HIGH RESOLUTION REMOTE
SENSING AND GIS FOR URBAN
ANALYSIS: CASE STUDY BURITIS
DISTRICT, BELO HORIZONTE,
MINAS GERAIS
Hermann Johann Heinrich Kux
Senior Researcher III
INPE, Remote Sensing Division
DAAD Thematic Seminar
“Natural Hazards: Research on natural disasters, civil defense,
disaster prevention, and aid”
Teresópolis, RJ, Brazil, June 15th - 17th 2012
Introduction
Some typical problems of metropolitan areas in
Brazil:
- Inadequate planning, not considering, among others, geo-
technological characteristics of underground substratum;
- Failure from local government on adequate control of
constructions;
- Strong financial speculation by private companies to raise the
prices of real estate.
Introduction
Buritis district in Belo Horizonte
Technical data sheet: Quickbird-2
satellite
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Launch: Oct. 18th 2001
Expected life span : > 5 years
Orbit: 97,2º sun-synchronous
Orbit duration: 93,5 minutes
Width of imaged area: 16,5 Km (nadir), 20,8 Km (off-nadir)
Revisit time: 1 – 3,5 days according to latitude
Spatial resolution: Panchromatic mode – 61 cm at nadir, 72 cm at 25º
Multispectral mode - 2,44 m at nadir, 2,88 m at 25º
Radiometric resolution: 11 bits, 2048 gray levels
Bands: Panchromatic:
0,45 - 0,90 µm
Blue:
0,45 - 0,52 µm
Green:
0,52 - 0,60 µm
Red:
0,63 - 0,69 µm
Near IR:
0,76 - 0,90 µm
Objectives of the case study
To evaluate the use of digital analysis (OBIA approach) of high
spatial resolution satellite images (Quickbird-2) and spatial
inference methods in a critical urban area (Buritis), emphasizing
its characteristics, specifics and limitations, in order to contribute
with information to urban planning.
Specific tasks:
1.
Evaluation on the efficiency of geometric correction from
Quickbird-2 images.
2.
Evaluation on the precision of ortho-rectification from Quickbird2 images, applying a rigorous model and using terrestrial
acquired D-GPS control points.
3.
Evaluation on the performance of image classification using the
OBIA (Object-based image analysis).
4.
Evaluation on the integration of information extracted from
Quickbird-2 images and other sources in a GIS, as a subsidy for
urban planning.
Localization of test-site Buritis
2004 image
Buritis
Belvedere
2002 image
Quickbird-2 images used, delivered by DIGITALGLOBE.
Methodology III
Geometric correction – D-GPS points (field survey)
Features at both images
Collection of coordinates by
a D-GPS receiver
Methodology I
Ortho-correction of Quickbird-2 image
Definition of test-site
Geometric correction
Contours
Ortho-images
DEM
Evaluation of precision
Field survey
D-GPS control points
Information acquired
Information generated
Methodology II
Object-oriented analysis (OBIA)
Area under study
Geometric correction
Object-oriented classification
Edited ortho-images
Classified images
Edited cadastre
Class definition;
Evaluation:
Multi-resolution segmentation;
Kappa;
Hierarchy;
Stability.
Membership rules;
Evaluation
Information
generated
Methodology III
Classification – Definition of classes
Classes
Composition bands 3,2,1
RGB
Composition bands 4,3,2
RGB
Characteristics of interest
Asphalt
Use of urban cadastre.
White cover
High brightness,
Constitution of materials
not discernible.
Gray cover
Impervious. Cover of high
buildings. Much variation.
Methodology III
Classification– Definition of classes (cont.)
Flare
Quantization level close to
2048.
Swimming pool
High response in the blue
band. In some cases in the
green band.
Bare soil
Modified terrain (earth
works). No vegetation.
Response in the red band.
Shadow
Low brightness. Close to
high buildings and
arboreal vegetation.
Methodology III
Classification – Definition of classes (cont.)
Ceramic tile
Linear borders.
Standardized according
to legislation. Variable
geometry. Response in
the red band.
Arboreal
vegetation
High response in the
NIR. Texture due to
different height of trees
(shadow).
Grass vegetation
High response in the
NIR. Uniform.
Response in the red
band (soil).
