Analysis of land use/land cover changes and driving forces during the period 1992–2022: a case study of Jinan City, China Open Access

The alteration of land use/land cover (LULC) is the most direct indication of the impact of human activities on the Earth's surface system and plays a crucial role in the process of global environmental change (Wulder et al. 2008; Mooney et al. 2013; Lawler et al. 2014). Through interactions with the biosphere and atmosphere, human activities directly or indirectly influence surface albedo, surface energy, surface roughness, and evapotranspiration, leading to significant effects on the surface radiation energy balance, biogeochemical cycles, and ecosystem services (Deng et al. 2014; Zhu & Woodcock 2014). Additionally, LULC is a critical factor in determining human responses to global change and serves as a vital input parameter for simulating global climate and biogeochemical effects. Understanding its spatio-temporal process and dynamic mechanism, and accurately measuring and simulating its changes, have become top priorities in scientific research (Jin et al. 2019; Debnath et al. 2022; Gaur & Singh 2023). The alteration of LULC patterns can result from a plethora of driving forces, including urbanization, economic growth, and natural calamities. As a result, it is imperative to study the changes in LULC at both the temporal and spatial scales to gain a comprehensive understanding of the underlying mechanisms driving these changes. The temporal analysis focuses on the examination of LULC changes over time, including the evolution of the extent and distribution of various LULC types. Contrarily, the spatial analysis examines how various LULC types are arranged and patterned, as well as how they relate to one another. In China, research on LULC has primarily focused on examining the changing patterns, driving mechanisms, and environmental impacts of land use at the regional level (Wear & Bolstad 1998; Yu & Yang 2002; Hietel et al. 2004). The selected areas typically fall into two main categories. The first category comprises ‘fragile areas’ that are characterized by vulnerable ecological environments, which are often found in unique geographic locations such as coastal areas (Zhu et al. 2022), plateaus (Wang et al. 2022), or delta regions (Zhang et al. 2020). The second category includes ‘hot spot areas’ with high levels of human activities and natural drivers, which are often found in international metropolises such as Beijing, Shanghai, or Guangzhou (Yin et al. 2011; Ding & Shi 2013; Wu et al. 2016). The diverse purposes and patterns of land use lead to changes in LULC that are influenced by various factors. Understanding the forces driving land-use change is crucial to addressing land system-related challenges. Scholars have different views regarding these factors, generally classifying them into natural geographic and socioeconomic factors (Sanderson et al. 2002; Hersperger & Bürgi 2009; Arifasihati 2016). Some researchers emphasize demographic factors such as population growth (Nagy & Lockaby 2011; Tiitu 2018; Tavares et al. 2019), population density (Xu et al. 2013; De la Luz Hernández-Flores et al. 2017), and rural-to-urban migration as key drivers of LULC change (Essien & Cyrus 2019; Islam et al. 2021). On a macro-scale, factors such as climate change and soil processes play significant roles but tend to have long-term and stable effects, accumulating over time. At the regional level, socioeconomic factors have a more pronounced impact (Stephen et al. 1993; Turner et al. 1993; Briassoulis & van der Straaten 2000; Xie et al. 2005).

Remote sensing (RS) has become a reliable monitoring tool for LULC changes because of the increasingly diverse and high-quality RS databases that meet various research needs. The availability of MODIS, Landsat, and Sentinel images has made RS imagery interpretation research more efficient and cost-effective. Academic studies show that image processing and classification methods have improved accuracy (Lu et al. 2011; Shao & Lunetta 2012; Thanh Noi & Kappas 2017; Toure et al. 2018). Numerous studies at local, national, continental, and global scales show that RS and spatial analysis in geographical information systems (GISs) enable fast and efficient LULC change detection. Some studies examined local areas (Wu et al. 2006; Rawat & Kumar 2015; Tadese et al. 2020), while others examined national issues (Sánchez-Cuervo et al. 2012; Schoeman et al. 2013). Overall, the amalgamation of RS and spatial analysis techniques in GISs, coupled with advancements in image processing and classification techniques, has simplified and made the monitoring of land-use changes more cost-effective.

