HIGH RESOLUTION REMOTE SENSING AND GIS FOR URBAN ANALYSIS: CASE STUDY BURITIS DISTRICT, BELO HORIZONTE, MINAS GERAIS

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1 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 15 th - 17 th 2012

2 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.

3 Introduction Buritis district in Belo Horizonte

4 Technical data sheet: Quickbird-2 satellite Launch: Oct. 18 th 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

5 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 Quickbird- 2 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.

6 Localization of test-site Buritis 2004 image Buritis Belvedere 2002 image Quickbird-2 images used, delivered by DIGITALGLOBE.

7 Methodology III Geometric correction D-GPS points (field survey) Features at both images Collection of coordinates by a D-GPS receiver

8 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

9 Methodology II Object-oriented analysis (OBIA) Area under study Geometric correction Object-oriented classification Edited ortho-images Classified images Edited cadastre Class definition; Multi-resolution segmentation; Hierarchy; Membership rules; Evaluation Evaluation: Kappa; Stability. Information generated

10 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.

11 Methodology III Classification Definition of classes (cont.) Flare Quantization level close to 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.

12 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).

13 Methodology IV Spatial inferences Definition of area under study Information acquired Info. generated Ortho-rectification Object-oriented classification Spatial inferences Data preparation Classified images Geology Geotechnical map Geologic risks Urban expansion Landslides Irregular constructions Critical areas Injury/losses f. population Legislation

14 Methodology V Spatial inferences Data preparation Geologic map Adaptation from geologic map of Belo Horizonte; 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 Generated from DEM; Interest on intervals limited by Law. Map of slope orientation Generated from DEM; Interest on slopes oriented to SE.

15 Methodology VI Spatial inferences Injury/losses for population Geologic risks associated Permissive Zoning Critical areas Classifications LEIPUOS Slopes Areas available for construction High geologic risks Spatial Inference - AHP Injury/losses f. population Information acquired Information generated

16 Results I Geometric correction Ortho-rectification Image without correction 2004 Corrected image 2004 RMS = 14,48m RMS = 0,86m

17 Results III Classification Buritis district (2004) Red Source: Araújo (2006)

18 Results IV Classification Confusion matrix Source: Araújo (2006).

19 Results V Inferences Risk of landslides Buritis Belvedere High Medium Low Nil Source: Araújo (2006)

20 Results VI Inferences Irregular constructions

21 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..!

22 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. 30 th 2011, 18:14 h. Which building will be the next to collapse...?

23 Localization of collapsed buildings in Buritis Places condemned by Civil Defense Low Vulnerability High

24 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: 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 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 , available at: 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

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