EN中文

Journal of Geographical Sciences >

2024 , Vol. 34 >Issue 1: 41 - 61

DOI: https://doi.org/10.1007/s11442-024-2194-2

Research Articles

Evaluation of suitability, adaptability, and reserve potential of construction land on the Qinghai-Tibet Plateau

  • YANG Hua , 1, 2 ,
  • XU Yong , 1, 2, * ,
  • ZHOU Kan 1, 2 ,
  • WANG Lijia 1, 2 ,
  • XU Lin 1, 2
Expand
  • 1. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
*Xu Yong (1964-), PhD and Professor, specialized in mechanistic modeling of land use and human-land relationships, and carrying capacity of resources and environment. E-mail:

Yang Hua (1995-), specialized in mechanistic modeling of land use and human-land relationships, and territorial function zoning. E-mail:

Received date: 2023-10-08

  Accepted date: 2023-11-20

  Online published: 2024-01-08

Supported by

The Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0406)

Fold

Abstract

Construction land is the leading carrier of human activities such as production and living. Evaluating the construction land suitability (CLS) on the Qinghai-Tibet Plateau (QTP) holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization. However, no relevant studies provide a complete CLS assessment of the QTP. In this study, we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model. Then, using the land-use data of 1990, 2000, 2010, and 2020, we examined the adaptability of existing construction land (ECL) to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land. Finally, we calculated the potential area of reserve suitable construction land. This article includes four conclusions: (1) The highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable CLS classes cover areas of 0.33×104 km2, 10.42×104 km2, 18.06× 104 km2, 24.12×104 km2, and 205.29×104 km2, respectively. Only approximately 11% of the study area on the QTP is suitable for large-scale permanent construction land, and approximately 79.50% of the area is unsuitable under current economic and technological conditions. (2) The ECL adaptability index is 85.16%, 85.93%, 85.18%, and 78.01% during 1990-2020, respectively, with an average adaptability index exceeding 80% on the QTP. The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference. (3) From 1990 to 2020, the maladaptive ECL was dominated by rural settlement land, transport land, and special land, with a rapidly increasing proportion of urban and other construction land. The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau, the Hengduan Mountains, and the Qilian Mountains. In contrast, elevation is significantly more limiting than slope in the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains. (4) The potential area of reserve suitable construction land is 12.41×104 km2, accounting for 4.81% of the total land area of the QTP, and the per capita area is 9928 m2. Regions of Qaidam Basin, Gonghe Basin, and Lhasa-Shannan Valley have the richest and most concentrated land resource of reserve suitable construction land. The research results provide spatial decision support for urban and rural settlement planning and ecological migration on the QTP.

Key words： land resource; construction land suitability; existing construction land adaptability; human activity; Qinghai-Tibet Plateau

Cite this article

YANG Hua , XU Yong , ZHOU Kan , WANG Lijia , XU Lin . Evaluation of suitability, adaptability, and reserve potential of construction land on the Qinghai-Tibet Plateau[J]. Journal of Geographical Sciences, 2024 , 34(1) : 41 -61 . DOI: 10.1007/s11442-024-2194-2

