Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socio-economic conditions in the Central Plains, China

Journal Pre-proof Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socioeconomic conditions in the Central Plains, China

Cuihong Jiang, Guangyong Li, Juan Du, Yunpeng Jia, Ju Bai PII:

S0048-9697(19)35927-3

DOI:

https://doi.org/10.1016/j.scitotenv.2019.135932

Reference:

STOTEN 135932

To appear in:

Science of the Total Environment

Received date:

18 July 2019

Revised date:

2 December 2019

Accepted date:

2 December 2019

Please cite this article as: C. Jiang, G. Li, J. Du, et al., Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socio-economic conditions in the Central Plains, China, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.135932

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Spatial differentiation characteristics of internal ecological land structure in rural settlements and its response to natural and socio-economic conditions in the Central Plains, China Author names and affiliations. Cuihong Jianga, Guangyong Lia,b, Juan Dub, Yunpeng Jiab, Ju Baib a

Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097,

National Geomatics Center of China, Beijing 100830, China

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b

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China

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Corresponding author.

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Guangyong Li

Tel.: +86 01063881207;

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Address: No. 7, Dawning garden, Haidian District, Beijing, China; 100097

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E-mail address: [email protected]

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1 Introduction As a crucial component of the human settlements system, rural settlements (RS) are a complex combination of rural life and production, ecological environment and social managements essential to support rural residents’ daily life (Afshar, 1998). There were still 3.4 billion the world’s population living in rural areas in 2018, and cumulatively

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accounting for 90% of rural dwellers worldwide living in Asia and Africa with

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concentrated developing countries (UN DESA, 2019). Industrialization, urbanization and climate change have arguably left a greater imprint on RS environment throughout the

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developing world (De Sherbinin et al., 2008; Saraswat and Kumar, 2016), increasing the

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life and health risks of vulnerable groups in rural areas (Horton et al., 2010; IPCC, 2007).

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Furthermore, the dualistic structure of urban and rural ecological environments is

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becoming increasingly evident with the rapid development of urbanisation in recent years, especially in developing counties (Xu et al., 2019). The ecological land in rural

improving

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settlements (ELRS) is one of the land uses types which plays an important role in the

living

environment.

However,

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the

guidance

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construction-centric economic policy over the past 40 years, the Chinese government and scholars have explored the use of internal space in RS (Zhang et al., 2016) and highlighted its economic and social attributes (Cao et al., 2008). Insufficient attention has been paid to the ecological attributes of RS and internal ecological land (EL) has even been classified as "idle land" or "unused land" (Long et al., 2015), neglecting its ecosystem service function (Delgado et al., 2013). To avoid "polarisation" between urban and rural human settlements, the Chinese government has gradually paid more attention to the

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improvement of RS and is emphasising "green ecological villages" based on a "rural revitalisation strategy" aimed at building a new pattern of rural development in which humans and nature coexist harmoniously (Li et al., 2019). Under the new paradigm of rural development in China, both the government and the public have identified an urgent need for research on ELRS.

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The concept and classification of EL have been proposed by scholars as early as the

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1970s (Wiken and Ironside, 1977) but are still unclear internationally. EL can be defined

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as an ecosystem (land unit) and a spatial location that are important for maintaining key

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ecological processes at different spatial scales and that can provide direct or indirect

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ecosystem services necessary for human well-being (Capotorti et al., 2012; Grondin et al., 2014). Scholars pay more attention to the spatio-temporal characteristics, demands and

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spatial optimisation of EL in urban areas where human settlement is highly concentrated

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(Peng et al., 2017; Huang et al., 2018). However, the land use area of a single rural settlement around the city is smaller than that of the urban, and its spatial distribution is more scattered, which makes it difficult to extract micro-ecological land structure information on a large scale (Winkler et al., 2018). Most of the related studies on RS regard its land use as a whole unit and have performed an important amount of fruitful research on the structure, distribution, evolution and driving forces of RS (Song and Liu, 2014; Yang et al., 2015; Hedlund and Lundholm, 2015; Gong et al., 2018). Some scholars regard all kinds of EL as idle land in the study of the internal structure of RS, pay more attention to the intensive use of built-up land, tap the potential of renovation, and study the internal quantity and distribution characteristics of RS (White et al., 2009; Zhang et al.,

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2019). The few studies conducted on the structural characteristics of ELRS have limited the scale to the perspective of typical villages (Dahms, 1995; Fang and Liu, 2014; Zhu et al., 2018), and relatively few studies have been conducted on a large scale (Zhang et al., 2016).

