1. Introduction
Natural disasters happening more frequently and humans engaging in more activities have caused a variety of environmental issues (e.g., global warming, local climate change, air pollution, energy shortage), expedited the evolution of landscape structure, and hindered the long-term sustainability of both nature and humanity [
1]. Green space, a vital part of the urban ecosystem [
2], is capable of sequestering carbon and releasing oxygen [
3], reducing the greenhouse effect [
4], and carrying out rainwater regulation and storage [
5,
6]. It also takes on a certain significance in alleviating heat islands and can increase property values. Zhao et al. [
7,
8] studied the relationship between land use/land cover (LULC) type and surface temperature (LST) in Shenyang City. It was discovered that different types of LULC had dramatically different temperature distributions and that greenery and water had a considerable impact on the urban heat island (UHI) effect. In addition, the urban surface heat island intensity (SUHII) varies significantly in different months, and the applicability of the local climate zone (LCZ) scheme to land surface temperature (LST) differentiation also varies with month. Zhang et al. [
9] found that the growth of green roof implementation (GRI) can be potentially motivated by property value enhancement and employment improvement, and the lack of government policies, unsound technical level, unsound economic benefit assessment, and weak personal will restrict the development of GRI. According to the classification system of green space in previous studies [
10,
11,
12], green space includes cultivated land, forest, grassland, wetland, and water area, whereas non-green space comprises artificial surfaces and bare land. The research objects cover cultivated land, forest, grassland, wetland, and water area in Harbin.
Urban landscape structure and ecological service function are dependent on the green space landscape pattern, affecting how much it functions [
13,
14,
15]. Landscape pattern analysis can determine the law, which takes on a significance for guiding the future in the chaotic landscape unit [
16]. The evolution characteristics of landscape patterns were analyzed, the spatiotemporal change rule of landscape patterns was revealed, and the structural characteristics of different landscapes were compared using the quantitative analysis method, the landscape pattern index method, and the moving window method. Dadashpoor et al. [
17] analyzed land use and land cover change (LUCC), urbanization, and landscape pattern using spatial indicators and the landscape expansion index (LEI). They used ordinary least squares (OLS) and geographically weighted regression (GWR) to analyze the relationships between the three changes. Su et al. [
18] qualitatively examined the effects of urbanization on an eco-regional scale while analyzing the changes in landscape patterns and ecosystem service value in the Hangjia-Hu region. Lv et al. [
19] examined the spatial and temporal changes in landscape patterns in the Dongjiang River Basin from 1990 to 2016 using a transfer matrix, moving window approach, and landscape pattern index.
The simulation of a future landscape pattern change in terms of LULC has been the subject of an increasing number of studies. Future scenarios can be built to model and study LUCC, the sources of change can be examined [
20], and a reference base can be offered for regional planning to help decision-makers make wise decisions about land use planning [
21,
22]. Landscape dynamic attitude can quantitatively describe the speed of regional land use change, which is convenient for the comparison of regional differences and the prediction of the future trend of land use change [
23,
24]. Studies of future LULC should consider the growth of the regional economy besides environmental preservation. A wide range of land use simulation models (e.g., cellular automata (CA) [
25], the conversion of land use and its effects modeling framework (CLUE) model [
26,
27], and the patch-generating land use simulation (PLUS) model [
28]) have been developed to more effectively balance the conflict between ecological protection and economic needs to forecast future land use change. Multi-scenario simulation is essential for future planning. Our LUCC projections in this study are based on the future land use simulation (FLUS) model [
29], a method that interactively combines bottom-up CA models with top-down system dynamics (SD) models. Since most of the other models cannot consider the effects of quantity and space-time on land use, this model considers the mutual effects of a wide variety of land-type conversion processes, eliminating the limitations of previous studies in obtaining land-type conversion rules by linear regression method [
30], and studies show that the simulation accuracy of the FLUS model is higher [
31]. Huang et al. [
32] investigated the Shenyang urban growth boundary development model in a wide range of development scenarios using the CLUE-S model through an evaluation of the feasibility of the land for development. Zhang et al. [
33] studied land use change in the Aksu region in multiple scenarios using the MOP-PLUS model. Fu et al. [
34] evaluated the three types of space in Panlong District based on information entropy and dominance, in conjunction with the ecological protection red line, the permanent basic cultivated land protection red line, and the FLUS model.