1 Introduction

Known as the “Roof of the World” and “Third Pole of the Earth,” the Qinghai-Tibet Plateau (QTP) is a significant ecological security barrier for China and Asia. Recent reports indicate that humans settled permanently in the Yellow River-Huangshui River Valley at least 5200 years ago and slowly expanded into the valleys of the southeastern and southern QTP and higher altitude areas (Chen et al., 2015). The intensity of human activity in the highlands has undergone a significant increase since the beginning of the 21st century (Li et al., 2018). Recent research reveals that ~23% of human activity expansions occurs in Asian highlands, with more than 20% coming from construction land (Yang et al., 2022). However, long-term exposure to hypoxic environments in high-altitude regions is harmful to physical function and clinically manifests as chronic mountain sickness (CMS) and acute mountain sickness (AMS) (Villafuerte and Corante, 2016; Bhatnagar, 2017; Mallet et al., 2021). For a long period, the average life expectancy on the QTP has been significantly lower than the national average (Zhou et al., 2019). Meanwhile, falling into poverty because of illnesses is an important factor leading to the QTP becoming the most concentrated region of poverty-stricken populations and severely impoverished areas (Zhou and Wang, 2016). Since 2000, in response to the ecological protection and inhabitant livelihood of the QTP, the Chinese government has conducted projects such as the Sanjiangyuan ecological migration, the nomad settlement project, and the poverty alleviation relocation project. However, these efforts have not achieved the expected results, as these projects covered a small spatial area and benefited a small part of the population. Nowadays, the Chinese government is committed to improving the development quality and enhancing ecological protection of the QTP. This region is projected to experience a boom in tourism and clean energy industries, a rise in population and urbanization rates, and an increase in the demand for town and rural settlements (Fang, 2022). As the major spatial carrier of human activity such as production and living, assessing the suitability of construction land on the QTP and clarifying the land potential for further development in the future is of great significance for optimizing the human activity space and the territorial space pattern for development and protection, as well as for coordinating ecological protection and human activity.
Land Use Suitability Assessment (LUSA) was developed from the Framework for Land Evaluation (FLE) proposed by FAO in 1976 and has become the most widely applied toolbox for land use suitability assessment and mapping (Food and Agriculture Organization of the United Nations, 1976). Designed to determine the suitability of spatial patterns for future land use according to specific requirements and preferences (Collins et al., 2001; Malczewski, 2004), LUSA has been widely used to evaluate the suitability of farming, forestry, and grazing land, while few studies have focused on construction land (Akpoti et al., 2019). Since the beginning of the 21st century, large-scale industrialization and urbanization have led to arable land decrease and environmental problems, and land suitability assessment for construction land has become a new research hotspot. Currently, the CLS research focuses on urban construction land (Zhang et al., 2013; Liu et al., 2014b), rural settlement land (Romano et al., 2015; Zhang et al., 2020), industrial and mining land (Amirshenava and Osanloo, 2021), and finding suitable land for construction of solar photovoltaic and wind power plants (Ayodele et al., 2018; Gouareh et al., 2021). Multi-criteria Decision Analysis (MCDA) is the most commonly used technique to solve complex CLS decisions with different criteria (Malczewski, 2006a; Greene et al., 2011). The general analytical MCDA framework uses topographic, climatic, geological, water, and socio-economic indicators as evaluation indicators. The Analytical Hierarchy Process (AHP), Entropy Method, and Fuzzy Logic Technique are used to determine the indicator weight. Weighted summation methods of Weighted Linear Combination (WLC) (Ustaoglu and Aydinoglu, 2020) and Ordered Weighted Averaging (OWA) (Malczewski, 2006b) are applied to obtain a comprehensive suitability index (Steiner et al., 2000; Luan et al., 2021; Bamrungkhul and Tanaka, 2022). With the abundance of geospatial big data, Machine Learning-Related (MLR) methods are increasingly used in suitability evaluation for construction land, including Artificial Neural Networks (ANN) and Cellular Automata (CA), and their evaluation results are more objective compared to MCDA (Saxena and Jat, 2020; Kang et al., 2021). In summary, most CLS assessment research has focused on small-scale regions and lacks large-scale regional modelling. In terms of methods, MCDA is limited by the research scale, data availability, validity, mathematical translation, and subjectivity of the indicator weights and combinations. Most research methods and analytical frameworks are difficult to replicate in other regions or to apply on larger scales (Nguyen et al., 2015). Furthermore, some uncertainties urgently need to be tested, including whether the MCDA analytical framework suffers from indicator duplication and covariance and whether this uncertainty leads to biased results. More importantly, the existing research focuses more on improving the evaluation model and pattern analysis of assessment results and lacks a discussion of evolution process of land use in response to suitability assessment results and its influence mechanism. There is no coupling analytical framework from pattern to process of land use suitability evaluation based on the model (Fu, 2014). Finally, these data-driven CLS assessment methods are challenging to apply to the QTP directly, which generally lacks basic data.
For the 2008 Wenchuan earthquake reconstruction, a method combining topographic elevation and slope was used to select the available construction land resources. This method relies on few indicators, can be rapidly applied to large-scale regions, and satisfies the essential suitability for engineering construction and human physiology, which helps decision-makers quickly select suitable land for urban and rural construction land in mountainous regions with complex environments (Fan, 2009; 2010; 2014). Since then, this method has been optimized and applied to evaluate the suitability of urban and town settlements within the context of Major Function Zoning and Territory Spatial Planning in China (Fan, 2019a; 2019b). Based on this method, Xu et al. (2022) mapped the atmosphere density and coefficient of sloping land suitability for construction onto topographic elevation and slope, respectively, and developed an evaluation framework reflecting the essential suitability for engineering construction and human physiology in the vertical direction. This framework addresses shortcomings of the original method, such as subjective decision-making and the lack of a maximum elevation limit for the suitability class and is highly accurate for the typical alpine-gorge region of Mainling county, southeast Xizang.
Exploring suitable space for human activity is an important part of the ecological security barrier construction on the QTP and a key component of the Second Tibetan Plateau Scientific Expedition and Research. Current research on the QTP focuses mainly on ecological changes and human activity effects, with rare studies on human activity suitability. Studies have evaluated the arable land suitability (Jin et al., 2014; Yao et al., 2021) and climate suitability (Liu et al., 2022) on the QTP, but a complete assessment for CLS is still lacking, and the suitable area for human settlement and industrial and mining activity on the QTP remains unknown. In this study, we extend a previously developed evaluation model for the suitability of land resources for human activity to the whole region of the QTP. First, we constructed a coupling research framework integrating the suitability patterns mapping in spatial dimension and the adaptability process analysis of existing construction land in the temporal dimension. Under this research framework, we evaluated the CLS on the QTP and analyzed its spatial pattern. Then, using the land use data in 1990, 2000, 2010, and 2020, we analyze the ECL adaptability process to the CLS in the temporal dimension and further analyzed the internal limitations and its influencing mechanism of maladaptive existing construction land. Finally, we also calculated the potential of reserve suitable construction land on the QTP. The conclusions scientifically support the search for suitable spaces of construction land and ecological migration on the QTP.