Jinan, the capital of Shandong Province and one of China's 14 mega cities, holds a strategic geographical position, situated between the capital economic circle in the north, the Yangtze River Delta economic circle in the south, the Shandong Peninsula in the east, and Central China in the west. Benefiting from substantial national resources and favorable policies, Jinan has experienced rapid development in recent years, becoming a prominent example of swift urbanization and industrialization over the last three decades. In 2022, Jinan's gross domestic product (GDP) grew by 3.1% year-on-year and the economic growth rate is 0.1% higher than the national average rate, which ranks eighth among all cities.

While research on land-use change is prevalent, the current studies investigating the characteristics of land-use change in China have predominantly centered on first-tier cities such as Beijing, Shanghai, and Guangzhou (Yin et al. 2011; Ding & Shi 2013; Wu et al. 2016), as well as on a national scale (Jiyuan et al. 2002; Li et al. 2018). Previous research on the Jinan region mainly focused on various ecosystems and specific resources, such as examining the changes in groundwater supply in the Jinan spring area, exploring the atmospheric particulate pollution and water pollution due to LULC (Sun et al. 2016). However, few studies have focused on long-term (30 years) LULC changes and analyses of driving factors in the Jinan region. In addition, few scholars have taken policy factors into account in land research in the Jinan region. The policies and regulations issued by the government are among the factors that cannot be ignored due to their impact on sudden land-use changes (Feng & Wang 2021). Numerous scholars have highlighted the significance of institutional elements, particularly local government policies (Xu et al. 2013; Kontgis et al. 2014; Nassar et al. 2014), rules and regulations (Dai et al. 2018; Tiitu 2018; Feng & Wang 2022), and alternations in the ownership of land (Zhang et al. 2015; Adam 2019; Whiteside 2020), which can influence the trends of the development of cities. Thus, it is meaningful and valuable to fill the research gaps and analyze the LULC changes over the past three decades, including the several factors and policies behind these changes.

The study delivers detailed information on Jinan's LULC change characteristics between 1992 and 2022. The land resource management department can create planning strategies based on the quantitative and spatial variations of LULC. These strategies aim to detect the regional properties of LULC variations and optimize the utilization of farmland, forest, bare land, and impervious surfaces accordingly. In addition, farmland and woodland should be protected to encourage sustainable development in Jinan, lessen farmland fragmentation, and enhance the woodlands in terms of quality (refers to condition and characteristics of the woodland such as the health and diversity of tree species, the density of trees, and the age structure of the forest) and quantity (refers to the extent or size of the woodland). Additionally, finding the main LULC driving factors can spur land management organizations to develop focused land control plans. It is anticipated that the study's findings will deepen our understanding of how the changes take place and what impact they have, offering insightful information for efficient land resource management and planning. Hence, the primary goals of this paper are (i) to analyze and depict the primary LULC changes in the Jinan region over a 30-year period beginning in 1992; (ii) to investigate the impact of major driving factors, such as GDP or population, on these LULC changes; and (iii) to assess the key implications of these changes by principle component analysis (PCA), analyzing them in the context of national and local policies.

The USGS Earth Explorer, accessible through the official website earthexplorer.gov, provides the essential satellite data (Landsat-8 OLI and Landsat-5TM) required for analyzing changes in LULC in Jinan. The Landsat-5 TM data and the Landsat-8 OLI data offer a 30 m resolution. To ensure greater accuracy in determining the period, the period from April to September is generally selected as the weather is generally good and the cloud content is low. Additionally, all satellite imagery data include cloud cover below 5%, which can plummet differences between months and improve the precision of the classification process by the random forest (RF) method. In this research, seven images were downloaded from USGS, which are related to Landsat 5-TM (Type 1) and Landsat-8 OLI (Type 2). Information related to Band 1–Band 7 (B1–B7) of Type 1 is used, whereas for Type 2, B1–B11 is applied (Table 1).

