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Journal of Geographical Sciences >

2023 , Vol. 33 >Issue 6: 1205 - 1225

DOI: https://doi.org/10.1007/s11442-023-2126-6

Research Articles

How to identify transitional geospace in mountainous areas? An approach using a transitional index from the perspective of coupled human and natural systems

  • DENG Wei , 1, 2, 3 ,
  • ZHANG Hao , 2, 3, * ,
  • ZHANG Shaoyao 1 ,
  • WANG Zhanyun 2, 3 ,
  • HU Maogui 4 ,
  • PENG Li 1
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  • 1. College of Geography and Resources, Sichuan Normal University, Chengdu 610101, China
  • 2. Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, China
  • 3. School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 4. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
*Zhang Hao, PhD Candidate, E-mail:

Deng Wei, PhD and Professor, E-mail:

Received date: 2022-05-07

  Accepted date: 2023-02-21

  Online published: 2023-06-26

Supported by

The Key Programme of National Natural Science Foundation of China(41930651)

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Abstract

The coupling of humans and nature differs in terms of distribution and intensity, thus producing a gradient of synthetic geographical environments. Within this variety of gradients, the transitional zone represents a complex space where dynamic processes and unstable conditions are observed. Based on the concepts of ecotone and transitional zone, we propose a conceptual framework for the transitional geospace of coupled human and natural systems and a quantitative identification method for the zone. Taking the Sichuan Basin as an example, this study defined the strength and direction of the coupling of the natural ecosystem and socioeconomic system and divided different types of transitional geospace. The transitional geo- space of the strong coupling type accounted for approximately 16.7% of the study area. Nine of the ten counties with the largest proportion of the type were formerly nationally poor counties in the study area. In the strong coupling type, human and nature jointly explained a high proportion of the variance in transitional stability (e.g., in Shifang city, with an unexplained proportion of 1.7%). The discovery and characterization of the transitional geospace types is crucial for facilitating more effective land use planning and sustainable balance among the population, resources, and environment.

Key words： land use change; urbanization; coupled human and nature; transition area; mountainous areas

Cite this article

DENG Wei , ZHANG Hao , ZHANG Shaoyao , WANG Zhanyun , HU Maogui , PENG Li . How to identify transitional geospace in mountainous areas? An approach using a transitional index from the perspective of coupled human and natural systems[J]. Journal of Geographical Sciences, 2023 , 33(6) : 1205 -1225 . DOI: 10.1007/s11442-023-2126-6