The maximization of land use benefits through multi-objective decision making has become a prevalent area of research in land use planning [
35]. Due to the rationale for creating a scenario, multi-scenario simulation frequently fails to produce the ideal development scenario. The Pareto optimal solution set, aiming to resolve the multi-objective scenario without abandoning any goals and maximizing the advantages of other goals, was proposed by economist Vilfredo Pareto [
36]. Stewart et al. [
37] optimized the spatial distribution of land resources and the ideal point approach using the evolutionary algorithm to address the multi-objective decision-making problem. Zhou et al. [
38] took future land use parameters as fuzzy variables to optimize the county land use structure under multi-objective conditions. Zhao et al. [
39] linked the MOP and FLUS model and optimized the production–life–ecological space’s spatial layout to maximize the spatially comprehensive advantages of the production–life–ecological space. In addition, these researchers used the landscape pattern index to examine and evaluate the optimization results.
The relevant studies [
40,
41,
42,
43,
44] have mostly concentrated on multi-scenario simulations for areas of South China and North China, such as Shenzhen City [
45], Chongqing City [
46], Tibet [
47], and the middle and lower reaches of the Yangtze River [
48,
49,
50], with less research on green space in Northeast China. Cai et al. [
50] analyzed the changes in landscape pattern and ESV in coastal areas of Fujian over the past 20 years using a patch generation land use simulation model, landscape pattern index, and ecological service value estimation method. Yang et al. [
51] forecasted the land use change and landscape pattern of Zhangjiajie in 2030 using GeoSOS-FLUS software and proposed an optimization strategy for the future development of Zhangjiajie City. Nie et al. [
52] built a land use simulation model using the PLUS model with coupling constraints of an ecological security model (ESP) and a multi-scenario (MS) model to develop land management policies for Anji County. Park et al. [
53] showed the different performance of landscape indicators under different urbanization conditions, and which type of landscape was most likely to be sensitive to future urbanization process. Troupin et al. [
54] simulated two scenarios of unregulated and regulated development in the Mediterranean region of Israel for the next 20 years and compared the two scenarios under different development rates.
In recent years, scholars at home and abroad have focused on the evolutionary driving mechanism [
55,
56], dynamic evolution analysis [
57], and the cooling effect of green space [
58,
59]. The amounts of green patches from remote sensing data were primarily used in the investigation of the dynamic evolution of the green spatial pattern [
12,
60]. The study techniques mainly concentrated on large data analysis [
61], landscape index analysis [
62], spatial correlation analysis [
63], and remote sensing technology [
64]. The majority of current studies are qualitative studies, most of which have undertaken extensive studies and produced conclusive findings on the dynamic evolution traits and driving mechanisms of green spatial landscape patterns and ecological function effects in the past time and space. However, only a small number of studies have been carried out to forecast the future green spatial pattern with diverse scenarios, and only a limited number of studies have quantitatively examined the optimization of green spatial structure and its comprehensive advantages.
Combining the aforementioned applied studies with associated scientific theories reveals that most scholars are only capable of analyzing and forecasting changes in land use areas. They do not, however, provide multi-scenario forecasts or benefit assessments for patterns of urban green space, and their focus is also skewed toward southern cities at the expense of Northeast China. However, the northeast is the key to high-quality development in the new era [
65]. Green space is the ecological base of a city, and the study of the spatiotemporal dynamic evolution of urban green space patterns is helpful for us to have a more intuitive understanding of the green space situated in the study area. The scenario simulation prediction of green space can assist in analyzing the cause-and-effect relationship of its changes, expanding the knowledge and experience of decision-makers in guiding rational land use and planning, and promoting the positive evolution of urban green space landscape patterns [
11]. A reasonable green spatial pattern takes on a critical significance in optimizing the urban ecological environment and improving urban biodiversity, while the direction of urban development planning directly affects the urban green spatial pattern [
66]. In addition, based on the objective conditions and combined with the current situation of the study area, this study constructed the objective function of economic benefit and ecological benefit and realized the optimization of the quantity structure of green space.
