The spatial distribution of land use at different terrain gradients has been used as an important index for the land management and ecological restoration in the Loess Plateau of China. Based on the land use data surveyed in 2015 and digital elevation model data with a resolution of 30 m from typical village transects in the Loess Plateau area in Yan’an City, Shaanxi Province, this study analyzed the terrain niche index, distribution index, land utilization comprehensive index, and land use equilibrium degree for four village transects. The results demonstrated that the land use types in the four village transects were mainly grassland, forest land, and cropland. Land use types showed obvious differences in respect to topographical gradient with built-up land, traffic land, water bodies, cropland, and orchard distributed in low terrain gradient areas, forest land, and grassland distributed in medium- and high-terrain areas. As terrain gradient increased, the land utilization comprehensive index and land use equilibrium degree showed a generally decreasing trend. These findings may provide a useful reference for land use planning and land resource allocation in the Loess Plateau region.
Land use and cover change is a multi-disciplinary issue that needs to be considered comprehensively [1,2,3,4]. The study of land use change has become an important topic related to the dynamic process of regional environmental changes, which has also stimulated considerable research around the world. Changes in spatial distribution patterns are key issues of research on land use [5,6,7]. Land use can be affected by a variety of influencing factors. Terrain, as one of the basic factors of the natural environment, has significant impacts on the land use process and degree [8,9] and is an important factor influencing regional land use . In the Loess Plateau of China, the terrain is complex and fluctuates dramatically. In addition, there are obvious differences in temperature, humidity, solar radiation, and soil conservation, thereby further enlarging the effect of topographic factors on land use [11,12,13].
In recent years, numerous studies that focused on the impact of terrain on populations [14,15], economy [16,17], environmental pollution [18,19], landscapes [20,21], hydrological processes [22,23], vegetation change trends , regional land use and cover, ecological characteristics [25,26,27], vertical changes in elevation and slope , spatiotemporal changes in land use [29,30,31], and the suitability of forest landscape [32,33] have been reported. In practice, elevation and slope are closely related and typically affect the spatial distribution patterns of land use simultaneously. Therefore, analyzing the influence of topographic factors on land use spatiotemporal distribution patterns using the terrain niche index [34,35] which combines the effect of elevation and slope has attracted a lot of attention. For example, the terrain niche index was used to study the land use spatiotemporal distribution patterns in Znojmo and Třebíč in the Czech Republic . In addition, the terrain niche index was also used to examine the spatial distribution characteristics of land use in Wanzhou District . The Loess Plateau, located in the central and northern parts of China, is one of the largest loess plateaus in the world and is also one of the most ecologically vulnerable regions in China. Soil erosion has led to soil degradation and reduction of agricultural yields. Land use pattern is undergoing an intensive change in the Loess Plateau. Therefore, the subjects of spatial change and distribution patterns of land use in the Loess Plateau have become research focuses for a long time [38,39,40,41]. The change of cropland area is an important part of land use change in the Loess Plateau, especially the abandoned cropland . The influencing factors were analyzed to understand how different factors affect the spatio-temporal changes in land use in the Loess Plateau . The knowledge of how the changing land cover affects the response of vegetation to drought in the Loess Plateau can provide a guidance on environmental protection and ecological restoration . The dynamic impact of land management practices on the Loess Plateau through field investigation and quantitative analysis from the perspective of returning farmland to forests was revealed. Based on the results, the researchers proposed environmental restoration and optimization of land use in the Loess Plateau . However, there is still a lack of research on smaller scales, especially on land use spatial distribution patterns at different terrain gradients in village transects in the Loess Plateau. A transect is a linear area type that changes regularly or has obvious differences under a certain dominant driving factor . The transect research method was first successfully applied to the study on global change  and further applied to the study on borderland change . The study on village transects is important for reasonable use of natural resources and sustainable development of the village economy since the preliminary investigation has shown that more and more economic production activities have been performed in transects . Therefore, the purpose of this study is to analyze the land spatial patterns in village transects in the Loess Plateau, which is expected to reveal the characteristics of differentiation in rural development, help further examination of changes in the relationships between urban–rural development and topographic gradients, and lay a foundation for the optimization of overall layout of villages and towns.
2 Materials and methods
2.1 Study area
Yan’an City, located in the middle reaches of the Yellow River, had been eroded and cut by the river flow for a long time, resulting in the formation of special terrains in this region, such as ridges and hills, and numerous valleys. It is a typical hilly and gully area in the Loess Plateau. Considering such topographical and geomorphological characteristics there, we chose Yan’an City as a study area. The geographical environment, such as the terrain, is high in the northwest and low in the southeast. The city is adjacent to Yulin City in the north; Xianyang City, Tongchuan City, and Weinan City in the south; Qingyang City of Gansu Province in the west; and Linfen City and Lyuliang City of Shanxi Province across the Yellow River in the east (Figure 1).