1 Introduction

The terrain gradient leads to the diversity and complexity of the land surface, and the gradient shapes a differentiated geographical space, especially in a mountainous country like China, where various territorial spaces reveal the regionality, differentiation and complexity of the human-nature system (Xu et al., 2020; Liu et al., 2021). There is a wide range of transitional geospace in mountainous areas, highlighting the particularity and complexity of the coupling system between humans and nature (Baker et al., 2011; Vizzari et al., 2018). Since China proposed and implemented national new-type urbanization planning and rural revitalization strategic plans, significant changes have taken place in relation to its urban-rural-natural regional system (Liu et al., 2020a). The pattern of the urban-rural transition zone is constantly evolving, rural space is rapidly changing, and the number and area of ecological protection areas are increasing (Arnaiz-Schmitz et al., 2018). These changes lead to significant spatiotemporal changes in the coupling relationship of human and natural systems in the transitional geospace. Socioecological coupled systems in the urban-rural-natural gradient have various types in transitional geospace at different scales (Liu et al., 2020a). These areas will encounter more challenging trade-offs between social development and ecological protection and require a rational layout of production-living-ecological space, an optimization of the regional development pattern, and an improvement in the capacity of regional sustainable development (Antonelli et al., 2018; Xu et al., 2019). Therefore, the research on the identification and classification of transitional geospace and the coupling relationship of humans and nature in the transitional gradient has important scientific significance and application value for the coordinated development between mountainous areas and China’s modernization and the guarantee of ecological security.
The term transitional is used to describe something that occurs or exists at a transitional stage or during a transitional period (Hanks, 1979). In terms of coupled human and natural systems, the state of the transitional geospace is neither a pure natural ecological state (PNS) nor a pure social economy state (PHS) (Deng et al., 2020). Rather, it is a composite system between the two aspects, and is a networked integrated space intertwined with ecological processes and socioeconomic processes. The attribute of transitional geospace is between the PNS and PHS, with spatiotemporal indeterminacy. The theories and methods of previous studies, such as ecological ecotones (Gosz, 1993; Laurance et al., 2001; Williams et al., 2009; Vizzari et al., 2018; Dawson et al., 2019), agro-pastoral ecotones (Qiao et al., 2018; Feng, 2019), urban-rural fringe (Wehrwein, 1942; Pryor, 1968; Yu et al., 2009; Yu et al., 2010; Yang et al., 2018), and oasis desert ecotones (Kutuzov, 2018; Chen et al., 2021; Li et al., 2021; Sun et al., 2021), have important reference significance for the transitional geospace of coupled human and natural systems (hereinafter referred to as the TG) (Lloyd et al., 2000; Strayer et al., 2003; Shea et al., 2021). The theory of spatial ecology produces a new ecological-geographic paradigm (Peng et al., 2016), which indicates that such a compound ecosystem has spatial heterogeneity, structural grading, and multiple functions with local randomness and scale (spatial or temporal) dependence (Xiao et al., 1997; Chen et al., 2015; Shea et al., 2021). Meanwhile, components, structures, and functions inevitably change with the passage of time under the comprehensive influences of various human and natural variables (Strayer et al., 2003).
Thus, objectively determining the location of the TG is an essential step in researching its structure, characteristics, and function. However, there are no classic methods for identifying TG locations, and the most prominent approaches are either nonobjective or challenging to popularize in a complex TG (Hufkens et al., 2009a; Dale et al., 2014; Shea et al., 2021). Subjective TG detection approaches qualitatively estimate a discrete boundary line between systems (natural, social or economic) or define the edge of the transitional zone at a random buffer distance beyond a line (Dauber et al., 2004; Urbina-Cardona et al., 2006; Blackwood et al., 2013). This method will eventually cause an inconsistent reconstruction of the transitional zone, and it is difficult to objectively quantify its characteristics. Objective methods for identifying the location of a TG are usually based on a certain property of the transitional zone: the turnover property or the co-occurrence property. The turnover property is derived from the definition of a transitional zone, in which the vegetation structure or species composition (also known as turnover) changes relatively sharply (Di Castri et al., 1992; Dale et al., 2014). For example, for sigmoid wave curve fitting, the maximum and minimum of the second derivative can be used to determine the margins of the transitional zone (Hufkens et al., 2008; Foster et al., 2015). This method is effective when it is not difficult to detect and measure the differences between systems (Hufkens et al., 2009b). However, when the situation is reversed, methods of using the co-occurrence property may be more suitable. Fuzzy logic detection, displaying zones with a high degree of mixing of different coverage types, is based on this property (Arnot et al., 2004; Hill et al., 2007). With the filling of basic data and the improvement of GIS and RS technologies, the identification methods of transitional zones have developed toward quantitative analysis, such as wavelets (Camarero et al., 2006), the moving split window method (Yu et al., 2015), assessment model construction (Liu et al., 2011), and spatial clustering (Li et al., 2018). Furthermore, tipping point detection is a common way to determine the transitional zone and fringes because of its accuracy and objectivity in identifying sharp changes (Peng et al., 2018). However, these transitional zones only delimitated blurry spatial boundaries with specific climatic, topographical, ecological, economic or agricultural characteristics. Transitional research simply lacks a conceptual foundation to implement a delineation for transitional regions covering various geospatial types in a concise but informative way.
Therefore, seeking a scientific approach to identify a comprehensive transitional zone of a region, that is the TG, is meaningful. This information can not only enrich the research of transitional zones but also provide a new vision for national spatial planning under the background of new urbanization and ecological civilization construction in China. This study was conducted to (1) propose the conceptual framework of the TG; (2) construct an index system for the identification of the TG and improve the traditional coupling model; (3) identify and characterize the TG of the Sichuan Basin, Southwest China; and (4) explore the differentiated coupling mechanism of humans and nature based on the type of TG. The findings will provide new ideas for addressing regional economic and eco-environmental coordination, land management, and future spatial planning by identifying and understanding this special comprehensive transitional zone.

2 Materials and methods

2.1 Study area

The Sichuan Basin is located in the transitional zone between the Tibetan Plateau and Yangtze Plain in Southwest China, where the Chengdu-Chongqing economic circle, a national-level urban agglomeration, is situated here. The basin, located between 101°50′E- 10°12′E and 27°40′N-33°03′N (Figure 1), covering an area of approximately 274.15 thousand km2, including 22 cities and 170 counties. The Sichuan Basin, one of the most populous areas in China and the world, is inhabited by most of the population of Sichuan and Chongqing, with a population of nearly 100 million. In addition, the Chengdu-Chongqing economic circle is an urbanized area with the highest development level and greatest development potential in western China. There is clustered cultivated land at the bottom of the basin, which is the largest rice- and rapeseed-producing area in China. The surrounding mountains are between 1000 m and 3000 m above sea level, accounting for more than 38.5% of the total area of the Sichuan Basin, and the hills and plains in the middle of the basin range from 250 m to 750 m above sea level, accounting for approximately 61%. From west to east, the bottom of the Sichuan Basin is divided into the Chengdu Plain, Sichuan Hilly Basin, and parallel ridge-valley of east Sichuan. The basin has a subtropical monsoon humid climate. The spatial distribution of temperature shows a trend of being high in the east and low in the west, high in the south and low in the north, and high in the basin bottom and low at the edge. The mean annual temperature is 17.1℃, and the annual precipitation is 1133.7 mm.
Figure 1 Location of the study area (Sichuan Basin)