ELRS structure is influenced by multiple factors such as natural and socio-economic

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conditions (Bański and Wesołowska, 2010). Natural environmental factors (elevation,

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slope, climate, etc.) played a controlling role during the period of low social productivity

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when agriculture was the leading industry of the social economy (Sevenant and Antrop,

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2007). With rapid urbanisation, developed countries in Europe and North America first

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encountered the diversity and transformation of RS functions caused by socio-economic factors, which eventually led to structural changes in ELRS (Tonts and Atherleya, 2005;

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Gong et al., 2018). During the same period, the ELRS structure in different regions varied

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to meet the needs of settlements during their social and economic development stage (Peng et al., 2016). Social transformation, location conditions, traffic conditions, industrial income, land prices (Kiss, 2000; Hammer et al., 2004; Hawbaker et al., 2004; Gonzalez-Abraham et al., 2007; Ihlanfeldt, 2007) and other socio-economic factors may affect the structure of ELRS. Furthermore, the explanatory effect of socio-economic factors on ELRS structure has gradually increased with societal and economic development.

The outer spatial morphological structure and the inner structure of RS belong to the category of rural land use planning, which is an organic and unified whole serving the

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RS functional orientation together, reflecting the socio-economic development and the human-land relationship in the process (Long, 2012). The spatial RS morphological structure in different regions determined by the interaction of multiple factors is variety, and the allocation of ELRS are diversified (Tian et al., 2012), closely related to the size, density and shape of RS（Li et al., 2014）.At the micro-scale, the lack of systematic spatial

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planning in rural areas further aggravates the confusion surrounding ELRS structure

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(Fang et al., 2019). Understanding the relationship between the ELRS structure and the outer spatial morphological structure of RS is of great importance for improving the

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environment by adjusting the planning and layout of RS. However, we have little

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knowledge of the relationship between them at present.

The process of urbanisation and industrialisation in China still lags behind

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developed countries and regional development is unbalanced. Most scholars still focus

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on spatial restructuring, functional evolution, the driving forces of land use type transition and rural residential land remediation of RS on a large scale (Long et al., 2009; Guo et al., 2015; Lang et al., 2016; Zhou et al., 2013). "Rural hollowing" is a special case of the evolution of RS internal structure derived from contradicting intensification and problems that precipitate under an urban-rural dual structure background. At the micro-scale, previous works focused on the salient economic and social problems of "rural hollowing" caused by idle and abandoned houses in RS, and carry out in-depth studies on the mechanism of "rural hollowing" from the core and external system of rural development, analysing the driving forces of core and foreign aid (Zhang and Liu, 2008; Liu et al., 2009). In the study of land use structure within RS, scholars focus their

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attention on the production and living functions of land use types within RS under economic and social conditions and confine the research area to the suburbs of large cities (Ma et al., 2018), ignoring the relationship between ELRS structure and natural environments regional differences, socio-economic development imbalance (Zhu et al., 2014).

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The development thought of “heavy city, light country” has led to confusion about

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ELRS structure for many years, which has become an obstacle to rural environmental

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renovation. This paper combines the current academic research on the status of ELRS and

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selects RS in the Central Plains (CP) of China as the research object. The main purpose is

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to explore the regional differentiation characteristics and laws of different types of ELRS on a large scale, analyse the contribution of multiple natural environmental and

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socio-economic factors to the spatial differentiation of ELRS and explain the mechanism

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of the effects of various factors on the ELRS. It is expected to provide a scientific reference for the structure, direction and mode selection of ELRS spatial utilisation and to promote the further optimisation of the ecological environment of new RS.

2 Materials and Methods 2.1 Study region The CP are in the Henan Province of China, situated in the middle and lower reaches of the Yellow River (31 23' - 36 22'N, 110 21'- 116 39'E) with a total area of 167,000 km2 (Fig.1). The region is located in the transition zone from the second-level geomorphological step to the third-level geomorphological step, displaying a

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west-high-east-low terrain and complex and varied geomorphology (Wang et al., 2017). The Taihang, Funiu, Tongbai and Dabie Mountains partly encircle the boundary of the CP and are distributed on its northern, western and southern margins respectively. In the southwest is the Nanyang Basin which has a hilly distribution on its outer margin; the northwestern part is the Loess Hilly area, which has a landform dominated by loess with

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many valley plains. The central and eastern regions, with their flat terrain and fertile soil,

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represent the North China Plain (NCP). The Yellow River runs through the central and northern part of CP from west to east. The region adjacent to the south bank of the

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Yellow River is a relic of the flooding and alleviation of the Yellow River in modern times

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and possesses a complex micro-geomorphology. The central and southern part, with its

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(Storozum et al., 2018).