In the process of selecting the transects, the accuracy of land use data, topographic trends, and area size were considered. After a comprehensive consideration of various factors, four representative transects were selected from the perspective of the village administrative district, i.e., channels passing through villages between two county/district government areas. Transect 1 is located in the east of Yan’an City, with an average altitude of 834.95 m. The villages along the channels between Qilicun Town and Luozishan Township in Yanchang County were selected as the county–township transect. Transect 2 is located in the south of Yan’an City, with an average altitude of 939.68 m. The villages in Fucheng Town in Fu County and Yongxiang Township in Luochuan County were selected as the county–township transect. Transect 3 is located in the west of Yan’an City, with an average altitude of 1369.90 m. The villages along the channel between Wuqi Town in Wuqi County and Danba Town in Zhidan County were selected as the county–county transect. Transect 4 is located in the north of Yan’an City, with an average altitude of 1142.91 m. The villages along the channel between Jianhua Town in Ansai County and Hezhuangping Town in Baota District were selected as the county–county transect (Figure 1).
Three major reasons were considered for selecting the four transects. First, the four transects are located in different directions to Yan’an City, a prefecture-level city in the Shaanbei region of Shaanxi province, China. Second, the average elevations of the four transects are significantly different, with an elevation interval of about 100 m. Third, two “county–county” transects with the channels passing through villages between two county government areas, and two “county–township” transects with the channels passing through villages between county and township government areas were selected.
Two county–county transects include 14 administrative villages in four towns, and two county–township transects include 12 administrative villages in four towns and 19 administrative villages in four towns (Figure 2). The administrative districts in the typical village transects are summarized in Table 1.
|Transect code||Transect type||Village number||District|
|Transect 1||“County–township” transect||12||Qilicun Town, Yanchang County|
|Zhangjiatan Town, Yanchang County|
|Anhe Town, Yanchang County|
|Luozishan Township, Yanchang county|
|Transect 2||19||Fucheng Town, Fu County|
|Yongxiang Township, Luochuan County|
|Fengqi Town, Luochuan County|
|Jiaokouhe Town, Luochuan County|
|Transect 3||“County–county” transect||14||Wuqi Town, Wuqi County|
|Baibao Town, Wuqi County|
|Jingding Town, Zhidan County|
|Danba Town, Zhidan County|
|Transect 4||14||Jianhua Town, Ansai County|
|Zhenqudong Town, Ansai County|
|Yanhewan Town, Ansai County|
|Hezhuangping Town, Baota District|
2.2 Data sources
The land use data and village boundaries were vector data based on the database surveyed at the end of 2015 by the Yan’an Natural Resources Bureau. According to the quantity, quality, and distribution of the land use types in the study area and the purpose of this study, a single spot was processed. The map spots of cropland, orchard, forest land, grassland, traffic land, water bodies, built-up land, and unused land were merged. The description of land use types is summarized in Table 2. The terrain factors included the digital elevation model (DEM), slope, and terrain niche index. The DEM was extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model Version 2 products with a spatial resolution of 30 m downloaded from the Geospatial Data Cloud (www.gscloud.cn/). Using the surface analysis function in the toolbox within ArcGIS 10.3 software, the slope was calculated from the DEM. The raster calculator was used to obtain the terrain niche index (see Section 2.3.1) from the elevation and slope. The land use, slope, DEM, and terrain niche index maps are shown in Figure 3.
|Land use type||Description|
|Cropland||The land where crops are produced|
|Orchard||The land planted with perennial woody and herbaceous crops, mainly for collecting fruits and leaves|
|Forest land||The land where trees, bamboos, shrubs, and mangroves grow|
|Grassland||The land mainly for growing herbaceous plants|
|Traffic land||The land used for transportation, such as ground lines, stations, airports, ports, docks, and bridges|
|Water bodies||Water bodies refer to the land areas where beaches, ditches, marshes, hydraulic structures, etc. are located|
|Built-up land||The land for different types of houses and ancillary facilities mainly used for human activities|
|Unused land||The land without being used|
2.3.1 Terrain niche index
Slope and elevation are the two important environmental factors that affect land use and land carrying capacity. The terrain niche index combines slope and elevation information to comprehensively reflect the terrain conditions of certain locations . The calculation formula is as follows:
where TNI represents the terrain niche index, e and s represent the elevation and slope at any point on the grid in the area, and E and S represent the average elevation and average slope of the grid where the point is located, respectively. The larger the terrain niche index is, the higher the elevation and/or the slope are, and vice versa.
