Multi-sensor microclimate and tree canopy dataset from a linden tree for green infrastructure validation

Area of Science:

• Environmental Science
• Urban Ecology
• Sensor Technology

Background:

• Accurate microclimate data is crucial for validating simulation models.
• Urban vegetation significantly influences local microclimates.
• Assessing cost-effective sensor networks is vital for expanding monitoring capabilities.

Purpose of the Study:

• To provide a comprehensive dataset of in-situ microclimate observations around a linden tree.
• To evaluate the performance of low-cost sensors against professional-grade instruments for urban microclimate monitoring.
• To support research in sensor calibration, microclimate model validation, and urban vegetation studies.

Main Methods:

• Collected high-resolution microclimate data (temperature, humidity, radiation, etc.) over three days using professional and low-cost sensors.
• Acquired thermal imaging for leaf surface temperatures and 3D point cloud data of the tree canopy.
• Validated low-cost sensor data against reference instruments, achieving high agreement (R² > 0.99 for temperature).

Main Results:

• The dataset includes diverse microclimate parameters and canopy structure information.
• Low-cost sensors demonstrated high accuracy, comparable to professional-grade instruments, particularly for air temperature and radiative fluxes.
• Validation confirmed the reliability of affordable alternatives for urban microclimate monitoring.

Conclusions:

• The collected dataset is valuable for microclimate model validation and urban vegetation research.
• Cost-effective sensor networks are a feasible solution for high-resolution spatial microclimate monitoring in urban environments.
• This study demonstrates the potential of affordable sensor technology to enhance urban ecological studies.