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numerous tributaries and abundant water, belongs to the Huaihe River watershed

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The superior geographical position and suitable natural environment of the CP not only prepared the material basis for early agricultural production but also provided the ideal living place for early settlers (Betts et al., 2014). It is considered one of the main birthplaces of the Chinese nation and civilization. During the development of ancient farming culture in China, politics, ideology and culture converged, collided and were exchanged on the CP, forming a pluralistic rural cultural model that became a microcosm of Chinese rural culture (Bielenstein, 1987). Henan Province is still in the stage of rapid urbanization with 49.1 million rural population at the end of 2016, accounting for 51.5% of its total population (Henan Statistical Yearbook-2016, 2017). There are 159 counties under the jurisdiction of Henan Province, and the per capita cultivated land area is 0.08

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hectare, which is lower than the national average. In this study, we selected all RS in

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Henan Province as the research object and used “county” as the statistical unit.

2.2 Data source

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2.2.1 ELRS data acquisition

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Fig. 1. Location and topographic map of the study area

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In this study, we extracted the land cover patches of farmland, garden land, woodland, grassland and waterbody as ELRS data in Henan Province according to the "village land" scope of land use data in 2016 through spatial extraction based on “Intersect” tool of ArcGIS 10.0. Land use and land cover vector data in 2016 are both provided by Department of Natural Resources of Henan Province. Both sets of data are interpreted manually based on multi-source satellite images (ZY-3, Worldview-II, GF-2, etc.) with a resolution better than 2 m as the main image source during 2016, and with the same Gauss-Kruger projection and World Geodetic System 1984 coordinate system. While land use and land cover are the most closely interrelated interactions between

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natural and human processes, they are two completely different concepts. Land use is a process of transforming the natural ecosystem into an artificial ecosystem; it emphasises the purpose of human activities and the social and economic attributes of the land. Land cover refers to the synthesis of various elements of the land surface covered by natural or artificial features, focusing on natural and ecological attributes (NOAA, 2018). Land with

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the same land use attributes can be used for different types of land cover. In the “Land

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Use Present Situation Classification” national standards of China, “village” is the second class under the first class “Urban village and industrial land” (Table S1). In the actual

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process of land use data collection, land use types within RS are not distinguished but

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are instead merged into the same attribute map patch, collectively referred to as the

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"village” (Fig S1). Land cover indicators were divided into 10 categories based on

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“Contents and Indicators of Geographical Conditions Census” of China (Table S2). Land cover data is no concept of village scope, direct interpretation of natural and artificial

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elements based on remote sensing images, such as farmland, woodlands, building, roads and waterbody, etc.

2.2.2 Natural and socio-economic data

Based on reviewing relevant research results (Tritsch and Tourneau, 2016; Xie et al., 2017), the natural and socio-economic factors involved in this study include average elevation, surface roughness, rural population, urbanisation rate, road density, industrial structure, per capita farmland and indicators to characterise the external spatial morphological structure of RS, including the number of patches (NP), patch density (PD), mean patch size (MPS), patch size standard deviation (PSSD) and mean patch fractal

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dimension (MPFD). Average elevation is the average digital elevation model (DEM) in a county, and surface roughness is the difference between the maximum and minimum DEM values in a county (Barnes et al., 2014). Surface roughness can describe the complexity of a region's topography together with average elevation. There are calculated by using DEM with a spatial resolution of 30 m in 2011 which was obtained

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from Geospatial Data Cloud site(http://www.gscloud.cn). Rural population reflects the

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size of the population of rural areas in a region (Zhou et al., 2018). Urbanisation rate is the process of urban civilisation spreading to the countryside and the transformation of

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rural lifestyles to urban lifestyles from a sociological perspective (Zeng et al., 2016). Road

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density is the length of the traffic line per square kilometre in a county, which generally

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reflects the level of development of the traffic line and the degree of traffic convenience in

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a region (Hawbaker et al., 2004). The total length of roads above the township level was used for the length of the traffic line. Industrial structure is the proportion of primary

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industry gross domestic product (GDP) to overall GDP based on national accounts data, which indicates the position of agriculture in the national economy. Per capita farmland is the farmland area occupied by the rural population per capita, reflecting the scarcity of farmland resources per capita. The data of the above five socio-economic factors are all from

Henan

Statistical

Yearbook-2016

(http://www.ha.stats.gov.cn/hntj/lib/tjnj/2017/indexeh.htm). The patches of “village land” in land use data from Department of Natural Resources of Henan Province in 2016 were used as the primary data to calculate the external spatial morphological structure parameters of RS using the Patch Analyst module in the ArcGIS environment. The

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meaning and calculation method of these five parameters are shown in Table S3.

