Remote sensing and the EU nature restoration Regulation: monitoring biodiversity indicators for integrated forest management
6 affiliations
- 1Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, 901 83, Umeå, Sweden; Corresponding author.
- 2Swedish University of Agricultural Sciences, Department of Forest Resource Management, Skogsmarksgränd, 901 83, Umeå, Sweden
- 3BOKU University, Department of Economics and Social Sciences, Feistmantelstraße 4, 1180 Vienna, Austria
- 4Finnish Geospatial Research Institute, Department of Remote Sensing and Photogrammetry, Vuorimiehentie 5, 02150 Espoo, Finland; Aalto University, Department of Built Environment, Otakaari 1B, 02150 Espoo, Finland
- 5Finnish Geospatial Research Institute, Department of Remote Sensing and Photogrammetry, Vuorimiehentie 5, 02150 Espoo, Finland
- 6Natural Resources Institute Finland (LUKE), Ecosystems and Modelling Group, Latokartanonkaari, 9, 00790, Helsinki, Finland
Abstract
Implementing the EU Nature Restoration Regulation (EU NRR) requires Member States to monitor multiple indicators of forest biodiversity, yet the role of remote sensing (RS) in this process remains unclear. This review synthesises current evidence on the use of RS for monitoring forest biodiversity indicators defined in the NRR and identifies methodological gaps and operational priorities. Following a systematic review of 222 studies, we evaluate the suitability of different platforms, sensors, and scales of observation. Our results show that RS capabilities are strongly indicator and scale dependent, with no single approach suitable across all indicators. Airborne laser scanning is most effective for capturing forest structural attributes, including uneven-aged forest structure, while satellite multispectral imagery provides consistent, large-scale monitoring of forest connectivity. Tree species composition and diversity can be assessed using multi-sensor approaches, though accuracy declines in mixed and structurally complex forests. In contrast, the common forest bird index cannot currently be monitored using remote sensing, and deadwood indicators are limited by the resolution required for national monitoring. Key challenges include limited availability of repeated high-resolution data, difficulties in detecting high-resolution features, and reduced classification accuracy in complex forest systems. Overall, RS has strong potential to support monitoring when integrated with National Forest Inventories and multi-sensor data streams. Advancing towards operational implementation of RS within the NRR will require harmonised products, improved methods for complex forest conditions, and greater emphasis on temporal monitoring.
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BibTeX
@article{parry2026remote,
title = {{Remote sensing and the EU nature restoration Regulation: monitoring biodiversity indicators for integrated forest management}},
author = {Jacob Parry and Inka Bohlin and Karin Öhman and Stefanie Linser and Hanna Elisabet Sorokina and Yunsheng Wang and Adriano Mazziotta and Ruben Valbuena},
journal = {Ecological Indicators},
year = {2026},
month = oct,
doi = {10.1016/j.ecolind.2026.115636},
url = {https://doi.org/10.1016/j.ecolind.2026.115636},
note = {CC BY},
}
RIS
TY - JOUR
TI - Remote sensing and the EU nature restoration Regulation: monitoring biodiversity indicators for integrated forest management
AU - Jacob Parry
AU - Inka Bohlin
AU - Karin Öhman
AU - Stefanie Linser
AU - Hanna Elisabet Sorokina
AU - Yunsheng Wang
AU - Adriano Mazziotta
AU - Ruben Valbuena
PY - 2026
DA - 2026/10/30
JO - Ecological Indicators
DO - 10.1016/j.ecolind.2026.115636
UR - https://doi.org/10.1016/j.ecolind.2026.115636
AB - Implementing the EU Nature Restoration Regulation (EU NRR) requires Member States to monitor multiple indicators of forest biodiversity, yet the role of remote sensing (RS) in this process remains unclear. This review synthesises current evidence on the use of RS for monitoring forest biodiversity indicators defined in the NRR and identifies methodological gaps and operational priorities. Following a systematic review of 222 studies, we evaluate the suitability of different platforms, sensors, and scales of observation. Our results show that RS capabilities are strongly indicator and scale dependent, with no single approach suitable across all indicators. Airborne laser scanning is most effective for capturing forest structural attributes, including uneven-aged forest structure, while satellite multispectral imagery provides consistent, large-scale monitoring of forest connectivity. Tree species composition and diversity can be assessed using multi-sensor approaches, though accuracy declines in mixed and structurally complex forests. In contrast, the common forest bird index cannot currently be monitored using remote sensing, and deadwood indicators are limited by the resolution required for national monitoring. Key challenges include limited availability of repeated high-resolution data, difficulties in detecting high-resolution features, and reduced classification accuracy in complex forest systems. Overall, RS has strong potential to support monitoring when integrated with National Forest Inventories and multi-sensor data streams. Advancing towards operational implementation of RS within the NRR will require harmonised products, improved methods for complex forest conditions, and greater emphasis on temporal monitoring.
SN - 1470-160X
SN - 1872-7034
ER -