Advances in image acquisition and processing technologies transforming animal ecological studies

Sajid Nazir*, Muhammad Kaleem

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
313 Downloads (Pure)


Images and videos have become pervasive in ecological research and the ease of acquiring image data and its subsequent processing can provide answers in research areas such as species recognition, animal behaviour, and population studies which are critical for animal conservation and biodiversity. Technological advances in imaging are enabling data collection from new areas such as from underwater, new modalities such as thermal and new ways of processing such as deep learning. These advances are accelerating due to ease of data collection, better storage and processing technologies with associated lowering costs. The advancements in state-of-the-art machine learning for image and video classification and analysis can directly be applied in ecology. Ecological applications are generally conducted in remote and harsh deployment environments, and therefore present formidable challenges that require appreciation of the limitations of such technologies. The ecological field is poised to make use of images acquired through drones, robotics, and satellites through machine learning for rapid advancements in critical research areas. Timely insights from such data help to understand and protect the species and environment. This paper provides a review of the advancements in image acquisition and processing technologies used in animal ecological studies. We also discuss concepts and technologies that would help foster future ecological research methodologies potentially opening new insights and quickening growth to an already rich and data-intensive field.
Original languageEnglish
Article number101212
Number of pages28
JournalEcological Informatics
Early online date20 Jan 2021
Publication statusPublished - Mar 2021


  • animal behaviour
  • big data
  • computer vision
  • deep learning
  • drone
  • image processing
  • population monitoring
  • robot

ASJC Scopus subject areas

  • Ecological Modelling
  • Applied Mathematics
  • Ecology, Evolution, Behavior and Systematics
  • Computer Science Applications
  • Ecology
  • Computational Theory and Mathematics
  • Modelling and Simulation


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