World's Best Scientists 2026 revealed!

D-Index & Metrics

Environmental Sciences

D-Index
52
Citations
10193
World Ranking
4471
National Ranking
119

Lammert Kooistra publication distribution in Environmental Sciences in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Environmental Sciences in 2026. The highlighted bar marks where Lammert Kooistra sits on this spectrum.

41–50 publications: 21 scientists 51–60 publications: 62 scientists 61–70 publications: 133 scientists 71–80 publications: 257 scientists 81–90 publications: 361 scientists 91–100 publications: 440 scientists 101–110 publications: 492 scientists 111–120 publications: 541 scientists 121–130 publications: 617 scientists 131–140 publications: 544 scientists 141–150 publications: 541 scientists 151–160 publications: 539 scientists 161–170 publications: 444 scientists 171–180 publications: 444 scientists 181–190 publications: 400 scientists 191–200 publications: 377 scientists 201–210 publications: 318 scientists 211–220 publications: 283 scientists 221–230 publications: 263 scientists 231–240 publications: 220 scientists 241–250 publications: 217 scientists 251–260 publications: 180 scientists 261–270 publications: 181 scientists 271–280 publications: 155 scientists 281–290 publications: 130 scientists 291–300 publications: 127 scientists 301–310 publications: 130 scientists 311–320 publications: 85 scientists 321–330 publications: 106 scientists 331–340 publications: 80 scientists 341–350 publications: 83 scientists 351–360 publications: 75 scientists 361–370 publications: 69 scientists 371–380 publications: 52 scientists 381–390 publications: 54 scientists 391–400 publications: 56 scientists 401–410 publications: 44 scientists 411–420 publications: 40 scientists 421–430 publications: 36 scientists 431–440 publications: 25 scientists 441–450 publications: 25 scientists 451–460 publications: 32 scientists 461–470 publications: 29 scientists 471–480 publications: 21 scientists 481–490 publications: 26 scientists 491–500 publications: 25 scientists 501–510 publications: 17 scientists 511–520 publications: 18 scientists 521–530 publications: 15 scientists 531–540 publications: 22 scientists 541–550 publications: 12 scientists 551–560 publications: 15 scientists 561–570 publications: 11 scientists 571–580 publications: 19 scientists 581–590 publications: 9 scientists 591–600 publications: 9 scientists 601–610 publications: 7 scientists 611–620 publications: 11 scientists 621–630 publications: 5 scientists 631–640 publications: 5 scientists 641–650 publications: 6 scientists 651–660 publications: 3 scientists 661–670 publications: 3 scientists 671–680 publications: 4 scientists 681–686 publications: 3 scientists 687+ publications: 100 scientists
41 publications 687+

This scientist: 238 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 687 publications or more.

Lammert Kooistra D-index placement in Environmental Sciences in 2026

The chart shows the D-index (discipline H-index) distribution of Environmental Sciences scientists ranked by Research.com in 2026. The highlighted bar marks where Lammert Kooistra sits on this spectrum.

