Abstract
Land use and land cover change research has been applied to landslides, erosion, land planning and global change. Based on the CA-Markov model, this study predicts the spatial patterns of land use in 2025 and 2036 based on the dynamic changes in land use patterns using remote sensing and geographic information system. CA-Markov integrates the advantages of cellular automata and Markov chain analysis to predict future land use trends based on studies of land use changes in the past. Based on Landsat 5 TM images from 1992 and 2003 and Landsat 8 OLI images from 2014, this study obtained a land use classification map for each year. Then, the genetic transition probability from 1992 to 2003 was obtained by IDRISI software. Based on the CA-Markov model, a predicted land use map for 2014 was obtained, and it was validated by the actual land use results of 2014 with a Kappa index of 0.8128. Finally, the land use patterns of 2025 and 2036 in Jiangle County were determined. This study can provide suggestions and a basis for urban development planning in Jiangle County.
Highlights
Land use research programs at a global scale have become central to international climate and environmental change research since the launch of land use and land cover (LULC) change project[1]
In terms of urbanization, a large amount of agricultural / forestry land has been transformed into urban land, and mining activities / oil exploitation have occurred worldwide to meet the demands of people and can directly and obviously lead to the LUCC[6, 7]
Substantial sand excavation equipment existed in the early stage and sediment built up
Summary
Land use research programs at a global scale have become central to international climate and environmental change research since the launch of land use and land cover (LULC) change project[1]. Remote sensing (RS) and geographic information system (GIS) are essential tools in obtaining accurate and timely spatial data of land use and land cover, as well as analyzing the changes in a study area[19–21]. In the Markov model, the change in an area is summarized by a series of transition probabilities from one state to another over a specified period of time These probabilities can be subsequently used to predict the land use properties at specific future time points[45]. This study seeks to utilize remotely sensed data and GIS tools to analyze the LULCC in Jiangle County in Fujian, China for the purpose of detecting changes in the area by comparing images between two years. The model simulation process mainly produces a land use area transfer matrix and a probability transfer matrix to predict land use change trends. TRLi is the transfer-out rate, IRLi is the transfer-in rate, and CCLi is the sum of TRLi and IRLi
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