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Bianchin
 

Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

Third International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"

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    Bianchin Bianchin Presentation Transcript

    • REMOTE SENSING AND URBAN ANALYSIS A. Bianchin, L. Bravin GEDDeS Laboratory – Planning Department University IUAV of Venice
    • Urban Analysis
      • depends on the document-source and related tools used
      • Traditionally is rooted on topographic map at local scale. Smaller scale is the result of generalization procedures. Visual analysis is the main tool.
      • RS allows working directly at regional as well at local scales. A large variety of processing techniques can be used
      • Types of urbanization extracted reflect the logic intrinsec to the nature of the algorithms and variable used.
    • Processing Techniques
      • Consider spectral and/or spatial information
      • Object properties: dimensional, geometrical, topological, radiometric, textural, relational.
      • Supervised and unsupervised classification (per pixel, spectral)
      • Segmentation (spatial-spectral information)
      • Object oriented classification (segmentation +classification)
      • Contextual classifiers ( ECHO, MRF-MAP, ecc.)
      • Texture analysis
    • Processing Techniques
      • Moreover:
      • Image fusion techniques
      • Creation of new channels (texture indeces, filtered images, ecc)
      • Post-classification techniques (majority filters or others)
      • Local filters (many approaches)
      • Morphological operators (structural and spatial information)
      • Markov Random Fields segmentation.
      • ……………… ..
    • Analist must
      • Be familiar with the range of processing techniques
      • Model the problem,i.e.dentify the variables of urban space macthing to the logic of processing tools.
      • Create criteria for the utilization of one or another technique
      • Design a procedure : a series of operations using a variety of processing techniques that acting according to diverse logic, better enable to converge on the desired outcome.
    • Case study Urban Phenomena at different scale
      • We compare the results obtained from images
      • at different resolution in the same area.
      • Analysis includes two phases:
      • • extracting urban areas
      • • defining different typologies.
      • Issues are related to:
      • 1. Implement the appropriate procedure according to the characteristics of images and urban spaces displayed
      • 2. The meaning of the results at different scales :
      • - Total built surface extracted
      • - But also by applying different analysis such as:
      • • Density function
      • • Landscape indexes
    • Summary
      • Automatic extraction of built space of three images (Landsat, Spot, Ikonos) and accuracy assessment.
        • 1.1 Comparison of built surfaces extracted at different resolution
      • 2 . Analysis techniques in order to characterize urban features and structures
        • 2.1 Built space density computed for the three images (plus 2 elaborations by buffers)
        • 2.1.1 Cross tables computing the distribution of density classes of a given image in the classes of an other image at a different resolution
        • 2.2 Landscape statistics ( PD, ED, MA) computed for 5 sub-areas in the three images.
    • EXTRACTION OF BUILT SPACE
      • Find out the appropriate procedure to extract the urban theme.
      • Images at different resolution:
      • Landsat 7 ETM+, 30m, frame 192/28, 09.08.2000
      • Spot 4 Pan, 10m, frame 062/258, 03.30.2002
      • Ikonos Pan-sharpened, 1m, 07.02.2001
    • LANDSAT
      • At 30 m of resolution, built space:
      • is composed by building and roads
      • can be differentiated from no built space
      • Procedure implemented:
      • 1. unsupervised ISODATA classification applied to all 7 bands at 30 m of resolution
      • 2. classes are labelled as built space/no-built space
      • 3. post classification through a majority filter of 3x3 pixels kernel
    • SPOT_PAN
      • At 10 m of resolution, built space:
      • is composed by building and roads
      • can be differentiated from no built space
      • In this case the strategy used for the extraction of built space requires two phases:
      • 1. generation of some new images
      • 2. classification ISODATA over new images
    • PROCEDURE
      • Creation of a new image by a 6x6 kernel low pass filter over the original image. Built areas become more homogenous.
      • 2. Creation of a second new image
      • by applying a morphological gradient. The gradient enphasizes radiometric jumps of the images.
      • erosion of the gradient image (structural element 3x3) then computation of variance (kernel 5x5). In the result: borders of fields have high value of variance, built area medium and no built areas low value.
      • 3. Unsupervised ISODATA classification over the three images: SPOT original, 1st and 2nd image.
      • 5. Classes are labelled as built space/no-built space.
      • 6. Post classification through a majority filter of 3x3 pixels.
    • IKONOS
      • At 1m of resolution :
      • in urban area can be identified different objects: building, roads, green areas…
      • built areas (buildings, roads) can be differentiated from no built areas (green areas)
      • Image has been processed as follows:
      • 34 cover classes have been identified
      • a maximum likelihood supervised classification has been implemented
      • the 34 classes are grouped into 2 super classes: built spaces (concrete covers, tar and tiles) and non built space (green area and water)
      • post classification through a majority filter of 3x3 pixel kernel
    • IMAGES Landsat7 ETM+, Spatial resolution 30 m R:4;G:3,B:2 SPOT 4 Spatial resolution 10 m PAN IKONOS Spatial resolution 1 m R:4;G:3,B:2
    • BUILT SURFACES Landsat 7 Spatial resolution 30 m SPOT 4 Spatial resolution 10 m IKONOS Spatial resolution 1 m
    • ACCURACY ASSESSMENT
      • Maps generated from Landsat, Spot and IKONOS are validated trough error matrix.
      • 400 pixel of test labelled as built or no built have been used for any image to obtain an estimation of 95% given an expected accuracy of 50% (Fitpatrick Lins 1981).
