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Engineering and Technology
Austria
2026

D-Index & Metrics

Engineering and Technology

D-Index
69
Citations
15620
World Ranking
1154
National Ranking
4

Linda See publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Linda See sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 303 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Linda See D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Linda See sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 69 D-Index — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in Austria Leader Award
  • 2025 - Research.com Engineering and Technology in Austria Leader Award
  • 2023 - Research.com Engineering and Technology in Austria Leader Award
  • 2022 - Research.com Engineering and Technology in Austria Leader Award

Overview

Linda See is affiliated with the International Institute for Applied Systems Analysis in Austria and specializes in the field of Environmental Science. Their work spans multiple subfields, including Global and Planetary Change, Ecology, Ecological Modeling, Environmental Engineering, and Geography, Planning and Development.

The scientist's research focuses on several key topics within environmental science. These include:

  • Land Use and Ecosystem Services
  • Species Distribution and Climate Change
  • Remote Sensing in Agriculture
  • Geographic Information Systems Studies
  • Remote Sensing and LiDAR Applications
  • Human Mobility and Location-Based Analysis
  • Conservation, Biodiversity, and Resource Management

Linda See has contributed to a number of publications in reputable scientific venues. Frequent publication outlets include:

  • Harvard Dataverse
  • Environment and Planning B Urban Analytics and City Science
  • Zenodo (CERN European Organization for Nuclear Research)
  • Scientific Data
  • IIASA PURE (International Institute of Applied Systems Analysis)

Several recent papers authored or co-authored by Linda See are as follows:

  • "Mapping citizen science contributions to the UN sustainable development goals," 2020, Sustainability Science
  • "A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand," 2021, Scientific Data
  • "Russian forest sequesters substantially more carbon than previously reported," 2021, Scientific Reports
  • "Global forest management data for 2015 at a 100 m resolution," 2022, Scientific Data
  • "Addressing the need for improved land cover map products for policy support," 2020, Environmental Science & Policy

Linda See frequently collaborates with several researchers in their field. Notable co-authors include:

  • Steffen Fritz
  • Ian McCallum
  • Liangzhi You
  • Myroslava Lesiv
  • Dilek Fraisl

Best Publications

  • HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts

    C. W. Dawson;R. J. Abrahart;L. M. See

  • Agent-based Models of Geographical Systems

    Alison J. Heppenstall;Andrew T. Crooks;Linda M. See;Michael Batty

  • Global livestock production systems.

    T. Robinson;P. Thornton;G. Franceschini;R. Kruska

  • Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments

    Robert J. Abrahart;Linda See

  • Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information

    Linda See;Peter Mooney;Giles Foody;Lucy Bastin

  • Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting

    Robert J. Abrahart;François Anctil;Paulin Coulibaly;Christian W. Dawson

  • Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning

    Barbara Cannas;Alessandra Fanni;Linda See;Giuliana Sias

  • Data-Driven Modelling: Concepts, Approaches and Experiences

    D. Solomatine

  • A comparison of global agricultural monitoring systems and current gaps

    Steffen Fritz;Linda See;Juan Carlos Laso Bayas;François Waldner;François Waldner

  • Generating WUDAPT Level 0 data – Current status of production and evaluation

    Benjamin Bechtel;Paul J. Alexander;Christoph Beck;Jürgen Böhner

  • Land consolidation in Cyprus: Why is an Integrated Planning and Decision Support System required?

    Demetris Demetriou;John Stillwell;Linda See;Linda See

  • Highlighting continued uncertainty in global land cover maps for the user community

    Steffen Fritz;Linda See;Ian McCallum;Christian Schill;Christian Schill

  • Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia

    Khalid Al-Ahmadi;Linda See;Alison Heppenstall;James Hogg

  • A hybrid multi-model approach to river level forecasting

    Linda See;Stan Openshaw

  • Applying soft computing approaches to river level forecasting

    Linda See;Stan Openshaw

  • Crime reduction through simulation: An agent-based model of burglary

    Nick Malleson;Alison J. Heppenstall;Linda M. See

  • Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa

    Steffen Fritz;Linda See;Felix Rembold

  • City-descriptive input data for urban climate models: Model requirements, data sources and challenges

    Valéry Masson;Wieke Heldens;Erwan Bocher;Marion Bonhomme

  • Multi-model data fusion for river flow forecasting: an evaluation of six alternative methods based on two contrasting catchments

    Robert J. Abrahart;Linda See

  • Comparing the quality of crowdsourced data contributed by expert and non-experts.

    Linda See;Alexis John Comber;Carl Salk;Carl Salk;Steffen Fritz

  • Assessing the Accuracy of Volunteered Geographic Information arising from Multiple Contributors to an Internet Based Collaborative Project

    Giles M. Foody;L. See;S Fritz;M. Van der Velde

  • Global bioenergy scenarios – Future forest development, land-use implications, and trade-offs

    Florian Kraxner;Eva-Maria Nordström;Eva-Maria Nordström;Petr Havlík;Petr Havlík;Mykola Gusti;Mykola Gusti

  • Using control data to determine the reliability of volunteered geographic information about land cover

    Alexis J. Comber;Linda M. See;Steffen Fritz;Marijn Van der Velde

Frequent Co-Authors

Steffen Fritz
Steffen Fritz International Institute for Applied Systems Analysis
Ian McCallum
Ian McCallum International Institute for Applied Systems Analysis
Dmitry Schepaschenko
Dmitry Schepaschenko International Institute for Applied Systems Analysis
Michael Obersteiner
Michael Obersteiner University of Oxford
Florian Kraxner
Florian Kraxner International Institute for Applied Systems Analysis
Anatoly Shvidenko
Anatoly Shvidenko International Institute for Applied Systems Analysis
Benjamin Bechtel
Benjamin Bechtel Ruhr University Bochum
Liangzhi You
Liangzhi You International Food Policy Research Institute
Marijn van der Velde
Marijn van der Velde European Commission Joint Research Centre
Giles M. Foody
Giles M. Foody University of Nottingham

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