2.2 Data sources

Relevant datasets used in the study involve topography, climate, land use, vegetation cover, soil, socioeconomic data, etc. (Table 1). Land-use data are available from the Resource and Environment Science and Data Center. According to the field data checking, the overall accuracy of classification interpreted from LandsatTM remote-sensing images approaches 98%. Climate data, including precipitation and temperature, were acquired from the China Meteorological Data Network and were collected from approximately 2400 meteorological observatories. The Anusplin interpolation model based on the consideration of terrain effects for meteorological data outperformed the general interpolation method, and the interpolation process was conducted using observation data by using a smooth spline function. The climate data for the study area were obtained by clipping the generated grid data of all of China. Population density data were derived from WorldPop Country Datasets, which produce data by using the unconstrained top-down modeling method. GDP data included 174 county-level administrative regions in Sichuan Basin. Soil data were obtained from the Harmonized World Soil Database and included soil organic matter, silt, clay and sand content. Except for the DEM and soil data, these data were from 2020. All data were processed into 500 m resolution raster data.
Table 1 The description and sources of data used in the study
Data types Purpose Sources
Land use data Soil erosion, land function and density of settlement patch Resource and Environment Science and Data Center (www.resdc.cn). (30-m resolution)
Digital elevation model Topographic position index and soil erosion Shuttle Radar Topography Mission dataset provided digital elevation model data. (90-m resolution).
Normalized difference vegetation index (NDVI) data Vegetation condition MOD13Q1 NDVI data was obtained from NASA’s (United States National Aeronautics and Space Administration) Earth Science Data Systems (available online: https://www.earthdata.nasa.gov/eosdis). (250-m resolution)
Nighttime light imagery data Density of GDP Harvard Dataverse (500-m resolution, https://doi.org/10.7910/DVN/YGIVCD).
GDP Density of GDP GDP data were derived from China Statistical Yearbooks (county-level), and they were allocated to each grid (500-m resolution) to implement downscaling.
Climate data Humidity index, soil erosion, and accumulative temperature The precipitation and temperature data were downloaded from the China Meteorological Data Network and interpolated at a 500 m × 500 m resolution. MOD16A2 Evapotranspiration data was provided by the NASA’s Earth Science Data Systems. (500-m resolution)
Soil properties Soil erosion Harmonized World Soil Database (https://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases). (1-km resolution)
Protected area data Natural component index The World Database on Protected Areas (vector data, https://developers.google.cn/earth-engine/datasets/catalog).
Road data Traffic accessibility Open Street Map (vector data, https://www.openstreetmap.org).

2.3 The conceptual framework for the TG

At present, challenging tasks confronted by the study of the TG involve the construction of general conceptual frameworks and index systems of range identification; additionally, there is difficulty regarding the integration of multiple disciplines and theories for the study based on coupled human and natural systems (CHANS) (Deng et al., 2020). Therefore, the TG focuses on the interaction between socioeconomic systems and natural systems, with the expression of geographical process uncertainty and the definition of regional spatial scope at its core (Figure 2). This framework proposes a systematic methodology to identify coupled natural and human systems, quantify their coupling degree, and recognize the TG. It involves linking submodels to provide coupled models capable of representing natural (e.g., hydrological, climate, soil) and human (e.g., economic, population) subsystems and, most importantly, the interactions between the two. It is the degree of coupling and interaction between natural ecosystems and social economic systems that ultimately determines the scope of the TG.
Figure 2 Conceptual framework for the TG
Ecological environmental elements can be used to manifest natural ecological properties. The geographic environment is an integral system composed of topography, air, soil, water, organisms, and other natural ecological factors that bear the sustainable development of human society. Natural ecological elements are the fundamental components of multiscale geographical patterns, among which natural site conditions, such as geology and landforms, directly dictate the type and spatial configuration of surface natural and human landscapes. Therefore, the strength of regional natural properties depends on the instability of the geological environment and the unevenness of the terrain.
The topographic position index is used to analyze the distribution characteristics of urban expansion (Zhang et al., 2022), which affects the spatial distribution and density of built-up land and cropland (Feng et al., 2008; Zhang et al., 2019). A higher topographic position index indicates a higher ecological risk and a lower intensity of human activities, thus showing strong natural components (Xue et al., 2019). Unsuitable climatic conditions, such as low temperature and little precipitation, restrict the survival and development of human society. Moreover, the quantity of water resources in a region directly affects agricultural irrigation, soil quality and vegetation growth and plays an important role in maintaining the regional basic living environment (it also determines the existence of strong natural components) (Li et al., 2012). In plain and hilly areas with a high population density, regional vegetation degradation is related to strong human activities, and the impact of human activities is often greater than the impact of natural factors (Chen et al., 2023). In Sichuan Basin, a low vegetation cover indicates strong human activities, and a high vegetation cover indicates a high natural component index. Studies have indicated that terrain, climate, and vegetation cover are important factors affecting the geographical distribution of unpopulated areas (salient natural ecological properties) (Li et al., 2021). In addition, soil erosion is often used as an indicator to measure the suitability of land, and high soil erosion will limit human activities. Soil erosion is more likely to occur in mountainous and hilly areas with a large slope and in areas that are far from the city. Therefore, this indicator was used to indicate natural components (Xiao et al., 2022).
Social and economic elements can be used to characterize human attributes. Population and gross domestic product (GDP) are human factor indicators reflecting the intensity of human activities, urban construction, and urbanization level, and these factors can better embody the main characteristics of human elements (Peng et al., 2019). Traffic accessibility is another very important indicator that supports the exchange and flow of personnel, material, and information between cities. It forms a network connection structure within the region, affects the intensity and scope of human activities and is an indispensable factor used to estimate the intensity of human attributes (Jin et al., 2010). Human changes in land use have resulted in the loss and abandonment of areas with strong natural properties and have enhanced the human attributes of the regions. Due to agricultural intensification, current land use changes cause serious shifts in human-natural interactions, while cropland is considered to have the largest range of land use worldwide, with a profound impact on biodiversity and ecosystem services (Plieninger et al., 2016).

