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Journal of Geographical Sciences >

2019 , Vol. 29 >Issue 4: 598 - 612

DOI: https://doi.org/10.1007/s11442-019-1617-y

Research Articles

Topographical relief characteristics and its impact on population and economy: A case study of the mountainous area in western Henan, China

  • ZHANG Jingjing ,
  • ZHU Wenbo ,
  • ZHU Lianqi , * ,
  • CUI Yaoping ,
  • HE Shasha ,
  • REN Han
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  • College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
*Corresponding author: Zhu Lianqi (1963-), Professor, specialized in mountain environment and regional development. E-mail:

Author: Zhang Jingjing (1991-), PhD Candidate, specialized in development and utilization of natural resources in mountain areas. E-mail:

Received date: 2018-09-16

  Accepted date: 2018-11-07

  Online published: 2019-04-12

Supported by

National Natural Science Foundation of China, No.41671090

National Basic Research Program (973 Program), No.2015CB452702

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Topographical relief is a key factor that limits population distribution and economic development in mountainous areas. The limitation is especially apparent in the mountain-plain transition zone. Taking the transition zone between the Qinling Mountains and the North China Plain (i.e. the mountainous area in western Henan Province) as an example and based on the 200-m resolution DEM data, we used the mean change-point analysis to determine the optimal statistical unit for topographical relief, and thereafter extracted the relief degree. Taking the 1:100,000 land use data, township population and county-level industrial data, population and economic spatial models were constructed, and 200-m resolution grid population and economic density maps were generated. Afterwards, statistical analysis was carried out to quantitatively reveal the impact of topographical relief on population and economy. In addition, the impacts of other topographical factors were discussed. The results showed the following. (1) The relief degree in western Henan is generally low, where 58.6% of the regional topography does not exceed half the height of a reference mountain (relative elevation ≤250 m). Spatially, the relief degree is high in the west while low in the east, and high in the middle while low in the north and south. There is a positive correlation between relief degree and elevation, and a much stronger correlation between relief degree and slope. (2) The linear fitting degree between the population and economic validation data and the corresponding simulation data are 0.943 and 0.909, respectively, indicating that the spatialized results can reflect the actual population and economic distribution. (3) The impact of topographical relief on population and economy was stronger than that of other topographical factors. The relief degree showed a good logarithmic fit relationship with population density (0.911) and economic density (0.874). Specifically, 88.65% of the population lives in areas where the topographical relief is ≤0.5 and 88.03% of the gross regional product was from areas where the relief is ≤0.3. Compared with the population distribution, the economic development showed an obvious agglomeration trend towards low relief areas.

Key words： topographical relief; population and economy; land use; spatialization; grid; western Henan mountainous area

Cite this article

ZHANG Jingjing , ZHU Wenbo , ZHU Lianqi , CUI Yaoping , HE Shasha , REN Han . Topographical relief characteristics and its impact on population and economy: A case study of the mountainous area in western Henan, China[J]. Journal of Geographical Sciences, 2019 , 29(4) : 598 -612 . DOI: 10.1007/s11442-019-1617-y

1 Introduction

Topography greatly impacts on agricultural production, population distribution, urban construction and economic development (Meybeck et al., 2001; Baumann et al., 2011; Li et al., 2015; Fang and Ying, 2016; Shi et al., 2018). At a global scale, the mountains have been classified based on surface roughness and elevation, and the impact of different types of mountain on water resources and population distribution have been analyzed (Meybeck et al., 2001). At the country level, the mountains in China have been classified based on elevation, relative height and slope (Fang and Ying, 2016). Taking county as the basic unit, the economic development in mountainous areas was classified into 4 major types and 23 subtypes, suggesting that the difference in economic development between mountainous and plain areas was one of the primary reasons for unbalanced economic development in China (Fang and Ying, 2016). From two different scales (grid scale and county scale), the impact of topographical relief on population distribution and economic development has been discussed, and relief, as an important factor for the suitability assessment of human settlements, has been suggested (Feng et al., 2007, 2011). Moreover, many studies have quantitatively analyzed the impact of topographical relief on population and economy from small to medium scale. The results showed that topographical relief also has a high practical application in the suitability assessment of human settlements at the small and medium scales (Wei et al., 2013; Yu et al., 2015; Xi et al., 2018). Using a geographical detector model to investigate the mechanism of poverty in village scale, Liu and Li (2017) concluded that surface slope was a dominant factor for poverty.
Previous studies have demonstrated that topographical factors are important limiting factors that influence population distribution and economic development. Topographical factors should be fully taken into consideration when selecting regional settlement sites, improving living environments and establishing economic development policies. However, previous small-medium scale studies were mostly based on statistical data of administrative units, which often conceal the internal spatial difference in population distribution and economic development, and thus potentially distort the results. A quantitative study at the grid scale, based on spatial data, can therefore better highlight the practical significance. With the support of geographic information system (GIS) and remote sensing (RS) technologies, especially the high resolution images, the spatialization and refinement of socioeconomic factors have become the hot topic of geographic research. The use of these technologies greatly improved the spatial resolution and precision of social and economic data (Kan, 2007; Zhao et al., 2017; Li et al., 2018). Commonly used spatial socioeconomic models include the spatial interpolation model, land use impact model and remote sensing inversion model. Factors that impact population and economic distribution mainly include climate, transportation, land use, residential area, and the distance to rivers and other cities. In particular, land use is most closely related to social and economic activities. Other impact factors also show a close relationship with population and economic distribution (Zhuang et al., 2002; Hu et al., 2017; Huang et al., 2018). Therefore, the land use impact model is widely used in the spatialization of population and economic data.
The western Henan mountainous area is located in the transition area between the second and the third steps in the topography of China, and between the Qinling Mountains and the Huanghuai Plain. The area is characterized by a complex topography and is classified as underdeveloped due poor transportation infrastructure, and delayed social and economic development (Zhang et al., 2017; Zhu and Li, 2017; Zhu et al., 2019). Social and economic scholars have investigated the probable reasons for the underdevelopment in this area from various aspects such as social structure, resources and condition, policies and management (Li, 2002; Du, 2015). However, the impact of topographical relief on population distribution and economic development in this area has rarely been studied. Significant relief is the main topographical feature of the study area. Topographical relief plays an important role in the formation of population and economic patterns. In addition, complex topographical conditions lead to more significant differences in population and economy within administrative units. Thus, it is of practical value to study the impact of topographical relief on population and economy at the grid scale. Topographical relief is a quantitative index to characterize topographic fluctuations and it can directly reflect geomorphological characteristics (Liu et al., 2015). However, the calculation of topographical relief involves inherent uncertainty and is dependent on the calculation scale. As such, scientifically defining the calculation scale (or the optimal statistical unit) is the key to determining regional topographical relief (Liu et al., 2010; Zhang et al., 2018). According to previous studies, the mean change-point analysis as a non-manual discriminate method can rapidly and accurately calculate optimal statistical units (Prima et al., 2006; Zhang and Dong, 2012). Thus, this method was used to determine the optimal statistical unit for topographical relief in western Henan mountainous area. Thereafter, the extraction method, based on the suitability assessment of human settlements (Feng et al., 2007), was used to calculate the topographical relief. Based on the land use impact model, the population and economic spatial models were constructed, and the 200-m resolution grid population and economic density maps were generated. Statistical analyses, based on grid units, were carried out to quantitatively reveal the impact of topographical relief on population and economy. In addition, the impacts of other topographical factors were analyzed as well. The ultimate goal is to explore mechanisms for reducing poverty and to implement targeted measures in alleviating poverty, and thereby provide a scientific basis and decision-making support for the coordinated development of population, resources and environment, and socioeconomics in the study area.

