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Environmental Sciences
South Africa
2026

D-Index & Metrics

Environmental Sciences

D-Index
70
Citations
19344
World Ranking
1665
National Ranking
2

Onisimo Mutanga 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 Onisimo Mutanga 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: 386 publications — 93rd percentile

93% of scientists in this discipline score the same or lower.

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

Onisimo Mutanga 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 Onisimo Mutanga 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: 70 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2026 - Research.com Environmental Sciences in South Africa Leader Award
  • 2025 - Research.com Environmental Sciences in South Africa Leader Award
  • 2023 - Research.com Environmental Sciences in South Africa Leader Award
  • 2022 - Research.com Environmental Sciences in South Africa Leader Award

Overview

Onisimo Mutanga is affiliated with the University of KwaZulu-Natal in South Africa, specializing in environmental science with a focus on remote sensing applications. Their research covers a variety of topics primarily within environmental science and related subfields.

Mutanga's main fields of study include:

  • Environmental Science

Within this broad field, key subfields of study include:

  • Ecology
  • Global and Planetary Change
  • Environmental Engineering
  • Plant Science
  • Ecological Modeling

Their research topics cover the following areas:

  • Remote Sensing in Agriculture
  • Land Use and Ecosystem Services
  • Remote Sensing and LiDAR Applications
  • Species Distribution and Climate Change
  • Soil Geostatistics and Mapping
  • Leaf Properties and Growth Measurement
  • Flood Risk Assessment and Management

Mutanga has co-authored extensively with several researchers, frequently collaborating with:

  • John Odindi
  • Mbulisi Sibanda
  • Timothy Dube
  • Kabir Peerbhay
  • Tafadzwanashe Mabhaudhi

The venues where Mutanga's research is most frequently published include:

  • Remote Sensing
  • Remote Sensing Applications Society and Environment
  • International Journal of Remote Sensing
  • International Journal of Applied Earth Observation and Geoinformation
  • Environmental Monitoring and Assessment

Among recent publications, the following papers provide insight into Mutanga's work:

  • "Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects," 2023, ISPRS Journal of Photogrammetry and Remote Sensing
  • "Advancements in the remote sensing of landscape pattern of urban green spaces and vegetation fragmentation," 2021, International Journal of Remote Sensing
  • "A quantitative framework for analysing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal landsat data," 2020, International Journal of Applied Earth Observation and Geoinformation
  • "The impact of land-use/land cover changes on water balance of the heterogeneous Buzi sub-catchment, Zimbabwe," 2020, Remote Sensing Applications Society and Environment
  • "A Comparative Estimation of Maize Leaf Water Content Using Machine Learning Techniques and Unmanned Aerial Vehicle (UAV)-Based Proximal and Remotely Sensed Data," 2021, Remote Sensing

Best Publications

  • Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review

    Elhadi Adam;Onisimo Mutanga;Denis Rugege

  • High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

    Onisimo Mutanga;Elhadi Adam;Moses Azong Cho

  • Narrow band vegetation indices overcome the saturation problem in biomass estimation

    Onisimo Mutanga;A. K. Skidmore

  • Google Earth Engine Applications Since Inception: Usage, Trends, and Potential

    Lalit Kumar;Onisimo Mutanga

  • Google Earth Engine Applications

    Onisimo Mutanga;Lalit Kumar

  • Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers

    Elhadi Adam;Onisimo Mutanga;John Odindi;Elfatih M. Abdel-Rahman

  • Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features

    O. Mutanga;A.K. Skidmore;H.H.T. Prins

  • Red edge shift and biochemical content in grass canopies

    Onisimo Mutanga;Andrew K. Skidmore

  • Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa

    Timothy Dube;Onisimo Mutanga

  • Remote Sensing of Above-Ground Biomass

    Lalit Kumar;Onisimo Mutanga

  • Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa

    O. Mutanga;A.K. Skidmore

  • Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry

    Elhadi Adam;Onisimo Mutanga

  • Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments

    Mbulisi Sibanda;Onisimo Mutanga;Mathieu Rouget

  • Discriminating tropical grass (Cenchrus ciliaris) canopies grown under different nitrogen treatments using spectroradiometry

    Onisimo Mutanga;Andrew K Skidmore;Sipke van Wieren

  • Detecting Sirex noctilio grey-attacked and lightning-struck pine trees using airborne hyperspectral data, random forest and support vector machines classifiers

    Elfatih M. Abdel-Rahman;Elfatih M. Abdel-Rahman;Onisimo Mutanga;Elhadi Adam;Riyad Ismail

  • Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions

    Onisimo Mutanga;Andrew K Skidmore

  • Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery

    Andrew K. Skidmore;Andrew K. Skidmore;Jelle G. Ferwerda;Onisimo Mutanga;Sipke E. Van Wieren

  • Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species

    Jelle G. Ferwerda;Andrew K. Skidmore;Onisimo Mutanga

  • Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects

    Unknown

  • Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas

    Timothy Dube;Onisimo Mutanga

  • Estimating tropical pasture quality at canopy level using band depth analysis with continuum removal in the visible domain

    O. Mutanga;A.K. Skidmore;L. Kumar;J.G. Ferwerda

  • Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu-Natal, South Africa

    Kabir Yunus Peerbhay;Onisimo Mutanga;Riyad Ismail

Frequent Co-Authors

Timothy Dube
Timothy Dube University of the Western Cape
Moses Azong Cho
Moses Azong Cho Council for Scientific and Industrial Research
Andrew K. Skidmore
Andrew K. Skidmore University of Twente
Lalit Kumar
Lalit Kumar University of New England
Mathieu Rouget
Mathieu Rouget Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Paramu L. Mafongoya
Paramu L. Mafongoya University of KwaZulu-Natal
Rob Slotow
Rob Slotow University of KwaZulu-Natal
Renaud Mathieu
Renaud Mathieu International Rice Research Institute
Chandrashekhar Biradar
Chandrashekhar Biradar International Center for Agricultural Research in the Dry Areas, Egypt
Herbert H. T. Prins
Herbert H. T. Prins Wageningen University & Research

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