2.3 Ecological land classification system

Scientific classification of the functional structure of land use in RS is an important prerequisite for improving the efficiency of land use in rural areas and realising the optimal allocation of land use. There are different perspectives on RS and their internal

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classification varies greatly (Long et al., 2015). From the point of view of land use

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function, this study takes the production, living and ecological functions of land use in

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RS as the dominant factors and classifies land units according to the physical, biological

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and ecological service function differences of land cover units at different scales within

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RS (Capotorti et al., 2012; Costanza et al., 2014). The types of EL in this study are

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household plots, woodland, grassland and waterbody, which are defined as follows.

Types Household plots

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Table 1. Ecological land types of RS and their definitions

Definition

According to the classification of land use, land mined within the "village land" area is used to grow crops such as grain and vegetables to meet self-sufficiency needs. Land for woody plants such as natural forests, secondary forests, plantations, shrubs

Woodland and fruit trees in the area of "village land". "Village land" is mainly covered by herbaceous plants, including natural grassland, Grassland artificial grassland, green grassland, etc.. Waterbody

The land covered by water in "village land" includes pits, rivers, artificial reservoirs,

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etc.

2.4 Methods

2.4.1 Landscape indexes

Land use of RS belongs to the theory of landscape ecology category (Sevenant and Antrop, 2007). Landscape index can be used to quantitatively describe the external and

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internal morphological structure of land use types in a region at different scales and then

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to analyse their spatial differentiation and allocation characteristics （Shaker, 2018). NP,

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PD, MPS, PSSD and MPFD were selected to characterise the outer margin morphology

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and external spatial aggregation characteristics of RS. The patch number ratio (PNR),

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percentage of landscape (PLAND), MPS and diversity index (DI) were selected to describe the fragmentation, dominance, stability and diversity of ELRS. All landscape

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indexes were chosen for the analysis of RS morphological structure obtained with

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Fragstats help (McGarigal, 2015). The meaning and calculation formula of each index are shown in Table S3.

2.4.2 Univariate Local Moran’s I

Local spatial autocorrelation mainly tests the degree of correlation between local unit attributes and the same attributes of adjacent units within the scope of the study, reveals the regional structure and displays them spatially with a LISA cluster map. When the value of a location is similar to its neighbouring value and exceeds a spatial randomness value, the region clusters are divided into high-value or low-value cluster areas. The spatial clusters shown in the LISA cluster map are only the cluster centre,

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indicating that both the centre and its surroundings present the cluster trend. To describe the spatial structure relationship of various indicators of ELRS, GeoDa software was used to analyse the spatial structure of PNR, PLAND, MPS and DI indicators of ELRS by using the local autocorrelation function and taking “county” as the unit. 𝑚

𝑋𝑖 − 𝑋 𝐼𝑖 = ∑ 𝑊𝑖𝑗 (𝑋𝑗 − 𝑋) 𝑆2 𝑗=1

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In the formula, 𝐼𝑖 is the index of local spatial autocorrelation, 𝑋𝑖 and 𝑋𝑗 are the

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attribute values of spatial unit i and space unit j, respectively, 𝑋 is the average value of

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𝑋𝑖 , m is the number of samples and 𝑊𝑖𝑗 is the spatial weight matrix. Local Moran's I

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spatial correlation models can be subdivided into four types. High-high and low-low

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represent positive local spatial autocorrelation and are typical spatial clusters, but high-low and low-high represent negative local spatial autocorrelation and are spatial

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outliers (Lalor and Zhang, 2001).

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2.4.3 Redundancy analysis

Sorting is the projection of sample points onto a two-dimensional plane composed of two sorting axes. It reflects the relationship between response variables and explanatory variables through the distribution of sample points in quadrants (Legendre and Gallagher, 2001). It also analyses the reasons for the variation in response variables. According to different data types and research objectives, modern sorting methods can be classified into two categories. Sorting using only species composition data is called unconstrained ordination, while using both species and environmental factors is called constrained ordination. Common restrictive sorting methods include linear redundancy

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analysis (RDA) and unimodal canonical correspondence analysis (CCA). Canoco is one of the most popular programs for multivariate statistical analysis using ordination methods in the field of ecology and several related fields, and is also applicable to the analysis of most sample-based observation data or experimental data (Braak and Smilauer, 2012). The results of software operation not only can show the contribution of

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every subset of explanatory variables and a single explanatory variable to the response

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response variable through the sorting diagram.