30 D-Index: 12 scientists 31 D-Index: 26 scientists 32 D-Index: 51 scientists 33 D-Index: 88 scientists 34 D-Index: 123 scientists 35 D-Index: 163 scientists 36 D-Index: 206 scientists 37 D-Index: 267 scientists 38 D-Index: 265 scientists 39 D-Index: 275 scientists 40 D-Index: 321 scientists 41 D-Index: 343 scientists 42 D-Index: 305 scientists 43 D-Index: 336 scientists 44 D-Index: 330 scientists 45 D-Index: 348 scientists 46 D-Index: 291 scientists 47 D-Index: 275 scientists 48 D-Index: 272 scientists 49 D-Index: 273 scientists 50 D-Index: 263 scientists 51 D-Index: 232 scientists 52 D-Index: 266 scientists 53 D-Index: 217 scientists 54 D-Index: 199 scientists 55 D-Index: 177 scientists 56 D-Index: 202 scientists 57 D-Index: 204 scientists 58 D-Index: 166 scientists 59 D-Index: 177 scientists 60 D-Index: 166 scientists 61 D-Index: 152 scientists 62 D-Index: 143 scientists 63 D-Index: 150 scientists 64 D-Index: 124 scientists 65 D-Index: 119 scientists 66 D-Index: 120 scientists 67 D-Index: 118 scientists 68 D-Index: 82 scientists 69 D-Index: 98 scientists 70 D-Index: 94 scientists 71 D-Index: 105 scientists 72 D-Index: 74 scientists 73 D-Index: 84 scientists 74 D-Index: 70 scientists 75 D-Index: 67 scientists 76 D-Index: 78 scientists 77 D-Index: 60 scientists 78 D-Index: 59 scientists 79 D-Index: 52 scientists 80 D-Index: 47 scientists 81 D-Index: 38 scientists 82 D-Index: 48 scientists 83 D-Index: 42 scientists 84 D-Index: 42 scientists 85 D-Index: 43 scientists 86 D-Index: 29 scientists 87 D-Index: 37 scientists 88 D-Index: 29 scientists 89 D-Index: 30 scientists 90 D-Index: 34 scientists 91 D-Index: 20 scientists 92 D-Index: 22 scientists 93 D-Index: 17 scientists 94 D-Index: 19 scientists 95 D-Index: 24 scientists 96 D-Index: 21 scientists 97 D-Index: 20 scientists 98 D-Index: 24 scientists 99 D-Index: 17 scientists 100 D-Index: 17 scientists 101 D-Index: 21 scientists 102 D-Index: 25 scientists 103 D-Index: 18 scientists 104 D-Index: 26 scientists 105 D-Index: 19 scientists 106 D-Index: 15 scientists 107 D-Index: 10 scientists 108 D-Index: 13 scientists 109 D-Index: 15 scientists 110 D-Index: 12 scientists 111 D-Index: 8 scientists 112 D-Index: 7 scientists 113 D-Index: 9 scientists 114 D-Index: 6 scientists 115 D-Index: 12 scientists 116 D-Index: 7 scientists 117 D-Index: 8 scientists 118 D-Index: 3 scientists 119 D-Index: 5 scientists 120 D-Index: 7 scientists 121 D-Index: 2 scientists 122 D-Index: 4 scientists 123 D-Index: 8 scientists 124 D-Index: 7 scientists 125+ D-Index: 99 scientists
30 D-Index 125+

This scientist: 52 D-Index — 55th percentile

55% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 125 D-Index or more.

Overview

Lammert Kooistra is affiliated with Wageningen University & Research in the Netherlands. Their research focuses primarily on Environmental Science and Agricultural and Biological Sciences, with significant contributions in several subfields including Ecology, Plant Science, Environmental Engineering, Global and Planetary Change, and Agronomy and Crop Science.

The main topics covered in their work are related to Remote Sensing in Agriculture, Remote Sensing and LiDAR Applications, Smart Agriculture and AI, Leaf Properties and Growth Measurement, Plant Water Relations and Carbon Dynamics, Land Use and Ecosystem Services, and Robotics and Sensor-Based Localization.

Frequent co-authors in their publications include Christina Kallimani, Ramin Heidarian Dehkordi, F.K. van Evert, João Valente, and Bert Rijk.

Common publication venues for their research are:

  • Harvard Dataverse
  • SSRN Electronic Journal
  • Smart Agricultural Technology
  • Computers and Electronics in Agriculture
  • Remote Sensing

Recent papers by Lammert Kooistra include:

  • Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches, 2021, published in Precision Agriculture
  • Automated crop plant counting from very high-resolution aerial imagery, 2020, published in Precision Agriculture
  • Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring, 2020, published in Pest Management Science
  • Diurnal variation of sun-induced chlorophyll fluorescence of agricultural crops observed from a point-based spectrometer on a UAV, 2020, published in International Journal of Applied Earth Observation and Geoinformation
  • The role of soils in habitat creation, maintenance and restoration, 2021, published in Philosophical Transactions of the Royal Society B Biological Sciences

