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Peer-reviewedCC BY 4.0Environmental Monitoring and Assessment

Technological approach to invasive species: the role of remote sensing and predictive modelling

  • Ayushi Gupta1
  • Damanti Murmu2
  • Prakash Rajak1
  • Leesh Ray3
  • Akhilesh Singh Raghubanshi2
  • Hema Singh1
  • Abhinav Wadhwa4
4 affiliations
  1. 1Ecosystems Analysis Laboratory, Department of Botany, Institute of Science, Banaras Hindu University, Varanasi, India
  2. 2Institute of Environmental Science and Development, Banaras Hindu University, Varanasi, India
  3. 3Ashoka Trust for Research in Ecology and the Environment (ATREE) Bengaluru, Bengaluru, Karnataka India
  4. 4Discovery Partners Institute, The Grainger College of Engineering, University of Illinois Urbana Champaign, Urbana Champaign, IL USA
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Abstract

Remote sensing, predictive modelling and machine learning are increasingly central to detecting and monitoring invasive plant species across broad and heterogeneous landscapes. This review synthesises 25 years of remote sensing–based invasive plant species research (2000–2025) to determine how sensor selection, analytical methods and reporting practices have evolved. Records from Web of Science and Scopus were screened using defined eligibility criteria and coded by platform, sensor, species, region, method, impact category, accuracy reporting and linkage with the United Nations Sustainable Development Goals. Bibliometric keyword analysis was used to examine thematic structure and emerging research directions. The evidence shows rapid publication growth after 2015, continued dominance of satellite data, increasing use of uncrewed aerial systems, light detection and ranging, synthetic aperture radar and machine learning classifiers and persistent geographic imbalance. Of 166 retained studies, 149 provided extractable impact information and 76 reported overall accuracy values. The literature indicates a shift from descriptive mapping toward integrated monitoring, risk assessment and decision support frameworks for biodiversity conservation, restoration planning and invasive plant management.

Graphical Abstract:

Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10661-026-16070-x.

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Cite

BibTeXRIS
BibTeX
@article{gupta2026technological,
  title = {{Technological approach to invasive species: the role of remote sensing and predictive modelling}},
  author = {Ayushi Gupta and Damanti Murmu and Prakash Rajak and Leesh Ray and Akhilesh Singh Raghubanshi and Hema Singh and Abhinav Wadhwa},
  journal = {Environmental Monitoring and Assessment},
  year = {2026},
  month = oct,
  doi = {10.1007/s10661-026-16070-x},
  url = {https://doi.org/10.1007/s10661-026-16070-x},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Technological approach to invasive species: the role of remote sensing and predictive modelling
AU  - Ayushi Gupta
AU  - Damanti Murmu
AU  - Prakash Rajak
AU  - Leesh Ray
AU  - Akhilesh Singh Raghubanshi
AU  - Hema Singh
AU  - Abhinav Wadhwa
PY  - 2026
DA  - 2026/10/31
JO  - Environmental Monitoring and Assessment
DO  - 10.1007/s10661-026-16070-x
UR  - https://doi.org/10.1007/s10661-026-16070-x
AB  - Remote sensing, predictive modelling and machine learning are increasingly central to detecting and monitoring invasive plant species across broad and heterogeneous landscapes. This review synthesises 25 years of remote sensing–based invasive plant species research (2000–2025) to determine how sensor selection, analytical methods and reporting practices have evolved. Records from Web of Science and Scopus were screened using defined eligibility criteria and coded by platform, sensor, species, region, method, impact category, accuracy reporting and linkage with the United Nations Sustainable Development Goals. Bibliometric keyword analysis was used to examine thematic structure and emerging research directions. The evidence shows rapid publication growth after 2015, continued dominance of satellite data, increasing use of uncrewed aerial systems, light detection and ranging, synthetic aperture radar and machine learning classifiers and persistent geographic imbalance. Of 166 retained studies, 149 provided extractable impact information and 76 reported overall accuracy values. The literature indicates a shift from descriptive mapping toward integrated monitoring, risk assessment and decision support frameworks for biodiversity conservation, restoration planning and invasive plant management.  Graphical Abstract:  Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1007/s10661-026-16070-x.
SN  - 0167-6369
SN  - 1573-2959
ER  -