2.4 Model to identify the TG in mountainous areas

According to the CHANS framework for TG identification in mountainous areas, the transitional nature of space implies the coexistence of natural properties and human attributes is as complex as possible. Human attributes have a negative correlation with natural properties; that is, the stronger the former is, the weaker the latter is. Based on the above definition, the study identifies the TG, taking the generalized Sichuan Basin as the target. Considering key issues when identifying the TG and actual characteristics of the study area (Changnon et al., 2002; Danz et al., 2011; Chen, 2018; Zhang, 2019; Liu et al., 2021), the TG identification index system is established in Table 2.
Table 2 Index system for identifying TG in mountainous areas
Target layer Criteria layer Index layer Sub-index layer
Transitional index of CHANS Natural component index Landform Topographic position index
Climate Humidity index
Accumulative temperature (≥10 ℃)
Soil Soil erosion
Vegetation Normalized difference vegetation index
Human component index Population Density of settlement patch
Land use Land function index
Economy Density of GDP
Traffic location Traffic accessibility

2.4.1 Overlying natural ecosystem components

The natural component index can be divided into five aspects: landform, climate, soil, and vegetation. First, counties with an average altitude greater than 3000 m near the Sichuan Basin were excluded because of their overly strong ecological properties. When evaluating terrains and landforms, to encompass slope and altitude information with a single indicator, the terrain position index was used to represent topography. The humidity index was selected from the climate factors to represent the natural property, which is expressed by the ratio of precipitation to evapotranspiration. Accumulative temperature reflects the heat demand of biological growth and development and is an important index used to measure regional heat resources. Soil erosion was calculated according to the Revised Universal Soil Loss Equation (Keller et al., 2015; Yao et al., 2016). Based on the soil texture, meteorological station, DEM and vegetation coverage datasets, we calculated several parameters required by the equation, involving the rainfall erosivity factor R, soil erosion factor K, terrain factor LS, vegetation coverage factor C, and soil conservation measure P. Vegetation cover can reflect the intensity of human activities and was expressed by the NDVI obtained by the maximum value composite method. The analytic hierarchy process (AHP) was used to determine the weight of each index. The weights for the topographic position index, humidity index, cumulative temperature, soil erosion, and NDVI were 0.3247, 0.0962, 0.2320, 0.1653, and 0.1818, respectively.

2.4.2 Overlying socioeconomic component

In this study, the human component index was summarized into four factors: population, land use, economy, and traffic location. GDP density and population density were used to represent the spatial characteristics of the economy and population, respectively. The productive function index of land function reflects the widely artificial influence of land use (Fan et al., 2021). The traffic accessibility model used to characterize traffic location was composed of the traffic network density, traffic trunk line proximity and location relationship of the central city (Jin et al., 2010). The density of the traffic network was calculated by the kernel density module of ArcGIS software. The next step was to determine the quantity and radiation range of important or large traffic facilities in an area and obtain the index of traffic trunk line proximity in each area, which could be achieved by establishing a buffer zone around these traffic facilities. The weights were determined according to the technology-economy characteristics and the radiation range of traffic facilities. By virtue of the shortest distance command in the network analysis module of ArcMap software, the time cost for each county to reach the central city (prefecture-level city) along the existing highway was calculated. The county-level city center was abstracted as a node in the traffic network, the road traffic speed was designed in combination with the terrain type, and GDP was taken as the weight factor of the economic strength of the central city. The human component index was the weighted sum of standardized indices of population (0.1662), land use (0.2346), economy (0.1567), and traffic location (0.4425), and the weights were acquired according to the AHP approach.