2 Overview of the study area

The western Henan mountainous area comprises the residuals of the Qinling Mountains in Henan Province. The area expands in a fan shape and includes Funiu Mountain in the southwest, Xiaoshan, Xiong’er and Waifang mountains in the northeast, and Songshan and Xiaoqinling mountains in the east-west direction (Zhang et al., 2017), which covers an area of 4.95×104 km2 and includes 29 county-level administrative units in Sanmenxia, Luoyang, Nanyang, Pingdingshan and Zhengzhou (Figure 1). The topographical relief gradually decreases from west to east and the elevation ranges between 29-2372 m. The landscape changes gradually from middle and low mountains to hills, tableland, plains and basins. At the end of 2014, the total population in the area was 17.908 million and the urbanization rate was 43.9%, which was lower than the average in Henan (45.2%). The gross regional product in 2014 was 853.03 billion yuan, accounting for 24.1% of the province’s total. Since the land area accounts for 30% of Henan, it is generally considered underdeveloped.
Figure 1 Location and elevation map of the western Henan mountainous area, China
Note: In this study, the municipal districts of every city were all considered in the total city area.

3 Data sources and processing, and research method

3.1 Data sources and processing

The digital elevation model (DEM) data were from ASTER GDEM, with a spatial resolution of 30 m. After splicing, projection, clipping and re-sampling, the 200 m resolution DEM of the study area was generated.
The 2014 population data were from the 2015 China Statistical Yearbook (Townships). After removing invalid data, 408 township units were reserved and 70 were randomly selected as validation samples for the results of population spatialization. The 2014 economic data were from the 2015 Henan Statistical Yearbook, which included the output values of different industries in 29 county-level units. The gross regional products of Songxian, Yichuan, Yiyang and Ruyang counties in Luoyang and Gongyi in Zhengzhou were from the 2015 statistical yearbook of all cities or counties. After removing invalid data, 68 township units were retained, which were used as validation samples for the results of economic spatialization. The population and economic data were divided by the area of the corresponding statistical unit to obtain the population density and economic density of every sector.
The 1:100,000 land use and vector diagrams of county and township boundaries in 2013 were from the National Science & Technology Infrastructure of China, Data Sharing Infrastructure of Earth System Science-Data Center of Lower Yellow River Regions (http://henu.geodata.cn). The land use data were divided into 6 primary types and 25 secondary types. As for the study area, the land use data consisted of 6 primary types and 16 secondary types.

