Ecological Informatics

Aims & Scope

The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography.

The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.

The nature of the journal is interdisciplinary at the crossover between ecology and informatics.

It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.

Metrics & Ranking

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Directory of Open Access Journals (DOAJ)
• Web of Science

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Agricultural and Biological Sciences, Computer Science, Environmental Science and Mathematics, designed to support cutting-edge academic discovery.

Licensing & Copyright

This journal operates under an Open Access model. Articles are freely accessible to the public immediately upon publication. The content is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), allowing users to share and adapt the work with proper attribution.

Copyright remains with the author(s), and no permission is required for non-commercial use, provided the original source is cited.

Policy Links

This section provides access to essential policy documents, guidelines, and resources related to the journal’s publication and submission processes.

• Aims scope
• Homepage
• Oa statement
• Author instructions
• License terms
• Review url
• Board url
• Copyright url
• Plagiarism url
• Preservation url
• Apc url
• License

Plagiarism Policy

This journal follows a plagiarism policy. All submitted manuscripts are screened using reliable plagiarism detection software to ensure originality and academic integrity. Authors are responsible for proper citation and acknowledgment of all sources, and any form of plagiarism, including self-plagiarism, will not be tolerated.

For more details, please refer to our official: Plagiarism Policy.

APC Details

The journal’s Article Processing Charge (APC) policies support open access publishing in Agricultural and Biological Sciences, Computer Science, Environmental Science and Mathematics, ensuring accessibility and quality in research dissemination.

This journal requires an Article Processing Charge (APC) to support open access publishing, covering peer review, editing, and distribution. The current APC is 2,200.00 USD. Learn more.

Explore journals without APCs for alternative publishing options.

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A novel numerical optimization algorithm inspired from weed colonization

  Citation: 1155

  Authors: A.R., C.

• A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

  Citation: 740

  Authors: Jin, Andrew D.

• Plant leaf disease classification using EfficientNet deep learning model

  Citation: 671

  Authors: Ãœmit, Murat, Kemal, Emine

• Spatial bias in the GBIF database and its effect on modeling species' geographic distributions

  Citation: 545

  Authors: Jan, Marianne, Andreas, Wolfgang

• BirdNET: A deep learning solution for avian diversity monitoring

  Citation: 485

  Authors: Stefan, Connor M., Maximilian, Holger

• A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants

  Citation: 329

  Authors: Emad, Victoria, Abdulaziz, Francis

• Deep convolution neural network for image recognition

  Citation: 328

  Authors: Boukaye Boubacar, Bernard, Fana

• The remote environmental assessment laboratory's acoustic library: An archive for studying soundscape ecology

  Citation: 323

  Authors: Eric P., Stuart H., Jordan, Wooyeong

• Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

  Citation: 319

  Authors: Duccio, Niko, Gregory A., Giles M., Thomas W., Kate S., Salit, Noam, Kelly, Miska, Harini, Jens, Carlo, Jane, Markus

• A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications

  Citation: 268

  Authors: Atiya, Amol D., Shankar, C.H.