Data regarding these driving factors were derived from the Jinan Municipal Bureau of Statistics (http://jntj.jinan.gov.cn/). Regional LULC change is caused by a variety of factors, according to research. LULC change is primarily impacted by the development of cities and the economical states (Dewan & Yamaguchi 2009). High correlations between the amount of developed land and arable land in a region and its economic growth have been found (Tendaupenyu et al. 2017). China has a dual urban–rural structure, and as a result, the infrastructure, employment opportunities, educational attainment, and access to healthcare in urban and rural areas differ significantly. These variations encourage rural residents to relocate to urban areas, which is a primary factor in the change in regional LULC. Therefore, we selected the three main factors that determine the population: permanent population (PP), urban population (UP), and population density (POD). As a result of the economy's growth and increasing salaries, the government invested in facilities and raised the living standards in urban districts, which raised the area available for impervious surfaces and decreased the amount of farmland. Likewise, the expansion of residential construction and infrastructure would substantially boost the Fixed Assets Investment (FAI). As a result, we settled on GDP and FAI as the key factors at the level of economic development. In addition, climate change can also affect LULC, such as the selection and scope of farmland and forest land. We selected annual temperature (AT) and annual rainfall (AR) as the driving forces at the level of climate change. The annual GDP value-added signifies the level of economical enhancement (unit: 109 yuan). Furthermore, other factors such as UP, population pressure (PP), POD, urban income (UI), and farmers income (FI) are listed in Table 2.

The Jinan City region consists of farmland (wheat, rice, sugarcane, etc.), forest, urban and rural impervious surfaces, and water bodies (rivers, canals, and ponds). Five LULC classes have been determined for this study after a review of the literature. Using the ENVI 5.3 Tool, the set of training area classes is created by selecting polygons for each class. The spectral properties and responses are used to separate these LULC classes. The region of interest tool is utilized for assessing spectral variations within defined pairs of LULC classes and demonstrated significant levels of separability between regions of interest. Table 3 displays RS image interpretation symbols.

Table 3

RS image interpretation symbols

No. .	Classification types .	Implications .	Image example .	Image features .
1	Farmland	Land on which crops are grown, including watered land, dry land, grassland, shrubs		True color images are green and divided into blocks of regular shapes by roads, generally surrounded by residential areas
2	Impervious surfaces	Includes urban sites and villages		Standard false color images show red with white dots and gray with blue, both in aggregates
3	Water bodies	Including rivers, lakes, and ponds		Dark blue on standard false color images, curved, or irregularly shaped
4	Woodland	Woodland and other forestlands		Red and irregularly shaped on standard false color images, distributed in mountainous areas
5	Bare land	Low-coverage areas, unused land		Irregular brownish-yellow color on true color images, mostly located in mountainous areas

No. .	Classification types .	Implications .	Image example .	Image features .
1	Farmland	Land on which crops are grown, including watered land, dry land, grassland, shrubs		True color images are green and divided into blocks of regular shapes by roads, generally surrounded by residential areas
2	Impervious surfaces	Includes urban sites and villages		Standard false color images show red with white dots and gray with blue, both in aggregates
3	Water bodies	Including rivers, lakes, and ponds		Dark blue on standard false color images, curved, or irregularly shaped
4	Woodland	Woodland and other forestlands		Red and irregularly shaped on standard false color images, distributed in mountainous areas
5	Bare land	Low-coverage areas, unused land		Irregular brownish-yellow color on true color images, mostly located in mountainous areas

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Due to the errors of the sensor itself and the refraction or absorption of the solar radiation entering the atmosphere, all of the factors produce different degrees of impact on the received signal of the sensor, so a series of preprocessing steps should be performed before applying the RS images of the study area, to improve the accuracy of the interpretation. In this study, we used ENVI 5.3 for image processing, which contains several steps: radiation calibration, atmospheric correction, image mosaic, and image cropping. In Figure 2, the entire LULC workflow is depicted.

Radiometric calibration, also referred to as radiometric correction, is essential for successfully converting unprocessed digital image data from satellite or aerial sensors to a typical physical scale based on known reflectance measurements acquired from objects' surfaces on the ground (Pourazar et al. 2019). This type of correction is essential for obtaining accurate quantitative assessments of imagery. The objective of atmospheric correction is to eliminate atmospheric effects from RS satellite images, allowing for the retrieval of surface reflectance. This crucial process is essential for quantitative RS, as it reduces atmospheric interference. Problems with atmospheric disturbance are resolved by using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) atmospheric correction technique, which eliminates absorption brought on by the effects of CO₂, O₂, O₃, and water vapor as well as the scattering brought on by molecules and aerosols (Chen et al. 2011). When extracting information from imagery data obtained under different conditions based primarily on spectral features, such as when detecting changes or mapping vegetation using extensive multi-temporal Landsat imagery data, atmospheric correction is particularly crucial. Image mosaic and image cropping are the processes of stitching multiple adjacent images that contain parts of the study area into one image that completely covers the study area, and then cropping the desired area according to the study boundary data.