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variables, but also display the relationship between each explanatory variable and the

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3 Results

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3.1 Spatial pattern characteristics of ELRS structure

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3.1.1 Spatial fragmentation characteristics

The mean PNR values of the household plots, woodland, grassland and waterbody

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in the whole region are 1.82, 2.33, 0.64 and 0.24, respectively. The spatial clusters of all types of ELRS PNR clearly show distinct regional characteristics (Fig. 2). In the western mountains and the areas surrounding large cities, the number of household plots in RS is lower than in other areas and the PNR value of most counties in these areas is less than 1, which means that fragmentation is low. The high-value areas of PNR are concentrated in the southern areas of Nanyang Basin and the NCP, and their PNR is more than 2, indicating that the degree of fragmentation is higher than that in other areas. Woodland in RS in the northern, western and southern edge mountainous areas has high integrity. Woodland PNR values of 24 counties in the central and northern NCP and Nanyang

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Basin exceed 3, showing high fragmentation. The counties with a grassland PNR of less than 0.3 in the eastern, western and northern regions of the CP are the areas with high homogeneity, accounting for 25.80% of the CP. The counties with a PNR greater than 1 in the southern and north-eastern NCP and the Nanyang Basin account for 18.23% of the whole area, especially in the southern part of the NCP, where a high concentration of

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grassland fragmentation exists. As a whole, the PNR of waterbody clearly increased from

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the northwest to southeast. The counties with a PNR less than 0.1 were concentrated in the northwest, accounting for 42.76% of the total counties. The PNR of waterbody in the

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southeast is higher and the fragmentation is more obvious than in the northwest.

Fig. 2. Different ecological land PNR spatial pattern and LISA cluster map of RS at the county scale. A, B, C, D is the spatial pattern of household plots, woodland, grassland and waterbody PNR in RS respectively, E, F, G, H is cluster analysis of household plots, woodland, grassland and waterbody PNR in RS respectively.

3.1.2 Spatial dominance characteristics

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The average PLAND of household plots, woodland, grassland and waterbody in RS accounted for 6.27%, 19.22%, 2.93% and 0.45% of village land in the CP respectively. Woodland is the dominant land use type in ELRS, followed by household plots. Waterbody is in the most disadvantaged position. The spatial patterns of all kinds of ELRS PLAND are different, and PLAND values are higher in the south than in the north.

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The PLAND value of the household plots in the eastern part of the CP is less than 5%,

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forming a low-low cluster area, which indicates that the percentage of the household plots in RS is lower than that in the surrounding areas. The western and southern areas

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are high-high cluster areas of the household plot PLAND and 23 counties with a

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household PLAND value higher than 10% are distributed in this area (Fig. 3). The

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PLAND value of woodland in the northern CP is less than 10%, which forms a low

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woodland cluster area in RS. The woodland PLAND value of 11 counties in southern CP is higher than 30% and the woodland occupies a substantial percentage in ELRS. The

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woodland PLAND value of counties in the central CP has great variance and high heterogeneity. The dominance of grassland in western and eastern CP is low, and the PLAND value for grassland in 35 counties is less than 1%. RS with dominant grassland in comparison with other areas are concentrated in the southern region of the CP, where the grassland PLAND value is more than 5%. The overall spatial dominance degree of waterbody in RS increases from the northwest to the south, and low-low and high-high cluster areas of waterbody dominance are formed in the northwest and south of the CP, respectively.

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Fig. 3. Different ecological land PLAND spatial patterns and LISA cluster maps of RS at the county scale.

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A, B, C, D is the spatial pattern of household plots, woodland, grassland and waterbody PLAND in RS

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PLAND in RS respectively.

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respectively, E, F, G, H is cluster analysis of household plots, woodland, grassland and waterbody

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3.1.3 Spatial stability characteristics

The average MPS values of the household plots, woodland, grassland and waterbody in RS are 0.13, 0.33, 0.19 and 0.09 respectively in the study region. Woodland areas show the highest spatial stability, followed by grassland and household plots. The MPS of waterbody is the smallest and showed poor stability. The counties with MPS values greater than 1.5 ha in the western margin of the NCP account for 60.9% of the total counties with MPS values higher than 1.5 ha in the CP. There are also sporadic distributions in the east and north of the NCP, showing high stability (Fig. 4). The MPS value of the household plots in the Funiu and Tongbai Mountains and the southern side of the Yellow River in the eastern part of the NCP is lower, accounting for 85.4% of the

