Satellite remote sensing for applied ecologists: opportunities and challenges
Abstract
Summary: Habitat loss and degradation, overexploitation, climate change and the spread of invasive species are drastically depleting the Earth's biological diversity, leading to detrimental impacts on ecosystem services and human well‐being. Our ability to monitor the state of biodiversity and the impacts of global environmental change on this natural capital is fundamental to designing effective adaptation and mitigation strategies for preventing further loss of biological diversity. This requires the scientific community to assess spatio‐temporal changes in the distribution of abiotic conditions (e.g. temperature, rainfall) and in the distribution, structure, composition and functioning of ecosystems. The potential for satellite remote sensing (SRS) to provide key data has been highlighted by many researchers, with SRS offering repeatable, standardized and verifiable information on long‐term trends in biodiversity indicators. SRS permits one to address questions on scales inaccessible to ground‐based methods alone, facilitating the development of an integrated approach to natural resource management, where biodiversity, pressures to biodiversity and consequences of management decisions can all be monitored. Synthesis and applications. Here, we provide an interdisciplinary perspective on the prospects of satellite remote sensing (SRS) for ecological applications, reviewing established avenues and highlighting new research and technological developments that have a high potential to make a difference in environmental management. We also discuss current barriers to the ecological application of SRS‐based approaches and identify possible ways to overcome some of these limitations.
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Satellite remote sensing has become an integral part to study animal habitats, biological diversity, and spatiotemporal changes of abiotic conditions, since it allows mapping land cover, soil moisture, or temperature at scale.
“Satellite remote sensing has become an integral part to study animal habitats, biological diversity, and spatiotemporal changes of abiotic conditions113, since it allows to map quantities like land cover, soil moisture, or temperature at scale.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [113]
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Self-citation
- Authors
- Pettorelli, N., Laurance, W. F., O'Brien, T. G., Wegmann, M., Nagendra, H., & Turner, W.
- Affiliations
- Zoological Society of London (GB)
- James Cook University (AU)
- Wildlife Conservation Society Mpala Research Centre PO Box 555 Nanyuki 10400 Kenya
- University of Würzburg (DE)
- Azim Premji University (IN)
- National Aeronautics and Space Administration (US)
- Corresponding author
- Pettorelli, Nathalie
- Venue
- Journal of Applied Ecology
- Publisher
- Wiley
- Published
- 2014-05-06
- Volume
- 51
- Issue
- 4
- Pages
- 839-848
- ISSN
- 0021-8901 (ISSN-L)
- 1365-2664
- OpenAlex
- W2170244367
- Cited by
- 707
- References
- 111
- Citations by year
- 2026: 101 · 2025: 98 · 2024: 85 · 2023: 69 · 2022: 48 · 2021: 49 · 2020: 37 · 2019: 57 · 2018: 48 · 2017: 49 · 2016: 26 · 2015: 29 · 2014: 10 · 2013: 1
- FWCI
- 32.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Applied Ecology (publishedVersion)
- ResearchOnline at James Cook University (James Cook University) (submittedVersion)
- elib (German Aerospace Center) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Remote Sensing and Land Use
- Physical Sciences › Environmental Science › Global and Planetary Change › Land Use and Ecosystem Services
- Keywords
- satellite remote sensing, biodiversity monitoring, biodiversity indicators, global change, habitat loss, invasive species, climate change, ecosystem services, natural resource management, spatio-temporal change detection
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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