Unmanned Aircraft Systems (UAS) have rapidly evolved from high-resolution mapping tools to strategic enablers of ecosystem-based disaster risk reduction (Eco-DRR). This systematic review of 51 peer-reviewed studies assesses how UAS support hazard monitoring, ecosystem resilience evaluation, and implementation of nature-based solutions across forests, freshwater, agricultural, coastal, grassland, and urban ecosystems affected by wildfires, hydrologic and climatic hazards, landslides, and multi-hazards. Recent advances reveal a shift from RGB photogrammetry to multisensory approaches integrating multispectral, LiDAR, thermal, and emerging hyperspectral or SAR data, enhancing detection of pre-hazard stress signals, fuel-load dynamics, hydrological connectivity, and coastal stability. Machine learning techniques increasingly automate risk mapping and ecological indicator extraction, improving operational transferability. UAS-derived biophysical metrics are progressively linked to ecosystem functions contributing to risk reduction, such as carbon storage, hydrological regulation, heat mitigation, and erosion control. However, explicit ecosystem service framing remains limited and uneven across ecosystems, with critical coastal and deltaic areas still underrepresented despite high hazard exposure. Policy integration is partial: although findings align with the Sendai Framework, the UN 2030 Agenda, and the Horizon Europe framework, only ≈ 45% of studies indirectly reference these agendas. Three main gaps were identified: (1) strong geographic bias toward high-income regions, (2) limited multi-temporal monitoring of resilience trajectories, and (3) insufficient standardization for decision support. UAS hold exceptional potential for quantifying ecosystem condition and modeling hazard dynamics, but realizing their full Eco-DRR potential demands stronger science-policy integration, regional scaling, and reproducible analytical frameworks.
A Systematic Review of UAS-based Remote Sensing for Ecosystem-based Disaster Risk Reduction: Applications, Policy Interfaces and Opportunities
Pirotti F.;
2026
Abstract
Unmanned Aircraft Systems (UAS) have rapidly evolved from high-resolution mapping tools to strategic enablers of ecosystem-based disaster risk reduction (Eco-DRR). This systematic review of 51 peer-reviewed studies assesses how UAS support hazard monitoring, ecosystem resilience evaluation, and implementation of nature-based solutions across forests, freshwater, agricultural, coastal, grassland, and urban ecosystems affected by wildfires, hydrologic and climatic hazards, landslides, and multi-hazards. Recent advances reveal a shift from RGB photogrammetry to multisensory approaches integrating multispectral, LiDAR, thermal, and emerging hyperspectral or SAR data, enhancing detection of pre-hazard stress signals, fuel-load dynamics, hydrological connectivity, and coastal stability. Machine learning techniques increasingly automate risk mapping and ecological indicator extraction, improving operational transferability. UAS-derived biophysical metrics are progressively linked to ecosystem functions contributing to risk reduction, such as carbon storage, hydrological regulation, heat mitigation, and erosion control. However, explicit ecosystem service framing remains limited and uneven across ecosystems, with critical coastal and deltaic areas still underrepresented despite high hazard exposure. Policy integration is partial: although findings align with the Sendai Framework, the UN 2030 Agenda, and the Horizon Europe framework, only ≈ 45% of studies indirectly reference these agendas. Three main gaps were identified: (1) strong geographic bias toward high-income regions, (2) limited multi-temporal monitoring of resilience trajectories, and (3) insufficient standardization for decision support. UAS hold exceptional potential for quantifying ecosystem condition and modeling hazard dynamics, but realizing their full Eco-DRR potential demands stronger science-policy integration, regional scaling, and reproducible analytical frameworks.
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