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total number of counties with MPS less than 1.0 ha in the whole province, forming an unstable accumulation area of the household plots. Woodland MPS in the NCP, Nanyang Basin and northwest CP is clearly higher than that in the surrounding areas, especially the six counties in the southern NCP with MPS of more than 0.5 ha, forming a high-high cluster area with high stability. The MPS of woodland in the northern, western and

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southern mountainous areas is less than 0.2 ha. The woodland ecological patches are

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smaller and their stability is poor. The high MPS value areas of grassland and waterbody are concentrated along the Yellow River in the northwest CP and the urban areas nearby,

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showing strong stability, while the MPS values of grassland and waterbody patches in

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the southern part of NCP and Funiu, Tongbai and Dabie Mountains are smaller and

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show poor stability.

Fig. 4. Different ecological land MPS spatial patterns and LISA cluster maps of RS at the county scale. A, B, C, D is the spatial pattern of household plots, woodland, grassland and waterbody MPS in RS respectively, E, F, G, H is cluster analysis of household plots, woodland, grassland and waterbody MPS

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in RS respectively.

3.1.4 Spatial diversity characteristics

From the overall spatial pattern of the province, the diversity and complexity of ELRS show a decreasing trend from the northwest to the south with local heterogeneity (Fig. 5). The structure of ELRS in the northwest CP is complex, forming two high-high areas with

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DI values close to 30. Moving to the south, the structure of ELRS tends to gradually

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simplify. The DI value of 11 counties in the southern CP is less than 15, forming a single

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cluster area of EL structure. Although there is no LISA cluster area in the central part of

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the NCP, the ELRS DI value of some counties in this area is significantly higher than that

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in the surrounding areas.

Fig. 5. Ecological land DI spatial pattern and LISA cluster map of RS at the county scale. A is the spatial pattern of ELRS diversity, B is cluster analysis of ELRS diversity.

3.2 Driving forces of ELRS structure

According to Table S4 and Fig. 6, four subsets composed of explanatory variables can explain 48.3% of the ELRS structure. That is, the ordination axis of ELRS characteristic indicators has a strong correlation with natural and socio-economic conditions. The first optimal subset composed of average elevation, surface roughness, urbanisation rate and

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road density, and the second optimal subset of RS external spatial structures composed of NP and PD have an obvious influence on the spatial pattern characteristics of the whole ELRS structure, explain approximately 28.02% and 13.94% respectively. The other two higher-order sorting axes have low explanatory power, explain 4.32% and 0.85% of ELRS structure characteristics respectively.

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The contribution of single explanatory variable to the whole structure of ELRS is

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quite different (Table S5). Average elevation and NP can explain 14.4% and 11.8% of the

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internal structural characteristics of ELRS, indicating that the topography and

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fragmentation of RS’ external space play a major role in controlling the internal structure

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of ELRS. Although the independent effects of the outer margin morphology of RS (MPS, PSSD, MPFD), road density and number of rural populations on the structure of ELRS

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are lower than those of average elevation and NP the cumulative explanatory response

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variable is 20.1% and the contribution is 41.5%. Other factors have no significant independent effect on the structure of ELRS.

The impact of each factor on the characteristics of ELRS is different, but in the direction of control, some factors are convergence (Fig. 6). Rural population, industrial structure and the outer edge morphological parameters of RS (MPS, PSSD, MPFD) show positive correlations with the all kinds of ELRS PNR. On the contrary, urbanisation rate, road density and terrain factors show negative correlations with PNR of all kinds of ELRS. Per capita farmland shows positive correlated with household plots and woodland PNR, but negatively correlated with grassland. The contribution of PD and NP to

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household plots, woodland and grassland is opposite to that of per capita farmland, and the relationship between them and water area is not obvious.

Except for the household plots, other types of EL PLAND show obvious negative correlations with average elevation and surface roughness, and the waterbody PLAND is the most obvious. The correlation between rural population, urbanization ratio and

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PLAND of household plots and waterbody is more obvious than that of woodland and

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grassland, and rural population, urbanization and PLAND show a opposite correlation

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with household plots and waterbody. The correlation between industrial structure, road

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density and other ELRS is more obvious except in household plots. The impact of NP and PD on all kinds of ELRS PLAND is opposite of the impact of MPS, PSSD and MPFD.

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Moreover, all RS outer edge morphology and structure factors have the greatest impact

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on household plots PLAND than other types of ELRS. Per capita farmland is negatively

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correlated with the PLAND index of various EL types, and the correlation with grassland PLAND is the highest compared with other ELRS.

