C. R. Fichera, G. Modica, M. Pollino (2011). Presented at "Computational Science and Its Applications - ICCSA 2011 International Conference", Santander, Spain, June 20-23, 2011. A relevant issue in Remote Sensing (RS) 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. The methodology described has been applied to a case-study conducted in the area of the Province of Avellino (Southern Italy). Firstly, aerial photos and Landsat imagery have been classified to produce LULC maps for a fifty-year period (1954÷2004). Then, through a GIS approach, change detection and spatiotemporal analysis has been integrated to characterize LULC dynamics, focusing on the urban-rural gradient. This study has shown that LULC patterns and their changes are linked to both natural and social processes whose driving role has been clearly demonstrated: after the disastrous Irpinia earthquake (1980), local specific zoning laws and urban plans have significantly addressed landscape changes.

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

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.

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.

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

11. Woodland

12. Cropland

13. Grassland/PastureIn 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

24. Analyze the relationships between demographic and physical feature that contribute to the urban sprawl phenomenon;

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.

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.

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

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