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

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.

Modeling Land Use Change in Sana’a City of Yemen with MOLUSCE

“…To clarify the difference, we will put a focused comparison between this study and some studies received in recent years: Manikandan Kamaraj and colleagues [51], with the MOLUSCE tool and the MLP-ANN model, could forecast and identify future land-use changes for 2025 and 2030 in the Bhavani basin for the two time periods, 2005 and 2015. While Muhammad et al [4] conducted a study that resulted to: physical and financial driving factors have a considerable impact on landscape designs. Within the final three decades the forecasts (2030-2050) back the expanding drift towards an impenetrable surface at the cost of necessary amounts of woodland and green space.…”

“…Within the final three decades the forecasts (2030-2050) back the expanding drift towards an impenetrable surface at the cost of necessary amounts of woodland and green space. By Modeling, Rizwan Muhammad and colleagues [4] used the tool MOLUSCE to analyze the strength and evolution of spatiotemporal LULC trends during the preceding four decades. They were speculating on the future of LULC in light of social and environmental variables and calculating the magnitude of the LULC shift and any potential effects on the regional pattern.…”

“…This study is distinct and unique compared to other literature studied in the same field example, Muhammad et al [4] conducted a study aimed: at the evolution of spatiotemporal LULC trends over the preceding four decades. The predicting LULC in the future utilizes socioeconomic and environmental factors.…”

“…Multiple theories can predict the future state of a system based only on the form that preceded it [3]. Creating a transitional probability matrix for LULC change from period 1 to two allows for predicting future change [4]. The researcher used multispectral satellite imagery and the MOLUSCE model to predict LULC change in different regions.…”

Spatial Dynamic Analysis of Changes in Land Use Applying Markov Chain and Cellular Automata

“…Based on literature searches for land use change, there are three dominant aspects: physical, social, and economic. The physical variable which can be the driving factors includes road distance, proximity to settlements [14], soil fertility [10], slope, and elevation [15]. Other aspects that also contribute are social and economic.…”