GIS and Remote Sensing to study urban-rural transformation during a fifty-year period

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GIS and Remote Sensing to study urban-rural transformation during a fifty-year period
Carmelo Riccardo Fichera, Giuseppe Modica -Mediterranea University of Reggio Calabria
Maurizio Pollino - National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA, UTMEA-TER)

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  • 1. Sixth International Workshop on "Geographical Analysis, Urban Modeling, Spatial Statistics"
    GEOG-AN-MOD 2011
    University of Cantabria, Santander - June 20th-June 23th2011
    GIS and Remote Sensing to study urban-rural transformation during a fifty-year period
    Carmelo Riccardo Fichera1, Giuseppe Modica1, Maurizio Pollino1,2
    1‘Mediterranea’ University of Reggio Calabria
    Department of Agroforestry and Environmental Sciences and Technologies (DiSTAfA)
    2ENEA - National Agency for New Technologies, Energy and Sustainable Economic Development “Earth Observations and Analyses” Lab
  • 2. Outline
    2
    • A relevant issue in Remote Sensing and GIS is related to the analysis and the characterization of Land Use/Land Cover (LULC) changes, very useful for a wide range of environmental applications and to efficiently undertake landscape planning and management policies.
    • 3. The development of the urban areas is able to transform landscapes formed by rural into urban life styles and to make functional changes, from a morphological and structural point of view.
    • 4. Historically, urban development (driven by the population increase) and agriculture are competing for the same land: cities expansion has typically take place on former agricultural use.
  • Materials and methods
    3
    • A multi-temporal image dataset has been processed and analyzed in order to identify the changing pattern of LULC during a fifty-year period (1954÷2004) and, as a result, to understand the changes within the area of interest.
    • 5. This dataset included aerial photos (1954, 1974 and 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004) and digital aerial orthophotos (1994 and 2006).
    • 6. In order to characterize the dynamics of changes, the study has integrated temporal trend analysis and GIS techniques, focusing on the urban-rural gradient.
    • 7. Aerial photos have been interpreted and satellite images have been classified: the results have been synthesized into maps of LULC changes during the period.
  • The study area: Conca di Avellino (Southern Italy)
    The study area is characterized by many small towns and villages scattered across the Province and surrounded by mountains: Massiccio del Partenio (Montidi Avella, Montevergine e Pizzod’Alvano) on NO and MontiPicentini on SE.
    4
    • Avellino was struck hard by the disastrous Irpinia earthquake of 23 November 1980.
    • 8. Consequently, to regulate the reconstruction activities, several specific acts, decrees, zoning laws and ordinance have been issued.
  • Multi-temporal Dataset: aerial photos
    5
    The aerial photos (1954, 1974 and 1990 surveys carried by “IstitutoGeograficoMilitareItaliano”, I.G.M.I.) and the digital orthophotos (1994 and 2006) used are listed into the following table:
  • 9. Multi-temporal Dataset: Landsat satellite images
    1975-07-15
    Landsat MSS
    (WRS-1, Path 203, Row 032)
    Res. 57 m
    1985-06-14
    Landsat TM
    (WRS-2, Path 189, Row 032)
    Res. 30 m
    1993-08-23
    Landsat TM
    (WRS-2, Path 189, Row 032)
    Res. 30 m
    2004-06-10
    Landsat ETM+
    (WRS-2, Path 189, Row 032)
    Res. 28.5-14.5 m
    6
    Source: Global Land Cover Facility (GLCF) http://glcf.umiacs.umd.edu
  • 10. LULC Maps
    Five different LULC maps have been produced from the classified image deriving from the classification of Landsat images and from the results of the visual interpretation of the aerial frames.
    Using the supervised approach (Maximum Likelihood Classification algorithm, MLC), four classes have been defined:
    In addition to the 2006 orthophotos, 1974 and 1990 aerial photos and 1994 orthophotos have been used as a reference material for the classification procedures. To evaluate the user’s and the producer’s accuracy, a confusion matrix was applied to the classified images.
  • 14. LULC Map
    8
  • 15. LULC Map
    9
  • 16. LULC Map
    10
  • 17. LULC Map
    11
  • 18. LULC Map
    12
  • 19. LULC distribution
    13
    +75.5%
    -29,8%
  • 20. Change detection
    14
    To determine the changes, the LULC maps have been compared by means of GIS tools.