3.2 Method

3.2.1 Extraction of topographical relief
1) The calculation of topographical relief has scale dependence. In this study, the moving window method was used to calculate the relief at different scales. The maximum and minimum elevation of every neighborhood grid in each window was collected, and the difference between the two represents the topographical relief of the corresponding grid. The equation is as follows:
$M=H\text{max}-H\text{min}$ (1)
where M is the topographical relief value of the central grid in the window; Hmax is the maximum elevation and Hmin is the minimum elevation in the window.
Based on the above equation, n × n windows with n = 2, 3, ..., 30 pixel sizes were sequentially applied to the DEM data. The average topographical relief under different windows was calculated. The variation in topographical relief with the window area shows a logarithmic relationship, and the fitting was 0.98 (Figure 2a). As can be seen in Figure 2a, there is a point on the curve between 2 and 8 km2 where the slope decreased sharply, this point is called the change-point. The window size corresponding to the change-point is the optimal statistical unit.
Figure 2 Relationship between the average relief degree and the window area (a), and the curve of the difference between S and Si (b)
In this study, the mean change-point method was used to calculate the optimal statistical unit for topographical relief in western Henan mountainous area. The method is able to identify the sudden change-point in a dataset and is most effective in conditions where there is only one change-point (Zhang et al., 2018). The procedure is as follows:
(1) The average topographical relief under different windows was divided by the area of the corresponding window to obtain the relief value per unit area for each window, Tt; and afterward taking its logarithm, the sequence of numbers {Xt} was obtained (Equation 2). The variance of the sequence S is 25.02:
Xt = ln Tt (2)
where t is the number of windows (X = 1, 2, 3, ..., 29).
(2) Let i = 2, 3, ..., 29, each i divides the sequence into two parts: {X1, X2, … , Xi-1} and {Xi, Xi+1, … , X29}. The arithmetic mean Xt1 and Xt2 of each part and their statistic Si were then calculated respectively.
${{S}_{i}}=\sum\limits_{t=\text{1}}^{i-1}{{{({{X}_{t}}-{{X}_{i1}})}^{2}}+}\sum\limits_{t=i}^{29}{{{({{X}_{t}}-{{X}_{i2}})}^{2}}}$ (3)
(3) The difference between S and Si was calculated. When the difference reaches the maximum, the corresponding window area is the optimal statistical unit.
It can be seen from Figure 2b that the maximum difference (18.04) is reached at the 9th point (i.e. i = 10), and the corresponding window area at this point is 11 × 11 pixels. Therefore, it can be concluded that by using 200 m resolution DEM, the optimal statistical unit size of the topographical relief in western Henan mountainous area is 4.84 km2.
2) Calculation of topographic relief. Due to topographical relief serving different purpose in different fields, there exist different extraction methods. This study explores the impact of topographical relief on population and economy, therefore, the extraction method proposed by Feng et al. (2007), based on the suitability assessment of human settlements, was used to calculate the topographical relief. The equation is as follows:
$RDLS=\{[\text{Max}(H)-\text{Min}(H)]\times [1-P(A)/A]\}/500$ (4)
where RDLS refers to “relief degree of land surface”, which is also called the topographical relief degree; Max(H) and Min(H) are the maximum and minimum elevation in the area (m), respectively. The difference between them is the relative elevation. P(A) is the flat land in the study area (km2). From the 200-m resolution DEM data, the average slope of the study area is 7.7°. In this study, areas with a slope of ≤2° were defined as flat land. A is the total area of the study area, also referred to the size of the optimal statistical unit (4.84 km2). Feng et al. (2007) calculated relief degree using 500 m as the height of China’s reference mountain, which gave the concept geographic meaning. In brief, when RDLS = 1, the relief degree is lower than the height of a reference mountain, but when RDLS = x, the relief degree is x times the height of a reference mountain.
3.2.2 Spatialization of population and economic data
In this study, the land use impact model was used to spatialize the population and economic data. For population density, based on town statistical data, X1 (farmland), X21 (forestland), X22 (shrubland), X23 (sparse forestland), X24 (other forestland), X31 (high-coverage grassland), X32 (medium-coverage grassland), X33 (low-coverage grassland), X51 (urban land), X52 (rural residential area) and X53 (construction land) were selected for spatialization. For economic density, a study has shown that spatialization per industrial sector may help improve accuracy (Han et al., 2012). However, the gross regional products of town level industries are difficult to obtain. Thus, the county-level data were used to select X11 (paddy field), X12 (dryland), X21, X22, X23, X24, X31, X32, X33, X41 (river and channel) and X43 (reservoir and pond) for economic density spatialization of the primary industry. For spatialization of the secondary and tertiary industries, X51, X52 and X53 were selected. The index of each type of land use is Xi, i.e. the proportion of each land use type in the statistical unit. It should be noted that the area of paddy field at the town level is generally small, thus different types of farmland were not subdivided during population spatialization. When determining the grid size, since the data resolution is limited, it is difficult to verify the simulation results obtained at a very small scale. Moreover, smaller grids will inevitably lead to data redundancy. Therefore, a grid size of 200 m × 200 m is commonly selected. The spatialization steps are as follows:
Firstly, the county and town boundaries were superimposed on the land use map to calculate the land use type index in every county and town. Next, a multiple regression model was established by taking the population density data of 338 towns, and the economic density data of different sectors in 29 counties as the dependent variables, and their corresponding land use type indices as the independent variables. Based on the Liao’s study the constant term was set to 0 (Liao et al., 2015). The regression function is shown in Table 1, it can be seen that the correlation coefficients are high, indicating good fitting effects.
Table 1 Relationship between population density, economic density and the corresponding land use type indices
Type Regression equation Relative coefficient
Population density $\begin{align} & Y=455.22X1+49.341X21+12.222X22-181.338X23+338.556X24-192.647X31 \\ & -64.721X32+351.082X33+2982.149X51+2453.164X52+779.709X53 \\ \end{align}$ 0.937
Economic density of the primary industry $\begin{align} & Y=1171.513X11+383.519X12+36.694X21+432.508X22+ \\ & \ \ \ \ \ \ 719.687X23-3094.677X24-345.41X31+179.08X32- \\ & \ \ \ \ \ \ 7331.019X33-1755.961X41+380.34X43 \\ \end{align}$ 0.990
Economic density of the secondary and tertiary industries $Y=54053.11X51+14161.954X52+31699.782X53$ 0.975
Secondly, a 200 m × 200 m grid was constructed and superimposed on the land use type data. The percentage of each land type in every grid was recorded. In the data layer attribute table, a new field was created for each land type. Using the “Select By Attribute” function, the regression coefficients were inputted. Using the “Field Calculator” tool, the regression operation was carried out. The results were fused as such that every grid represented a regression value. At this point, the gridding of population density and economic density of the primary, secondary and tertiary industries were completed. By superpositioning the economic density of every industry, the gridding of economic density was completed. Finally, the results were converted to the grid format, thereby generating the 200 m resolution population density and economic density maps.

4 Results and analyses

4.1 Distribution of topographical relief degree

As can be seen from Figure 3a, the topographical relief degree of western Henan mountainous area shows a spatial pattern of high relief degree in the west and low in the east, and high relief degree in the middle and low in the south and north. The highest values are distributed in the main ridge area of the Xiaoqinling, Funiu and Xiong’er mountains. The second highest values are distributed in the southeastern Taihang, Waifang, Songshan and Xiaoshan mountains. The lowest values are distributed in Jiaxian and Baofeng counties in the eastern part of the study area, in city proper of Luoyang in the northeastern part, and in Fangcheng, Zhenping and Xichuan counties in the southeastern part.
Figure 3 RDLS of western Henan mountainous area (a), and RDLS distribution ratios and area cumulative frequencies (b)
The distribution ratios and the cumulative frequencies of RDLS in the study area are shown in Figure 3b. It can be seen that the topographical relief degree is low generally. From Table 2, it can be seen that areas with a relief degree of 0-0.1 account for the largest proportion (24.21%), the average relative elevation is only 48.98 m and the area of flat land is the largest (70.41%). Areas with a relief degree above 1.6 account for only 0.49% of the study area, the average relative elevation is 864.74 m and the area of flat land is minimal (0.20%). Thus, as the relief degree increases, the relative elevation gradually increases and the proportion of flat land gradually decreases. From Figure 3b and Table 2, when the relief degree reaches 0.5, the average relative elevation does not exceed 250 m and the cumulative frequency reaches 58.6%. When the relief degree reaches 1, the average relative elevation is ≤500 m and the cumulative frequency reaches 89.76%. When the relief degree reaches 1.6, the average relative elevation is ≤800 m and the cumulative frequency reaches as high as 99.51%.
Table 2 Main RDLS parameters of western Henan mountainous area
RDLS [Max (H) - Min (H)] P (A) /A
Range Proportion (%) Average (m) Average (%)
0-0.1 24.21 48.98 70.41
0.1-0.3 19.86 122.56 44.78
0.3-0.5 14.53 217.88 7.25
0.5-0.7 15.05 307.59 2.80
0.7-1 16.11 424.10 1.31
1-1.3 7.57 568.53 0.62
1.3-1.6 2.18 707.43 0.35
1.6-2.23 0.49 864.74 0.20
From Figures 4a and 5a, RDLS shows a strong positive correlation with both elevation and slope, with a linear fitting degree of 0.926 and 0.934, respectively. In particular, RDLS shows a stronger correlation with slope. When the elevation is less than 150 m, there is some inconsistency between the trend of RDLS and elevation. This is possibly due to the fact that there are some ravine and gorge areas with low elevation but relatively high relief degree. When the elevation is under 1800 m or the slope is <30°, the variation in RDLS is low. For areas with >1800 m elevation or >30° slope, the variation in RDLS increases since the areas are located at the main ridge area of Xiaoqinling, Funiu and Xiong’er mountains, which have large land relief and significant fragmentation. From Figures 4b and 5b, with changes in elevation and slope, the proportion of the variation in RDLS in different areas can be classified into three types: continuous decrease (with changes of 0-0.2); first increase and then decrease (the changes include 0.2-0.5 and 0.5-0.8 in different elevations, and on different slopes, the changes include 0.2-0.5, 0.5-0.8 and 0.8-1.2); continuous increase (the changes include 0.8-1.2 and ≥1.2 in different elevations, and on different slopes, the changes only include ≥1.2). In summary, low RDLS areas are mainly located in the low elevation and low slope areas, and high RDLS areas are located in high elevation and high slope areas. With increasing elevation and slope, the proportion of high RDLS areas gradually increases and shows relatively strong consistency with elevation and slope.
Figure 4 Variations in RDLS along with elevation (a), and RDLS proportions at different elevations (b)
Figure 5 Variations in RDLS along with slope (a), and RDLS proportions on different slopes (b)

