1. Introduction
A large number of ecological and environmental problems have emerged, including reduced forest coverage rate [
1], degraded ecosystems [
2], increased soil erosion [
3], and loss of biodiversity [
4], following rapid economic and social development, urbanization, the increasingly prominent contradiction between man and land, and the forced change of the structure and pattern of many ecological lands [
5]. Human beings have brought about many negative ecological and environmental impacts while promoting socio-economic development [
6]. On the other hand, nowadays, human power has been regarded as the primary source of power to improve environmental systems and can be used to reduce ecological risks, improve regional natural environments, and adjust the internal structure of ecosystems with urban planning [
7], energy conservation and emission reduction [
8], and carbon peaking and neutrality [
9]. Thus, in order to manage urban ecological risk, plan for and restore the environment, and encourage the sustainable development of cities, people, and nature, it is indispensable to model and predict the changes in ecological risks under various scenarios and analyze the evolution characteristics of ecological risks in rapidly developing cities.
Landscape ERA, an extension of landscape ecology, is a method for monitoring and evaluating the negative impacts of human activities and the natural environment on ecosystem structure and function [
10,
11]. The landscape ecological risk values determine the influence of landscape spatial patterns on ecological risk processes and functions, with typical spatial heterogeneity and scale effects [
12,
13]. The scale effect is a fundamental feature of spatial heterogeneity, and the degree of heterogeneity and dynamic processes of landscape patterns differ significantly at various scales [
14], with different ecological risk evaluation results. Therefore, a suitable research scale is a prerequisite for ERA and can improve its accuracy and reliability.
At present, remote sensing data is the primary data source for environmental monitoring [
15] and management [
16], ecosystem protection [
17], landscape patterns [
18], and so on. In particular, the scale effect of ecological risk is closely related to the selection of remote sensing data. Scale effects are usually divided into spatial granularity and extent, and the appropriate scale for different study areas is not universal [
19,
20]. For the appropriate spatial granularity selection, current research has focused on the response of the landscape pattern index to its changes [
21,
22], the selection of granularity analysis methods, and the construction of models [
23,
24]. The landscape pattern index has been widely used in landscape-scale spatial analysis because it is highly condensed with information related to the landscape pattern and can better describe the structural composition and spatial configuration of different landscape elements [
23]. In the analysis of the spatial granularity effect of landscape pattern indices, scholars usually adopt the resampling method [
25] to analyze the scale effect of the study area and use the area loss evaluation [
26] and the inflection point identification methods of the landscape pattern response curve [
27] to determine the suitable granularity of the study area landscape. Although current studies have explored the effects of suitable spatial grain size on landscape pattern changes from a landscape pattern perspective, the impact of scale effects on landscape ecological risk has been ignored.
There are two methods for assessing landscape ecological risk, which are based on risk sources and sinks [
28] and landscape patterns [
29]. The method based on landscape patterns usually evaluates the ecological risk of the area directly from the perspective of the spatial pattern [
30], involving the rational selection of ERA units and the direct reflection of spatial extent in the landscape ERA. Depending on the evaluation area and the study’s purpose, assessment units can be delineated according to natural geographical units or artificially divided, mainly by directly taking watersheds [
31], administrative districts [
32], and nature reserves [
33] as the ERA units, thus ensuring the structure’s integrity and natural elements processes but, to a certain extent, ignoring spatial heterogeneity. The artificial division method mainly uses the risk cell or grid as the evaluation unit of ecological risk [
34]. Risk cells are the smallest units in the evaluation, and too small of a division extent will destroy or even change the internal structure’s integrity and the landscape’s function. At the same time, too large of a division scale will lead to a loss in information about the landscape patches and will not fully or accurately reflect the actual situation inside the landscape. Therefore, a suitable spatial extent is a basis for dividing risky plots into those that can genuinely and effectively carry out an ERA.
ERA aims to carry out risk prevention and ecological protection based on its results. Simulating ecological risks under different scenarios is beneficial for comparing and studying the effectiveness of different protection measures. The simulation and prediction of ecological risk are usually based on land use (LU). At present, the primary land use simulation models are the Markov [
35], the CA [
36], the CLUE-S [
37], and the Patch-generating Land Use Simulation (PLUS) models [
38]. Among these models, the Markov model is widely used but has limitations in simulating spatial changes in LU. The CA model has mighty spatial computing powers, but its different conversion rules will lead to different LU simulation results. The PLUS model can respond to the drivers of LU change and their contribution rates and has better LU simulation accuracy. It has been widely used in LU simulation [
33], carbon stock prediction [
39], ecosystem service value models [
40], etc.