2.3.2 Distribution index
The distribution index can be used to describe the distribution frequency of each land use type on different terrain gradients . The calculation formula is as follows:
where P is the distribution index, S ie is the area of the land type i on the terrain e, S i is the area of the land type i, S e is the area of terrain interval e, and S is the total area of the study area. When P > 1, it means that the land use type belongs to the dominant distribution .
2.3.3 Land utilization comprehensive index
The land utilization comprehensive index can quantitatively express the distribution characteristics of land use degree on different terrain gradients . The calculation formula is as follows:
where R is the land utilization comprehensive index, L i is the grading index of land type i, and C i is the proportion of land type i. The distributions of land utilization comprehensive index across the study area were calculated based on the previous results  and natural conditions. The grading index intensity of each category was assigned as follows: 1 for unused land; 2 for water bodies, forest land, and grassland; 3 for cropland and orchard; and 4 for built-up and traffic lands.
2.3.4 Land use equilibrium degree
The land use equilibrium degree can be used to indicate the order degree and spatial dispersion of land use distribution in the study area. Moreover, this degree can reflect the dynamic change law of land use from different aspects . The calculation formula is as follows:
where J is the land use equilibrium degree, J ∈ [0,1]; a ie is the proportion of land use type i on terrain gradient e; and n represents the number of land use types. The smaller the land use equilibrium degree is, the simpler the land use structure is. Theoretically, when J = 1, land use reaches the equilibrium state.
3.1 Influence of topographic factors on land use spatial distribution
3.1.1 Distribution index of land use type against terrain gradient
Based on the actual situation of the study area and the above formula, the distributions of terrain niche index in the four transects were calculated. To reflect the dominant distribution interval of each land use type, the raster map of the terrain niche index was reclassified using the equal interval reclassification method , which was divided into eight levels represented by codes 1–8. The classification standards and results are provided in Table 3. The area percentage of each transect is shown in the Table A1.
The distribution index of each transect against the terrain gradient is depicted in Figure 4. Transect 1 was one of the county–township transects (Figure 4a). The dominant distribution of water bodies was at the 1st, 2nd, and 3rd terrain gradients, distributed mainly along the transect and concentrated in low-elevation areas. At the 4th–8th levels, the distribution index of water bodies tended to be zero. The distribution indices of forest land and grassland increased and the dominant distributions were at 5th–8th terrain gradients. Built-up land was predominantly distributed at the 1st–4th and the 8th terrain gradients. The dominant distribution of built-up land at the 8th level was located at Chengguan Village. Cropland showed predominant distributions at the 1st–4th terrain gradients and was mainly scattered in low-terrain areas. The distribution index of orchards fluctuated obviously, and its dominant distribution was at the 2nd–5th and the 8th terrain gradients. Traffic land exhibited predominant distributions at the 5th–7th terrain gradients, where Dongjiahe Village’s road is situated in this area. Unused land was dominantly distributed at the 2nd, 3rd, and 8th terrain gradients and reached the highest index value (7.73) at the 8th terrain gradient.
Transect 2 was another county–township transect (Figure 4b). The dominant distribution of forest land was at the 4th–7th terrain gradients, which were mainly distributed in An’er, Duhe, Duanjiazhuang, Liuwan, Pingquan, and Yukou Villages in the northern part of the transect. The distribution indices of grassland and forest land were similar, and their dominant distributions were at the 4th–8th terrain gradients. Water bodies were distributed dominantly at the 1st–3rd and the 7th terrain gradients and in the valley area of the channel. At the 1st terrain gradient, the maximum distribution index was 3.62, indicating that water bodies had strong selectivities to the areas with small slopes and low elevations. The dominant topographical position of built-up land was at the 1st–4th topographical gradients, where topographical conditions of this area are relatively good and water resources are abundant, thereby meeting the requirements for living. The dominant distribution of traffic land was at the 1st–3rd terrain gradients, which were convenient for the construction of roads. A large amount of cropland was distributed at the 1st–3rd terrain gradients with low elevations, which were convenient for human activities. The dominant terrain position of the orchard was at the 1st–4th terrain gradients, indicating that the spatial distribution of the orchard was less affected by the altitude and slope. The dominant topographic position of unused land was at the 2nd and the 6th–7th terrain gradients.
Transect 3 was one of the county–county transects. Figure 4c shows that the distribution index in this transect had similarities and differences with respect to different terrain gradients. Water bodies, cropland, and built-up land had dominant distributions at the 1st–3rd terrain gradients. The distribution indices of the three land-use types gradually decreased as the topographic gradient increased. Water bodies were distributed along the channel, and cropland was distributed mainly along the water bodies in a strip. Meanwhile, built-up land was distributed mainly in the northwest of transect 3 and mostly located in Yaogoumen, Zhongyangqing, and Jinfoping Villages. The dominant distribution of forest land was at the 4th–6th terrain gradients. The dominant terrain position of grassland was at the 4th–8th terrain gradients, indicating that grassland was distributed mainly in the areas with relatively steeper terrain gradients. The orchard distribution index fluctuated as the terrain niche index increased, and the dominant topographic position of the orchard was at the 2nd–4th, 7th, and 8th terrain gradients. The dominant distribution of traffic land was located at the 1st–2nd and 7th terrain gradients with the Jinfoping Village roads passing through the area of the 7th terrain gradient. The dominant topographical position of unused land was at the 1st– 2nd terrain gradients. The distribution index gradually decreased to zero as the topographic gradient increased.