4.2 Results of population and economic spatialization analyses

4.2.1 Accuracy verification
To verify the reliability and accuracy of the regression model, the population and economic data selected for validation were linearly fitted to the corresponding simulated values (Figure 6). The fitting degree between the statistical and simulated values of population and economic data are 0.943 and 0.909, respectively, indicating that the simulation results are accurate and the accuracy in population density is higher than that in economic density. Thus, the 200 m × 200 m population and economic densities obtained from the proposed model are able to reflect the actual population and economic distribution in western Henan mountainous area.
Figure 6 Relationship between simulated and statistical values
4.2.2 Differences in spatial distribution of population and economy
As can be seen in Figure 7, population and economic densities have certain spatial coupling characteristics. At the grid scale, the correlation between population density and economic density is 0.787 (p<0.05), indicating that population distribution and economic development in western Henan mountainous area were in coordination at the 200 m grid level. Both densities increased from the central-western part to the northern, eastern and southern parts. The low value areas were all distributed in Lushi, Luanchuan and Xixia counties in the central-western part, where the population density was below 100 people/km2 and the economic density was under 3 million yuan/km2. The high value areas were mainly concentrated in the northeastern (e.g. Zhengzhou and Luoyang) and the northern part (e.g. Sanmenxia and Lingbao), as well as in some scattered counties and townships. The population density in high value areas was >800 people/km2 and the economic density was >20 million yuan/km2.
Figure 7 Spatial distribution of population density and economic density at a resolution of 200 m × 200 m

4.3 Impact of topographical relief on population and economy

4.3.1 Comparison with the impact of other topographical factors
In order to compare the differences between topographical relief and other factors in terms of their impact on population and economy, a regional statistical analysis model was used to collect the topographical relief degree, elevation, slope, population density and economic density at the grid level. Logarithmic fitting was then carried out (Figure 8). There are some similarities in the impact of various topographical factors on population and economy. Among these factors, RDLS had the strongest impact, followed by slope, and elevation had the lowest impact. From Figures 8b and 8e, it can be seen that in some low elevation areas, the population and economic densities are low as well. This is because these areas mostly comprise remote ravines and gorges, and although the elevation is low, the slope and RDLS are relatively large. Thus, social and economic activities are still limited. The fitting degree between topographical relief degree and population and economic densities are 0.911 and 0.874, respectively. Compared with other factors, RDLS showed a strong limitation towards population and economic development. From Figures 8a and 8d, it can be seen that the change-point for population and economic densities was at the relief degree of 0.7 and 0.5, respectively. Beyond the change-point, there was no significant change in the population and economic densities, which suggests that 0.7 and 0.5 are critical values for RDLS-dependent variations in population and economic densities.
Figure 8 Relationships of RDLS, elevation and slope with population density and economic density
Moreover, it is apparent that the correlations between various factors and population density were higher than that for economic density, and the critical value for population density was also higher than that for economic density. This suggests that the impact of topography on population was stronger than that on economy. This is likely due to the advantages that relatively flat terrain areas offer in terms of local financial budget, infrastructure and investment. The economic agglomeration effect is stronger than the population agglomeration effect, resulting in concentrated gross regional product values in these areas and thus unbalanced economic development.
4.3.2 Relationship between RDLS and distribution of population and economy
Figure 9 shows the variations in cumulative frequencies of total population, gross regional product and land area with changes in RDLS. With increasing RDLS, it can be seen that the cumulative frequency of gross regional product first reached the critical value (0.5), followed by total population (0.7) and land area (1.3). This indicates that the majority of population and gross regional product in western Henan are distributed in areas with a flat terrain, and that the population agglomeration degree lags behind the economic agglomeration degree. The population and economy were in a moderately coupled state. However, the coupling relationship with land area was weak. When the RDLS is 0 (i.e. flat; slope ≤ 2°), the cumulative frequencies of population and gross regional product accounted for 10.35% and 13.04%, respectively, while that of land area was only 4.98%. When the RDLS is 0.5 (i.e. relative elevation < 250 m), the cumulative frequencies of population and gross regional product were 88.65% and 94.39%, respectively, and that of land area was 58.6%. When the RDLS > 1 (i.e. relative elevation > 500 m), the cumulative frequency of land area accounted for 10.24% while that of population and gross regional product accounted for 1.84% and 0.75%, respectively.
Figure 9 Variations in cumulative frequencies of population, gross regional product and land area with changes in RDLS
As the RDLS increased, population density, economic density, total population and gross regional product all decreased sharply (Table 3). When the RDLS is 0-0.1, the population and economic densities peaked at 728.91 people/km2 and 45.43 million yuan/km2, respectively. The total population and gross regional product were 8.9242 million and 570.736 billion yuan, respectively. About 49.86% of the population lived in 25% of the study area and generated 64.07% of the gross regional product. When the RDLS increased from 0-0.1 to 0.3-0.5, the decrease in population and economic densities was the largest, with a decrease of 49.3% and 61.9%, respectively. The total population and gross regional product dropped by 3.28 million and 156.636 billion yuan, respectively. When the RDLS is 0.5-0.7, the decrease in population and economic densities gradually slowed, and the cumulative frequencies of population and gross regional product reached about 95%. When the RDLS ranged 1.6-2.23, the population and economic densities were 45.46 people/km2 and 0.604 million yuan/km2, respectively, and the total population and gross regional product accounted for 0.07% and 0.02% of that of the whole study area, respectively. In summary, 88.65% of the population in the study area lived in regions where the RDLS is < 0.5, which accounts for 58.6% of the total area. In terms of gross regional product, 88.03% was distributed in regions where the RDLS is < 0.3, which accounts for 44.07% of the total area. Therefore, the study area is characterized by unbalanced population distribution and strong economic agglomeration effect.
Table 3 Statistics of land, population and economy at different RDLS
RDLS Land Population density (person/ km2) Economic density
(104 yuan/km2)		 Population Gross regional product Cumulative frequency of
population
(%)		 Cumulative frequency
of gross
regional product
(%)
Area (km2) Percentage
(%)		 Total
(104 people)		 Percentage
(%)		 Total amount
(108 yuan)		 Percentage
(%)
0-0.1 11900.80 24.21 728.91 4542.99 892.42 49.86 5707.36 64.07 49.85 64.07
0.1-0.3 9760.52 19.86 511.11 2106.84 511.43 28.57 2133.51 23.95 78.43 88.03
0.3-0.5 7143.16 14.53 259.13 802.51 183.02 10.23 567.15 6.37 88.65 94.39
0.5-0.7 7399.40 15.05 132.73 349.79 98.93 5.53 255.89 2.87 94.18 97.27
0.7-1 7920.08 16.11 88.11 214.55 71.25 3.98 176.93 1.99 98.16 99.25
1-1.3 3720.68 7.57 67.63 143.66 25.23 1.41 53.59 0.60 99.57 99.85
1.3-1.6 1070.84 2.18 58.90 94.24 6.44 0.36 11.23 0.13 99.93 99.98
1.6-2.23 238.48 0.49 45.46 60.36 1.19 0.07 1.77 0.02 100.00 100.00

