High-throughput phenotyping for climate-resilient forests: integrating multi-sensor fusion and root-shoot dynamics | JoVE Visualize

Area of Science:

Forestry science
Plant physiology
Remote sensing

Background:

Compound drought and heat events, exacerbated by climate change, pose significant threats to global forest stability.
Genomic advancements allow rapid identification of tree genotypes, but phenotyping—characterizing adaptive traits—has not kept pace, creating a bottleneck in understanding stress responses.
Current high-throughput phenotyping (HTP) primarily focuses on above-ground canopy traits, neglecting the critical role of root systems and the soil-plant-atmosphere continuum (SPAC) in drought resilience.

Purpose of the Study:

To review the current state of HTP for forest climate resilience.
To highlight the limitations of canopy-focused phenotyping and the need to investigate below-ground traits.
To propose a framework integrating aerial sensing with eco-hydrological and modeling approaches to infer root functional strategies.

Main Methods:

Multi-sensor data fusion combining thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR on UAVs and robotic systems.
Application of deep learning models for tasks like tree-crown segmentation and stress classification.
Development of a constraint-based framework coupling aerial sensor data with eco-hydrological approaches and process-based modeling.

Main Results:

Integrated sensing approaches effectively detect pre-visual physiological stress, such as changes in stomatal conductance.
Deep learning models show promise but face challenges with overfitting, transferability, and domain shift in complex forest canopies.
The proposed framework aims to constrain plausible root functional strategies by linking above-ground signals to below-ground processes.

Conclusions:

Developing climate-resilient forests necessitates a shift towards below-canopy investigation.
Integrating above-ground sensing with eco-hydrological models offers a viable approach to understanding root system contributions to drought resilience.
Future research priorities include standardized protocols, open datasets, and Explainable AI (XAI) to bridge the gap between observed signals and underlying traits.