Transect 4 was another county–county transect and its topography exerted a considerable influence on the land use spatial distribution. Figure 4d shows that the dominant topographic positions of cropland, built-up land, and unused land were located at the 1st–3rd terrain gradients. Unused land had the largest distribution index (8.18) at the 1st topographic gradient, mainly distributed in Majiagou Village. From the 1st to 8th terrain gradients, the distribution index of cropland gradually decreased, indicating that the cropland in transect 4 was highly selective to low terrain gradients. Built-up land was distributed at low terrain gradients where industrial and mining land and residential areas were located. Forest land and grassland had a similar distribution index, which increased as the topographic level index increased, and the dominant interval of both land use types was at the 4th–8th terrain gradients. The orchard distribution index showed a fluctuating trend, and the dominant terrain was at the 2nd–4th and the 8th terrain gradients. The dominant distribution of traffic land was located at the 1st–3rd and the 8th levels with the roads of Lijiawan Village and Majiagou Village passing through this area at high terrain gradients.
3.1.2 Spatial distribution of land use types on terrain gradient
The land use types in the village transects on the Loess Plateau showed several common features and differences against terrain gradients (Figure 5). The built-up land and cropland distribution indices in the four transects were greater than 1 at the 1st–3rd terrain gradients, where water bodies required for human activities and agricultural production were available. The distribution indices of grassland in the four transects showed a gradual upward trend, and the dominant terrain positions were at the 5th–8th terrain gradients, indicating the strong vitality of grassland, which had good adaptability and can be distributed in the areas of high elevation and steep slope, with less demands for flat geographical conditions. For the traffic land, the dominant distributions were on the 5th–7th terrain gradients, the 1st–3rd terrain gradients, the 1st–2nd and 7th terrain gradients, and the 1st–3rd and 8th terrain gradients in transect 1, transect 2, transect 3, and transect 4, respectively. The orchard distribution indices in the four transects first showed a slowly increasing trend and then a decreasing and increasing trend. The dominant topographical positions of forest land in the four transects were on the 4th–8th terrain gradients. Unused land in the study area was relatively small and scattered on different terrain gradients.
3.2 Comprehensive land use index analysis based on terrain gradient
Figure 6 shows that the land utilization comprehensive index of the study area demonstrated a decreasing trend as terrain gradient increased. The reason was that in the low-terrain gradient areas, the elevation and slope were relatively small, which was suitable for human life and various production activities. The land use types were mainly construction land and arable land, increasing in land use intensity. As the terrain gradient increased, the proportion of forest land increased in general (Table A1), land use intensity decreased, and the land utilization comprehensive index decreased. When the terrain gradient was higher than the 5th level, the land utilization comprehensive index of the study area tended to be stable. Four transects had similar land use comprehensive index. On the middle–high terrain gradients, the population and the intensity of human activity were low, whereas shrubby areas were larger. Land use type composition was relatively simple, and the land comprehensive degree index was relatively stable. The land comprehensive degree index of transect 1 on the 1st–3rd terrain gradients, and transect 4 on the 1st–4th terrain gradients declined significantly, indicating that the areas were greatly affected by both elevation and slope.
3.3 Land use equilibrium degree analysis on terrain gradient
Figure 7 shows that the land use equilibrium degree of the transects in the typical Loess Plateau exhibited similar characteristics and differences. The equilibrium degree of the land use types in transect 1 reached the maximum value in the 2nd terrain gradient (0.81), indicating that human activities in this area were mainly concentrated in the low topographic places. On the 5th terrain gradient, the land use equilibrium degree decreased slowly, where the grassland area occupied more than 50% of transect 1 on the 5th terrain gradient (Table A1). Transect 2 had the highest land use equilibrium degree in the 1st and 2nd terrain gradients, which was 0.85 for both levels. In the 3rd–6th terrain gradients, equilibrium degree decreased rapidly. The land use equilibrium degree of transect 3 was the highest (0.78) in the 1st terrain gradient area. In the 3rd terrain gradient area, equilibrium decreased the most, where forest land and grassland occupied nearly the entirety of transect 3 (Table A1). The land use equilibrium degree index of transect 4 was 0.82 and 0.81 in the 1st and 2nd terrain gradient areas, respectively. As the terrain gradient increased, equilibrium decreased to as low as 0.49 in the 6th terrain gradient area and rose to 0.50 at the 8th level. This was because the forest and grasslands have a predominant distribution in transect 4, with less disturbance from human activities, and have few land use types.
