Optimization of landscape spatial structure aiming at achieving carbon neutrality in desert and mining areas
Introduction
Greenhouse gas emissions have led to an increased average temperature in globally, triggering many problems such as melting ice caps, extreme weather, droughts, and rising sea levels (Abelson, 1997; Touma et al., 2021). Global warming has become an important obstacle limiting the sustainable development of human economy and society (Gurney et al., 2021). Achieving the two goals of peak carbon and carbon neutrality can effectively prevent the increases in the greenhouse effect and mitigate the risk of climate change (Salvia et al., 2021). Peak carbon means that greenhouse gas emissions stop growing and slowly decline after reaching a peak in the target year (Li D.Z. et al., 2021). Carbon neutrality refers to the adoption of tree planting and other means to enhance carbon absorptions and offset carbon emissions to reach net-zero carbon emissions and net-zero carbon footprints (Finkbeiner and Bach, 2021; Niu et al., 2021).
Vegetation is considered to be the best ever carrier of carbon capture and sequestration (Qiu et al., 2020). However, it is often severely damaged, leading to changes in surface ecology, especially in desertification and mining areas (Li J.Q. et al., 2021). Desertification is usually caused by natural factors such as climatic drought, as well as by human factors such as destruction of desert vegetation (Reynolds et al., 2007). Desertification leads to loss of moisture and nutrients from the soil, degradation of vegetation, drying of the middle and lower atmosphere, and increase wind speeds. This in turn leads to intensified desertification, further degradation of vegetation, and eventually even unrecoverable or irreplaceable, turning into a desert with no grass to grow (Xu and Ding, 2018; Zhou et al., 2015). Mining, on the other hand, is man-made and causes damage to land resources, water resources and the environment (Huang et al., 2015). Open-pit mining must strip the surface soil, destroying surface soil and vegetation, easily causing soil erosion and even land desertification (Hou et al., 2019; Li X.H. et al., 2020), while underground mining causes surface collapse and completely changes the surface topography (Liu N. et al., 2019). The consequences of these damages on soil and vegetation are usually irreversible, so it is especially important to restore and protect vegetation in areas where desertification and extensive mining activities occur simultaneously (Liao et al., 2021; Peng et al., 2020).
Restructuring and planning the landscape spatial structure is an effective way to achieve large-scale vegetation restoration (Allaire et al., 2020; Marull et al., 2020). Atmospheric carbon is mainly absorbed by vegetation on the surface and underwater, and the amount of absorption is influenced by the type, amount and growth state of the vegetation. Thus, increasing carbon uptake can be achieved by increasing the amount of vegetation or by improving the growth state of existing vegetation without changing its species. The ability to increase vegetation or improve vegetation growth is related to the moisture and nutrient content of the surrounding environment and soil, and the circulation of materials such as moisture and nutrients is influenced by the landscape spatial structure (Wang Y.C. et al., 2018). A good spatial structure promotes ecological processes, which in turn change the spatial structure and achieve a virtuous cycle. In the current situation where the spatial structure of the desert and mining coexistence area is poor, optimizing the landscape spatial structure by setting up stepped patches and building protective corridors can promote the material cycle and energy conversion process within the landscape (Ureta et al., 2021), improve the surrounding environment and soil conditions, and provide favorable conditions for the growth of vegetation, thus increasing the carbon uptake of vegetation (Jin et al., 2021).
Ecospatial networks are the result of applying complex network theory to landscape ecology as an abstract representation of the landscape spatial structure. Complex network theory can quantify the overall characteristics of a network system and determine the states of individuals in the systems and the interactions between them (Gonzalez and Sueur, 2017; Jia et al., 2020; Liu C.X. et al., 2019; Maluck and Donner, 2015). Currently, many scholars have analyzed the structure of ecological spatial networks and the state of the elements in the network using the topological properties in complex network theory, and have proposed a series of network optimization strategies to optimize the landscape spatial structure, such as adding ecological sources using the similarity search method, adding stepping stones at corridor breakpoints, and adding corridors between sources with low degree values (An et al., 2020; Li S.C. et al., 2020; Luo et al., 2021; Wang S. et al., 2021; Yu et al., 2017). However, some of these strategies have a single direction of optimization and cannot effectively optimize the landscape spatial structure. Some are based on theory and lack consideration of the state and geographical location of elements in the network, resulting in strategies that cannot be implemented in reality.
In this study, we aim to restore vegetation and improve its carbon sink function by optimizing the landscape spatial structure in areas where desertification and mining co-exist. The EFCT optimization model proposed in this study takes into account the ecological functional state, connectivity and topological properties of the elements in the landscape spatial structure, and adopts different optimization schemes according to the differences in the synergy degree between the ecological function and connectivity. The optimization scheme is effective and highly feasible to implement. A comparison of the total carbon sink and robustness of the landscape spatial structure before and after optimization is used to evaluate the optimization effectiveness of the model.
Section snippets
Study area
Ordos City is located in the southwest of Inner Mongolia Autonomous Region, China (37°35′∼40°51′N, 106°42′∼111°27′E), adjacent to Wuhai in the same province to the west, across the Yellow River from Alashan, Bayan Nur, Baotou and Hohhot in the same province to the north, and bordering Shaanxi Province and Ningxia Province to the south. It spans 400 km from east to west and about 340 km from north to south, with a total area of 86,752 km2 (Fig. 1). The study area is rich in mineral resources,
Methods
The methodological framework is divided into 3 parts, as shown in Fig. 3. The first is the identification of the landscape spatial structure, which includes the matrix as a background as well as two important elements: ecological patches and ecological corridors. Ecological patches were identified by selecting land cover types with carbon sink function, and ecological corridors were the shortest paths from patches through the minimum cumulative resistance surface, which was constructed using
Landscape spatial structure of Ordos and ecological function of its elements
Ecological patches in the study area are relatively evenly distributed, with grass-shrub patches dominating, water-wet patches forming a protective fence mainly in the north, and forest patches being small and adjacent to high-cover grass-shrub patches (Fig. 5, top left). Patch vacancies include the Kubuqi Desert, the Maowusu Sands, and urban and mining areas in the east, where carbon sequestration capacity is weak or even largely absent. The coverage of grass-shrub patches decreases from east
Discussion
Overall, this study attempts to improve the carbon sink function of desertification and mining coexistence areas by optimizing the landscape spatial structure. We proposed the EFCT optimization model for the ecologically fragile and highly differentiated characteristics of desertification and mining coexistence areas, and the results show that it is feasible to optimize the landscape spatial structure using this model, and that the carbon sink function of the optimized structure is increased by
CRediT authorship contribution statement
Hongqiong Guo: Conceptualization, Methodology, Investigation, Formal analysis, Writing – original draft, Visualization. Qiang Yu: Writing – review & editing. Yanru Pei: Investigation, Resources, Formal analysis. Ge Wang: Resources, Formal analysis. Depeng Yue: Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
This work was supported by the Youth Science Foundation of National Natural Science Foundation of China (No.42001211). And we are grateful to the graduate students and staff of the Beijing Key Laboratory of Precise Forestry, Beijing Forestry University.
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