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Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Automated Assessment of Green Infrastructure Using E-nose, Integrated Visible-Thermal Cameras and Computer Vision

Areej Shahid¹, Sigfredo Fuentes², Claudia Gonzalez Viejo²

¹Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, Melbourne, VIC 3010, Australia.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This study introduces a new urban green infrastructure monitoring system using an electronic nose and computer vision to assess plant health and environmental conditions. The findings demonstrate strong links between air pollution, weather, and tree health, enabling optimized smart irrigation.

Keywords:

air pollution electronic nose predictive modelling urban green infrastructure vegetation indices

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Last Updated: Jan 10, 2026

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Environmental Science
Horticulture
Urban Planning

Background:

Conventional monitoring systems for urban green infrastructure (GI) face limitations due to pollution and vandalism.
Existing satellite, UAV, and sensor-based methods have documented shortcomings for effective GI management.

Purpose of the Study:

To develop and evaluate a novel urban GI monitoring system integrating gas exchange and vegetation indices (VIs).
To assess the impact of environmental factors and air contaminants on urban tree health.

Main Methods:

Utilized an electronic nose (E-nose) for volatile organic compound detection and computer vision for VIs.
Collected plant growth parameters (LAIe, Ig, CTD, TWSI) and meteorological data.
Mounted sensors on a vehicle for mobile data acquisition from 172 Elm trees.

Main Results:

Established strong correlations between air contaminants, ambient conditions, and plant growth status.
Demonstrated the feasibility of real-time data acquisition for urban tree monitoring.
Identified key environmental drivers affecting urban green infrastructure health.

Conclusions:

The integrated monitoring system provides a robust approach for smart irrigation and environmental management in urban settings.
Real-time data analysis enables optimized resource allocation for sustainable urban green infrastructure.