2.4.3 Measuring the interaction of nature and humans

The coupling model originated from physics, and the coupling degree was used to reflect the degree of correlation between the systems or between elements within the system. The coordination degree is the representation of the overall level between them, as well as the relevance (Zhang et al., 2016). The coupling degree (CPD) and coordination degree (CDD) between the natural system and human system can be calculated by the following formulas:
$CPD=2\sqrt{\frac{HI\cdot NI}{{{(HI+NI)}^{2}}}}$
$CDD=\sqrt{2\sqrt{\frac{HI\cdot NI}{{{(HI+NI)}^{2}}}}\cdot (HI\cdot {{\omega }_{H}}+NI\cdot {{\omega }_{N}})}$
where HI refers to the human component index characterized by social economic elements. NI represents the natural component index characterized by natural ecological elements. The factors wH and wN denote the weights of the indices.
The trade-off between the natural component and human component was calculated as follows. The benefit for a single object (NI or HI) was defined as the relative deviation from the mean for a given observation (Peng et al., 2019). The magnitude of trade-off (TO) was calculated as follows:
$TO\text{=}\left( \frac{HI-H{{I}_{\min }}}{H{{I}_{\max }}-H{{I}_{\min }}}-\frac{NI-N{{I}_{\min }}}{N{{I}_{\max }}-N{{I}_{\min }}} \right)$
where HI is the observed value of NI/HI and the Max (Maximum) and the Min (Minimum) of NI/HI are calculated from the entire population of NI or HI. An explanation of the three models is shown in Figure 3.
Figure 3 Illustration and example of coordination and trade-offs between two benefits. The order for the coupling, coordination, and trade-off of dots A, B and C is |A| > |B| = |C|. Note that B = -C can be obtained only in the trade-off model process.
To integrate the capabilities of the three models, we proposed a new model with the capability of identifying the coupling degree, the comprehensive level, and its orientation. We used the transitional index (TI) to represent the final coupling result, and we extended it to n dimensions. The specific formula of TI is as follows:
$TI=TO\cdot CI=\left( \frac{HI-H{{I}_{\min }}}{H{{I}_{\max }}-H{{I}_{\min }}}-\frac{NI-N{{I}_{\min }}}{N{{I}_{\max }}-N{{I}_{\min }}} \right)\cdot (HI\cdot {{w}_{H}}+NI\cdot {{w}_{N}})$
$TI\left( n \right)=\sqrt{\sum\limits_{i=1}^{n}{{{({{x}_{i}}-{{y}_{i}})}^{2}}}}\cdot \sum\limits_{i=1}^{n}{({{a}_{i}}\cdot {{\omega }_{i}})}$
where the factors wH and wN denote the weights of the indices. Given that the human component index is just as important to the whole CHANS as is the natural component index, the study identified the TG through equal weighting, adding up the two individual sub-TIs. That is, wH = wN = 1/2. HI refers to the human component index characterized by the social economic elements. NI represents the natural component index characterized by the natural ecological elements.
The variation partitioning analysis in R (R Core Development Team, Vienna, Austria) was used to quantify the contributions (disturbance to transition stability) of natural components and human components to the dynamics of the transitional index (Zhou et al., 2021).

3 Results

3.1 Social economic components and natural ecosystem components

Figure 4a depicts the natural component index constituted by the natural ecological elements such as landforms and climate conditions. Overall, the more adverse the geographical environment and the more severe the climatic conditions were, the higher the natural component index was. The natural component index was generally at low and extremely low levels in the whole region, whereas the two categories together accounted for 61.12%. The low index category was most extensive and comprised 32.73% of the whole study area, whereas the areas with an extremely high index accounted for only approximately 7.91%. Through spatial pattern analysis, the natural component index of the western area was found to be more than that in the east and that in middle and high mountainous areas was more than that in the plain and hills. Specifically, Maoxian and Wenchuan counties of Aba prefecture, Baoxing and Shimian counties of Ya’an city, and Luding county of Ganzi prefecture in the west were located in areas with higher mountains and steeper slopes, resulting in higher natural ecological properties. More than 60% of the area in these counties was the area with an extremely high natural component index. Furthermore, the alluvial flat area, typically represented by Qingyang and Wuhou districts of Chengdu city and Yuzhong district of Chongqing city, which was in the Chengdu Plain and parallel ridge-valley of eastern Sichuan, respectively, was completely dominated by extremely low natural properties due to its terrain conditions, such as the relatively low altitude and relatively flat slope.
Figure 4 The natural component index (a) and human component index (b) across the Sichuan Basin
Figure 4b shows the grade distribution of the human component (social economic component) index in the Sichuan Basin. This grade was obtained using a weighted sum of the population, economy, land use, and traffic location. The greater this sum is, the stronger the human attributes will be. Overall, the human component index of the study area was generally at an extremely high level, with the area of a category increasing with the value of the index. The highest index category (34.09%) comprised the largest proportion of the total area, and the lowest 11.80%) accounted for the smallest proportion of the total area. For the spatial distribution, the human component index of the central region was higher than that of the edge of the basin, and that of plains and hills was higher than that of mountainous areas, showing an overall trend of an increasing index from typical natural ecological areas (such as high-altitude areas) to urban agglomeration areas. Corresponding to the distribution of extremely high natural component indices, Maoxian, Wenchuan, Baoxing, Shimian, and Luding counties were dominated by extremely low human components, which accounted for more than 60% of their own areas. The 12 counties in the northeastern part of the study area, especially Chengkou, Wuxi, Wushan and Fengjie counties of Chongqing city, were covered by a large area with an extremely low human component. However, there were no areas with extremely high natural component indices in these counties, and the distribution of the extremely low human component index did not correspond to that of the extremely high natural index.

