The Spatiotemporal Land use/cover Change of Adana City
- 1Bursa Technical University, Urban and Regional Planning, Bursa, Turkey
- 2Cukurova University, Architecture and Urban Planning Department, Adana, Turkey
- 3Cukurova University, Landscape Planning, Adana, Turkey
Keywords: Remote sensing, Change detection, Landscape metrics, SLEUTH model
Abstract. The major driving factors for land use planning are largely limited to socio-economic inputs that do not completely represent the spatio-temporal patterns and ecological inputs have often been neglected. Integration of remote sensing and GIS techniques enabled successful applications in characterizing the spatiotemporal trends of land use/land cover (LULC) change.
This study demonstrated an approach that combines remote sensing, landscape metrics, and LULC change analysis as a promising tool for understanding spatiotemporal patterns of Adana city. Calculation of spatial metrics was based on a categorical, patch-based representation of the landscape. Landscape metrics are conceptual framework for sustainable landscape and ecological planning. LULC change analysis was performed by considering the metric calculation. Post-classification technique was used for the metric based change detection and two different remotely sensed data set recorded in 1967 (CORONA) and 2007 (ALOS AVNIR) were used for the analysis. Additionally, a LULC projection for the year 2023 was also generated and integrated to the change analysis. SLEUTH model was utilised as a urban growth model for the future developments of study area in the scope of Cellular Automata (CA). SLEUTH model contains the main elements that characterize the core characteristics of CA: it works in a grid space of homogeneous cells, with a neighburhood of eight cells, two cell states and five transition rules that act in sequential time steps.
Most useful and relevant metrics for landscape including: percentage of landscape, patch density, edge density, largest patch index, Euclidian mean nearest neighbor distance, area weighted mean patch fractal dimension and contagion were calculated for the 1967, 2007 and 2023 LULC maps and temporal changes were determined for the study area. Most considerable change was observed on the agricultural areas. Urban sprawl is the major driving factor of the LULC change.