  • 21. Change detection
    The resulting maps have allowed to make directly available the tables containing the spatial information of each class (area, perimeter, etc.) and the information about amount, location and nature of change.
    15
    Changes and dynamics of LULC during the overall period
  • 22. LULC changes (1954÷2004)
    16
    In the following transition matrix are reported the statistics of changes,
    aggregated for each LC class.
    The values (in hectares) reported along the diagonal express the area of the unchanged LC types; the other cells contain the measurement of the areas that have bore a transformation from a LC type to another class.
    The column on the right sum up the LC areas at 1954, while the last row sum up the LC areas at 2004.
    Statistical estimation of the amount of change through ‘‘from–to’’ information derived from the classifications maps
  • 23. Spatial analysis and GIS
    17
    • Characterize LULC dynamics, focusing on urban areas change patterns in relation to morphology, transportationnetwork, population growth, etc…;
    • 24. Analyze the relationships between demographic and physical feature that contribute to the urban sprawl phenomenon;
    • 25. Support land planning policies and decisions.
  • Urban expansion
    Urbanized areas represent the LULC type with the largest growth rate.
    18
    A considerable increase has occurred in the period after 1985 when reconstruction process started in consequence of the 1980 earthquake.
    In fact, this phase was characterized by significantly suburban spreading of some residential and industrial areas, leading to attrition process (gradual loss of remaining fragments).
  • 26. Rural transformations (1954÷2004)
    Cropland, the largest class at the beginning of the study period, was mainly
    19
    distributed in the lowland area in the centre of the Conca di Avellino and has changed the most because of human activities.
    A significant part of rural land transformed was converted into urban areas
  • 27. Rural transformations (1954÷2004)
    20
    Land conversion is mainly located on urban-rural fringe, whose spatio-temporal evolution has been forced by the sprawl process that interests the study area.
  • 28. Urban expansion vs Population growth
    LULC patterns and changes are also linked to social processes.
    21
    • Avellino is in a territorial continuity with other urban centers (Atripalda, Mercogliano and MonteforteIrpino, pop. over 10,000).
    • 29. Population displacement was the contributory cause of the urban expansion in the area surrounding is an “extended” urban area, with around 90,000 inhabitants.
    • 30. Urban areas expansion has been compared whit the demographic data achieved from ISTAT.
  • Urban expansion vs Transportation routes
    The urban sprawl is also a direct consequence of the courses of the A16 Motorway and the state-road S.S. 7bis (“Terra di Lavoro”).
    22
    These roads, along the SW-NE direction, connect Avellino to MonteforteIrpino, Mercogliano, on the West side and Atripalda, Manocalzati and Montefredane on the East side, underlining the relation between place of residence and place of work.
    Transportation routes are responsible for the so called “linear branch” development and represent a key catalyst of sprawl.
  • 31. Effects of Master plans
    After the disastrous Irpinia earthquake (1980), local specific zoning laws and urban plans have significantly addressed landscape changes: an important push to the urban expansion has come from the indications of Master plans.
    23
    The industrial estate of Avellino
    Residential areas
  • 32. Conclusions
    • The activities here presented are part of a wider research concerning the analysis and interpretation of urban-rural gradient at regional scale.
    • 33. The relationships between demographic and physical feature with the urban sprawl phenomenon have been analysed: these factors can outline some aspects of urbanisation.
    • 34. The results confirm the capability of multi-temporal RS data to provide accurate and cost-effective tools to understand LULC changes, through detailed spatiotemporal analysis.
    • 35. This approach, applicable to studies at various locations, can be used to improve land management policies and decisions. Moreover, it represents a valid contribution to land-use planning, especially considering the necessity to cope with matters related to the sustainable urban development.
    • 36. Finally, mapping periodically the structure of urban growth and the LULC changes via GIS spatial analysis,is useful to forecast future development (e.g. to monitor and to assess the effectiveness of planning policies).
    • 37. The analysis of urban-rural fringe areas (dynamics and evolution) represents one of the future research directions utilising, among others, VHR satellite images and very detailed digital cartography as reference data.
    24