Methodology IV
Spatial inferences
Definition of area under study
Information acquired
Info. generated
Ortho-rectification
Object-oriented classification
Spatial inferences
Data preparation
Urban expansion
Classified images
Landslides
Geology
Geotechnical map
Irregular constructions
Geologic risks
Legislation
Critical areas
Injury/losses f. population
Methodology V
Spatial inferences – Data preparation
 Adaptation from geologic map of Belo Horizonte;
 Geologic map
 Simplification;
 Field survey.
 Map of geologic risks
Edited from original in digital format;
 Map of legislation
 Adapted f. Law on Potential Land Use (LEIPUOS);
 Slope map
 Map of slope orientation
 Generated from DEM;
 Interest on intervals limited by Law.
 Generated from DEM;
 Interest on slopes oriented to SE.
Methodology VI
Spatial inferences – Injury/losses for population
Geologic
risks
associated
Critical areas
Classifications
Areas
available for
construction
LEIPUOS
Permissive
Slopes
High geologic
risks
Zoning
Spatial Inference - AHP
Injury/losses f. population
Information acquired
Information generated
Results I
Geometric correction – Ortho-rectification
Image without correction 2004
RMS = 14,48m
Corrected image 2004
RMS = 0,86m
Results III
Classification – Buritis district (2004)
Red
Source:
Araújo (2006)
Results IV
Classification – Confusion matrix
Source: Araújo (2006).
Results V
Inferences – Risk of landslides
Buritis
Belvedere
High
Medium
Low
Nil
Source: Araújo (2006)
Results VI
Inferences – Irregular constructions
Conclusions
● High resolution satellite images, such as QuickBird-2, and
since 2009 WorldView-2, allow detailed mapping of urban
growth, with emphasis on the construction at critical areas.
● The Object-based image analysis (OBIA) approach is of
fundamental importance to obtain reliable classification results,
to be stored in a data base, for a yearly multi-temporal analysis.
● As for the test site Buritis district, all geological and
geotechnical risks mentioned (and known since a long time!)
were not taken into account both by the construction companies
of buildings and by the municipal authorities.
● Due to such a behavior in 2011 a tragedy occurred in Buritis..!
Injury / Losses for population
Tragedy at Buritis district in Belo Horizonte
Bang in edifices surrounding the condemned building panic
inhabitants.
Civil Defense alerts that glasses, doors and windows can break in a
radius of 800 m from the condemned building, in case of collapse.
Source: Do Hoje em Dia, Oct. 30th 2011, 18:14 h .
Which building will be the next to collapse....?
Localization of collapsed buildings in Buritis
Places
condemned
by Civil
Defense
Low
Vulnerability
High
References (selection)
● ARAÚJO, E.H.G. Análise multi-temporal de cenas do satélite Quickbird usando um novo
paradigma de classificação de imagens e inferências espaciais: estudo de caso Belo
Horizonte (MG), 159 p. 2006, Mestrado em Sensoriamento Remoto, INPE, São José dos
Campos, available at: http://mtc-m13.sid.inpe.br/rep-sid.inpe.br/MTCm13@80/2006/07.24.19.43
● ARAÚJO, E.H.G., KUX, H.J.H., FLORENZANO, T.G. Análise multi-temporal de dois bairros
de Belo Horizonte (MG) usando classificação orientada a objetos de imagens Quickbird e
inferências espaciais. In: BLASCHKE, T. & KUX, H. (editores) Sensoriamento Remoto e SIG
avançados, 2ª edição, Oficina de Textos Ltda., São Paulo 2007, pp. 209-226.
● ARAÚJO, E.H.G., KUX, H.J.H., FLORENZANO, T.G. Ortorretificação de imagens do satélite
Quickbird para aplicações urbanas, Revista Brasileira de Cartografia, vol. 60/2, Agosto 2008,
pp. 205-213, available at: http://www.rbc.ufrj.br/_2008/60_2_10.htm
● KUX, H.J.H. & ARAÚJO, E.H.G. Object-based image analysis using Quickbird images and
GIS data, case study Belo Horizonte (Brazil). In: T. Blaschke, S. Lang, G.J. Hay (editors)
Object-based image analysis – spatial concepts for knowledge-driven Remote Sensing
Applications, SPRINGER Verlag, Berlin/Heidelberg 2008, pp. 571-588.
Thanks for your attention !
Danke für ihre Aufmerksamkeit !
Contact:
[email protected]
Telephone: +55-12-3945-6426