Exploring land use/land cover change by using density analysis method in yenice - Aksoy, H.; Kaptan, S.; Varol, T.; Cetin, M.; Ozel, H. B. (Springer Science and Business Media LLC, 2022)
In this study, the changes in forest areas and land cover in the period of 2001-2011 and 2011-2021 in the Yenice Forestry Operation Directorate of Turkey were examined in time interval, category and transition levels by using the density analysis method. Landsat images of the study area for the relevant years were classified according to the supervised classification method, land cover change maps and matrices were produced. Method of Intensity Analysis dissects the transition matrices of 2001-2011 and 2011-2021 time period for six categories: Coniferous, Broad-Leaved, Mixed, Settlement, Agriculture and Water. Interval results of the analysis show that the accelerated change in the second period resulted in a decrease in mixed forest and agricultural areas (2%, 8%, respectively) and an increase in residential and pure forests (1% and 9%, respectively) in 20 years. According to the category level results, Broad-Leaved category is active in gain in the first time interval and dormant in terms of loss and gain in the second time interval. Mixed, on the other hand, is an active loser in the first timeframe and active in terms of gains in the second timeframe. According to the Transition level results, the gains of Coniferous and Mixed categories showed that they targeted each other's losses in both time intervals. While Broad-Leaved targeted Mixed losses in the first timeframe, Agriculture losses were targeted as gains in the second timeframe.

The authors thank the supported by the Republic of Turkey Ministry of Agriculture and Forestry, General Directorate of Forest Engineering and the General Directorate of Meteorology for their prompt responses to our requests.

Republic of Turkey Ministry of Agriculture and Forestry, General Directorate of Forest Engineering
Keywords: Landsat Imagery; Remote Sensing; Land Classification; Turkey; Landsat imagery; Land classification; Remote sensing; General Agricultural and Biological Sciences