This document provides a standardised classification scheme (conventions and protocols) to estimate the vegetation cover of large areas with high resolution and accuracy, which has potential use to inform and propose climate change adaptation/mitigation strategies.
Despite the current evidence on the thermal benefits of vegetation and water bodies, further research is needed to investigate how cooling capacities are influenced by particular types, amounts, and spatial arrangements of green infrastructure (GI). However, there are no commonly agreed typologies that can be confidently used to compare and report the existing climatological effects of GI.
There is ample evidence of the cooling effects of green infrastructure (GI) that has been extensively documented in the literature. However, the study of the thermal profiles of different GI typologies requires the classification of urban sites for a meaningful comparison of results, since specific spatial and physical characteristics produce distinct microclimates.
The local climate zones (LCZ) scheme has attracted the interest of climate researchers as it enables the standardized study of urban heat islands by combining thermal and physical parameters of built and natural structures. Most recent work on LCZ has concentrated on understanding air temperature differences, adapting the scheme to different contexts and improving satellite-based classification methods. However, studies using very high-resolution imagery, including 3-D descriptors and analyzing their land surface temperature (LST) variability are scarcer.
165 studies from 2010 to 2017 investigating the cooling effects of green infrastructure were systematically reviewed.
Studies were analysed for their spatial patterns, investigation period, typologies studied and methodological aspects.
Five major gaps in the literature were identified.
Research opportunities for future development were identified.
This paper presents a methodological framework for a more accurate assessment of the thermal performance of green infrastructure (GI) using a combination of airborne remote sensing, field measurements and numerical modelling. The proposed framework consists of: (a) controlling intervening variables and classifying sites according to urban morphology, (b) classifying GI according to a newly developed typology, (c) quantifying and allocating a set of indicators/metrics to each typology, and (d) analysing and comparing data spatially and statistically.