3.2 Coupled human and natural systems

By integrating the natural component index and human component index, the integrated coupling degree and coordination degree of the natural ecosystem and socioeconomic system in the mountainous areas of the Sichuan Basin were calculated (Figure 5). Both a high human component index and a high natural component index corresponded to a low coupling degree of natural and human systems (Figure 5a). Moreover, the coupling degree value was high in the marginal areas, emerging with an almost ring-like spatial pattern. The result of the coordination degree was a reflection of the mutual promotion or reciprocal inhibition of natural and human systems with a spatial distribution similar to that of the coupling degree (Figure 5b). This result differentiated the regions of mutual promotion from those of mutual inhibition, both of which belong to the strong coupling region. The area with the strong coordination degree was much smaller than that with the strong coupling degree.
Figure 5 The coupling degree (a) and coordination degree (b) of human and natural components across the Sichuan Basin

3.3 Geospatial transitional zoning

By improving the coupling model and the trade-off method, the transitional index comprehensively characterizing the coupling degree, coordination degree and action direction of the natural ecosystem and socioeconomic system in mountainous areas of the Sichuan Basin was acquired. It was concluded that the higher the transitional index was, the stronger the human attributes were, and the lower the natural component was, producing a weaker coupling degree of the human-nature system. Based on transitional index values ranging from high to low, this study graded the coupling degree of human and natural components in the Sichuan Basin into five groups using the natural break method, namely, dominated by HC (HC means human component), high HC-low NC (NC means natural component), strong coupling, high NC-low HC, and dominated by NC. To accurately reflect the geospatial human and natural attributes, the grid mosaic tool was used to superimpose the protected area (absolute natural property) and built-up area (absolute human attribute) on the original layer. In addition, areas with an altitude greater than 3500 m were considered areas with absolute natural attributes. The final corrective spatial pattern of the coupling degree of human and natural systems in mountainous areas is revealed in Figure 6a. Based on the composition of the CHANS conceptual framework and considering the geospatial structural, functional, and composition features of the Sichuan Basin, the coupling degree presented the following overall characteristics: the areas dominated by natural components in the west were larger than those in the east, the central part of the basin had areas dominated by human components, and the strongly coupled area had a ring-like distribution between areas dominated by natural components and human components. The high HC-low NC areas, strong coupling areas, and high NC-low HC areas, namely, the TG, were the transition regions between the high human component area and the high natural component area.
Figure 6 The transitional index across the Sichuan Basin (a), proportion of each type (b), and 10 counties with a large area of a strong coupling region (c). Human components and natural components are represented by HC and NC, respectively.
Zone statistics were used to sum the proportion of the area as well as the county occupied by five types, ordered from high to low proportions as follows: areas dominated by HC, high HC-low NC areas, strongly coupled areas, high NC-low HC areas, and areas dominated by NC (Figure 6b). Almost all categories had a higher percentage of area than county, except for the category dominated by HC. The proportion of counties dominated by HC was 14.76% higher than that of areas dominated by HC. In addition, the top ten counties/districts with the largest proportion of strong coupling area in the area of the county were listed (Figure 6c). The strong coupling area of Gulin county accounted for 84.6% of the county area, covering an area of 2670.5 km2. In contrast, Gongxian county had the lowest proportion, accounting for 54.8% and with an area of 879.2 km2. Yucheng District (872.6 km2, 57.0%) and Lizhou District (1392.1 km2, 76.9%) were the main urban areas of Ya’an city and Guangyuan city, respectively, with the strong coupling area being small but accounting for a high proportion.
To examine the classification of TG for typical counties across multiple transitional index classifications, gradient transects of the transitional index of eight typical directions were constructed. The included angle between the gradient transects was 45°, which was exactly eight transects. Based on the trend (the difference between the two change rates) of the transitional index, the TG could be divided into the following three categories, ordered from large to small difference value: sharp transition (above 6/1 thousand km), rapid transition (between 2 and 3/1 thousand km), and slow transition (below 1.5/1 thousand km) (Figure 7 and Table 3). The transition of the gradient transect in the northwest was the sharpest. The gradient transect included Shifang city (county-level city), and the difference in the change rate was 6.656/1 thousand km. This area represented the transitional zone between the Qinghai Tibet Plateau and Chengdu Plain. The rapid transition of gradient transects in the east (the difference was 2.622/1 thousand km) and west (the difference was 2.12/1 thousand km) crossed Tianquan and Fengdu counties, respectively. Tianquan was in the transitional zone between the Hengduan Mountains and Chengdu Plain, and Fengdu county was located in the transitional zone between the parallel ridge valleys of the east Sichuan and Wushan Mountains. The southeast direction had a slow transition (Jiangjin District) from the Sichuan Hilly Basin to the Dalou Mountains, with the lowest difference in the change rate of 0.77.
Figure 7 Gradient transects of transitional index of typical directions. The change rate is represented by CR.
Table 3 Transitional index changes and transitional types of typical counties
Gradient
transect		 CR1 CR2 Difference Classification Geomorphic transition Typical county
a 0.066 -6.59 6.656 Sharp transition Tibetan Plateau-Chengdu Plain Shifang
b -0.31 -1.57 1.26 Slow transition Qinba Mountains-Sichuan Hilly Basin Jiangyou
c -0.25 -1.28 1.03 Slow transition Qinba Mountains-Sichuan Hilly Basin Tongjiang
d 0.022 -2.6 2.622 Rapid transition Hengduan Mountains-Chengdu Plain Tianquan
e 0.1 -2.02 2.12 Rapid transition Paralleled ridge-valley of east Sichuan- Wushan Mountains Fengdu
f -0.15 -1.31 1.16 Slow transition Hengduan Mountains-Chengdu Plain Muchuan
g -0.015 -1.16 1.145 Slow transition Sichuan Hilly Basin-Yunnan-Guizhou Plateau Changning
h -0.15 -0.92 0.77 Slow transition Sichuan Hilly Basin-Dalou Mountains Jiangjin

