in urban climate studies Daniele G. Ferreira , Eleonora S. Assis, - - PowerPoint PPT Presentation

Urban form representation of a heterogeneous city for application in urban climate studies

Daniele G. Ferreira, Eleonora S. Assis, Elisa M. C. Almeida, Priscila A. B. Tuzani Federal University of Minas Gerais, Brazil

ICUC 10

INTRODUCTION

□ Urban form representation for urban climate studies is a

challenge.

□ Representation methods □ Qualitative = Area Classifications (Local Climate Zones – LCZ) □ Quantitative = Urban descriptors (real buildings) □ Developing countries: heterogeneous cities □ Urban form is disordered, with informal occupation. □ Land cover types are different from those established in

classification models.

□ Quantitative methods are possible to apply by increasing

capacity of computers.

Compare 3 methods to represent the urban form of a heterogeneous city

GOAL

BELO HORIONTE, BRAZIL

3rd

largest Brazilian urban concentration

4%

• f the city area is informal occupation (favelas)

7,167 hab./km²

population density

BELO HORIZONTE

METHODOLOGY REPRESENTATION METHODS

Classification of satellite image □ low-rise □ mid-rise □ high-rise

urban land cover (Grimmond and Oke 1999)

Classification by number of stories □ up to 2 stories □ 3-8 stories □ above 9 stories Surface descriptors □ hm = mean height of buildings □ λp = plan area fraction of buildings □ λc = complete aspect ratio (Voogt and Oke 1997)

QUALITATIVE QUANTITATIVE

INTRODUCTION URBAN DESCRIPTORS

λp = plan area of buildings λc = complete aspect ratio λ𝑞 = 𝐵𝑞 𝐵𝑈

𝐵𝑈 𝐵𝑞 𝐵𝑈 𝐵𝑑

λ𝑑 = 𝐵𝑑 𝐵𝑈

RESULTS CLASSIFICATIONS Satellite classification

Classes % city % classes low-rise 46% 56% mid-rise 32% 38% high-rise 5% 6% total 83% 100%

Building height classification

Classes % city % classes ≤ 2 stories 18% 69% 3 - 8 stories 6% 25% > 9 stories 1% 5% total 26% 100%

RESULTS DESCRIPTORS Surface descriptors

sample hm (m) λp λc 1 6,4 0,31 1,09 2 40,9 0,64 3,87 3 6,7 0,37 1,16 4 6,6 0,16 0,42 5 5,2 0,31 1,01

1 2 3 5

FINAL REMARKS

□ Classifications of land cover could not be the best method to

represent the urban surface for heterogeneous cities.

□ Disadvantages of classifications: □

Satellite image classification

■

Confusion in the recognition of classes.

■

Overestimation of the classified urban area.

□

Building height classification

■

Insufficient to represent the impact of the city on climate.

□ Surface descriptors □

hm masks the height difference observed in the classification by number of stories.

□

λp shows an area ratio only in one dimension (plan area).

FINAL REMARKS

□ Surface descriptors: □ λc ■

Representation of the three-dimensions of the buildings.

■

Express the density difference and the absolute size of the active surface area responsible for thermal exchanges in cities.

■

Used to represent the city in surface energy balance simulation and also as an input parameter in weather simulations.

THANKS! Daniele Gomes Ferreira dani.gferreira@yahoo.com.br Eleonora Sad de Assis eleonorasad@yahoo.com.br