Land cover and land use change detection and analyses in Plovdiv, Bulgaria, between 1986 and 2000

Document Type

Conference Paper


This paper compares three remote sensing techniques to monitor land-cover change in Plovdiv County, Bulgaria (1986-2000) using multitemporal Landsat TM data. Following the fall of dictatorship in 1989, Bulgaria experienced major political and economic changes, not accompanied by the implementation of necessary instruments for managing land-use transitions. As a result, the country experienced rapid and widespread land-cover conversionsthe extent of which is still unknown. The context of the study is based on three major issues in remote sensing science: 1) Unsupervised classification algorithms are currently under-utilized in land-cover applications; 2) Many developing countries, such as Bulgaria, are data-poor and the main information source for proper land management and monitoring are non-spatial summary reports; 3) A lack of reliable ground reference data in these countries demands the development of alternative methodologies to address this paucity. Supervised and unsupervised classifications were used to map land-cover in 1986 and 2000 and postclassification comparison of map products produced maps of land- cover conversion. To assess land cover modifications, change vector analysis was then used to produce images of change direction and magnitude between the two dates based on two Kauth Thomas transformation features: brightness and greenness. Landscape pattern metrics were subsequently used to determine the status and trends in the condition of land-cover change process. The results indicate that there was a significant increase in urban areas and decrease in agriculture. The research demonstrated that the applied techniques were successful tools for monitoring land-cover change in countries with limited or inexistent spatial data, where the only available information is represented in reports provided by international organizations. Results also showed that the unsupervised classification provided better land-cover change estimation and accuracy in the study area. Overall accuracies of land cover change maps using unsupervised classification algorithm were higher (74-84%) then the accuracies achieved, when the supervised classification was applied (54-68%).

Publication Title

American Society for Photogrammetry and Remote Sensing - Annual Conference of the American Society for Photogrammetry and Remote Sensing 2006: Prospecting for Geospatial Information Integration

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