© by PSP Volume 20 – No 5. 2011 Fresenius Environmental Bulletin 1190 A CHANGE VECTOR ANALYSIS TECHNIQUE TO MONITOR LAND-USE/LAND-COVER IN THE YILDIZ MOUNTAINS, TURKEY Murat Özyavuz 1, *, Onur Şatır 2 and Bayram Cemil Bilgili 3 1 Namık Kemal University, Faculty of Agriculture, Department of Landscape Architecture, 59030,Tekirdağ, Turkey 2 Çukurova University, Faculty of Agriculture, Department of Landscape Architecture, Adana, Turkey 3 Karatekin University, Faculty of Forestry, Department of Landscape Architecture, Çankırı, Turkey ABSTRACT This paper presents the method to detect the intensity of change and the dimension of change using Change Vector Analysis (CVA) method. The aim of this study is to con- firm the changes in the Yıldız Mountains using proof from natural ecosystems (natural deciduous forest, wetland and sand dune ecosystems) and other various ecosystems ecologically significant all over Europe and Turkey by using the CVA technique. Two Landsat TM scenes recorded in 1990 and 2009 were used to minimize change detection error introduced by seasonal differences. Images were geo- metrically and atmospherically corrected. As a result of the ratings, it is observed that agricultural areas decreased by 17.5 and Crimean pine forests by 16.4% whereas oak for- ests increased by 4.1%. Ash trees, sand dunes and bare ground reflected insignificant changes, too. KEYWORDS: CVA analysis, change detection, remote sensing, habitat monitoring. 1. INTRODUCTION Land-use/land-cover change is an important field in the global environmental change research. Inventory and monitoring of land-use/land-cover changes are indispensa- ble aspects for further understanding of change mechanism, and modeling the impact of change on the environment and associated ecosystems at different scales [1, 2]. Remote sensing is a valuable data source from which land-use/land-cover change information can be extracted efficiently [3]. Information derived from remote sensing has played an important role in natural resources manage- ment and planning by providing insight into land-cover and land use patterns and multi-temporal trends [4, 5]. The measurements of remote sensing in visible bands and ther- mal infrared can be used to extract characteristics of land- cover, derive biophysical parameters of vegetation, and support many study fields including global change [6]. * Corresponding author Landsat satellite imageries are widely used in remote sensing. The launch of Landsat-1 in 1972 initiated a new era of providing satellite data in digital format to users. Efforts to develop algorithms for image classification have continued since the 1970s. Combined with the rapid evolu- tion of Geographic Information Systems, researchers have quickly created computer-aided analysis tools to produce land-use and land-cover (LULC) maps [7]. These maps have been used to analyze the impacts of land-use change on the environment [8], improve land-use planning and natural resource management [5], and better understand ecological processes on earth [9]. Change detection is the process of identifying differences in the state of an object or phenome- non by observing it at different times [10]. Timely and accu- rate change detection of Earth surface features provides the foundation for greater understanding of the relationships and interactions between human and natural phenomena. Different change detection algorithms have their own mer- its and no single approach is optimal and applicable to all cases. In practice, different techniques are often compared to find the most useful change detection results for a spe- cific application [11]. These algorithms have a common characteristic in that they all involve selecting a threshold to determine the changed areas. Four change detection techniques, image differencing, image rationing, image regression and change vector analy- sis (CVA), are used widely in the remote sensing context. Among them, CVA is a valuable technique for land-use/ land-cover change detection [3]. Image differencing is a common change detection approach for forested and agri- cultural areas [10, 13-15]. CVA is typically applied to multi- spectral images acquired by passive sensors, by using all the spectral channels that contain useful information with respect to the considered kind of change. The CVA tech- nique is based on three steps: (1) image comparison by vector subtraction, (2) magnitude of the spectral change vectors computation (sometimes also the direction of SCVs is computed), and (3) thresholding. The first step computes the vector difference of spectral feature vectors associated with pairs of corresponding pixels in two images acquired on the same geographical area at two different times, and results in a multispectral difference image. Each pixel in this image is associated with a multidimensional vector