Additionally, a single RS map cannot completely cover the whole study area, hence we stitch the two RS maps after radiometric calibration and FLAASH into one map that can completely cover the study area. And this function is achieved by the function of SeamlessMosaic in ENVI 5.3. To create the final area map that can be used for the classification that follows, the stitched image is cropped in accordance with the vector boundary of the study area.

In this paper, we conducted RF by using ENVI software (Van der Linden et al. 2015), and all parameters are set as follows: n_ Estimators = 400; Impurity Function: Gini Index; Minimum impurity threshold = 0; and Minimum number of samples = 1.

The confusion matrix is a widely accepted evaluation tool in the field of LULC classification that provides a visual representation of the agreement between predicted and actual classifications. By using the confusion matrix, it is possible to offer a quantitative measure of accuracy, providing the ability to compare results with other classifications or previous results (Congalton 1991).

To ensure correctness, the ground truth data were matched to the categorized image. To assess categorization accuracy, the user accuracy (UA) and producer accuracy (PA) were tested. The accuracy of the producer is determined by dividing the total number of correctly categorized pixels. The inaccuracy of misclassified pixels, as well as misclassification into a different class, was recorded. Furthermore, the user's accuracy is a measurement of the individual class acquired from the pixels classified in the same group (Voss & Sugumaran 2008). The confusion matrix produced for UA and PA was used to calculate the total accuracy. UA, PA, and overall accuracy (OA) are described as follows.

UA and PA are two important performance metrics that are derived from the confusion matrix in the evaluation of LULC classification accuracy. UA is the percentage of samples that are correctly categorized by the classifier relative to the actual sample class. It is calculated as the amount of correctly classified samples (i.e. true positives) divided by the total number of samples (i.e. true positives plus false negatives). PA, on the other hand, is defined as the proportion of samples in a particular class that was correctly classified by the classifier. It is calculated as the number of samples that were correctly categorized (i.e. true positives) divided by the total number of samples that were classified as that particular class (i.e. true positives plus false positives).

For the analysis of various types of land-use change and to determine its rate, we employed four indicators. These indicators include the net total change area, annual change area, annual change rate, and dynamic degree.

In the given context, s_i represents the initial area of land-use type i at the beginning of monitoring. Δs_(i,j) denotes the total net area of changes between land-use types, where j changes to or from land-use type i during the time period t. K_i is calculated for the case study over the time period t.

The interaction between human activity and the natural environment leads to land-use change, which is a crucial aspect of regional sustainable development (Van Asselen & Verburg 2013; Ge et al. 2019). Human activities have surpassed the constraints imposed by the physical geography of the planet by implementing advanced farming methods and engineering solutions, such as land consolidation, which have changed the geographic distribution and type of regional LULC (Zhou et al. 2020). Land-use change is primarily driven by human activities related to urbanization, population rise, and LULC management policies. These factors have indirect effects on LULC (Du et al. 2014). The PCA was employed to calculate the principal component using the following variables: total annual precipitation, mean temperature, GDP, total population, UP, resident population per unit area, urban people's income, farmer income, and fixed asset investment (Table 2). During the period 1992–2022, we collected each dataset on an annual basis for the purposes of pre-processing. SPSS software was utilized to conduct the PCA.

This study introduces a ‘two-step’ analytical approach. Initially, all identified factors from the general LULC analysis undergo a PCA. In the second step, the key variables are identified within the significant dimensions, and stepwise regression modeling is conducted to assess the interaction between typical LULC and these essential variables.

To examine the factors influencing LULC change, we included a range of socioeconomic and natural climate indicators. However, there might be significant correlations (informational overlap) among these various indicators. To address this issue, we initially filtered the variables using PCA.

PCA is a multivariate statistical technique used to assess the level of correlation among different variables. It replaces the initial variables with a new set of composite variables that are uncorrelated by combining multiple variables with specific correlations. The primary goal is to retain as much information as possible while reducing the dimensionality of the variable dataset. This is achieved by orthogonally transforming a set of potentially correlated variables into a new set of linearly independent variables, with the top few variables retaining the majority of the variance present in the original variables (Li et al. 2021). These transformed variables are referred to as principal components and are used in subsequent analyses.

