Thermal Infrared Remote Sensing of Stress Responses in Forest Environments: a Review of Developments, Challenges, and Opportunities

遥感 环境科学 热成像 植被(病理学) 压力源 天蓬 计算机科学 环境资源管理 地理 生态学 红外线的 生物 医学 物理 病理 神经科学 光学
作者
Magdalena Śmigaj,Avinash Agarwal,Harm Bartholomeus,Mathieu Decuyper,Ahmed Elsherif,Arjen de Jonge,Lammert Kooistra
出处
期刊:Current forestry reports [Springer Nature]
卷期号:10 (1): 56-76 被引量:6
标识
DOI:10.1007/s40725-023-00207-z
摘要

Abstract Purpose of Review The successful application of thermal infrared (TIR) remote sensing in the agricultural domain, largely driven by the arrival of new platforms and sensors that substantially increased thermal data resolution and availability, has sparked interest in thermography as a tool for monitoring forest health. In this review, we take a step back to reflect on what physiological responses are reflected in leaf and canopy temperature and summarise research activities on TIR remote sensing of stress responses in forest environments, highlighting current methodological challenges, open questions, and promising opportunities. Recent Findings This systematic literature review showed that whilst the focus still remains on satellite imagery, Uncrewed Aerial Vehicles (UAVs) are playing an increasingly important role in testing the capabilities and sensitivity to stress onset at the individual tree level. To date, drought stress has been the focal point of research, largely due to its direct link to stomatal functioning at leaf level. Though, research into thermal responses to other stressors, e.g. pathogens, is also gaining momentum. Summary Disentangling stress-induced canopy temperature variations from environmental factors and structural influences remains the main challenge for broader application of TIR remote sensing. Further development and testing of approaches for thermal data analysis, including their applicability for different tree species and sensitivity under different climatic conditions, are required to establish how TIR remote sensing can best complement existing forest health monitoring approaches.

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