Spatiotemporal Change Analysis and Prediction of Future Land Use and Land Cover Changes Using QGIS MOLUSCE Plugin and Remote Sensing Big Data: A Case Study of Linyi, China

Rizwan Muhammad, Wenyin Zhang, Zaheer Abbas, Feng Guo and Luc Gwiazdzinski
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Rizwan Muhammad: School of Information Science and Engineering, Linyi University, Linyi 276000, China
Wenyin Zhang: School of Information Science and Engineering, Linyi University, Linyi 276000, China
Zaheer Abbas: School of Geography, South China Normal University, Guangzhou 510631, China
Feng Guo: School of Information Science and Engineering, Linyi University, Linyi 276000, China
Luc Gwiazdzinski: Institut de Géographie Alpine (IGA), Université Grenoble Alpes, 38100 Grenoble, France

Land, 2022, vol. 11, issue 3, 1-24

Abstract: Land use and land cover (LULC) change analysis is a systematic technique that aids in the comprehension of physical and non-physical interaction with the natural habitat and the pursuit of environmental sustainability. Research regarding LULC’s spatiotemporal changing patterns and the simulation of future scenarios offers a complete view of present and future development possibilities. To simulate the spatiotemporal change transition potential and future LULC simulation, we utilized multi-temporal remotely sensed big data from 1990 to 2020 with a 10-year interval. Independent variables (DEM, slope, and distance from roads) and an integrated CA-ANN methodology within the MOLUSCE plugin of QGIS were utilized. The findings reveal that physical and socioeconomic driving variables have a substantial effect on the patterns of the terrain. In the last three decades, the study area had a significant rise in impervious surface from 10.48% to 26.91%, as well as a minor increase in water from 1.30% to 1.67%. As a result, forest cover decreased from 12.60% to 8.74%, green space decreased from 26.34% to 16.57%, and barren land decreased from 49.28% to 46.11%. Additionally, the predictions (2030–2050) support the increasing trend towards impervious surface at the expense of significant quantities of forest and green space.

Keywords: LULC change; remote sensing; big data; QGIS; impervious surface; prediction; Linyi (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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