Dr. Ulf Mallast Profile

Dr. Ulf Mallast

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Proceedings Article | 13 November 2024 Presentation + Paper

Urban green infrastructure monitoring using remote sensing techniques

Tesfaye Tessema, Dale Mortimer, Sharad Kumar Gupta, Ulf Mallast, Stephen Uzor, Fabio Tosti

Proceedings Volume 13197, 131970Z (2024) https://doi.org/10.1117/12.3034031

KEYWORDS: LIDAR, Vegetation, Remote sensing, Clouds, Satellites, Multispectral imaging, Spatial resolution, Environmental monitoring, Image resolution, Environmental sensing

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Urban Green infrastructure is essential part of the urban ecosystem and is a sink for extreme heat and carbon dioxide. The urbanisation has been developing in alarming rate with taller and dense built environment. The effect of the green infrastructure in combating the new development should be quantified to address and balance the phenomena. Most of the green urban infrastructure are covered by trees in the park, woodlands and along the streets. The physical characteristics of these trees such as canopy height and density, distribution, and health should be monitored. The monitoring has twofold benefits: understanding the existing situation of the trees and identifying the impact they bring during extreme weather conditions, in turn the implication of hazard mitigation. Existing urban tree inventories and monitoring schemes are based on spatial sampling assessment techniques and visual inspections but are limited in space and time. Remote sensing applications towards assessing the green infrastructure has become a prominent technology to quantify parameters and identify changes. In this study, different remote sensing datasets and techniques such as LiDAR and satellite remote sensing are used for inventory. The inventory includes the canopy height, density, and health of the trees with respect to their spatial and temporal variations. The study can be implemented in physical processing units and/or in cloud based geospatial platform. The results can be integrated with the available urban green infrastructure database and contribute towards regular monitoring. As a case study to demonstrate the methodology, we investigate sample trees selected in urban settings. Such studies have an impact on quantifying existing green infrastructure and inform data driven decision making for a more sustainable environment.

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