The common feature of the four transects was that with the increase in topographic gradient, the equilibrium degree decreased. The reason was that the terrain in this region was relatively flat. Convenient transportation, abundant water, and soil resources were conducive to human life and agricultural production activities. Land use types were diverse, and the equilibrium degree was higher.
In this study on the spatial distribution of land use, most of the transects were selected based on the scales at county levels. Two county–county transects and two county–village transects were selected to pursue the research on land use distributions with respect to terrain gradients. The results revealed the characteristics of changes in the rural land type, which can be further used to reveal the relationships between urban and rural development and lay a foundation for optimizing the layout of villages and towns. The study on the scales smaller than a village, however, remain a research topic in the future.
The distribution index can effectively eliminate the effects of different terrain segments and/or areas. It can be used to illustrate the dominant distribution of different land types. The decrease in comprehensive land use index with an increase in terrain gradient was attributed to the relatively small elevation and slope in low terrain gradient areas, which were suitable for human production activities. Moreover, built-up land and cropland were the main land use types that resulted in an increase in land use intensity in low terrain gradient. The main reason for the low land utilization comprehensive index in high terrain gradient areas was that a large amount of irrigated land and dry land had been converted into forest land and grassland. More specifically, the land utilization comprehensive index of transect 3 and transect 4 at the 1st–4th terrain gradients decreased rapidly, indicating that elevation and slope had a great impact in these areas. With the implementation of the policy of returning cropland to forest land and grassland, the forest land and grassland had become dominant in the areas with medium- and high-terrain gradients. Similar results were also reported by Liang and Liu . The land types with competitive relationships on the same topographic gradient should be rationally allocated.
Aside from the influence of natural factors such as elevation and slope, the comprehensive land use index and land use equilibrium degree can be significantly affected by human activities . Terrain affects the formation of land distribution types. More human activities appeared on lower terrain gradients. For example, built-up land was relatively fixed, and different land use types were difficult to change the current land use type, so they were in a state of balance with a high degree of land use equilibrium. In higher terrain gradient areas, the land use types were mostly woodland and grassland, and the land use balance was lower. Similar effects were observed in this study because both the comprehensive land use index and the land use equilibrium degree in the four transects decreased as the terrain gradient increased.
In this study, four village transects in the Chinese Loess Plateau of Yan’an City, Shaanxi Province, China, were selected as representatives to examine the land use distribution patterns in 2015 against terrain gradients. Major conclusions drawn from this study are as follows:
The land use types in the four transects are mainly grassland and forest land. The distribution of the land use types at different topographic gradients exhibited significant hierarchical characteristics. Cropland, built-up land, traffic land, water bodies, and unused land had predominant distributions at low terrain gradients, whereas forest land and grassland were mainly distributed at medium to high terrain gradients.
Differences existed in the comprehensive land use index at different terrain gradients. Fundamentally, it was relatively high in low topographic areas and decreased as the topographic level increased. At the middle and low terrain levels, the land use comprehensive degree index decreased rapidly, while at the high terrain level, the land use comprehensive degree index tended to be stable because of less influence from human activities.
Overall, the land use equilibrium degree decreased as the terrain gradient increased. The land use equilibrium degree was high in low terrain gradient areas. This finding indicated that the land use diversity in the study area was concentrated mainly in low-terrain areas because the terrain in such areas was relatively flat, transportation was convenient, and water and soil resources were abundant, which were conducive to human activities and agricultural production. At the 2nd–4th terrain gradients, the order of land use equilibrium degree in the four transects was transect 2 > transect 1 > transect 4 > transect 3.
Although several common features were observed in land use spatial distribution in the Loess Plateau, differences existed in each transect. The reason was that different natural conditions and socio-economic development in the four transects lead to land use restrictions in the Loess Plateau. As a result, the four transects demonstrated differences in land use distribution. The findings obtained from this study can help better understand the current status of land use distribution and planning of future land use in the Loess Plateau region.
The authors acknowledge the financial support from the National Natural Science Foundation of China (41571346).
Funding information: National Natural Science Foundation of China (41571346).
Conflict of interest: Authors state there is no conflict of interest.
Author contributions: Y. Z.: Original draft, Methodology, Data analysis; J. C.: Funding acquisition; X. Z.: data acquisition; M. Z.: Manuscript framework, Review and editing.
