‘evaluating the thermal performance of urban green

‘Evaluating the thermal performance of urban green infrastructure at local scale: A methodological framework’

Carlos Bartesaghi Koc MBEnv. (SusDev.); B.Arch. – PhD Candidate UNSW Supervisors : Dr Paul Osmond, Prof Alan Peters Co-supervisor : Dr Matthias Irger

GREEN INFRASTRUCTURE (Tree canopy, green open spaces,

green roofs, vertical greenery systems)

URBAN MICROCLIMATE (Surface- & Canopy Layer- Urban heat

island – SUHI, CLUHI)

Airborne Remote Sensing

As a method to map and

assess the thermal effects

of GI

Research outline

What is Green Infrastructure (GI)? An interconnected network of high quality natural and semi-natural areas and environmental features, that are designed and managed to deliver a wide range of ecosystem services (ESS), maintaining natural processes and protecting biodiversity in both rural and urban settings’ (Benedict et al. 2000, 2006, Williamson 2003, EMGIN 2006, EEA 2013, Faehnle 2014) Ecosystem Services provided by GI: - Multi-functionality - Interconnected network - Spatial heterogeneity Right diagram: Millennium Ecosystem Assessment (MEA) (2005), Ecosystems and Human Well-being: Synthesis.

Image: http://www.greenroofs.com Image courtesy: Michael Van Valkenburgh Associates

Urban heat island phenomenon Urban areas experiences warmer temperatures than rural areas. GI’s climate regulation through: - Shading - Evaporative cooling - Wind modification (Hunter Block et al. 2012, Forest Research 2010b, Motazedian 2012, Lehmann 2014).

Images courtesy of CSIRO. [Irger, M. (2014), The Effect of Urban Form on Urban Microclimate]

What is the thermal performance of

different green infrastructure

typologies on urban microclimate

and which amounts, compositions

and distributions are more effective

in providing cooling benefits at the

local scales?

Q1

Q2

Q3

Questions & Objectives

Q4

• How can be classified GI to support climate studies? O1 Propose a standardised classification scheme for identifying and

characterising GI from a climatological perspective.

• What are the most suitable methods and indicators to evaluate and predict the thermal behaviour of GI at the local scales and across different urban contexts? O2 Evaluate different methods, principles and indicators utilised for

investigating the cooling effects of GI.

O3 Propose a methodological framework for a more accurate and precise mapping, analysis and visualisation of the thermal performance of GI.

• What is the thermal profile of different GI typologies and which typologies are more effective in providing cooling benefits? O4 Apply the methodological framework to evaluate the relationship

between different aspects of GI and the thermal profile of a case study within Sydney metropolitan region.

O5 Develop a statistical model to predict the thermal performance of different GI typologies.

• What are the recommendations that can be drawn from the evidence? O6 Propose a list of evidence-based guidelines and recommendations

for practitioners, industry and policy makers.

Data sources & Indicators

INFRARED Seasonal / day- & night- time Surface Temperature (SurfT)

IN-SITU MEASUREMENTS

Car transects Relative humidity (RH)

Air Temperature (AirT)

Meteorological stations Wind speed (WS)

Solar radiation (SR)

CADASTRAL

Location Distance to coast (DtC)

Street geometry Street width (W)

Aspect ratio (H/W)

LIDAR

Buildings Building heights (H)

Building surf. Fraction (BSF)

Ground Altitude (DTM/DSM)

Vegetation configuration Patch density (PD), aggregation index (AI), landscape shape index (LSI), contagion (CONTAG)

Vegetation height/extent Low (L), medium (M), high (H) vegetation fractions

HYPER-/MULTI- SPECTRAL

Spectral Reflectivity Impervious surface fraction (ISF)

Water fraction (WF)

NDVI

Deciduous/Evergreen (D/E) fractions

Leaf area index/density (LAI-LAD)

Evapotranspiration (ET)

Climatic indicators

Intervening variables

Independent variables

Urban Morphology indicators

GI- Configurational indicators

GI- Structural indicators

GI- Functional indicators

Dependent variables

Data collection techniques: 1. Airborne remote sensing 2. In-situ measurements (mobile and weather stations)

Ongoing data collection summer Data collected winter 2012

Data collected and pre-processed by Dimap, and kindly provided by Dr. Matthias Irger

- Hyperspectral - Lidar - Cadastral - Thermal infrared - Car transects’ data

- Multispectral - Thermal infrared - Car transects’ data - Weather stations’ data

Data to be collected as part of a project managed by Dr. Matthias Irger

Methodological framework

LCZ 2 LCZ 3 LCZ 1

A LCZ classification Hy

Li

In

Image in process of publication, Bartesaghi et al. (2016c)

Ca

- Wind speed

- Dist. to coast - Street width - H/W ratio

- Building heights - Building SF - DSM / DEM

- Impervious SF

II. Classification of case study into LCZs to: - Reduce the effect of

urban morphology aspects.

