GRD Journals- Global Research and Development Journal for Engineering | Volume 5 | Issue 12 | November 2020 ISSN- 2455-5703
Simulation and Prediction of LULC Change Detection using Markov Chain and Geo-Spatial Analysis, A Case Study in Ningxia North China Elhamem. M. Abdalla Department of Information Engineering Ningxia University Du Fang Department of Information Engineering Ningxia University
Hazem T. Abd El-Hamid Department of Marine Environment National Institute of Oceanography and Fisheries (NIOF)
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. Keywords- LULC, Simulation, Markov, Prediction, Cellular, Remote Sensing, Change Detection
I. INTRODUCTION Remote sensing is an important science in estimating and assessing changes in LULC in different years. Remote sensing has many advantages as safe time and give accurate data about features and phenomena that human cannot explore it easily (Hong and Abd El-Hamid 2020). Modern technology of remote sensing serve many researchers in the world for assessing change in LULC and provide them with accurate temporal and spatial data (Mustafa el at.2019). Land use Land Cover (LULC) is considered as one of the most important components of the terrestrial environment system. Changes in LULC reflects of human activities on global environment (1999). The NDVI algorithm directly correlates with the green biomass, and it is properly applied to positively enhance the vegetation spectral. The climate has been getting dryer and warmer. Since 1999, the Chinese government has inaugurated middle to long term development strategy on the northwestern regions in China. Land use and land cover change research has been applied to landslides, erosion, Land planning and global change. Remote sensing (RS) and geographic information system (GIS) techniques have been utilized as powerful tools in predicting and monitoring urban growth. Environmental and simulation models as Land Change molder (LCM) and Cellular Automata (CA) - Markov model were proposed using IDRISI.The MARKOV module evaluates two images of LULC and outputs a transition probability matrix, a transition regions matrix, and a set of conditional probability images. Then, it forecasts future LULC according to comparative transition potential maps Fung T and Siu W. (2010). Based on the CA-Markov model, this study predicts the spatial patterns of land use in 2019 and 2025 based on the dynamic changes in land use patterns using remote sensing and geographic information All rights reserved by www.grdjournals.com
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