 Arnold C, Wilson E, Hurd J, Civco D. 30 Years of land cover change in Connecticut, USA: a case study of long-term research, dissemination of results, and their use in land use planning and natural resource conservation. Land. 2020;9(8):255. 10.3390/land9080255.Search in Google Scholar
 Wang QZ, Guan QY, Lin JK, Luo HP, Tan Z, Ma YR. Simulating land use/land cover change in an arid region with the coupling models. Ecol Indic. 2021;122(3–4):107231. 10.1016/j.ecolind.2020.107231.Search in Google Scholar
 Mansour S, Al-Belushi M, Al-Awadhi T. Monitoring land use and land cover changes in the mountainous cities of Oman using GIS and CA-Markov modelling techniques. Land Use Pol. 2020;91(C):104414. 10.1016/j.landusepol.2019.104414.Search in Google Scholar
 Gong W, Wang H, Wang X, Fan W, Stott P. Effect of terrain on landscape patterns and ecological effects by a gradient-based RS and GIS analysis. J For Res. 2017;28:1061–72. 10.1007/s11676-017-0385-8.Search in Google Scholar
 Gebrelibanos T, Assen M. Land use/land cover dynamics and their driving forces in the Hirmi watershed and its adjacent agro-ecosystem, highlands of Northern Ethiopia. J Land Use Sci. 2013;10(1):81–94. 10.1080/1747423X.2013.845614.Search in Google Scholar
 Ispikoudis I, Lyrintzis G, Kyriakakis S. Impact of human activities on Mediterranean landscapes in western Crete. Landsc Urban Plan. 1993;24(1–4):259–71. 10.1016/0169-2046(93)90105-M.Search in Google Scholar
 Ramachandra TV, Bharath S, Bharath A. Spatio-temporal dynamics along the terrain gradient of diverse landscape. J Environ Eng Landsc Manag. 2014;22(1):50–63. 10.3846/16486897.2013.808639.Search in Google Scholar
 Zang YZ, Liu YS, Yang YY. Land use pattern change and its topographic gradient effect in the mountainous areas: a case study of Jinggangshan city. J Nat Resour. 2019;34(7):1391–404. 10.31497/zrzyxb.20190704.Search in Google Scholar
 Huang H, Zhou Y, Qian MJ, Zeng ZQ. Land use transition and driving forces in Chinese Loess Plateau: a case study from Pu County, Shanxi Province. Land. 2021;10(1):67. 10.3390/land10010067.Search in Google Scholar
 Liu ZJ, Wang JY, Wang XY, Wang YS. Understanding the impacts of ‘Grain for Green’ land management practice on land greening dynamics over the Loess Plateau of China. Land Use Pol. 2020;99:105084. 10.1016/j.landusepol.2020.105084.Search in Google Scholar
 Yan R, Zhang XP, Yan SJ, Zhang JJ, Chen H. Spatial patterns of hydrological responses to land use/cover change in a catchment on the Loess Plateau, China. Ecol Indic. 2018;92:151–60. 10.1016/j.ecolind.2017.04.013.Search in Google Scholar
 Cheng DY, Li XD. Relationship between population distribution and topography of the Wujiang River Watershed in Guizhou province. Geogr Res. 2020;39(6):1427–38. CNKI:SUN:DLYJ.0.2020-06-015.Search in Google Scholar
 Liu YX, Li CY, Ren ZY. Study on the flow of rural labor force and the contribution of terrain factor in Shaanxi, China. China J Popul Resour Environ. 2013;10(4):77–83. 10.1080/10042857.2012.10685113.Search in Google Scholar
 Wei W, Guo ZC, Xie BB, Zhou JJ, Li CH. Quantitative simulation of socio-economic effects in mainland China from 1980 to 2015: a perspective of environmental interference. J Cleaner Prod. 2020;253(C):119939. 10.1016/j.jclepro.2019.119939.Search in Google Scholar
 Huang XG, Zhao JB, Sun CJ, Tang HL, Liang XQ. Orographic influences on the spatial distribution of PM2.5 on Fei-Wei Plain. Env Sci. 2021;42(10):1–14. 10.13227/j.hjkx.202102024.Search in Google Scholar
 Lai HC, Lin MC. Characteristics of the upstream flow patterns during PM2.5 pollution events over a complex island topography. Atmos Env. 2020;227:117418. 10.1016/j.atmosenv.2020.117418.Search in Google Scholar
 Ribeiro D, Visković NR, Čarni A. Landscape dynamics at borderlands: analysing land use changes from Southern Slovenia. Open Geosci. 2020;12(1):1725–35. 10.1515/GEO-2020-0212.Search in Google Scholar
 Wang LJ, Ma S, Jiang J, Zhao YG, Zhang JC. Spatiotemporal variation in ecosystem services and their drivers among different landscape heterogeneity units and terrain gradients in the southern hill and mountain belt, China. Remote Sens. 2021;13(7):1375. 10.3390/rs13071375.Search in Google Scholar