In this equation, the coefficient l_ij is defined with the aid of two main principles: (i) z_i is not influenced by z_j and (ii) z₁ exhibits the highest variance among all linear combinations, while z_m displays the lowest variance. The new variables (i.e., z₁, z₂, and z₃) represent the first, second, and third principal components of the original variables. In practical applications, to simplify the relationship between variables, the top few principal components with the highest variances are often utilized.

The reliability of the classification results is shown by the accuracy of the classification. The OA is more than 90%, and the Kappa value is more than 85. Thus, the accuracy results show that the results of the classification are highly reliable (Table 4).

For these six time periods, their dynamic degree is ranked as 2002–2006 > 1998–2002 > 2017–2022 > 1992–1998 > 2006–2011 > 2011–2017 (Table 6). Among them, the dynamic degree is the highest in 2002–2006 (7.35%), indicating that the changes in various types of LULC are the greatest during this time period, and the degree of change in bare land is significant (−74%), which may be due to the government's vigorous construction of houses or factories on bare land during this period of accelerated urbanization. On the contrary, the period from 2011 to 2017 showed the least dynamic attitude (0.32%), indicating that there was little change in the various LULC types during this period.

During the study period, 45.63 km² of water area was converted to other uses, primarily forest, construction, and agricultural land. Conversely, 91.67 km² of water surface was created primarily from impervious surface, including a large lake, a network of channels and dams, serving various urban development and water management purposes. Consequently, despite fluctuations over time, the water surface area grew from 81.36 km² in 1992 to 127.40 km² in 2022, with a mean that has always been relatively small at approximately 100 km² (Table 5).

The evolution of bare land area within the studied region reveals a nuanced landscape transformation. In 1992, the expanse of bare land covered 1,888.05 km², signifying a substantial portion of the terrain. By 1998, this area witnessed a reduction to 1,184.74 km², hinting at shifts in land use or possible natural vegetation regrowth. Notably, in 2002, the bare land area increased to 1,645.15 km², suggesting land cover adjustments. However, a pivotal transformation occurred in 2006 when the bare land area dramatically plummeted to a mere 421.11 km². This substantial decrease may be attributed to urban development, reforestation, or alternative land utilization strategies. By 2011, the bare land area showed a slight increase, reaching 550.10 km², implying continued land-use changes. In 2017, the area experienced a modest reduction, lowering it to 422.30 km², and it continuously decreased to 91.29 km² in 2022. This pronounced reduction likely reflects substantial shifts in land use, possibly driven by urbanization, agricultural expansion, or deliberate afforestation efforts.

There was a fluctuation trend in woodland over the last 30 years (Figure 4). The area grew from 1992 to 1998, and it decreased slowly from 1998 to 2011 to achieve a stable line until 2022. Over the past 30 years, the total woodland area that was converted to other types of land was 686 km², mainly for impervious surfaces. Therefore, from accounting for the biggest part of about 37% of the total area in 1992, Jinan province had approximately 30% area in 2022 (Table 5). This decline is most likely caused by the growth of urban areas, the creation of infrastructure, and the commercial exploitation of forests. However, over the past 30 years, the primary spatial distribution of woodland in Jinan City has remained largely unchanged. The majority of woodland is concentrated in the southern parts of the Changqing, Licheng, Zhangqiu, and Laiwu Districts. Further investigation revealed that the southern region is predominantly mountainous, with forested areas covering a significant portion of the landscape.

Unlike the abovementioned types of land, farmland experienced growth as well as decline, and fluctuated over the last 30 years (Figure 4). But the area between the beginning and the end of the studied period was almost the same, changing from 2,110 to 2,104 km², respectively, with a maximum in 2006–2011 of around 2,800–2,900 km². The majority of the cultivated land in Jinan City is concentrated in the northern region, where it is interspersed with clusters of impervious surfaces. While the patch-like areas of cultivated land have gradually expanded over time, the distribution remains primarily focused in the northern area.

Between 1992 and 1998, approximately 14% of the woodland had been converted to other land types, such as impervious surfaces, farmland, and bare land (Table 7). Some parts of the bare land were transformed into impervious surfaces and woodland, while farmland was mainly converted to woodland and impervious surfaces. During this period, the area of concessions for bare land was lower than the area of transfers, amounting to approximately 700 km², while the area of concessions for other types of land was higher than the area of transfers. In particular, both farmland and impervious surface areas increased by approximately 13%.