Based on the above background, this study chooses the green space of Harbin as the research object. Harbin, the capital of Heilongjiang Province, is an important city in Northeast China. It is undergoing rapid economic development and facing increasingly acute ecological and environmental problems. The emphasis on the environment is progressively deepening, and the pattern of green space in this region is constantly changing [
67]. According to the Harbin City people’s government website (
http://www.harbin.gov.cn/col/col394/index.html accessed on 1 January 2023), district national spatial planning and related policies are temporarily not issued. Accordingly, the temporal and spatial evolution characteristics of green space spatial patterns in the study area were explored using the landscape dynamic attitude model, the FLUS model, the MOP model, and the landscape pattern index. This investigation was conducted to clarify the intensity and trend of green space expansion in Harbin City from 2010 to 2020, as well as the trend of the landscape pattern of green space under different scenarios, and the land use structure under the optimal sustainable development scenario. The ideas elucidated are as follows: First, the landscape dynamic degrees of the respective components of Harbin’s green space were investigated from 2010 and 2020 (i.e., cultivated land, forest, grassland, wetland, and water area). Second, the evolution characteristics of green space coverage in status quo development scenarios, ecological protection scenarios, and economic development scenarios in 2030 were predicted using the FLUS model. Third, the evolution traits and degree of green space fragmentation at the class level and landscape level were analyzed using the method of the landscape pattern index in different scenarios. Lastly, the MOP model and LINGO 12.0 were integrated to determine the green space coverage of the optimal sustainable development scenarios. On that basis, more insights can be gained into the land use of Harbin’s green spaces, which takes on a great significance in optimizing the spatial distribution of the above-described areas, implementing sustainable urban growth in Harbin, and providing the rationale for future green space planning in Harbin.
5. Conclusions
The dynamic attitude of green space composition in Harbin from 2010 to 2020 was calculated in this study. Subsequently, the green space in 2030 was modeled using GeoSOS-FLUS software in accordance with the views of status quo development scenarios, ecological preservation scenarios, and economic development scenarios. Next, the changes in landscape pattern index in 2010, 2020, and three different scenarios were compared and analyzed. Lastly, based on multi-objective conditions, the land cover needs in the ideal sustainable development scenario were determined using the MOP model. This study has filled the gap in the research of the cities in Northeast China and provides a reference for the future development of Northeast China and similar cities. The results are summarized as follows:
From 2010 to 2020, the dynamics of the landscape’s cultivated land, forests, and grasslands experienced a slow change, whereas wetlands and water areas showed quick and beneficial changes. Moreover, the total landscape composition’s dynamic degree, which merely reaches 0.20%, has changed quite slowly.
The primary landscape type in the three 2030 scenarios is cultivated land. The status quo scenario anticipates a rise in cultivated land and forest, a decline in grassland, and little to no change in water bodies and wetlands. The increase in forest land is expected to be the largest in the ecological protection scenario, while the rise in cultivated land is expected to be the smallest. The cultivated land is expected to expand rapidly in the economic development scenario, whereas the forest area will be shrinking and the grassland will undergo the most significant drop, which is primarily manifested in the southeast of Acheng District, the northwest of Shangzhi City, and the Songhua River basin of Yilan County.
At the class level, between 2010 and 2020, the patch edges of cultivated land, forest, and grassland turned out to be regular, the patch fragmentation of wetland was intensified, and the distribution of wetland and cultivated land patches tended to be scattered. In the status quo development scenario, the fragmentation of cultivated land, forest, and grassland is anticipated to increase and disperse progressively in the status quo development scenario, while the largest patch of forest land is anticipated to shrink and the edges of the grassland and water areas are expected to become simpler. In the ecological scenario, the scattered and fragmented state of cultivated land, forest, and grassland has improved compared with that of the status quo scenario, the maximum patch index of forest land is expected to increase, and the overall conditions of water areas are expected to improve. Overall patch fragmentation and edge regularization are expected to be most severe in the economic development scenario.
At the landscape level, from 2010 through 2020, both SHDI and SHEI have increased, and the overall landscape level tends to be diversified and homogenized. The connectivity between cultivated land and forest decreased. The SHDI and SHEI are predicted to decrease significantly in each of the three future scenarios, with the economic development scenario showing the most significant decline. CONTAG is expected to decrease slightly in the status quo scenario and is expected to increase slightly in the ecological scenario and the economic scenario, and the relative increase is relatively large in the economic scenario. The 2030 divisions all show small declines, with the largest decline in the economic scenario.
Among the wide variety of development scenarios, the sustainable development scenario has the most significant ecological and economic benefits, and the total benefit is anticipated to be CNY 435,860.88 million. According to this scenario, the study area should have 22,299.88, 19,379.10, 4271.00, 920.74, 4648.54, 2453.68, and 17.24 km2 of cultivable land, forest, grassland, wetland, water, artificial surface, and bare land in 2030, respectively.