 Yang J, Zhang HL, Pang JZ. Study on spatial-temporal variation and driving factors of precipitation concentration in Jialing River Basin. Resour Environ Yangtze Basin. 2021;30(4):849–60. CNKI:SUN:CJLY.0.2021-04-008.Search in Google Scholar
 Peng SL, Wang CY, Eguchi S, Kuramochi K, Kohyama K, Yoshikawa S, et al. Response of hydrological processes to climate and land use changes in Hiso River watershed, Fukushima, Japan. Phys Chem Earth, Parts A/B/C. 2021;123:103010. 10.1016/J.PCE.2021.103010.Search in Google Scholar
 Qiu ZC, Liu HJ, Zhang XL, Meng LH, Xu MY, Pan Y, et al. Analysis of spatiotemporal variation of site-specific management zones in a topographic relief area over a period of six years using image segmentation and satellite data. Can J Remote Sens. 2019;45(6):746–58. 10.1080/07038992.2019.1690439.Search in Google Scholar
 Li Q, Shi XY, Wu QQ. Exploring suitable topographical factor conditions for vegetation growth in Wanhuigou catchment on the Loess Plateau, China: a new perspective for ecological protection and restoration. Ecol Eng. 2020;158:106053. 10.1016/J.ECOLENG.2020.106053.Search in Google Scholar
 Wang L, Wu L, Zhang W. Impacts of land use change on landscape patterns in mountain human settlement: The case study of Hantai District (Shaanxi, China). J Mt Sci. 2021;18(3):749–63. CNKI:SUN:SDKB.0.2021-03-015.Search in Google Scholar
 Carlier J, Doyle M, Finn JA, hUallacháin D, Moran J. A landscape classification map of Ireland and its potential use in national land use monitoring. J Environ Manage. 2021;289:112498. 10.1016/J.JENVMAN.2021.112498.Search in Google Scholar
 Cao Z, Li YR, Liu ZJ, Yang LF. Quantifying the vertical distribution pattern of land-use conversion in the loess hilly region of northern Shaanxi Province 1995–2015. J Geogr Sci. 2019;29(5):730–48. 10.1007/s11442-019-1624-z.Search in Google Scholar
 Gomes LC, Bianchi FJJA, Cardoso IM, Fernandes Filho EI, Schulte RPO. Land use change drives the spatio-temporal variation of ecosystem services and their interactions along an altitudinal gradient in Brazil. Landsc Ecol. 2020;35:1–16. 10.1007/s10980-020-01037-1.Search in Google Scholar
 Prijith SS, Srinivasarao K, Lima CB, Gharai B, Rao PVN, SeshaSai MVR, et al. Effects of land use/land cover alterations on regional meteorology over Northwest India. Sci Total Environ. 2021;765:142678. 10.1016/j.scitotenv.2020.142678.Search in Google Scholar
 He Y, Wang WH, Chen YD, Yan HW. Assessing spatio-temporal patterns and driving force of ecosystem service value in the main urban area of Guangzhou. Sci Rep. 2021;11(1):3027. 10.1038/S41598-021-82497-6.Search in Google Scholar
 Chen MY, Zeng LX, Huang ZL, Lei L, Shen YF, Xiao WF. Evaluating suitability of land for forest landscape restoration: a case study of Three Gorges Reservoir, China. Ecol Indic. 2021;127:107765. 10.1016/J.ECOLIND.2021.107765.Search in Google Scholar
 Cui NX, Zou HT, Zhang MS, Guo L. The effects of terrain factors and cultural landscapes on plateau forest distribution in Yushu Tibetan Autonomous Prefecture, China. Land. 2021;10(4):345. 10.3390/LAND10040345.Search in Google Scholar
 Jozef M, Ian SE, Marián J. A comprehensive system of definitions of land surface (topographic) curvatures, with implications for their application in geoscience modelling and prediction. Earth Sci Rev. 2020;211(11):103414. 10.1016/j.earscirev.2020.103414.Search in Google Scholar
 Brovkina O, Zemek F, Novotný J, Heřman M, Štěpánek P. Analysing changes in land cover in relation to environmental factors in the districts of Znojmo and Třebíč (Czech Republic). Eur. Environ Sci. 2017;7:108–18. 10.14712/23361964.2017.9.Search in Google Scholar
 Xing R, Zhou Q, Li H, Chen Q, Chen D. Analysis on spatiotemporal variations of land use change in Wanzhou District of three gorges reservoir based on the terrain gradient. Res Soil Water Conserv. 2019;26(2):297–304. 10.13869/j.cnki.rswc.2019.02.042.Search in Google Scholar
