling (40 citations)
  • Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam (29 citations)
  • Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models (26 citations)

Papers citation over time

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 Geocarto International (based on the number of publications) are:

  • Kamlesh Lulla (81 papers) published 1 paper at the last edition the same number as at the previous edition,
  • M. Duane Nellis (52 papers) published 1 paper at the last edition,
  • Michael R. Helfert (30 papers) absent at the last edition,
  • James H. Everitt (25 papers) absent at the last edition,
  • Biswajeet Pradhan (25 papers) published 4 papers at the last edition, 1 less than at the previous edition.

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 Geocarto International (based on the number of publications) are:

  • Indian Institute of Remote Sensing (66 papers) published 14 papers at the last edition, 1 less than at the previous edition,
  • Indian Space Research Organisation (58 papers) published 13 papers at the last edition, 1 more than at the previous edition,
  • Chinese Academy of Sciences (47 papers) published 16 papers at the last edition, 6 more than at the previous edition,
  • Indian Institute of Technology Roorkee (38 papers) published 5 papers at the last edition, 2 less than at the previous edition,
  • University of KwaZulu-Natal (32 papers) published 4 papers at the last edition the same number as at the previous edition.

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.

Publication chance based on affiliation

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, 3.65% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 22.08% were posted by at least one author from the top 10 institutions publishing in the journal. Another 8.83% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 16.09% of all publications and 53.00% were from other institutions.

Returning Authors Index

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.

Returning Institution Index

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.

The experience to innovation index

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:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Top Publications

  • An interpretable model for the susceptibility of rainfall-induced shallow landslides based on SHAP and XGBoost

    Unknown

    (2022)
    163 Citations
  • A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers

    Binh Thai Pham;Indra Prakash;Jie Dou;Sushant K. Singh

    (2020)
    146 Citations
  • Spatial flood susceptibility prediction in Middle Ganga Plain: comparison of frequency ratio and Shannon’s entropy models

    Aman Arora;Manish Pandey;Masood Ahsan Siddiqui;Haoyuan Hong

    (2021)
    129 Citations
  • Decision Tree based ensemble machine learning approaches for landslide susceptibility mapping

    Alireza Arabameri;Subodh Chandra Pal;Fatemeh Rezaie;Rabin Chakrabortty

    (2021)
    123 Citations
  • Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins

    Amirhosein Mosavi;Mohammad Golshan;Saeid Janizadeh;Bahram Choubin

    (2020)
    106 Citations
  • An ensemble architecture of deep convolutional Segnet and Unet networks for building semantic segmentation from high-resolution aerial images

    Abolfazl Abdollahi;Biswajeet Pradhan;Abdullah M. Alamri

    (2020)
    101 Citations
  • Analysing past land use land cover change and CA-Markov-based future modelling in the Middle Suluh Valley, Northern Ethiopia

    Solomon Hishe;Woldeamlak Bewket;Jan Nyssen;James Lyimo

    (2020)
    101 Citations
  • Ensemble modeling of landslide susceptibility using random subspace learner and different decision tree classifiers

    Binh Thai Pham;Tran Van Phong;Trung Nguyen-Thoi;Kajori Parial

    (2020)
    93 Citations
  • Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam

    Tran Van Phong;Trong Trinh Phan;Indra Prakash;Sushant K. Singh

    (2021)
    81 Citations

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