NEWS AND EVENTS / MARCH 2026
Enhancing Insect Outbreak in Monitoring Mediterranean Forests through Multi-Source Remote Sensing Data
Thu 12 March 2026, NGC Communication Team
The second scientific paper of
the year from NextGenCarbon was published in the journal Rendiconti Lincei –
Scienze Fisiche e Naturali on 20 February. NGC colleagues
The study shows that combining very
high-resolution
This
integrated approach allowed the researchers to detect early signs of insect
outbreaks several days before they became clearly visible in PlanetScope
images, thanks to Sentinel-2’s higher spectral sensitivity. Validation results
confirmed the robustness of the method, with Sentinel-2 yearly maps reaching an
overall accuracy of 92% and an average detection delay of only 8.5 days. The
study was carried out in the Castelporziano Presidential Estate, a
pine-dominated peri-urban forest located near Rome, Italy. In recent
years, this peri-urban forest has been severely affected by two invasive insect
species,
The insect outbreak near Rome led to the removal of a large portion of mature stone pine stands, significantly altering forest structure and directly affecting carbon stocks and carbon fluxes. Developing remote sensing–based systems to monitor infestations and forest disturbances is therefore a key objective of NextGenCarbon, supporting improved assessment of forest health and carbon dynamics. - Giovanni D’Amico
- Our contribution focused on generating the reference (ground-truth) dataset using very high-resolution satellite imagery and implementing the annual disturbance mapping workflow based on Sentinel-2 data and the
Graphical Abstract from the study illustrates the process of creating a raster map from satellite data. Graphic: Petti et al. (2026), https://doi.org/10.1007/s12210-025-01387-5
The research demonstrates the value of integrating multi-platform, multi-temporal and multi-resolution data. By combining complementary satellite systems, researchers were able to leverage the strengths of each sensor and build a more robust and reliable monitoring framework. Such integrated approaches are increasingly essential to address growing forest vulnerability under climate change.
- The paper is the result of a well-coordinated team effort that required time, collaboration and methodological refinement, and it is always rewarding to see this work published and shared with the scientific community and stakeholders. Looking ahead, the next step is to further optimize change detection algorithms to enhance early warning capabilities. Improving detection sensitivity and reducing uncertainty will enable more timely interventions when forests enter stress conditions. Strengthening early detection systems is crucial not only for biodiversity conservation and forest health, but also for maintaining carbon storage and supporting the climate mitigation role of Mediterranean forests, a central goal of the NextGenCarbon project, summarises Giovanni D’Amico.
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*References:
Francini, S., McRoberts, R. E., Giannetti, F., Marchetti, M., Scarascia Mugnozza, G., & Chirici, G. (2021). The Three Indices Three Dimensions (3I3D) algorithm: a new method for forest disturbance mapping and area estimation based on optical remotely sensed imagery. International Journal of Remote Sensing, 42(12), 4693–4711. https://doi.org/10.1080/01431161.2021.1899334
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Read the open access paper from the link below:
Beatrice Petti, Giovanni D’Amico, Cesar Alvites, Francesco Parisi, Emma Bambagioni, Roberta Bruno, Giovanni Santopuoli, Bruno Lassere, Gherardo Chirici, Marco Ottaviano, Marco Marchetti & Saverio Francini. Remote sensing across scales and platforms: monitoring Castelporziano nature reserve forest insect outbreaks. Rend. Fis. Acc. Lincei (2026). https://doi.org/10.1007/s12210-025-01387-5