      • Pixels of test are drawn at random on the image then compared with the results of photo- interpretation considered as truth.
    • Landsat OverallAccuracy 354/400=88,5% Spot OverallAccuracy 340/400=85,00% IKONOS OverallAccuracy 363/400=90,75%
        • 1. Built area surface
        • 2. Density function
        • 3. Landscape indexes
      • Analysis of the same area at different resolutions:
        • Landsat 7 ETM+, 30m, 09/08/2000
        • Spot 4 Pan, 10m, 03/30/2002
        • IKONOS Pansharpened, 1m, 07/02/2001
        • IKONOS_5 generateded by applying a buffer of 5m
        • IKONOS_10 generated by applying a buffer of 10m to the IKONOS map
      ANALYSIS TECHNIQUES CHARACTERIZING URBAN SPACE
    • IKONOS_5 IKONOS_10
    • 1. BUILT AREA SURFACE
    • RESULTS
      • Landsat has the higher percentage of built space (51%) while it decreases a little in the Spot (43%).
      • Lowest percentage occurs in IKONOS (26%).
      • IKONOS_5 built surface is near to the one of Spot, IKONOS_10 to the LANDSAT.
      • Buffers indeed fill voids between objects and increase the area of objects.
    • 2. DENSITY FUNCTION
      • Local density is generated by a low pass filter corresponding to 1Km 2 of surface
      • Kernel of:
      • 33x33 pixels for Landsat
      • 99x99 pixels for Spot
      • 999x999 pixels for IKONOS
    • DENSITY FUNCTION
      • Landsat
      IKONOS Built space density classes over 5 images
    • RESULTS
      • Landsat gets the highest percentage of areas ranked in class A (A =15.35%), and IKONOS the lowest (A= 7.90%)
      • Behaviour over intervals of density is similar in Landsat and Spot with a maximum for D (D=30.15% for Landsat and D=29.20% for Spot)
      • Behaviour over intervals of density in IKONOS is quite different from Landsat and Spot. IKONOS shows higher values for class E (E= 35.89%)
      • With IKONOS_5 and IKONOS_10 we obtain, for classes A, B and C, percentage values similar to Spot and Landsat respectively
    • DENSITY FUNCTION - CROSS TABLES
      • Cross table analyses the distribution of density classes of a given image in the classes of another image at a different resolution
      • The cross table allows us to answer questions such as: to what density class of IKONOS do pixels of class A of Spot pass?
    • In blue : Distribution of each density class of Spot into IKONOS classes. (percentage values) In pink: Distribution of each density class of IKONOS into Spot classes (percentage values) Cross table Graphic representation of cross table
    • RESULTS
      • Most (more than 50%) of the pixels of a given class of IKONOS pass to the higher density class in Landsat and Spot.
      • Landsat and Spot density classes distributions are similar even spatially.
      • IKONOS_5 and Spot density classes distributions are similar: nearly all pixels belong to the same classes of density, and this mainly for classes A (92%) and B (63%).
      • The same remarks are valid for IKONOS_10 with regard to Landsat.
      • The results drawn from this test:
        • Density function shows great diversity with regard to the attribution of pixels to a given class of density.
        • The maximum of built area is registered for Landsat, the maximum of low density surface for IKONOS
        • A density class of IKONOS migrate towards higher density classes of LANDSAT .
        • .
        • Working at different scales (IKONOS vs Spot and Landsat) entails working on different phenomena.
      DENSITY FUNCTION - CONCLUSIONS
    • 3. LANDSCAPE INDEXES
      • Landscape ecology statististics :
      • Patch density (PD) _n°patches/100h
      • is the ratio between number of patches and total area.
      • Edge density (ED) _ ml/hectare
      • is the ratio between perimeter of all regions in the area and total area.
      • Low values
      • High values
      • Mean patch area (MA) _ km2
      landscapes with few regions landscapes with several regions
    • WORKING STEPS
      • Definition of an area including concentrated as well as diffuse urbanization ( Veneto region)
      • Definition of a set of variables that can qualify the different spatial configurations of settlements
      • Computation of these variables through analysis tools
      • Comparison of the results over different scales and different urban contexts
    • Five sub-areas
      • The work computes the landscape indexes over five sub-areas, which are the same identified in built space maps from satellite images at different resolution (the 5 maps of the previous test).
      • In the study area five different sub-areas can be identified:
        • compact core
        • suburban area
        • connection area
        • scattered settlements
        • industrial area
    • 1_compact core; 2_suburban area; 3_connection area; 4_scattered settlements; 5_ industrial area
    • Study Area Indexes for the 5 sub-areas 1-2 Concentrated urban areas: uniform and compact in Landsat and Spot; a dense set of single elements in IKONOS. 3-4 Fragmented areas are represented by numerous patches spread over the territory whose minimum size is defined by pixel size. 5 Representation of industrial area similar over scales, shaped into a set of medium-wide sized patches
      • Normalized values of indexes calculated for IKONOS are opposite to normalized values calculated for other images.
      • Meaning of landscape indexes is completely different when applied to different resolutions.
      • ED is an index of dispersion and fragmentation only at Spot and Landsat and not at IKONOS resolution.
      LANDSCAPE STATISTICS - CONCLUSION
    • CONCLUSIONS
      • Great attention must be payed in drawing considerations about urban development, working with Remote Sensing.
      • For exemple, the total surface of built space vary from the single value to the double
      • The meaning of the results of analysis techniques must be verified because, when scale changes, to the same result can correspond different meaning. For exemple Density function and ED in IKONOS
      • Working at different scale entails working on different phenomena.
      • Anyway: as long as the analysis is adressed with the same kind of image for analysing changes at two different times, indexes computed in the same way and for the same subareas can be compared.