Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature...

Area of Science:

• Environmental Science
• Ecology
• Remote Sensing

Background:

• Global warming increases river temperatures, threatening cold-water species.
• Riparian shading is crucial for regulating river temperatures, especially in sparsely forested areas.
• Accurate characterization of riparian canopy is vital for effective river temperature modeling.

Purpose of the Study:

• To develop and validate a novel approach combining drone-based Structure-from-Motion (SfM) photogrammetry with a process-based river temperature model.
• To accurately quantify riparian shading and its impact on river temperatures.
• To assess the potential of riparian tree planting for climate change adaptation.

Main Methods:

• Utilized drone-acquired SfM photogrammetry to map riparian canopy structure.
• Integrated SfM data into the Heat Source process-based river temperature model.
• Compared model performance with SfM data against conventional LiDAR data and a scenario without riparian woodland.

Main Results:

• SfM photogrammetry accurately estimated canopy elevation (R² = 0.96).
• The integrated model accurately simulated river temperatures (RMSE < 0.7°C).
• Significant spatial heterogeneity in shading was identified, impacting temperature regulation.

Conclusions:

• Drone-based SfM is an effective tool for characterizing riparian shading for ecological modeling.
• Accurate river temperature models are essential for understanding and managing aquatic ecosystems under climate change.
• Bankside tree planting strategies can be optimized using improved modeling for climate change adaptation.