1. Introduction
There is growing use of the term “big data” to characterize the various new data formats being generated by our increasingly digitized, linked, and GNSS-enabled lifestyles. There are enormous and frequently noisy collections of observations that are becoming increasingly geographic and time referenced, and changing the character of data analysis. From a time when all data were spatial, we are moving toward a time-and-space-collected era of spatial-temporal data. GIS research is centered on spatial–temporal relationships [
1]. While the importance of closeness in geographical processes is widely established, its relevance in temporal processes is far more ambiguous. Numerous processes, however, exhibit distinct periodicities that require synchronization between the phase of the process being viewed and the time of observations, rather than just temporal proximity [
2]. Identifying temporal patterns needs an informed treatment of time series to guarantee that the phase of observation (data) corresponds to the process’s frequency. Remote sensing data are enormous, with a significant deal of variety in addition to volume, from what is captured by sensors to how data are presented to users, with variations in pixel size, sampled spectral regions, revisit rate, and so on. Due to huge volume and variety, remote sensing data are considered “remote sensing big data” [
3,
4,
5]. New analytical methodologies for large remote sensing data sets have been advocated, in part to address the pervasive challenge and need for real-time processing [
6]. Thus, remote sensing big data are “modular” and ensure that a particular pixel depicts the same geographical ground location across time, allowing for the capturing of changes in environment [
7]. Variations at the pixel level can be tied to temporal steps in order to quantify activities occurring within and between years (e.g., wildfire, harvesting, urban expansion, and other disasters), as well as seasonal processes (e.g., snowfall, foliage) [
8]. Medium- to long-term processes (e.g., climate change, soil deterioration, and chemical deposition) can also present themselves as a shifting trend in the value of a particular attribute over time.
Changes in the external environment and sociodemographic characteristics contribute to the process of landscape transformation [
9]. The fast pace of economic expansion and rising population results in substantial urbanization and land use and land cover (LULC) changes [
10]. These shifts have a major effect on the dynamics of LULC, as well as on the cycle and structure of the ecosystem [
11]. Human influence on landscapes is a major driver of regional LULC change mechanisms [
12]. Human utilization of the natural habitat, such as urbanization, agricultural land segmentation, and the loss of green space, profoundly disrupts the local ecology [
13]. Among these activities, increased urbanization is believed to be the primary driver of farmland and green space loss, which can have a significant impact on climate change and human existence. Urbanization processes and industrial development have resulted in a dramatic increase in built-up areas in peri-urban areas [
14]. Increased impervious surfaces have had an effect on the urban environment [
15]. To better understand complex LULC processes, long time series of satellite imagery are used. Short- and long-term patterns can be recognized, allowing for the investigation of periodic functions and feedback, so improving our understanding of the factors ranging from climate change to economic pressures [
16]. Studying trends in such data enables the modeling and extrapolation of future prospects or processes, as well as large-scale scenario simulations over extended time periods, with attendant issues of geographical organization in prediction [
17,
18,
19]. Globally, urbanization is expected to exceed 55%, and therefore more than the majority of the global population will reside in cities by 2050; this figure is expected to increase to about 70% by the end of 2050 [
20,
21]. However, urbanization is growing at a rate twice that of the worldwide population. In industrialized countries, the urbanization trend is more integrated, and the majority of these countries have stabilized their urbanization levels. Developing economies are either experiencing excessive or insufficient urbanization. Urban development and economic growth are inextricably linked, as socioeconomic growth serves as a catalyst for urbanization [
22]. Nearly 80% of world GDP is created in cities, and economic expansion activities encourage migration, which is one of the primary drivers of urbanization [
23]. Additionally, the world’s urban poverty is expanding, owing largely to increased migration (
www.unfpa.org/urbanization, accessed on 17 July 2021). As a result of rapid urban and industrial expansion, and population growth, LULC changes occur, which may have environmental implications such as significant erosion, air pollution, global warming, and deterioration of water sources. Additionally, rapid urbanization and socioeconomic growth have increased the strain on resources, habitats, and agricultural land dispersion, resulting in substantial environmental disturbances, food security concerns, and adverse health effects around the world [
24].