2 Study area

Located between 26°00′12″N-39°46′50″N, 73°18′52″E-104°46′59″E, the QTP extends over six provincial-level regions, namely the Xizang, Qinghai, Sichuan, Gansu, Xinjiang, and Yunnan, comprising 213 county-level administrative units and covering an area of approximately 2.58 million km2, i.e., 26.9% of China’s total land area. The QTP is the highest highland in the world in terms of average elevation, with 73% of its area lying above 4000 m. The topography of the QTP is high in the northwest and low in the southeast, distributed with some important geographical regions such as the Pamir-Kunlun Mountains, northern Tibet Plateau, southern Qinghai Plateau, Gangdis Mountains, Nyingchi Tanggula Mountains, Himalayan Mountains, Yarlung Tsangpo River Valley, Qaidam Basin, Qilian Mountains, and Yellow River-Huangshui River Valley (Figure 1a). The climate is dry and cold on the QTP, with -6-20℃ average annual temperature and 20-4500 mm average annual precipitation. Grassland is the major land cover type on the QTP, accounting for more than 60% of the total area, and forest is mainly distributed in the alpine-gorge region in Southeast Xizang and west Sichuan (Figure 1b). Dubbed “the water tower of China,” the QTP has numerous rivers, lakes, and glaciers and is home where the well-known rivers of Yangtze River, Yellow River, Lancang River, Nujiang River, and Yarlung Tsangpo River originate. According to the 2020 Seventh National Population Census, the resident population of the QTP is 18.10 million, accounting for 1.25% of the total population of China, while the resident urban population is 7.77 million, the urbanization rate is 57.08%, and the overall population density is about 7 person/km2. The QTP is an important pastoral region in China, and the Yellow River-Huangshui River Valley in Qinghai and Three-Rivers Region (the region of the Yarlung Tsangpo, Lhasa, and Nyangchu rivers in Xizang) are the main urban and agricultural spaces. In recent decades, population growth, overgrazing and engineering construction have increased the human activity intensity on the QTP, leading to ecological problems such as grassland degradation and destruction of plant and animal habitats.
Figure 1 Topographic elevation (a) and spatial distribution of land use types (b) on the Qinghai-Tibet Plateau in 2020

(Note: Produced based on the standard map GS (2022) 4312 of the Ministry of Natural Resources Standard Map Service website, with no modifications to the base map)