4 Discussion

4.1 Coupled mechanism of the natural ecosystem and socioeconomic systems

Central to the regions of coupled human and natural systems, humans and nature are organized into interacting subsystems at multiple spatial and temporal scales, which show the characteristics of spatial transition from natural ecosystems to socio-economic systems (Chen et al., 2015). The TG provides a powerful framework for understanding the highly dynamic interactions between ecological and societal changes (Ostrom, 2009; Gatzweiler, 2014; Arnaiz-Schmitz et al., 2018). The interaction between the social economy and natural ecology in the study area had an obvious geographical transition (Figure 4). The coupled system was complex (Figures 5 and 6), which was directly related to the spatial correlation of the terrain niche and the central location (Zhang et al., 2022). Variation partitioning analysis indicated that human components explained a greater proportion (64.6%) of the variance in the transitional index than did the natural components (32.5%) in the high NC areas (Figure 8a). This result was because in the high NC area, namely, the natural ecological area, the geographical environment difference was small, and human activities had a strong interference on the transition stability. Likewise, in high HC areas, such as urban areas, some abrupt natural attributes can destroy the stable state of the transition of strong human attributes. Natural components (72.5%) dominated the variance in the transitional index and were followed by human components (11.2%) (Figure 8c). Variation partitioning analysis also indicated that natural components explained just a slightly greater portion of the variance (60.9%) in the transitional index than did human components (48.7%) (Figure 8b). Human components and natural components together explained 89.7% of the transitional index variation, and the unexplained proportion of only 10.3% in the strong coupling areas was below that in the high NC areas (35.3%) and in the high HC areas (21.3%). The landscape and internal configuration of the TG represented complex interfaces characterized by particular socioeconomic and ecological processes involving their relationships with their surroundings (Vizzari et al., 2018). As a result, different evolving environmental and socioeconomic interactions were observed. This result clearly shows that the strength of the complex interaction of human and natural systems presents a spatial gradient and difference and confirms the basic characteristics and laws of the spatiotemporal evolution of the human-nature relationship in the TG (Liu et al., 2020b). The mechanism of their interaction is directly affected by the development, planning and functional positioning of territorial space, as well as by relevant policies.
Figure 8 Relative contributions (%) of natural components and human components to the transitional index in the areas dominated by NC (a), TG (b), and HC (c)
Four counties with typical transition types did not exhibit the same trait (Figure 9). As a strong economic county in Sichuan, Shifang has a balance between economic development and ecological protection. Shifang city had a type of sharp transition covering all types of transitional geospace from areas dominated by HC distributed in plain cities to areas dominated by NC distributed in mountains. This makes the positioning of land use and function in the region clear, and the areas where human and natural systems are interlaced and ambiguous were rare. The joint explanation from humans and nature was as high as 98.3%, which also confirmed this classification result (Figures 9d). The joint explanation in Changning and Fengdu counties also exceeded 90% (Figures 9a and 9c). There is less conflict between production-living-ecological space in these regions, and the coupling between humans and nature is strong. Tianquan county has a large amount of ecological space and high ecological components, and it has won many honors related to ecological protection. There are many conflicts between the natural systems and human systems. It is not easy to realize strong coupling, so the proportion of joint interpretation was the lowest (Figures 9b). Moreover, human components explained a greater portion of the variance in the transitional index than did natural components in the rapid and sharp transition areas. In these regions, the stability of transition was more sensitive to human factors. However, in the slow transition area, this predominant contribution of natural components was observed for the transitional index. What these regions had in common was that the unexplained proportion from humans and nature was very small in the strong coupling region. Based on the analysis of the coupled human and natural characteristics of these typical transitional counties, it is clarified that the mountainous TG is a complex region with human-nature interactions, and its component, structure and function are closely related to the coordination of regional sustainable development.
Figure 9 Relative contributions (%) of natural components and human components to the transitional index in Fengdu (a), Tianquan (b), Changning (c), and Shifang (d)