Rapid urbanization in China increased from 17.9% in 1978 to 56.7% in 2016, with an increment of 500 million residents in urban areas between 1980 and 2011. By 2050, urbanization is anticipated to rise to 70%, adding 255 million urban residents [
25]. China’s farmland area has changed unevenly during the past four decades. While the fast expansion of built-up land has benefited the economic growth, it has also raised serious problems about meeting sustainable development goals. As a result, investigating and comprehending the complex link between internal environment and external environment requires a clear understanding of the LULC change process [
26,
27]. China has experienced tremendous urbanization since its economic reforms, leading to the loss of significant agricultural areas and greenery [
28]. The Chinese government has implemented a number of land administration regulations aimed at minimizing fragmentation of agricultural land and greenery, but given that urbanization is the primary determinant of economic development, enforcing the policy without jeopardizing economic growth is difficult [
29]. Additionally, urbanization has an effect on air pressure, precipitation, thermal diffusivity, and ultraviolet output, altering the surface temperature parameters dramatically [
30]. As a result, man-made disasters tend to cause a greater degree of environmental deterioration than the adjacent LULC categories. All of these factors combine to make LULC change analysis a critical component of environmental sustainability [
31]. It is critical in the study and management of natural resources, the ecosystem, and urbanization. Numerous studies [
32,
33,
34,
35,
36,
37] have demonstrated that LULC changes, particularly urbanization, forest and resource depletion, cropland segmentation, degraded aquatic environments, increasing carbon output, and heat, all contribute significantly to the exploitation of the environment. Thus, surveillance and recognizing LULC change trends over time, particularly under the impact of urban and environmental variables and their impact on local landscapes, is crucial for the ecosystem’s maintenance and integration, as well as for environmental sustainability [
38,
39].
Techniques for studying LULC mechanisms have advanced fast in terms of spatial analysis, simulation, and changing transition potentials. Effective and reproducible simulation models can be used to examine the determinants of past, present, and future projections and their importance in different contexts. Numerous spatially distributed models, including Dinamica [
40], Markov-FLUS [
41], SLEUTH cellular automata [
42], artificial neural network-Markov chain [
43], CA-ANN [
44], and CLUE-S [
45] have been proposed by researchers for analyzing and projecting LULC. Each model is unique in its approach to tackling the complex challenges of LULC. Neural network models are a popular method for simulating LULC because they accurately reflect nonlinear spatially probabilistic land-use transformation [
46]. CA are an effective way to comprehend land-use systems and their underlying dynamics, particularly when combined with certain other tools, such as artificial neural networks. Because the CA-ANN is based on “what-if” scenarios, it is applicable to development and land change simulation studies [
47,
48,
49]. Starting with transitions and change detection mechanisms, conventional methods for determining the spatial extent of a LULC change are used. Areal imaging and historic records have been utilized in tandem with geospatial technology and remote sensing big data to define landscape patterns and produce valid scientific findings and policy initiatives that have assisted authorities and planners in advancing sustainable development, particularly in fast expanding metropolitan contexts [
50,
51,
52]. As a result, the methodologies of transitional potential modeling and anticipating potential LULC change under the effect of geographical variables aim to pinpoint the locations of changes that have occurred and may occur in the future. The majority of such models examine LULC transitions using temporal land-use data, which when combined with geographical characteristics can forecast future LULC situations [
53].
