Monitoring of land use/land cover changes using GIS and CA-Markov modeling techniques: a study in Northern Turkey

The purpose of this study, covering the northern Ulus district of Turkey, was to analyze the forest and land use/land cover (LULC) changes in the past period from 2000 to 2020, and to predict the possible changes in 2030 and 2040, using remote sensing (RS) and geographic information systems (GIS) together with the CA–Markov model. The maximum likelihood classified (MLC) technique was used to produce LULC maps, using 2000 and 2010 Landsat (ETM +) and 2020 Landsat (OLI) images based on existing stand-type maps as reference. Using the historical data from the generated LULC maps, the LULC changes for 2030–2040 were predicted via the CA-Markov hybrid model. The reliability of the model was verified by overlapping the 2020 LULC map with the 2020 LULC model (predicted) map. The overall accuracy was found to be 80.90%, with a Kappa coefficient of 0.74. The total forest area (coniferous + broad-leaved + mixed forest) grew by 10,656.4 ha (15.4%) in the 2000–2020 period. Examination of the types within the Forest Class revealed that the coniferous forest area had grown by 5.9% in the period 2000–2010, whereas it had decreased by 4.7% in the period 2010–2020. The broad-leaved forest area had grown by 1.2% and 3.1%, respectively, between 2000 and 2010 and 2010 and 2020. The mixed forest area had been reduced by 7.1% in the period 2000–2010 but had grown by 1.7% in the 2010–2020 period. In the Non-Forest Class, although the water area had increased in the 2000–2020 period, agricultural land and settlement areas had decreased by 11,553.9 ha (32.3%) and 34.6 ha (0.5%), respectively. According to the 2020–2040 LULC simulation results, it was predicted that there would be 3.8% and 26.4% growth in the total forest and water surface areas and 13.9% and 5.3% reduction in the agricultural and settlement areas, respectively. Using the LULC simulation to separate the Forest Class into coniferous, broad-leaved, and mixed forest categories and subsequently examining the individual changes can be of great help to forest planners and managers in decision-making and strategy development.