5 Discussion and conclusions

5.1 Discussion

(1) Discussion on the spatialization process
Existing research on the spatialization of population and economy, using the land use impact model, mostly used grid data layers during computation, i.e. one grid layer for every land use type (Han et al., 2012). This method may cause information loss or damage in the process of data extraction and conversion. By contrast, we used the grid land use vector data (recording the percentage of every type in a grid). Based on the “Select by Attribute” and “Field Calculator” tools, the grids of population and economic data were obtained. Compared with other studies, the present study successfully avoided potential errors that may be introduced during the vector and grid data conversion. The results show large improvement in accuracy and calculation efficiency.
(2) Analysis of the spatialization results
As shown in 4.2.1, the spatialization results of population and economic data have good accuracy and are reliable. The simulation accuracy for population density was higher than that for economic density. According to the results, the average population density was 364.17 people/km2, and the economic densities of primary, secondary and tertiary industries were 1.99 and 16.14 million yuan/km2, respectively. In order to further verify the accuracy of the results and deviation from the actual values, the simulated data of population, output of each industry and gross regional product were compared with the corresponding statistics (Table 4). It can be seen that the relative error of total population was the lowest (0.66%), indicating that the land use impact model works well in population simulation. Some researchers have simulated the spatial distribution of population density in Henan using geostatistic methods and have reported that western Henan mountainous area is sparsely populated. The high and low population density areas are consistent with the results of this study (Zhang et al., 2016). The relative error of the primary industry was the second lowest (1.39%). However, the simulation accuracy for the second and tertiary industries was relatively low, resulting in low accuracy for the gross regional product. Thus, there are some limitations in spatializing economic data solely based on land use data. With the development of RS and GIS technologies, the remote sensing inversion of economic data and multi-source data fusion models have emerged (Zhao et al., 2017). Thus, we will integrate remote sensing data, land use and other geographic data in future studies, and use the principal component analysis method to detect information redundancy in various factors. Accordingly, the spatial economic data model will be constructed from the aspect of different rural-urban areas and different industries.
Table 4 Error analysis of simulated results
Type Simulated data Statistical value Residual error Relative error (%)
Total population (104 people) 1802.64 1790.8 11.84 0.66
Primary industry (108 yuan) 989.7 976.09 13.61 1.39
Secondary and tertiary industries (108 yuan) 7988.51 7554.16 434.35 5.75
Gross regional product (108 yuan) 8978.21 8530.25 447.96 5.25
(3) Impact of RDLS on population and economy
In western Henan mountainous area, the impact of RDLS on population was stronger than that on economic development (Figures 8a and 8d). Correlation analysis showed that RDLS is negatively correlated with both population and economy at the significance level of 0.01, with correlation coefficients of -0.784 and -0.687, respectively. This result is consistent with a study by Yu et al. (2015), which showed that the correlation between RDLS and population in the Three Gorges Reservoir area was -0.821, higher than that between RDLS and economic development (-0.663). This may be due to the advantages of flat terrain areas in terms of financial budget, infrastructure and investment. The strong economic agglomeration effect, as compared to the population agglomeration effect, resulted in highly concentrated gross regional product and unbalanced economic development.
Compared with previous studies on the impact of medium-small scale topographical factors (Wei et al., 2013; Yu et al., 2015; Xi et al., 2018), this study discussed the limitation of RDLS on population distribution and economic development in western Henan mountainous area. However, previous studies mostly focused on statistical data of administrative units, which often concealed the internal spatial difference and potentially distorted the results. In this study, first the spatialization of population and economic data was conducted and then the quantitative analysis at the grid scale was performed, which better highlights the practical significance and enables comparison with other topographical factors. Thus, helps reveal the formation of population and economic patterns in the mountain-plain transition zone. However, due to some limitations in this study such as the resolution problem in land use data and the low accuracy in economic data when using county-level sub-industry data, the results may have some uncertainty. In addition, the spatial variation in RDLS, population and economic data have certain scale dependency. However, this study only focused on the 200 m resolution grid scale, which may also introduce some uncertainty. On the basis of improving data resolution, future research should explore the scale effect while analyzing the impact of RDLS on population and economy, and thereby more accurately demonstrate the relationship between RDLS and socioeconomic factors, and provide a scientific basis for the implementation of poverty alleviation projects in mountainous areas.

5.2 Conclusions

(1) RDLS in western Henan mountainous area is dominated by low values. Specifically, the topography in 58.6% of the area is below half the height of a reference mountain (relative elevation ≤250 m). Flat land (slope ≤2°) accounted for 46.06% of the area. The spatial pattern is characterized by “low in the east and high in the west” and “high in the middle and low in the north and south”. RDLS is positively correlated with elevation and slope, while the correlation with slope is higher than that with elevation.
(2) The linear fitting degrees between population and economic data and simulated data were 0.943 and 0.909, respectively, suggesting that the spatialization results can reflect the actual population and economic distribution. Both population and economic densities increased from the central-western to the northern, eastern and southern parts. The low value areas are generally distributed in Lushi, Luanchuan and Xixia counties in the central-western part. The high value areas are mainly concentrated in Zhengzhou and Luoyang in the northeast, and Sanmenxia and Lingbao in the north, as well as in several scattered counties and townships.
(3) The impact of RDLS on population and economy was stronger than that of other topographical factors. There is a logarithmic relationship between RDLS and population and economic densities, the fitting degrees are 0.911 and 0.874, respectively. In western Henan mountainous area, 88.65% of the population lived in areas with a RDLS < 0.5 (58.6% of the total area) and 88.03% of the gross regional product was generated in areas with a RDLS < 0.3 (44.07% of the total area). Compared with population distribution, economic development agglomerates more obviously in low RDLS areas.

The authors have declared that no competing interests exist.

