Using the Remote Sensing Method to Simulate the Land Change in the Year 2030

Abstract

This is study is based with the support of RS-GIS technology on the land use of Samsun Center, as well as the coastal districts of Ilkadım,Atakum,Bafra Plain, through the processing and interpretation of satellite images in the summer months of 2000,2010,2020. Spatial and temporal variability properties of LU/LC were determined using MLC algorithm, controlled classification approach. The predictive values of the LU/LC change that will occur in 2030, calculated with the MLP‑ANN model based on Machine Learning algorithms and mapped with the QGIS 3.16 program. To determine the accuracy coefficient of the model, 2020 LU/LC simulation performed using the transition potential matrix of 2000 and 2010 LU/LC data. The results of simulation were compared the data of land use land cover with the 2020 to evaluate the accuracy of the simulation model. The model of MLP‑ANN provided an accuracy of 72% based on the kappa fit index. According to MLP‑ANN model 2030 results were an increase of 73.33 km² in built up areas, an increase of 56.89 km² in bare areas, and a decrease of 129.66 km² in green areas. It provided a reference basis for future Samsun urban to rural coastline LU planning and management and LU structure optimization.

Keywords

• remote sensing
• gis
• lu/lc
• machine learning
• mlp‑ann