Elevation and surface roughness have little effect on the stability of grassland and waterbody but have a strong correlation with the stability of household plots and woodland. Rural population, urbanization ratio, industrial structure and road density all have obvious correlations with household plots and woodland. The stability of grassland and waterbody has no obvious relationship with industrial structure and road density. The spatial outer edge morphology and structure of RS have the most significant impact on the internal stability of ecological land.

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Terrain factors, per capita farmland and road density show positively correlated with DI, but it is contrary influence between industrial structure and DI. The effects of rural population and urbanization ratio on the diversity of ELRS are unclear. The diversity index of ELRS is positively correlated with MPS, PSSD and MPFD and negatively correlated with NP and PD. Per capita farmland is positively correlated with

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DI.

Fig. 6. The relationship between ecological land characteristic indicators and natural, socio-economic factors. Blue arrows represent response variable and the red arrows are explanatory variables; PNRhp, PNRwo, PNRgr, PNRwa represent household plots, woodland, grassland and waterbody of patch number ratio, respectively; PLANDhp, PLANDwo, PLANDgr, PLANDwa is percentage of landscape of household plots, woodland, grassland and waterbody, respectively; MPShp, MPSwo, MPSgr, MPSwa is mean patch size of household plots, woodland, grassland and waterbody.

4 Discussion

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The spatial differentiation of the ELRS structure is the result of multiple factors of the natural

environment and socio-economic conditions. The spatial pattern

characteristics of RS are related to the scale of the study (de Koning et al., 1998). On a large scale, the structure of RS is mainly influenced by natural conditions (Tian et al., 2012). At landscape scale, socio-economic factors have more obvious controlling internal

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structure of RS (Bański and Wesołowska, 2010). Furthermore, the structure of ELRS is

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also related to the stage of socio-economic development (Kiss, 2000). As one of the main determinants of land use in rural areas, natural factors play a key role in the formation of

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the initial land use pattern, especially in the primary stage of urbanization (Sevenant and

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Antrop, 2007). In the later stage of the development of RS, socio-economic factors play an

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important role in the process of remodelling the internal structure of RS (Bański and

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Wesołowska, 2010). The geographical differences of the terrain condition and urbanization process in Henan Province are obvious (Wang et al., 2017). The overall

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regional characteristics differentiation of ELRS structure necessarily reflect the elements of terrain conditions and socio-economic conditions. Therefore, the subsets of terrain factors and urbanization ratio play an important role in controlling the structure of ELRS.

The process of urbanisation and industrialisation is not only reflected in the change in economic and industrial structure but also in social structure and the improvement of public services, that is, the transition of rural populations to urban populations and the gradual optimisation of the transportation network (Fang et al., 2019). Therefore, there is synergy among industrial structure, rural population, urbanization ratio and road density in controlling the structural characteristics of ELRS. Under the same level of

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agricultural productivity, the high urbanisation rate, the weakening status of the agricultural industry and the low population in rural areas show that agricultural production is no longer the main way for farmers to earn a living. The "part-time farming" and "non-agricultural" production of farmers' livelihoods lead to their reduced dependence on agricultural production. Farmers are more inclined to abandon "private

production within and around

RS (Zhang

et

al., 2016).

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agricultural

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plots", "edge and corner land" and agricultural production-related land used for

non-construction land will be fully exploited and utilised to bring into play greater

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economic benefits, resulting in the reduction of ELRS, decreasing fragmentation and the

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simplification of diversity. The advantages of convenient transportation corridors in the

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vicinity of cities have enhanced the radiation and infiltration of urbanisation and

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industrialisation into the countryside (Yang et al., 2018). The high output value of part-time land and scientific village planning has promoted the intensive use efficiency

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of RS land use. The fragmentation, dominance and stability of ELRS are lower than those in remote agricultural production areas and the diversity is higher. In areas with slow urbanisation and industrialisation or weak radiation from cities and insufficient transportation infrastructure, RS land use tends to be extensive and EL tends to be fragmented. Under these circumstances, a large number of rural residents have moved out, accelerating the decline of rural areas and further aggravating the fragmentation of various types of EL (Zhang and Han, 2018). The government's reclamation of abandoned houses after the inhabitants left the countryside has not only increased the patches of household plots and woodland in RS but also enhanced the spatial stability (Li et al.,

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2014).