Nanning is the political and economic center of Guangxi, China, and is known as the “Green City of China”, one of the first “National Ecological Garden Cities” in China, and a “Beautiful Mountain City” for three consecutive years. The urbanization rate in Nanning was estimated at 68.91% in 2020, so coordinating economic development with ecological protection and optimizing the spatial pattern of cities and towns is both the focus of and difficulty for Nanning’s future sustainable development. Currently, there is little research focused on evaluating and understanding the ecological risks in Nanning and the simulation of future scenarios. There is an urgent need to analyze and predict the phenomenon of changes in ecological risks in the region and to propose appropriate ecological protection strategies. Using LU data, this study determines the optimal scale for ERA based on the granularity response curve of the landscape pattern index and the relevant results of the semi-variance function of ecological risk in the landscape. This study uses the ERA model to analyze the spatial and temporal variation characteristics of ecological risk in Nanning at the optimal scale and then simulated and predicted the changes in LU patterns and ecological risks for the year 2036. Our study explored the scale effects of ecological risks from the perspectives of spatial granularity and spatial amplitude, which improved the single perspective of previous studies on scale effects and improved the accuracy of ERA to a certain extent. At the same time, based on the research results, suggestions for the ecological protection of Nanning are put forward to ensure the rational planning and layout of landscape garden cities under the rapid development of cities and towns.
4. Discussion
4.1. Analysis of the Impact of Scale Effect on Ecological Risk
Scale has always been the focus and difficulty of researching geography, ecology, and related disciplines. Scale effects run through many research fields, such as landscape patterns and processes, ecosystem structure and function, and topographic characteristics [
51]. Different disciplines have different understandings of the scale concept and content. Landscape ecological risk research divides scale into spatial granularity and extent, considering its typical spatial heterogeneity and scale dependence. Current research focuses on the granularity of the landscape pattern index response and less on the two aspects of spatial granularity and extent to determine the best measure of landscape ecological risk. Thus, this research uses the granularity of the response curve to determine the best landscape pattern index granularity, based on the fitting results of ecological risk area semi-variance functions, to choose the best spatial extent and to determine the best measure of ERA in Nanning.
The sensitivity of different landscape pattern indices to changes in spatial granularity in different regions varies. According to the coefficient of variation of landscape pattern indices to grain size changes in Nanning, the SPLIT, SHAPE_MN, and CONTAG indices have high sensitivity to the changes in spatial grain size, which is due to the changes in data raster cells caused by the changes in spatial scale. The shape and aggregation degree in the plaque changes significantly. With the spatial granularity change, the internal structure of the landscape and its pattern index change accordingly [
52]. The increase in spatial granularity changes the complexity of landscape patch boundaries, leading to the aggregation of some patches and the gradual merging of small patches with other patches, resulting in the reduction in the number and aggregation of patch boundaries and the simplification of patch shape [
53]. Different landscape pattern indices have different responses to spatial grain size changes, so it is very difficult to find an optimal spatial grain size conforming to all indexes. In practical application, we balance the sensitivity of each index to spatial grain size to determine the optimal spatial grain size. Based on the turning point and gentle interval of each landscape pattern index grain size response curve, 120 m is identified as the inflection point of most landscape pattern response curves and is in the moderate interval. Therefore, this study believes 120 m is the best landscape spatial granularity in Nanning.
The appropriate spatial extent can directly reflect the changed law of regional landscape patterns and improve the accuracy and scientific validity of ERA. Comprehensively considering the turning and extreme points of the parameters in the semi-variogram fitting model of ecological risks in Nanning city under different amplitudes, it can be seen that when the space range is 7 km, C0, and C0 + C is a significant turning point and 7 km is the minimum. This shows that 7 km is a random part of the minimum point of spatial heterogeneity, and the ecological risk of variations in the amplitude are stable. At the same time, the fitting accuracy of the ecological risk semi-variogram in Nanning was highest when the spatial amplitude was 7 km, so this study believed that 7 km was the best spatial extent in Nanning.
