“Remote sensing applications for biodiversity monitoring: Scales, transparency and drone – Outcomes from the BioMonWeek 2026 workshop on remote sensing application”
Published: June 2026 | DOI: 10.5281/zenodo.20810758
This report summarises the outcomes of the BioMonWeek 2026 workshop “Remote sensing applications for biodiversity monitoring”, held on 7 May 2026 in Montpellier, France. The workshop brought together participants from research, monitoring and data management backgrounds to explore how remote sensing and predictive modelling can support biodiversity monitoring in a transparent, scalable and policy-useful way.
Context
Biodiversity monitoring needs to be scalable, comparable across regions and able to detect change over time. Remote sensing can help address some of these needs by providing spatially consistent and repeatable data from satellites, aircraft and drones.
When combined with field observations, remote sensing can support model-based indicators for habitat extent, condition and change. It can help fill spatial and temporal gaps in monitoring coverage and provide information at scales that would be difficult to achieve through field surveys alone.
However, the workshop participants stressed that remote sensing is not a simple substitute for field-based monitoring. Its value depends on data quality, sampling design, model performance, validation and the clarity with which uncertainty is communicated. This is especially important when outputs are used to inform policy, reporting or management decisions.
Main takeaways
- Model performance must be evaluated transparently. Predictive models based on remote sensing can be powerful, but their reliability varies depending on data quality, modelling choices and scale. The workshop underlined the need for structured validation, clear separation between training and validation data, and transparent reporting of uncertainty, limitations and risks of overfitting.
- Scale matters. A model developed and validated at one scale may not perform well when applied at another. The workshop highlighted the risk of using local or plot-level data to support regional, national or European outputs without clearly assessing scale mismatches. Validation and application scales need to be aligned.
- Remote sensing works best when connected to field data. Satellite, airborne and drone-based observations can complement field monitoring, but they depend on reliable ground-truth data for calibration and validation. The workshop stressed the need to better connect plot-based observations, drone surveys and satellite monitoring.
- Drones offer strong potential, but standards are needed. Drone data can provide very high-resolution information on vegetation structure, habitat features and fine-scale ecological patterns. They can also support training datasets and calibration for satellite-based models. However, shared standards are needed for sensor choice, flight design, timing, data processing and alignment with field surveys.
- Policy users need clear information on uncertainty and fit-for-purpose use. Remote-sensing outputs can support exploratory analysis, trend monitoring and large-scale reporting, but may not always be suitable for fine-scale management or regulatory use. Workshop participants called for clearer communication of assumptions, limitations and appropriate use cases so that decision-makers can interpret results with confidence.
Future steps
The report identifies several priorities to strengthen the use of remote sensing and modelling in biodiversity monitoring:
- Strengthen validation and reporting practices, including structured validation strategies, uncertainty reporting and assessment of overfitting.
- Clarify fit-for-purpose modelling approaches by distinguishing between exploratory, predictive, monitoring and policy-reporting uses.
- Develop harmonised standards for drone data collection, including sensor selection, flight design, timing, processing workflows and alignment with field sampling.
- Improve integration across scales and data sources by linking plot-based field data, drone imagery and satellite observations.
- Enhance communication with policy users so that data sources, assumptions, limitations and appropriate uses are clearly understood.
