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Remote Sensing of Foliar Insect Herbivory in Broadleaved Forests: A Systematic Review
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DOI：10.1007/s40725-026-00277-9.png)
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Abstract
En 中文
Foliar insect herbivory is a growing global threat to the health and productivity of forests. Timely and spatially explicit monitoring is essential for effective silvicultural interventions. Remote sensing (RS) technologies are powerful tools for detecting, mapping, and monitoring insect herbivory, offering scalable alternatives to traditional ground-based methods. This systematic review synthesises findings from 60 studies published between 2010 and February 2026, categorising them by insect feeding guilds and operational scales to identify key advancements, research gaps, and future opportunities. Research has predominantly focused on a limited number of host-pest systems and geographic regions. Results reveal a strong emphasis on landscape-scale assessments of leaf-chewing guilds, while tree-level studies remain underrepresented. Post-2020 adoption of Sentinel-2 has demonstrated strong potential for herbivory characterisation across feeding guilds. Leaf-chewing studies used spectral, structural, textural, and polarimetric features achieving high accuracy (R2 = 0.34-0.9, overall accuracy = 73-97.7%), while other guilds remain methodologically underexplored. Phenology-aware time-series approaches combined with machine learning algorithms offer strong potential for near-real-time detection and mapping of outbreak extent, severity, timing, and frequency. Future research should focus on (a) expanding studies into underrepresented domains; (b) developing flexible yet standardised host-herbivory specific ground-truthing protocols; (c) refining methods to separate foliage types and confounding stressors; (d) advancing time-series analysis for monitoring outbreak dynamics; and (e) quantifying herbivory impacts on tree growth and forest productivity. Integrating multi-platform, multi-sensor, multi-source and multi-scale RS frameworks will enable more robust, scalable, and actionable forest health monitoring, supporting adaptive forest management under accelerating environmental change.
Keywords:
Forest health
LiDAR
Machine learning
Satellite imagery
Time series
UAV
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