World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
36
Citations
6300
World Ranking
11158
National Ranking
4625

Bahram Salehi publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Bahram Salehi sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 103 publications — 9th percentile

9% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Bahram Salehi D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Bahram Salehi sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 36 D-Index — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Bahram Salehi is affiliated with the SUNY College of Environmental Science and Forestry in the United States. Their primary field of study is Environmental Science, with focused contributions across several subfields including Environmental Engineering, Ecology, Global and Planetary Change, Aerospace Engineering, and Ocean Engineering.

Their research topics extensively cover Remote Sensing and LiDAR Applications, Remote Sensing in Agriculture, Land Use and Ecosystem Services, Flood Risk Assessment and Management, Coastal Wetland Ecosystem Dynamics, Automated Road and Building Extraction, and Peatlands and Wetlands Ecology.

Frequent collaborators in their research include Masoud Mahdianpari, Fariba Mohammadimanesh, Brian Brisco, Jean Granger, and Haifa Tamiminia.

Bahram Salehi has published articles in several scientific venues notable for remote sensing and environmental science, including:

  • Remote Sensing
  • Canadian Journal of Remote Sensing
  • ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
  • International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Recent publications associated with their work include:

  • Google Earth Engine for geo-big data applications: A meta-analysis and systematic review, 2020, ISPRS Journal of Photogrammetry and Remote Sensing
  • A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland, 2020, GIScience & Remote Sensing
  • Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review, 2020, Remote Sensing
  • Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform, 2020, Canadian Journal of Remote Sensing
  • Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America, 2020, Remote Sensing

Best Publications

  • Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

    Haifa Tamiminia;Bahram Salehi;Masoud Mahdianpari;Lindi Quackenbush

  • Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

    Masoud Mahdianpari;Bahram Salehi;Mohammad Rezaee;Fariba Mohammadimanesh

  • Remote sensing for wetland classification: a comprehensive review

    Sahel Mahdavi;Bahram Salehi;Jean Granger;Meisam Amani

  • The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

    Masoud Mahdianpari;Bahram Salehi;Fariba Mohammadimanesh;Saeid Homayouni

  • Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery

    Masoud Mahdianpari;Bahram Salehi;Fariba Mohammadimanesh;Mahdi Motagh

  • Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery

    Mohammad Rezaee;Masoud Mahdianpari;Yun Zhang;Bahram Salehi

  • A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem

    Fariba Mohammadimanesh;Fariba Mohammadimanesh;Bahram Salehi;Masoud Mahdianpari;Masoud Mahdianpari;Eric Gill

  • Temperature-Vegetation-soil Moisture Dryness Index (TVMDI)

    Meisam Amani;Bahram Salehi;Sahel Mahdavi;Ali Masjedi

  • A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

    Masoud Mahdianpari;Hamid Jafarzadeh;Jean Elizabeth Granger;Fariba Mohammadimanesh

  • Spectral analysis of wetlands using multi-source optical satellite imagery

    Meisam Amani;Meisam Amani;Bahram Salehi;Bahram Salehi;Sahel Mahdavi;Sahel Mahdavi;Brian Brisco

  • Object-Based Classification of Urban Areas Using VHR Imagery and Height Points Ancillary Data

    Bahram Salehi;Yun Zhang;Ming Zhong;Vivek Dey

  • Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review

    Sarina Adeli;Bahram Salehi;Masoud Mahdianpari;Lindi J. Quackenbush

  • Multi-temporal, multi-frequency, and multi-polarization coherence and SAR backscatter analysis of wetlands

    Fariba Mohammadimanesh;Bahram Salehi;Masoud Mahdianpari;Brian Brisco

  • Big Data for a Big Country: The First Generation of Canadian Wetland Inventory Map at a Spatial Resolution of 10-m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

    Masoud Mahdianpari;Masoud Mahdianpari;Bahram Salehi;Fariba Mohammadimanesh;Fariba Mohammadimanesh;Brian Brisco

  • Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration

    Meisam Amani;Bahram Salehi;Sahel Mahdavi;Jean Granger

  • Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America

    Masoud Mahdianpari;Jean Elizabeth Granger;Fariba Mohammadimanesh;Bahram Salehi

  • WetNet: A Spatial-Temporal Ensemble Deep Learning Model for Wetland Classification Using Sentinel-1 and Sentinel-2

    Benyamin Hosseiny;Masoud Mahdianpari;Brian Brisco;Fariba Mohammadimanesh

  • Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

    Roghieh Eskandari;Masoud Mahdianpari;Fariba Mohammadimanesh;Bahram Salehi

  • Fisher Linear Discriminant Analysis of coherency matrix for wetland classification using PolSAR imagery

    Masoud Mahdianpari;Bahram Salehi;Fariba Mohammadimanesh;Brian Brisco

  • A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

    Rabia Munsaf Khan;Bahram Salehi;Masoud Mahdianpari;Fariba Mohammadimanesh

  • Wetland Classification Using Multi-Source and Multi-Temporal Optical Remote Sensing Data in Newfoundland and Labrador, Canada

    Meisam Amani;Bahram Salehi;Sahel Mahdavi;Jean Elizabeth Granger

  • Object-Based Classification of Wetlands in Newfoundland and Labrador Using Multi-Temporal PolSAR Data

    Sahel Mahdavi;Bahram Salehi;Meisam Amani;Jean Elizabeth Granger

Frequent Co-Authors

Brian Brisco
Brian Brisco Natural Resources Canada
Yun Zhang
Yun Zhang University of New Brunswick
Mahdi Motagh
Mahdi Motagh University of Hannover
Weimin Huang
Weimin Huang Memorial University of Newfoundland
Laura L. Bourgeau-Chavez
Laura L. Bourgeau-Chavez Michigan Technological University
Timothy A. Volk
Timothy A. Volk SUNY College of Environmental Science and Forestry
Derek R. Peddle
Derek R. Peddle University of Lethbridge
Heather McNairn
Heather McNairn Environment and Climate Change Canada

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