References

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Liu Y, Deng W, Song X Q, 2015. Relief degree of land surface and population distribution of mountainous areas in China.Journal of Mountain Science, 12(2): 518-532.Evaluation on the population pressure in the mountainous areas is a necessary condition for the protection and good governance. The evaluation depends on accurate population density assessment. Traditional methods used to calculate population density often adopt the administrative region as a scale for statistical analysis. These methods did not consider the effects of the relief degree of land surface(RDLS) on the population distribution. Therefore they cannot accurately reflect the degree of population aggregation, especially in mountainous areas. To explore this issue further, we took the mountainous areas of China as the research area. China has A total area of 666 km2 can be classified as mountainous area,accounting for 69.4% of the country's total landmass. The data used in this research included the digital elevation model(DEM) of China at a scale of 1:1,000,000, National population density raster data, the DEM and the national population density raster data. First, we determined the relief degree of land surface(RDLS). Next, we conducted a correlation analysis between the population distribution and the RDLS using the Statistical Package for Social Science(SPSS). Based on the correlation analysis results and population distribution, this new method was used to revise the provincial population density of themountainous areas. The revised results were used to determine the population pressure of different mountainous areas. Overall, the following results were obtained:(1) The RDLS was low in most mountainous areas(with a value between 0 and 3.5) and exhibited a spatial pattern that followed the physiognomy of China;(2) The relationship between the RDLS and population density were logarithmic, with an R2 value up to 0.798(p0.05), and the correlation decreased from east to west;(3) The difference between the revised population density(RPD) and the traditional population density(PD) was larger in the southeastern region of China than in the northwestern region;(4) In addition, compared with traditional results, the revised result indicated that the population pressure was larger. Based on these results, the following conclusions were made:(1) the revised method for estimating population density that incorporates the RDLS is reasonable and practical,(2) the potential population pressure in the southeastern mountainous areas is substantial,(3) the characteristics of the terrain in the high mountainous areas are important for the scattered distribution of the population, and(4) the population distribution of mountainous areas in China should be guided by local conditions, such as social, economic, and topographic conditions.

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[16]
Liu Yansui, Li Jintao, 2017. Geographic detection and optimizing decision of the differentiation mechanism of rural poverty in China.Acta Geographica Sinica, 72(1): 161-173. (in Chinese)Rural poverty has long aroused attention from countries around the world, and eliminating poverty and achieving realize common prosperity is an important mission to build the well- off society in an all- round way. Scientifically revealing the regional differentiation mechanism of rural poverty has become an important issue of implementation of national poverty alleviation strategy. This paper, taking Fuping County of Hebei Province as a typical case, diagnoses the dominant factors of differentiation of rural poverty and reveals the dynamic mechanism of rural poverty differentiation by using the Geodetector model and multiple linear regressions, and puts forward the poverty alleviation policies and models for different poverty regions. The result shows that the dominant factors affecting rural poverty differentiation include slope, elevation, per capita arable land resources, distance to the main roads and distance to the center of county, and their power determinant value to poverty incidence differentiation are 0.14, 0.15, 0.15, and 0.17. These factors affect the occurrence of poverty from different aspects and their dynamic mechanism is also different. Among various factors, the slope and per capita arable land resources affect the structure and mode of agricultural production, while distance to the main roads and distance to the center of county have influence on the relationship between the interior and exterior of the region. There are significant differences in the four types identified of regional rural poverty, namely,environment constrained region mainly affected by slope(seven towns), resource oriented region mainly affected by per capita arable land(seven towns), area dominated by traffic location affected by distance to the main roads(three towns), and economic development leading area mainly affected by distance to the center of county(four towns). Then, Fuping County is divided into single core, dual core and multi- core area according to the number of core elements of the township. The county has shown a multi differentiation of rural poverty with a horizontal center of dual core area, and both sides have a single core and multi- core,which are affected by different dominant factors. Finally, this paper suggests that policy of targeted poverty alleviation should take science and technology as the foundation and form innovation of targeted poverty alleviation according to the core dominant factors of the differentiation mechanism of rural poverty. The county's poverty alleviation and development under different driving mechanisms need orderly promotion of poverty alleviation and integration of urban and rural development strategy with adjusting measures to local conditions, respecting for science, and stressing practical results.

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[17]
Meybeck M, Green P, Vörösmarty C, 2001. A new typology for mountains and other relief classes: An application to global continental water resources and population distribution.Mountain Research and Development, 21(1): 34-45.

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[19]
Shi Z, Deng W, Zhang S, 2018. Spatio-temporal pattern changes of land space in Hengduan Mountains during 1990-2015.Journal of Geographical Sciences, 28(4): 529-542.Hengduan Mountains offer land space for a variety of ecological services. However, the sustainable development and management of land space has been challenged by increased human activities in recent...

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[20]
Wei W, Shi P, Zhou J, et al., 2013. Environmental suitability evaluation for human settlements in an arid inland river basin: A case study of the Shiyang River Basin.Journal of Geographical Sciences, 23(2): 331-343.The paper selects slope, aspect, Relief Degree of Land Surface, land use, vegetation index, hydrology, transportation density and climate as evaluation indexes and sets up the Human Settlements Environmental Index (HEI) model to evaluate the environmental suitability for Human Settlements in Shiyang river basin. Through using spatial analysis technology of GIS such as spatial overlay analysis, buffer analysis and density analysis to establish the spatial situation of nature suitability and spatial pattern for human settlement. The Results showed that: the index of nature suitability for human settlement in Shiyang river basin was between 17.13 and 84.32. In general, nature suitability for human settlement decreased from southwest to northeast. Saw from area pattern, the suitable region mainly distributed in Minqin oasis, Wuwei oasis and Changning basin, which accounting for about1080.01 km2, 2.59% of the total area. Rather and comparatively suitable region mainly distributed around the county in Gulang, Yongchang and north of Tianzhu, which accounting for about1100.30 km2.The common suitable region mainly distributed outside of the county inYongchang, Jinchuan and most area of Minqin county, which accounting for about 23328.04km2, 56.08% of the total area. The unsuitable region mainly distributed upstream and north of river, which accounting for about 9937.60 km2, 23.89% of the total area. Meanwhile, the least suitable region distributed around the Qilian Mountain which covered by snow and cold desert and the intersecting area between Tenger Desert and Badain Jaran Desert. The total area was about 6154.05 km2, which accounting for 14.79% of the total area. Suitable regions for human inhabitance mainly distributed around rivers in the form of ribbons and batches, while others are scattered. Their distribution pattern was identical with the residential spatial pattern. In addition, the relationships between HEI and some factors were also analyzed. There was a clear logarithm correlation between situation of residential environment and population, that is, the correlation coefficient between evaluation value and population density reached 0.851. There was also positive correlation between situation of residential environment and economics, which reached 0.845 between evaluation value of residential environment and GDP. Results also showed the environment was out of bearing the existing population in Shiyang river basin. Spatial distribution of population was profoundly affected by severe environment such as the expanded deserts, the wavy terrains, and the changeful climate. Surface water shortage and slowly economic growth was the bottleneck of nature suitability for human settlement in Shiyang river basin. So according to these problems and various planning, some of residential parts need to relocate in order to improve situation of residential environment.