 Liang W, Fu BJ, Wang S, Zhang WB, Jin Z, Feng XM, et al. Quantification of the ecosystem carrying capacity on China’s Loess Plateau. Ecol Indic. 2019;101:192–202. 10.1016/j.ecolind.2019.01.020.Search in Google Scholar
 Ostwald M, Chen D. Land-use change: Impacts of climate variations and policies among small-scale farmers in the Loess Plateau, China. Land Use Pol. 2006;23(4):361–71. 10.1016/j.landusepol.2005.04.004.Search in Google Scholar
 Ji WJ, Huang YN, Shi PJ, Li Z. Recharge mechanism of deep soil water and the response to land use change in the loess deposits. J Hydrol. 2021;592:125817. 10.1016/j.jhydrol.2020.125817.Search in Google Scholar
 Zhu GY, Shangguan ZP, Deng L. Variations in soil aggregate stability due to land use changes from agricultural land on the Loess Plateau, China. Catena. 2021;200:105181. 10.1016/J.CATENA.2021.105181.Search in Google Scholar
 Zhou X, Zhou Y. Spatio-temporal variation and driving forces of land-use change from 1980 to 2020 in Loess Plateau of northern Shaanxi, China. Land. 2021;10(9):982. 10.3390/LAND10090982.Search in Google Scholar
 Ding YB, Wang FZ, Mu Q, Sun YN, Cai HJ, Zhou ZQ, et al. Estimating land use/land cover change impacts on vegetation response to drought under ‘Grain for Green’ in the Loess Plateau. Land Degrad Dev. 2021;32(17):5083–98. 10.1002/ldr.4093.Search in Google Scholar
 Zhang XS, Yang DA. Allocation and study on global change transects in China [in Chinese with English abstract]. Quat Sci. 1995;1:43–52+99–100. CNKI:SUN:DSJJ.0.1995-01-004.Search in Google Scholar
 Liu D, Li LN. Spatiotemporal change and driving factors of land use in the northern border transect of China, 1995–2015. Resour Sci. 2021;43(6):1208–21. CNKI:SUN:ZRZY.0.2021-06-012.Search in Google Scholar
 Tong XW, Wang KL, Brandt M, Yue YM, Liao CJ, Fensholt R. Assessing future vegetation trends and restoration prospects in the Karst Regions of southwest China. Remote Sens. 2016;8(5):357. 10.3390/rs8050357.Search in Google Scholar
 Xue LQ, Zhu BL, Wu YP, Wei GH, Liao SM, Yang CB, et al. Dynamic projection of ecological risk in the Manas River basin based on terrain gradients. Sci Total Environ. 2019;653:283–93. 10.1016/j.scitotenv.2018.10.382.Search in Google Scholar
 Li S, Shen ZF, Liu KJ, Xu ZY, Wang HY, Jiao SH, et al. Analysis of terrain gradient effects of land use change in Daqing River Basin [in Chinese with English abstract]. Trans Chinese Soc. Agric Eng. 2021;37(5):275–84. CNKI:SUN:NYGU.0.2021-05-032.Search in Google Scholar
 Zhang ZY, Yang X, Xie FM. Macro analysis of spatiotemporal variations in ecosystems from 1996 to 2016 in Xishuangbanna in Southwest China. Environ Sci Pollut Res. 2021;28(30):1–11. 10.1007/S11356-020-12330-6.Search in Google Scholar
 Li J, Liao HP, Cai J, Li T, Zhang T. Distribution characteristic on terrain gradient of land use pattern and change in the fringe of mountainous cities: a case study of Banan District in Chongqing [in Chinese with English abstract]. Resour Environ Yangtze Basin. 2018;27(2):296–305. CNKI:SUN:CJLY.0.2018-02-008.Search in Google Scholar
 Ma LB, Cui XJ, Yao Y, Liu SC. Gradient difference of structure of rural construction land in Loess Hilly region: a case study of Yuzhong county, Gansu province, China. Land. 2021;10:349. 10.3390/LAND10040345.Search in Google Scholar
 Wu J, Wang SS, Tan J. Analysis on terrain gradient effect based on land use change in Anhui Province. Resour Environ Yangtze Basin. 2016;25(2):239–48. 10.11870/cjlyzyyhj201602009.Search in Google Scholar
 Liang CF, Liu LM. Analysis on distribution characteristics of land use types based on terrain gradient: a case of Liuyang city in hunan province. Resour Sci. 2010;32(11):2138–44. CNKI:SUN:ZRZY.0.2010-11-015.Search in Google Scholar
 Zhou Y, Li XH. Geographical pattern and mechanism of poverty differentiation in plain areas: a case study of Lixin County, Anhui Province. Sci Geogr Sin. 2019;39:1592–601. 10.13249/j.cnki.sgs.2019.10.008.Search in Google Scholar
© 2022 Jiannong Cao et al., published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.