Simulation and Prediction of LULC Change Detection Using Markov Chain and Geo-spatial Analysis, A Case Study in Ningxia North China

Abstract This paper describes a synthetic research on Land use Land cover changes in Ningxia North China achieved by an intensive change detection using the multi-temporal remotely sensed data Landsat 5 TM images 2005, Landsat 8 OLI images 2013 and 2019). This study is aiming at producing the fundamental land use change data, understanding the driving forces and change mechanism in order to develop a dynamic monitoring system and provide useful references for the local governments in their sustainable land use planning and making decision. Remote sensing and GIS are important tools for studying Land use / Land cover change and integrating the associated driving factors for deriving useful outputs. Geo spatial techniques Such as remote sensing and Geographic Information system are very important for studying the dynamic process of land use. Transition images and matrix ere applied using Arc GIS 10.5. with the Excel Pivot Table function. To determine the driving forces of land use change in the study area. Results showed that, the images of the study area were categorized into (Vegetation , Water , Bare land and Urban). NDVI was applied in the present study showing the vegetation cover was precisely approximately 224.61 Km2 (3.08%), 3051.11 Km2 (41.94%) and 3206.06 Km2 (44.08%) in 2005, 2013 and 2019, respectively, NDWI was applied in the present study typically showing Water bodies represent the largest area of North Ningxia. Water bodies were approximately 3613.67 Km2 (49.67%), 288.99 Km2 (4.91%) and 357.27 Km2 (4.91%) in 2005, 2013 and 2019, respectively, NDBI The reflectance of bare lands has a high frequency in band 5, the bare land have total of area 2065.78 Km2 / 28.39 % in 2005 and increased to 40.37% in 2013 to decreased again in 2019 to 35.20 %. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA-Markov) Chain Model were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 2005 and 2013, were used for calibration and optimization of the Markov algorithm, while 2019 was used for validating the predictions of CA-Markov using the ground based land cover image. After 3 validating the model, plausible future LULC changes for 2025 were predicted using the Cellular Automata (CA-Markov) Chain Model. Remote sensing is a good technique for assessing the actual Sequence in the development of any area that may be caused by human activities. Citation: Elhamem. M. Abdalla, Du Fang, Hazem T. Abd El-Hamid. "Simulation and Prediction of LULC Change Detection Using Markov Chain and Geo-spatial Analysis, A Case Study in Ningxia North China." Global Research and Development Journal For Engineering 5.12 (2020): 20 - 32.