Best Publications

  • Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content

    J. G. P. W. Clevers;L. Kooistra

  • Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications

    Caroline M. Gevaert;Juha Suomalainen;Jing Tang;Lammert Kooistra

  • Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

    Jan G. P. W. Clevers;Lammert Kooistra;Marnix M. M. Van den Brande

  • Possibilities of visible–near-infrared spectroscopy for the assessment of soil contamination in river floodplains

    L Kooistra;R Wehrens;R.S.E.W Leuven;L.M.C Buydens

  • Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series

    Ben DeVries;Jan Verbesselt;Lammert Kooistra;Martin Herold

  • Spectral reflectance based indices for soil organic carbon quantification

    H. M. Bartholomeus;M. E. Schaepman;L. Kooistra;Antoine Stevens

  • Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR.

    Benjamin Brede;Alvaro Lau;Alvaro Lau;Harm M. Bartholomeus;Lammert Kooistra

  • Estimating canopy water content using hyperspectral remote sensing data

    Jan G. P. W. Clevers;Lammert Kooistra;Michael E. Schaepman

  • Identification of soil heavy metal sources and improvement in spatial mapping based on soil spectral information: A case study in northwest China

    Tao Chen;Qingrui Chang;Jing Liu;J.G.P.W. Clevers

  • Assessing capacities of non-Annex I countries for national forest monitoring in the context of REDD+

    Erika Romijn;Martin Herold;Lammert Kooistra;Daniel Murdiyarso

  • Integrating remote sensing in Natura 2000 habitat monitoring: Prospects on the way forward

    Jeroen Vanden Borre;Desiré Paelinckx;Caspar A. Mücher;Lammert Kooistra

  • Improved estimation of leaf area index and leaf chlorophyll content of a potato crop using multi-angle spectral data – potential of unmanned aerial vehicle imagery

    Peter P.J. Roosjen;Benjamin Brede;Juha M. Suomalainen;Juha M. Suomalainen;Harm M. Bartholomeus

  • The potential of field spectroscopy for the assessment of sediment properties in river floodplains

    L Kooistra;J Wanders;G.F Epema;R.S.E.W Leuven

  • Exploring field vegetation reflectance as an indicator of soil contamination in river floodplains.

    L Kooistra;E.A.L Salas;J.G.P.W Clevers;R Wehrens

  • Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR

    Benjamin Brede;Kim Calders;Alvaro Lau;Pasi Raumonen

  • A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles

    Juha Suomalainen;Niels S. Anders;Shahzad Iqbal;Gerbert Roerink

  • Using spectral information from the NIR water absorption features for the retrieval of canopy water content

    Jan G. P. W. Clevers;Lammert Kooistra;Michael E. Schaepman

  • Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

    Jelle ten Harkel;Harm M. Bartholomeus;Lammert Kooistra

  • Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

    Harm M. Bartholomeus;Lammert Kooistra;Antoine Stevens;Martin van Leeuwen

  • Study of heavy metal contamination in river floodplains using the red-edge position in spectroscopic data

    J.G.P.W. Clevers;L. Kooistra;E.A.L. Salas

  • Environmental risk mapping of pollutants: State of the art and communication aspects

    Joost Lahr;Lammert Kooistra

Frequent Co-Authors

Michael E. Schaepman
Michael E. Schaepman University of Zurich
Jan G. P. W. Clevers
Jan G. P. W. Clevers Wageningen University & Research
Juha Suomalainen
Juha Suomalainen Wageningen University & Research
Harm Bartholomeus
Harm Bartholomeus Wageningen University & Research
Martin Herold
Martin Herold Wageningen University & Research
Lutgarde M. C. Buydens
Lutgarde M. C. Buydens Radboud University
Jan Verbesselt
Jan Verbesselt Wageningen University & Research
Okke Batelaan
Okke Batelaan Flinders University
Paul Scheunders
Paul Scheunders University of Antwerp
Rob S. E. W. Leuven
Rob S. E. W. Leuven Radboud University

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