The formation and spatial structure of RS are the result of the comprehensive effect of the human-land relationship at different scales of rural areas. Therefore, there must be a relationship between the characteristics of RS external and internal morphological structure. There are obvious regional differences in the morphological structure of RS

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(Dong et al., 2017), influenced by topography, climate conditions, customs and policies

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(e.g., relocation and consolidation of villages, resettlement of new villages). Small patches

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of RS mostly exist for single families; the distance between houses is large, which

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improves the number and density of patches and is not conducive to control by

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government departments. In addition, the efficiency of RS land utilisation is extensive. The PNR of all types of ELRS declined under extensive efficiency, PLAND did not

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decrease, and the types of EL tended to be in individual patches. RS with concentrated

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farmers are large in scale and low in density, and the land use of RS is more intensive, ultimately leading to a direct decline in PLAND for all types of EL. At the same time, due to the urgent need for construction land and the lack of binding rural planning, farmers have led to the disorderly and extensive expansion of RS (Tian and Li, 2013). The shape of the outer edge is now more complex, the fragmented EL has increased and the land use types have tended to diversify. The disorderly expansion of RS is accompanied by “rural hollowing” (Liu et al., 2009), and the fragmentation of EL continues.

In addition to natural and socio-economic factors, traditional culture policy systems also affect the structural characteristics of internal ELRS (Long et al., 2007; Li et al., 2014).

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The CP is the birthplace of cultivation culture in China; as a result, local farmers have special feelings for the land. They prefer to reclaim non-construction land in front of and behind houses in RS as their own and cultivate crops self-sufficiently to meet their needs (Ma et al., 2018). In areas with limited farmland resources per capita in particular, the scarcity of farmland resources highlights the advantages of reserved land in RS. Based on

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the long history and culture of the CP, the RS in the plain areas are mostly "inhabited by

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ethnic groups" living in traditional fort and stockade settlements (Gustafsson and Sai, 2009). The overall and local design of RS reflects the architectural style of Chinese

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landlord manors, which further aggravates the instability of the internal household plot,

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grassland and waterbody. This phenomenon is most prominent in the central part of the

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NCP. In terms of policy systems, governments at all levels have carried out such projects

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as comprehensive renovation of the rural environment, village relocation and combination, migration and security projects in recent years. The scientific planning and

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design of ELRS driven by policy further optimises the structure of EL use and improves the rural human settlement environment (Zhao and Zhang, 2017).

5 Conclusion

RS are naturally and socio-economically complex ecosystems. This study shows that ELRS in the CP has obvious regional differentiation characteristics from the global spatial pattern that are the result of indirect and direct comprehensive effects of natural and socio-economic factors. Woodland has higher fragmentation, dominance and stability than other types of EL and is the main EL type of RS in the CP. Average elevation, surface roughness, urbanization ratio and road density constitute the best subset of natural and

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socio-economic factors, and NP and PD constitute the external factors subset of RS spatial structure, which plays an important role in controlling the spatial pattern of ELRS structure. The independent effects of individual explanatory variables on the overall characteristics of ELRS structure are quite different. Elevation is the primary factor controlling the structure of EL, followed by the external morphological structures of RS,

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road density and rural population. The external spatial morphological structure of RS is

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closely related to the internal EL structure. RS with large quantities, high densities, small average areas of land use, poor uniformity and shape rules are conducive to the integrity

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and superiority of ELRS but hinder its stability and diversity. The formation of RS on a

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large scale is a long historical process, particularly in the CP, that integrates many factors

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such as history, culture, ethics, religious beliefs. However, the research in this field is still

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insufficient. Therefore, the structure of ELRS still needs to be studied in depth to better provide data support for the large-scale regionalization of the environmental renovation

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of rural human settlements and to further improve the environmental performance of rural human settlements.

Acknowledgements

This study was financially supported by the National Natural Science Foundation of China (41671519). We wish to thank Professor Paulo Pereira and referees for this journal for constructive comments on an earlier version of the paper.

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Authors： Cuihong Jianga, Guangyong Lia,b, Juan Dub,Ju Baib a

Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097,

China b

National Geomatics Center of China, Beijing 100830, China

Corresponding author.

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Guangyong Li

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Address: No. 7, Dawning garden, Haidian District, Beijing, China; 100097

E-mail address: [email protected]

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Conflict of interest statement

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Tel.: +86 01063881207;

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We declare that we have no conflict of interest.

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Graphical abstract

Journal Pre-proof Highlight 1. ELRS structure is significantly spatially different in the Central Plains. 2. Woodland has higher fragmentation, dominance and stability than other ELRS types. 3. Elevation is the most important independent factor in controlling ELRS structure. 4. The subset consisting of terrain, urbanization and road density mainly

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controls ELRS.