4.2 From the spatial identification of the transitional zone to its cascading effect

The general delineation approaches of transitional regions are based on the superposition of environmental factors. To be applicable for transitional regions with complex coupled natural-human systems, approaches must consider human influences and their interactions. The transitional index is a feasible methodological tool used to quantify the interactions between society and nature. It can measure the intensity and direction of their interaction and realize the zoning of the transition region. Additionally, this index is a systematic and comprehensive index that can be used to characterize the transitional zone, which is worthy of more detailed research in the future. The ideal classification of the transitional index may be used to divide a variety of transitional zones. The area with a high index refers to the transition between urban and rural areas, and a low index may represent the agro-pastoral ecotone.
The TG is the complex region presented by the coupled systems with multilevel and multitype spatial functions and structures. In the view of human geography and economic geography, the TG is different from the geographical space dominated by natural ecological processes or by human process, and it does not represent a single natural ecological space or a single urban space (Vejre et al., 2010). It is an ecosystem with special functions and attributes where urbanization cannot be simply copied from other places.
From the perspective of human activity mode and intensity, the TG, where human activities are uncertain and complex (e.g., diverse livelihoods), is a paradigm of the instability, vulnerability, and susceptibility of coupled human and natural systems. It is influenced by many factors, including climate change, urbanization, markets, and government policies (Dewey et al., 2007). In a traditional agricultural society, the basic requirement of human beings is survival. Farming in a traditional approach forms the rural geographical space, where the population is related to the amount of land available and the mode of production. Especially in mountainous villages, the suitability of cultivated land is more constrained, and the land function is mainly agricultural production, which embodies the attributes of an agricultural society (Wang et al., 2016). In the modern society of industry and urbanization, the integration of the construction of villages and market towns with township enterprises and tourism and leisure formats leads to land shouldering the functions of developing primary industry, secondary industry, and tertiary industry simultaneously. Meanwhile, the livelihoods of rural residents have become diversified, and the production and living spaces are endowed with new connotations and attributes of land function in modern society. The TG in mountainous areas is the complete embodiment of production function, living function, and ecological function. The three are interdependent and have trade-offs and synergies of multifunctional land use (Zou et al., 2021).
From the perspective of systematic and comprehensive geography, the TG has the attributes of semi-nature and semi-humanity. Here, population, resources, and the environment take on unusual temporal and spatial patterns and evolution laws. The transitional zone, the priority and hot region to study geography, provides an enriched research connotation regarding the interaction between humans and nature, especially in mountainous areas, spotlighting the particularity of the regional system of human-nature relationships (Liu et al., 2021). The TG in mountainous areas has a wide environmental gradient, high spatial heterogeneity, intense action processes, and a significant cascading effect. It is also a momentous region for the coupling and synergy of multiple elements in regional planning and land management, where a given tool is needed for development policies and governance means. It is necessary to deepen the understanding of the temporal and spatial evolution characteristics and laws of the TG in mountainous areas.

5 Conclusions

The TG is widely distributed in mountainous areas, where the human-nature relationship is special and shows regionality, and the intensity of human activities has obviously spatiotemporal uncertainty. Identifying TG and revealing the most directional indicators require the creation of new analysis methods from the perspective of a coupled human and nature system. It is essential to construct an indicator system to identify the TG in mountainous areas, so as to better understand and manage the many interacting natural and human processes. Therefore, this study constructed a conceptual framework, recognition indicator system, and the analysis model and used the Sichuan Basin as a case study to verify the feasibility of the indicator system and method. The following conclusions could be drawn:
(1) Natural components, especially the topographic position index, were the key indicators needed to identify the TG, and human components were auxiliary indicators. In natural ecological areas, the geographical environment difference was small, and human activities strongly interfered with the transition stability. Likewise, in urban areas, some abrupt natural attributes destroyed the stable state of the transition of strong human attributes.
(2) The complex relationship of human and natural systems in the TG is diverse, manifesting the trade-offs between ecological protection and economic development. Due to the terrain, location and its external connection, including the impact of policy, the coupling of human and natural systems in the TG has uncertainty with spatiotemporal evolution.
(3) Human activities have gradually increased from the high mountains around the basin to the hills and low mountains in the center of the Sichuan Basin, forming various types of coupled human and natural systems. The TG of the strong coupling type accounted for approximately 16.7% of the study area and had a stronger conflict in between economic development and ecological conservation. Nine of the ten counties with the largest proportion of the type were formerly nationally poor counties in the study area.
(4) In the strong coupling type, human and nature jointly explained a high proportion of the variance in transitional stability (e.g., in Shifang city, with 98.3% explained proportion). The identification of the TG and an in-depth analysis of the coupling process, coupling pattern and coupling mechanism between humans and nature along the TG are vital in allowing for territorial space governance and high-quality development in mountainous areas.
China’s mountainous areas have complex territorial space and various human activities, with spatiotemporal diversity and uncertainty. The socio-economic development of mountainous areas will face some challenges in the future, and it is an arduous task to achieve sustainable development in mountainous areas based on the rural revitalization strategy. It is hoped that research on the coupled human and natural systems in mountainous areas will provide a key to solving the relevant problems.

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