Linyi is among China’s fastest growing cities and has developed into the industrial and technological heart of Shandong province [
54]. Linyi city underwent a metamorphosis as a result of the recent regional socioeconomic and urban development, having a profound effect on the spatial structure of LULC alterations. We modeled the spatiotemporal transitioning prospects and future scenario of LULC in this work using the Modules for Land-Use Change Simulation (MOLUSCE) plugin underneath QGIS [
55,
56]. The MOLUSCE plugin is an open-source model for QGIS 2.0 and above, developed by Asia Air Survey to analyze, model, and simulate land use/cover changes (Asia Air Survey). The plugin incorporates a number of well-known algorithms, such as utility modules, cross tabulation techniques, and algorithmic modules, e.g., artificial neural networks (ANNs), multi-criteria evaluation (MCE), weights of evidence (WoE), logistic regression (LR), and Monte Carlo cellular automata (CA) models. To simulate spatiotemporal transitioning possibilities and future LULC predictions for 2030, 2040, and 2050, we used the CA-ANN technique with remotely sensed big data from 1990 to 2020 with a ten-year interval, along with spatial attributes, digital elevation model (DEM), slope, and closeness to roads. After simulating and forecasting the LULC, we supplemented it with indicators to estimate the annual rate of change in LULC classes. Taking all of these factors into consideration, we structured our research with the following specific objectives:
Analyzing the degree and change of spatiotemporal LULC trends over the previous four decades by modeling.
Forecasting future LULC using socioeconomic and environmental parameters as predictors.
Determining the magnitude of LULC change and its possible effects on the geographical pattern.
Determining the future LULC intensity scenario.
4. Discussion
Globally, and particularly in the 21st century, enormous urbanization processes have altered the natural habitat and landscape layout. Urbanization is primarily driven by physical and socioeconomic reasons such as geography, demography, and economic expansion. Socioeconomic development, however, has a higher impact on the urban expansion than overpopulation. The size and speed with which cities are expanding and fragmenting landscape patterns has raised worries about climate change, food security, and natural resource shortages.
Changes in LULC are inextricably tied to geography and development policies. Following China’s late 1970s ‘opening up’ strategy, economic reforms resulted in huge movement, immigration, and urban expansion. We examined the shift from 1990 to 2020 using spatiotemporal LULC data and physical and socioeconomic driving factors, and produced a transition probability matrix for each interval using the MOLUSCE plugin within QGIS software. Additionally, we predicted the LULC for 2030, 2040, and 2050 using the CA-ANN multilayer perceptron technique included in the MOLUSCE plugin.
Our findings suggest that during the research period, physical and socioeconomic factors had a substantial impact on landscape patterns. In general, locations with lower elevations have more rapid LULC changes, as their geography is more conducive to human activity. The greatest modifications happened in Linyi’s plain sections, particularly along the Yi River, where the slope is relatively lower than in other parts. The northern, eastern, and western regions, which are mountainous and hilly, do not suffer from rapid fragmentation.
Linyi’s vision of a trade gateway encapsulates broader objectives of market-oriented reforms and cooperation in the socioeconomic sector, education, innovation, international business, and technical advancement. Numerous studies have demonstrated that population increase and economic development are the primary factors driving built-up expansion [
66,
67]. The growing urban area has a detrimental influence on the environment, aquatic habitat, and biodiversity. According to our findings, Linyi’s LULC has undergone a remarkable transformation over the last three decades due to rapid urbanization, most notably in the rapid conversion of green space and barren land to impervious surface during the last decade. From 1990 to 2020, the impervious surface increased from 10.48 to 26.91%, and forest contributed 5.72%, green area contributed 21.24%, barren land contributed 19.45%, and water contributed 0.44%. Additionally, the future simulation results show that impervious surface will continue to increase from 2030 to 2040 with percentages of 34.36% to 34.50%, and decrease from 2040–2050 with percentages of 34.50% to 30.75%. However, the green area will continue to decrease from 2030–2050 with percentages of 11.79% to 6.97%.
Ultimately, dramatic changes in LULC, particularly urban growth and fragmentation of green space, could jeopardize natural resources, the environment, and food security. Thus, the spatiotemporal and prospective LULC simulation results will aid policymakers in analyzing the change in LULC intensity and the socioeconomic elements that influence it, as well as in promoting environmental conservation and sustainable development policies. Additionally, we modeled and predicted LULC using solely physical and socioeconomic characteristics. However, future research can incorporate development policies and climate variables.