Between 2002 and 2006, the largest gap between inward and outward transfers occurred for bare land, resulting in a decrease of approximately 12% in its area. Farmland, impervious surfaces, and water were converted into each other, and the transfer volume for these three land types was higher than the transfer out. By contrast, the transfer volume for woodland was lower than the transfer out, with most of it being converted to impervious surfaces. As a result, the area of bare land and woodland decreased during this period, with a portion of the reduction being transformed into the other three land types.

During the three time periods of 2006–2011, 2011–2017, and 2017–2022, the area of land transfer for all types was less, except for the increase of impervious surfaces in the last period. The transfer area of impervious surfaces exceeded the transfer out area, resulting in an 18% increase in their total area between 2017 and 2022. During these three time periods, the total area of impervious surfaces increased, while the areas of farmland decreased. These types of land were converted into each other, with the Sankey diagram indicating that the transfer area for various types of land during the 2006–2017 period was lower than that of the other research periods.

We used nine factors, such as GDP and population, as input data for the 30 consecutive years from 1992 to 2022, in other words, 9 * 30 sets of data were used as raw data for PCA input. Afterward, all data were standardized in SPSS, followed by the Kaiser–Meyer–Olkin (KMO) and Bartlett tests. The results of this experiment were KMO = 0.775 > 0.5 and P < 0.05. Therefore, it was concluded that these 30 sets of data have structural validity and can be used for PCA.

Using PCA, each of the nine indicators' change data could be reduced to two components (Table 8). The first principal component (F1) is a description of economic growth, reflecting primarily the changes in the GDP (X1), UI (X5), FI (X6), PP (X2), UP (X3), and POD (X4). The second principal component (F2) primarily reflects AT (X8) and AR (X9) to characterize regional climate change.

Multiple linear regression analysis (Table 9) could be used to derive the statistical model of the relationship between the changes in the land area and the primary driving factors. The significance test was passed by the driving relationship models of every kind of land. The particular models demonstrated the following.

This reflects the previous expansion of construction areas, which occurred concurrently with the swift growth of the national economy and the consistent acceleration of Jinan's population growth.

Water area exhibited a negative (reverse) correlation with economic factors (F1) and a positive correlation with climate factors (F2), with F1 having the most significant influence; however, it is not significant, nor is it significant for farmland. Therefore, we will not discuss it.

Some traditional water bodies are near urban and rural construction sites, although their impact is not expected to be substantial. Conversely, climate change-related factors, such as increased precipitation, will likely result in denser water bodies in Jinan. Farmland areas had a negative correlation with the economy (F1) and a positive correlation with climate (F2). It was influenced primarily by economic expansion. The woodland area had a positive correlation with climate change and a negative correlation with the economy (F1). This is a result of the traditional woodland being encroached upon by urban development and impervious surface expansion, but this encroachment is not a significant factor. However, the expansion of woodlands will be brought on by climate change, particularly the increase in precipitation. The area of bare land exhibited a negative correlation with both the economy (F1) and climate change (F2), with F1 being the primary influencing factor. This suggests that the development of construction or urban expansion utilized the bare land area, resulting in significant development for construction purposes.

The traditional planning concept for Jinan's ‘central urban area’ has been replaced with ‘main city’. The eastern urban area, western urban area, and old urban area are named Dongcheng, Xicheng, and Zhongcheng, respectively. As a consequence, regional development primarily occurred in centralized areas, leading to the expansion of developed land in the connecting zones. These changes illustrate the significant impact of policies on regional LULC modifications. The expansion of developed land has resulted in a significant loss of bare land, which is now the largest of the four types of land resources.

China has implemented several initiatives to preserve agricultural land to ensure food security. The most well-known program is the ‘Basic Farmland Protection System,’ which ensures China has at least 1.2 million km² of essential farmland available for agriculture and the nation's total food self-sufficiency. The alternative strategy, known as the ‘Balance of Arable Land’ policy, requires that any conversion of arable land must be offset by the acquisition of land elsewhere. These land policies have postponed the loss of arable land. In the current study, we found that between 1992 and 2022, a sizeable percentage of impervious surfaces was converted into farmland (Tables 5 and 6). This finding is in line with other researchers' findings and may be explained by the terrain. Because Jinan is a plain region, it is more practical to convert developed land to farmland. This was significantly dissimilar from other areas, such as Nairobi, Kenya, and the Bale Mountains, Ethiopia, where farmland was primarily created by clearing existing vegetation (Du et al. 2014). Unused land should be carefully utilized, considering it is a vital resource for the land reserve (Li et al. 2021). In urban growth, it is crucial to regulate the construction of new structures on farmed and unused land. Both the quantity and the quality of farmland should not be impacted, and the balance of farmed land must be properly managed.