- Select zones of relatively similar urban characteristics.

I. Control of intervening variables by selecting appropriate location and day for measurements

In= In-situ; Ca= Cadastral; Li= Lidar; Hy= Hyper-/multispectral; IR= Infrared


B

GIT 3 GIT 2 GIT 1 GIT 2

GIT classification


- PD, ED, LSI (Fragstats) - L,M,H Veg. fract.

- Impervious SF - Water fraction

Li

Hy

Hy - Dec./everg. fract. - LAI - NDVI - ET

- RH - Air Temp. - Solar radiation - Wind speed

V. Calculation of NDVI and derivation of LAI VI. Estimation of ET by adapting the FAO-56 Penman-Monteith method. VII. Allocation of functional values (LAI, ET) to each GIT.

In

III. Subdivision of LCZ into GIT. IV. Characterisation and classification of GITs according to structural and configurational indicators.



C Statistical analysis


IR

- Winter & summer, diurnal & nocturnal surface temperature

VIII. Statistical analysis and formulation of a predictive model according to: a. Functional aspects

(LAI; ET; NDVI)

b. Structural aspects (L, M, H; Dec/ev.%)

c. Configurational aspects (PD, AI, LSI, CONTAG).


GI Classification criteria

Principles for the classification of GI. Bartesaghi et al. (2016a).

Combined in different ways and arrangements to form:

TC GOS GR VGS

Green infrastructure

Vegetation layers (VL)

GV

L

D E

M

D E

H

D E

CV

S

D E

T

D E

Ground surfaces (GS)

TS

Ip

N A

Pr

B V*

WB

V* NV

Building structures (BS)

RS

In

SV* V*

Si

SV* V*

Ex

SV* V*

VS

Rg

P Es

Rw

D I

Func

tiona

l St

ruct

ural

Conf

igur

atio

nal

Cate

gorie

s Cl

asse

s Su

b-cla

sses

Ty

polo

gies

Un

ivers

e Su

b-ca

tego

ries

Contextual classification (Spatial configuration)

GREEN INFRASTRUCTURE

(GI)

Functional classification

Network

Individual

Structural classification

Network

Individual

- Network & connectivity

- Hierarchy & significance

- Ecosystem services (ESS)

- Structure and morphology

- Land cover structure

- Vegetation structure

- Supporting structure

Multi-scale approach for characterising GI elements. Based on Oke (2006) and Erell et al. (2011).

Logical division of GI according to the climatic function, structure and combination of its elements. Bartesaghi et al. (2016b) Sub-categories: GV= ground vegetation, CV= climbing vegetation, TS= terrestrial surfaces, WB= water bodies, RS= roof structures, VS= vertical structures. Classes: L= low, M= medium, H= high, S= short, T= tall, Ip= impervious, Pr= pervious, V= vegetated, NV= non-vegetated, In= intensive, Si= semi-intensive, Ex= extensive, Rg= rooted on ground, Rw= rooted on wall. Sub-classes: D= deciduous, E= evergreen, N= natural, A= artificial, B= bare, V= vegetated, SV= semi-vegetated; P= panel; Es= elevated substrate, D= Direct system, I= Indirect system. Typologies: TC= tree canopy; GOS= green open space; GR= green roofs; VGS= vertical greenery systems. *Vegetated and semi-vegetated surfaces can be viewed as part of vegetation layers.

Spatial conceptualisation of GI Identification of main GI categories as a combination of different vegetation layers, surfaces and building structures [Bartesaghi et al. (2016a, 2016b)]

Research contributions to knowledge

• A green infrastructure typology that works in line with LCZ to support climatic studies.

• Use of high resolution imagery for a more precise and accurate analysis.

• Estimation of evapotranspiration in urban areas and heterogeneous contexts.

• Formulation of a framework to evaluate existing critical urban areas and to predict thermal profiles of vegetation to plan more future interventions.

• Formulation of guidelines as a communication and visualisation tool for designers and policy-makers.

Image: EEA (2013). Building a green infrastructure for Europe.

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‘evaluating the thermal performance of urban green

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