4.2. Analysis of Temporal and Spatial Changes in Ecological Risk
As a typical landscape garden city in China, Nanning is rich in mountains, forests, fields, lakes, and grass resources, but the rapid development of urban construction has led to many ecological contradictions and problems. There are pronounced regional differences in the results of the ERA in Nanning, which is due to the obvious differences in natural conditions and human activities in different regions. Different natural environments determine human activities, and human activities, in turn, change the regional natural environment. The woodland area of Nanning is wide, concentrated in the northern and marginal areas of the city, where the temperature is suitable, the precipitation is abundant, the vegetation coverage is high, and the ecological risk is relatively low. The seven districts under the jurisdiction of Nanning have high temperatures, low precipitation, and flat terrain. The area has a large population, a high GDP, and frequent human activities, and the construction land is distributed chiefly here, so the ecological risk is relatively high. Water resources in Hangzhou and Binyang are widely distributed, but the water quality is poor, the pollution problem is serious, and the scattered distribution of construction land in this area leads to a higher ecological risk.
The change in LU and human activities influences spatial and temporal changes in ecological risk. From 2000 to 2018, the ERA of Nanning was significantly improved, and the area of high-risk and medium-high-risk areas decreased significantly, totaling 1272.72 km
2, mainly distributed in the seven districts under the jurisdiction of Nanning, which was related to the change of construction land in this region. From 2000 to 2018, the construction land expanded significantly, reaching 336.33 km
2, and its fragmentation and separation indexes decreased significantly (
Table 6), indicating that the continuous and contiguous development of construction land enhanced the degree of aggregation, increased internal stability, and gradually reduced ecological risk. In recent years, Nanning has carried out a series of ecological construction and ecological restoration work, such as remediation of black and smelly water, soil pollution prevention and control, and rural environment treatment, which have achieved remarkable results and improved the ecological risk of Nanning to a certain extent.
4.3. Ecological Risk Prediction and Control
The rapid development of urbanization significantly affects the spatial distribution of regional land, and different LU types convert into each other, resulting in changes in ecological risks. In 2036, the spatial distribution and changing trends in ecological risks in Nanning differed under various scenarios. Compared with the ecological risks in 2018, the area of high-risk regions increased under the NDS, while the area of high-risk regions decreased under the EPS, indicating that the EPS was more conducive to reducing the ecological risks in Nanning. It is worth noting that in 2036, the NDS of Nanning’s ecological risk level improvement and deterioration is greater than the ecological protection area. This is because the NDS follows the law of LU change and does not impose artificial control factors and various LU type transformations, so the areas of improvement and deterioration fluctuate considerably.
Based on the current situation of the ecological environment of Nanning, combined with the ERA and the prediction results, and given the high-risk and medium-high-risk areas, we suggest strictly limiting the expansion of urban construction land disorder, focusing on the improvement of black and odorous water bodies and ecological restoration in the Youjiang, Yongjiang, Yujiang and other river basins, establishing a cultivated land protection system, and vigorously promoting land consolidation and high-standard basic farmland construction. For medium-risk areas, we suggest rationally developing and constructing the unused land, reducing the degree of fragmentation and separation of unused land, increasing forest and grass coverage, and strengthening the stability of ecosystem structure and function. For the low-risk and low-medium-risk areas, we suggest continuing to conserve forest resources such as Qingxiu Mountain and Daming Mountain and building an ecological barrier in the northern region of Nanning. In general, ecological risk aversion and ecological environment governance in Nanning should be controlled according to the regional characteristics. In addition, we suggest delineating the ecological protection red line and reasonably planning urban space, agricultural space, and ecological space.
4.4. Study Shortcomings and Recommended Process Improvements
Due to the complex dynamic characteristics and spatial heterogeneity in ecological processes, any scale will be affected by the interaction of social, economic, or decision-making factors at other scales, so there are often many uncertainties in the scale of ecological risks [
54]. In the ERA, the division of risk plots reduces the scale effect to a certain extent, but the risk plots segment the spatial continuity of the original natural landscape. Therefore, future research can explore the spatial and temporal variation characteristics of ecological risks at multiple scales. Secondly, when the PLUS model was used in this study to simulate and predict ecological risks, no restricted conversion areas were added due to the data acquisition situation. Therefore, in subsequent studies, natural reserves, scenic spots, and restricted development zones in Nanning should be set aside as restricted conversion areas. At the same time, in the simulation and prediction of future ecological risks, only the multi-scale effects of LU were taken into account without considering the scale effects of other driving factors, which will inevitably affect the results of ecological risk prediction. Therefore, future studies must take into account the uncertainties introduced by the drivers of varying scales when interpreting the research results. In addition, the current research on ecological risk optimization and management is relatively weak, and a scientific and mature framework system has not yet been formed.