Usage of Airborne LiDAR Data and High-Resolution Remote Sensing Images in Implementing the Smart City Concept

Abstract

The cities of the future should not only be smart, but also smart green, for the well-being of their inhabitants, the biodiversity of their ecosystems and for greater resilience to climate change. In a smart green city, the location of urban green spaces should be based on an analysis of the ecosystem services they provide. Therefore, it is necessary to develop appropriate information technology tools that process data from different sources to support the decision-making process by analysing ecosystem services. This article presents the methodology used to develop an urban green space planning tool, including its main challenges and solutions. Based on the integration of data from ALS, CLMS, topographic data, and orthoimagery, an urban green cover model and a 3D tree model were generated to complement a smart-city model with comprehensive statistics. The applied computational algorithms allow for reports on canopy volume, CO (Formula presented.) reduction, air pollutants, the effect of greenery on average temperature, interception, precipitation absorption, and changes in biomass. Furthermore, the tool can be used to analyse potential opportunities to modify the location of urban green spaces and their impact on ecosystem services. It can also assist urban planners in their decision-making process.