‘a remote sensing framework for assessing the...

‘A remote sensing framework for assessing the microclimatic effects of green infrastructure at local scales’

Carlos Bartesaghi Koc PhD Candidate UNSW, CRC-LCL | MBEnv, BArch 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

GI characteristics:

- Multi-functionality - Interconnectivity - Spatial heterogeneity

Climate regulation:

- Shading (LAI-NDVI) - Evaporative cooling (ETc) - Wind modification

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?

Questions & Objectives

O1 Propose a standardised classification scheme for GI.

O2 Evaluate different methods, principles and indicators.

O3 Propose a methodological framework for a more accurate and precise evaluation of GI.

O4 Examine the relationship between different GI characteristics and the thermal profile of a case study in the context of Australia.

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

O6 Propose a list of evidence-based

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) Initial set of indicators. 95 out of 150 articles reviewed (ongoing systematic review)

Missing data: 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. POSSIBLE PROJECT CANCELATION.

Key issues!

?

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; Th= Thermal


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


Th

- 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).


Key interventions & contributions:

• 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 urban areas and to predict thermal profiles of vegetation (for end-users i.e. councils and governmental agencies)

• Formulation of guidelines as a communication and visualisation tool for designers and policy-makers. Image: EEA (2013). Building a green infrastructure for Europe.

Key publications

(1) Journal paper (review) submitted to the Urban Ecosystems Journal. (Under review)

(1) Conference paper accepted at IC2UHI June 2016, Singapore. (4th International conference on countermeasures to Urban Heat Islands).

(1)Conference abstract submitted. iHBE (International High-performance Built Environments

Conference) (SBE16 Sydney) November 2016. (1)Conference abstract submitted. Climate Adaptation Conference, Adelaide - July 2016. (1)Poster presentation at the Annual CRC-LCL Forum, November 2015. (1) Abstract and oral presentation at 11th ACCARNSI National Early Career Researcher Forum

and Workshop, February 2016, Canberra.

(1) Working paper for journal > Second systematic literature review (150 pap.) on methodologies and indicators.

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‘a remote sensing framework for assessing the...

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