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

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...

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Bibliographic Details
Published inLand (Basel) Vol. 11; no. 3; p. 419
Main Authors Muhammad, Rizwan, Zhang, Wenyin, Abbas, Zaheer, Guo, Feng, Gwiazdzinski, Luc
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.03.2022
Subjects
Barren lands
Big Data
Climate change
Economic growth
Green infrastructure
impervious surface
Independent variables
Land cover
Land use
LULC change
Neural networks
prediction
QGIS
Remote sensing
Simulation
Social factors
Sustainable development
Trends
Asia
China
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Summary: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.
ISSN:2073-445X
2073-445X
DOI:10.3390/land11030419