In terms of sustainable LULC management, Jinan will assume a greater number of economic development responsibilities, thereby introducing new challenges. This study indicates that the main obstacles affecting the sustainable utilization of land resources in Jinan are the rapid increase in impermeable water surface and the reduction of woodland (Figure 4). Moreover, the economy (UP and GDP) and climate change were the primary drivers of LULC change (Tables 7 and 8). Therefore, departments tasked with the sustainable management of Jinan should prioritize the reasonable utilization of various types of impervious surfaces. Some new construction projects should first utilize existing impervious surfaces, strictly control new land, fully utilize unused land, and minimize the occupation of arable land. Furthermore, departments ought to regulate UP growth and guarantee an appropriate range of GDP increase to enhance the use efficiency of impervious surfaces and the level of sustainable LULC. The policies of the government have a significant impact on regional LULC. The implementation of sustainable LULC policies by land management departments in Jinan will promote the effective utilization and long-term growth of regional land as well as enhance the level of regional land management.

Future studies could use the data on LULC quantity and spatial distribution as the foundation for assessing Jinan's LULC sustainability. To evaluate the spatial variation in sustainable LULC in Jinan, the level of sustainable LULC will be determined. This will allow departments of land management to continue to develop their regional sustainable LULC policies.

This study employed RS imagery, GIS, and socioeconomic data to quantitatively assess LULC changes and distributions in Jinan City from 1992 to 2022 (Figure 3). The findings highlight substantial urban expansion, with impervious surfaces more than doubling while bare land decreased significantly (Table 5). Farmland predominantly clustered in the north, while woodland, located primarily in the south, decreased over time. The analysis shows water bodies undergoing conversions and expansions in the past three decades, with new surfaces such as lakes, channels, and dams primarily originating from impervious areas to aid urban development and water management. However, these changes in water areas have been relatively minor compared with the shifts in other land cover types. The main driving forces behind these changes encompass economic development, population growth, and climate variability (Table 8). Notably, qualitative assessments indicate that government policies, notably the ‘Construction of Harmonious Jinan,’ exerted a noteworthy influence on LULC changes. Consequently, this research underscores the imperative for ecologically sound policies and sustainable urban planning to mitigate adverse environmental consequences. This study employed methods such as supervised classification, PCA, and regression analysis, to construct a spatial database of evaluation factors based on seven time periods from 1992 to 2022.

This study aligns harmoniously with the existing body of literature that has diligently examined the intricate facets of LULC transformations and the underlying forces propelling them. The judicious amalgamation of RS imagery, GISs, and socioeconomic data in deciphering the nuanced dynamics and conversions of LULC in response to urbanization reflects a methodological continuity with analogous investigations conducted in various regions across China (Tian et al. 2014). In congruence with prior research, the findings of this study underscore the pivotal roles played by economic development, burgeoning population, and the strategic influence of government policies as primary drivers of LULC alterations (Wulder et al. 2008). However, it is imperative to underscore the unique contribution of this study, which distinguishes itself by offering an intricate dissection of LULC transitions specifically within Jinan City. This contribution is marked by its meticulous delineation of spatial patterns, the precise quantification of change magnitude, and the resounding emphasis on the exigency of ecologically sensitive policies and sustainable urban planning, thereby enriching the scholarly discourse in this domain.