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[21]
Xi C, Qian T, Chi Y, et al., 2018. Relationship between settlements and topographical factors: An example from Sichuan Province, China.Journal of Mountain Science, 15(9): 2043-2054.Terrain can influence the spatial distribution of settlements. Studies on the terrain characteristics of settlements can help to understand the effects of the environment on human activities. This paper provides a quantitative analysis of the relationship between settlements and topographical factors. A statistically significant sample of residential locations and ASTER GDEM V2 were used to investigate terrain traits and settlements distributions. We selected eight topographical factors and introduced a practical concept, distributive entropy, into assessing the aggregation extent of the settlements spatial distribution. The study showed that topography varies within the study area, and distributive entropy indicates that settlements have distinctive distribution tendency in statistic approach. According to the results of this study, mountain inhabitants prefer to settle in valleys. Additionally, with distributive entropy, residential suitability was divided to three levels: suitable, normal, and unsuited. The results showed that suitable area is small in Sichuan Province, accounting for 8.2%~29.9%; however, unsuited area is large, accounting for 33%~63.3%.

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[22]
Yu H, Luo Y, Liu S Q et al., 2015. The influences of topographic relief on spatial distribution of mountain settlements in Three Gorges area.Environmental Earth Sciences, 74(5): 4335-4344.Topographic analysis is an important component of research on geomorphologic and surface process. The threshold value method was used to obtain the topographic relief of the study area based on 1:100,000 digital elevation model (DEM). Through mean change-point analysis method, the optimal statistical unit of topographic relief was determined as 0.1502km 2 . The swath profile method was used to analyze topographic and geomorphological characteristics of the Three Gorges Area. Combined with geosciences research method, the spatial analysis technique was applied to quantitatively analyze the relationship between topographic relief and settlements as well as settlement density (SD) in the Three Gorges Area. The results show that: (1) The Three Gorges Area is mainly covered by hilly regions, accounting for 58.9302% of the study area. The cascade structure of planation surface is very significant. Northeast undulating mountains have greater topographic relief. The peaks are generally located at the elevation from 1500 to 200002m, i.e., high planation surface; southwest low mountain valley regions have greater topographic relief. The summit level is generally located at the elevation from 500 to 150002m, i.e., low planation surface with an elevation from 1200 to 150002m, pediment with an elevation from 500 to 120002m; the general elevation gradually decreases from east to west, showing a geomorphological shape as narrow ridge and wide valley. (2) Topographic relief is a key factor influencing the spatial distribution of mountain settlements. In the vertical direction, the mountain settlements have significant change characteristics. In the horizontal direction, the mountain settlements have significant spatial aggregation characteristics.

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[23]
Zhang Haixia, Niu Shuwen, Qi Jinghui, et al., 2016. Geological statistics analysis of population distribution at township level in Henan Province.Geographical Research, 35(2): 325-336. (in Chinese)

[24]
Zhang J, Zhu W, Zhao F,et al., 2018. Spatial variations of terrain and their impacts on landscape patterns in the transition zone from mountains to plains: A case study of Qihe River Basin in the Taihang Mountains.Science China Earth Sciences, 61(4): 450-461.Terrain plays a key role in landscape pattern formation, particularly in the transition zones from mountains to plains.Exploring the relationships between terrain characteristics and landscape types in terrain-complex areas can help reveal the mechanisms underlying the relationships. In this study, Qihe River Basin, situated in the transition zone from the Taihang Mountains to the North-China Plain, was selected as a case study area. First, the spatial variations in the relief amplitudes(i.e.,high-amplitude terrain undulations) were analyzed. Second, the effects of relief amplitudes on the landscape patterns were indepth investigated from the perspectives of both landscape types and landscape indices. Finally, a logistic regression model was employed to examine the relationships between the landscape patterns and the influencing factors(natural and human) at different relief amplitudes. The results show that with increasing relief amplitude, anthropogenic landscapes gradually give in to natral landscapes. Specifically, human factors normally dominate the gentle areas(e.g., flat areas) in influencing the distribution of landscape types, and natural factors normally dominate the highly-undulating areas(e.g., moderate relief areas). As for the intermediately undulating areas(i.e.,medium relief amplitudes), a combined influence of natural and human factors result in the highest varieties of landscape types. The results also show that in micro-relief areas and small relief areas where natural factors and human factors are more or less equally active,landscape types are affected by a combination of natural and human factors.The combination leads to a high fragmentation and a high diversity of landscape patterns. It seems that appropriate human interferences in these areas can be conducive to enhancing landscape diversity and that inappropriate human interferences can aggravate the problems of landscape fragmentation.

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[25]
Zhang Jingjing, Zheng Hui, Zhu Lianqi, et al., 2017. Multi-dimensional changes of vegetation NDVI and its response to climate in Western Henan Mountains.Geographical Research, 36(4): 765-778. (in Chinese)Western Henan Mountains, the extent of Qinling Mountains in Henan province and the transition from subtropical to warm temperate zone, are sensitive to climate change. This study sought to analyze vegetation NDVI change and its response to climate change in this sensitive area in multi-dimensions because the multi-dimensional ecological unit analysis is conducive to vegetation protection and ecology restoration in mountain ecosystems. We firstly used S-G filtering algorithm to reconstruct the MODIS-NDVI time-series data from 2000 to2013 and combined DEM, temperature and precipitation data in the study area; then we used statistical analyses(i.e., linear regression, correlation analysis, and so on) to study vegetation NDVI change and its response to climate variables(temperature and precipitation) in different terrain factors(elevation, slope, and aspect). The results showed that:(1) in 2000-2013, there was a significant growth of vegetation NDVI in the study area, and the growth rate was 0.041/10 a. The finding suggested that, in general, the vegetation in the Western Henan Mountains was positively developed in this area. Meanwhile, the mean NDVI value increased with the increase of elevation, and then the trend became decreased; while it gradually increased as the slope increased. The mean NDVI value, however, had no significant differences in each aspect.(2)The recovery probability of vegetation in 1100 m regions was the highest, whereas the degradation probability in 1700 m regions was the highest. Regarding the slope, the recovery probability of vegetation in 10 ~20 regions was the highest, while the degradation probability in 0 ~5 regions was the highest. The variation of recovery(or degradation) probability of the aspect was not obvious somehow.(3) Vegetation in different terrains was affected by distinctive climate factors. Specifically, vegetation NDVI change at high elevations had stronger correlation with precipitation than with temperature, which indicated that the vegetation dynamics in this range was mainly affected by precipitation change. Inversely, vegetation NDVI change on different slopes had closer relationship with temperature than with precipitation. Not surprisingly, in different aspects there was little difference in terms of the response of vegetation NDVI to climate variables.(4) NDVI growth rates on the north slopes of sub-mountains, such as Xiaoshan, Xionger, and Funiu, were much higher than those on the south slopes. Moreover, the vegetation on the north slopes was more sensitive to precipitation change, whereas on the south slopes it was more sensitive to temperature change. All this echoes the importance of studying the response of local ecological environment to mountain ecosystems in transition zone under the background of global climate change.