Compared with previous land-use studies, our approach exhibits notable distinctions in several pivotal aspects. Conventional supervised classification methods are conventionally utilized for land-use change classification, relying on prior knowledge and sample training, yet they are vulnerable to the intricacies and noise inherent in RS data. By contrast, we have opted for the RF classification method, a machine learning technique renowned for its adeptness in handling large-scale RS data (Abdel-Rahman et al. 2014; Rodriguez-Galiano & Chica-Rivas 2014; Tian et al. 2014). The RF method affords us the capability to glean intricate patterns and correlations from the data, consequently facilitating a more precise identification of the driving forces behind land-use changes. Furthermore, we have applied PCA to pinpoint the principal influencing factors (Jolliffe & Cadima 2016), then used multiple linear regression to explore the mechanisms by which these influencing factors affect different types of LULC. The implementation of this amalgamated methodology augments our holistic comprehension of the mechanisms governing land-use changes. Our study shares similarities with the research conducted by Yao et al. (2021), as they also employed a similar theoretical framework. However, our study delves deeper within this framework to conduct a more in-depth analysis of the relative importance of different factors, particularly with the support of PCA analysis, to ascertain which factors hold greater influence in different time periods. This comparison highlights that our research offers a more detailed and specific understanding of the driving mechanisms behind land-use change, further enriching the application of the theoretical framework.

This study harnesses data derived from RS imagery, GISs, and socioeconomic factors. However, it is important to acknowledge inherent data limitations regarding accuracy. Notably, the selected study period spans three decades, from 1992 to 2022, potentially overlooking the intricate subtleties and fluctuations in LULC changes that could manifest over shorter time intervals. Within this chosen timeframe, certain finer-grained temporal dynamics may not have received exhaustive scrutiny. Additionally, this study falls short in conducting a comprehensive exploration of the specific environmental impacts and ecological repercussions stemming from the observed LULC alterations. A more extensive analytical approach, encompassing the assessment of effects on biodiversity, ecosystem services, and hydrological systems, could provide a more profound comprehension of the multifaceted environmental implications at hand. In the future, there is potential for advancements in RS data accuracy and resolution, allowing for a more refined observation of land-change trends over shorter time intervals. Emphasis will be placed on investigating the precise impacts of LULC changes on ecosystems. These multifaceted approaches aim to deepen our comprehension of the environmental repercussions stemming from LULC changes, thereby providing invaluable support for sustainable environmental management and policy development.

The availability of information regarding spatio-temporal land-use variation and city development status is crucial for effective land-use projection and decision-making. However, in many urban districts, such as the city of Jinan, such data are often limited. In this study, LULC maps were derived from multi-temporal Landsat images, and spatial analysis techniques and statistical analysis were employed to analyze the spatial–temporal patterns of land-use variation and the driving forces in Jinan from 1992 to 2022.

The study findings revealed diverse trends in the changes of LULC types, with significant transitions observed from bare land and woodland to other types, particularly an increase in impervious surfaces over the course of 30 years. From the present research, it can be concluded that the impervious surfaces of Jinan expanded more than two times than previously. The expansion rates were high and expanded from being concentrated only in two districts to being significantly displayed in each district. Farmland and woodland were located in the northern and southern parts, respectively, and farmland experienced fluctuations while still staying stable in 2022 compared with 1992. The factors affecting the LULC change in Jinan were also discussed. The factors comprise the social economy (GDP and population) and climate. The economy accounts for the main influencing factor in LULC changes.

Urban income, POD, and climate change are the primary drivers of the increase in developed land. Population agglomeration is substantially affected by LULC patterns. When developing LULC policies, the Department of Land Management ought to take into account rationally planning land resources to avoid excessive population concentration. When the Department develops land development tactics, it is critical for it to concentrate on areas experiencing rapid LULC changes. To ensure adequate reserve land, the Department should, if necessary, limit the rate of LULC transfer in an area of volatile LULC change. In response to national low-carbon policies and to reduce harmful gas emissions, attention should also be paid to the climate-influencing factors of urban areas. Further studies on these issues are required. For example, issues such as the urban heat island effect, carbon emissions from impermeable surfaces, and the coupling relationship between urban land change and urban development can provide a basis and reference for sustainable urban development from a more comprehensive perspective. Researchers and stakeholders can refer to or base their further analyses on this information, which may require the support of futher information about LULC. They can also adjust certain parameters of RFs to potentially improve classification accuracy. Additionally, they can apply PCA tests to other areas in combination with other regression methods.

L.T. contributed to the conceptualization, methodology, software development, visualization of this research, and wrote the original draft. Z.Z. contributed to the resources and data curation selection, and supervised this work. R.T.L.K. participated in writing the original draft and reviewed and edited the manuscript.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.