[26]
Zhang X R, Dong K, 2012. Neighborhood analysis-based calculation and analysis of multi-scales relief amplitude. Advanced Materials Research, 468-471: 2086-2089.Abstract. Relief Amplitude is a quantitative index that describes topographic features and reflects the degree of relief amplitude. It is widely used in the field of topographic mapping and resource environment assessment. In the present study, the relief amplitudes in different neighborhood-scale units in east mountainous area of Tibetan Plateau were calculated based on the high space-resolution ASTER GDEM data by calling the Focal Function used for neighborhood analysis by means of AML language program. The results revealed that the size of the unit of neighborhood scale affected crucially the relief amplitude. The value of relief amplitude increased rapidly with increasing area of the statistic unit of neighborhood at first. Whereas when the area of neighborhood-scale unit was close to some threshold value, the increasing rate started to slow down and became stable, and during the process in which the increasing rate started to decline, there was a prominent turning point, namely the optimal unit of neighborhood scale. The optimal unit of neighborhood scale was 5.06km2 based on the significance test of elevation difference. A classification map of relief amplitude was established based on the optimal unit of neighborhood scale and it was found that, in the study area, the relief amplitude increased gradually northwestward-southeastwardly.

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[27]
Zhao M, Cheng W, Zhou C, et al., 2017. GDP spatialization and economic differences in South China based on NPP-VIIRS nighttime light imagery.Remote Sensing, 9(7): 673. doi: 10.3390/rs9070673.Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R2 > 0.88) than other nighttime light indices (R2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened.

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[28]
Zhu W, Li S, 2017. The dynamic response of forest vegetation to hydrothermal conditions in the Funiu Mountains of western Henan Province.Journal of Geographical Sciences, 27(5): 565-578.This paper uses HJ-1 satellite multi-spectral and multi-temporal data to extract forest vegetation information in the Funiu Mountain region. The S-G filtering algorithm was employed to reconstruct the MODIS EVI (Enhanced Vegetation Index) time-series data for the period of 2000 2013, and these data were correlated with air temperature and precipitation data to explore the responses of forest vegetation to hydrothermal conditions. The results showed that: (1) the Funiu Mountain region has relatively high and increasing forest coverage with an average EVI of 0.48 over the study period, and the EVI first shows a decreasing trend with increased elevation below 200 m, then an increasing trend from 200 1700 m, and finally a decreasing trend above 1700 m. However, obvious differences could be identified in the responses of different forest vegetation types to climate change. Broad-leaf deciduous forest, being the dominant forest type in the region, had the most significant EVI increase. (2) Temperature in the region showed an increasing trend over the 14 years of the study with an anomaly increasing rate of 0.27 C/10a; a fluctuating yet increasing trend could be identified for the precipitation anomaly percentage. (3) Among all vegetation types, the evergreen broad-leaf forest has the closest EVI-temperature correlation, whereas the mixed evergreen and deciduous forest has the weakest. Almost all forest types showed a weak negative EVI-precipitation correlation, except the mixed evergreen and deciduous forest with a weak positive correlation. (4) There is a slight delay in forest vegetation responses to air temperature and precipitation, with half a month only for limited areas of the mixed evergreen and deciduous forest.

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[29]
Zhu W, Zhang X, Zhang J,et al., 2019. A comprehensive analysis of phenological changes in forest vegetation of the Funiu Mountains, China.Journal of Geographical Sciences, 29(1): 131-145.This paper reports the phenological response of forest vegetation to climate change(changes in temperature and precipitation) based on Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) time-series images from 2000 to 2015. The phenological parameters of forest vegetation in the Funiu Mountains during this period were determined from the temperature and precipitation data using the Savitzky olay filter method, dynamic threshold method, Mann-Kendall trend test, the Theil-Sen estimator, ANUSPLIN interpolation and correlation analyses. The results are summarized as follows:(1) The start of the growing season(SOS) of the forest vegetation mainly concentrated in day of year(DOY) 105 120, the end of the growing season(EOS) concentrated in DOY 285 315, and the growing season length(GSL) ranged between 165 and 195 days. There is an evident correlation between forest phenology and altitude. With increasing altitude, the SOS, EOS and GSL presented a significant delayed, advanced and shortening trend, respectively.(2) Both SOS and EOS of the forest vegetation displayed the delayed trend, the delayed pixels accounted for 76.57% and 83.81% of the total, respectively. The GSL of the forest vegetation was lengthened, and the lengthened pixels accounted for 61.21% of the total. The change in GSL was mainly caused by the decrease in spring temperature in the region.(3) The SOS of the forest vegetation was significantly partially correlated with the monthly average temperature in March, with most correlations being negative; that is, the delay in SOS was mainly attributed to the temperature decrease in March. The EOS was significantly partially correlated with precipitation in September, with most correlations being positive; that is, the EOS was clearly delayed with increasing precipitation in September. The GSL of the forest vegetation was influenced by both temperature and precipitation throughout the growing season. For most regions, GSL was most closely related to the monthly average temperature and precipitation in August.

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[30]
Zhuang D, Liu M, Deng X, 2002. Spatialization model of population based on dataset of land use and land cover change in China.Chinese Geographical Science, 12(2): 114-119.The spatialization of population of counties in China is significant. Firstly, we can gain the estimated values of population density adaptive to different kinds of regions. Secondly, we can integrate effectively population data with other data including natural resources, environment, society and economy, build 1km GRIDs of natural resources reserves per person, population density and other economic and environmental data, which are necessary to the national management and macro adjustment and control of natural resources and dynamic monitoring of population. In order to establish population information system serving national decision making, three steps ought to be followed:1) establishing complete geographical spatial data foundation infrastructure including the establishment of electric map of residence with high resolution using topographical map with large scale and high resolution satellite remote sensing data, the determination of attribute information of housing and office buildings, and creating complete set of attribute database and rapid data updating; 2) establishing complete census systems including improving the transformation efficiency from census data to digital database and strengthening the link of census database and geographical spatial database, meanwhile, the government should attach great importance to the establishment and integration of population migration database; 3) considering there is no GIS software specially serving the analysis and management of population data, a practical approach is to add special modules to present software system, which works as a bridge actualizing the digitization and spatialization of population geography research.

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