3 Materials and methods

3.1 Evaluation model of construction land suitability

3.1.1 Evaluation model and key parameters

The suitability of construction land is generally influenced by geological, hydrological, climatic, topographical, natural disaster and socio-economic factors (Wu and Li, 2010). However, the suitability of engineering construction and human physiology are the most fundamental dimensions, especially on the QTP, where physiological unsuitability due to high altitude is a direct limiting factor for construction activities. We used a previously developed evaluation model for the suitability of land resources for human activity to assess CLS on the QTP (Xu et al., 2022). In this distributed model, air density was mapped into topographic elevation to express the physiological suitability for the human body, and the coefficient of sloping land suitability for construction derived from step transformation of sloping land was mapped into topographic slope to reflect the construction convenience/difficulty (Xu et al., 2020). Their multiplication expresses the comprehensive suitability value. The formula is as follows:
$ H A_{b}=\delta_{T E} \theta_{T S}$
$ \delta_{T E}=1.1822-0.0002 T E$
$ \theta_{T S}=0.9904-0.0098 T S-0.0004 T S^{2}$
where HAb is the CLS index. δTE is air density parameter, TE is the average value of each elevation class, and TE∈[2000,5000]. θTS is the coefficient of sloping land suitability for construction, TS is the average value of each slope class, TS∈[0,43], and when TS>43°, $ \theta_{T S}=0$.

3.1.2 Elevation and slope classification

Reasonable classification for topographic elevation and slope is a precondition and basis for CLS evaluation. According to the different physiological responses of the human body at different altitudes, altitude is classified internationally as low (≤1500 m), intermediate (1500-2500 m), high (2500-3500 m), very high (3500-5800 m), and extreme (≥5800 m) (Barry and Pollard, 2003). Modern medical research has shown that the maximum altitude of 2500 m corresponds to lower altitude limit of CMS (León-Velarde et al., 2005). The human body functions begin to be affected by CMS above 3000 m, but these effects can be reduced or eliminated by High Altitude Acclimatization (Barry and Pollard, 2003). In addition, the probability of AMS is significantly increased above 4000 m (Bärtsch and Saltin, 2008). The altitude of the highest permanent human habitation in the world may up to 5000 m (West, 2002), but the highest large-scale human settlements on the QTP, such as county city, tend to be around 4500 m.
Meanwhile, the slope classification criteria of ≤3°, 3°-8°, 8°-15°, 15°-25° and ≥25° have been used to measure the difficulty of stepped transformation for construction land. This slope classification is also widely used in land suitability evaluation, such as the resource environment carrying capacity of earthquake restoration and reconstruction (Fan, 2009, 2010, 2014), Territory Spatial Planning (Fan, 2019a), Major Function Zoning (Fan, 2019b), and available construction land potential (Xu et al., 2011) (Table 1). Specifically, areas with less than 3° slope suit large-scale construction activity. The terrain of areas with a slope of 3° to 8° imposes certain restrictions on construction activity. The technical difficulty and engineering cost of carrying out construction activity will increase significantly with a slope of 8° to 15°, while engineering measures such as stepped transformation of sloping land can reduce this impact. When the slope exceeds 15°, it is unsuitable for large-scale construction activity. Areas with a slope of more than 25° cannot be used to centralize construction land because of the high technical difficulty, engineering costs, and the greater risk of soil erosion (Xu et al., 2020).
Table 1 Topographic elevation and slope classification in the construction land suitability (CLS) evaluation
Types Elevation classification thresholds (m) Slope classification thresholds (°)
Resource environment carrying capacity of earthquake restoration and reconstruction 800, 1200, 1600, 2000, 2500, 3000 3, 5, 8, 10, 15, 25, 30
Major Function Zoning 500, 1000, 2000, 3000 3, 8, 15, 25
Territory Spatial Planning 1000, 2000, 3000, 4000 3, 8, 15, 25
Available construction land potential 500, 1000, 2000, 3000 3, 8, 15, 25
In this study, we referred to existing elevation and slope classification methods, considering model computational efficiency and classification equivalence. The elevation was distributed into eight classes of ≤2000 m, 2000-2500 m, 2500-3000 m, 3000-3500 m, 3500-4000 m, 4000-4500 m, and ≥5000 m, and the slope was distributed into eight classes of ≤3°, 3°-5°, 5°-8°, 8°-12°, 12°-15°, 15°-20°, and ≥25° (Yang et al., 2023).

3.2 Adaptability analysis of existing construction land

3.2.1 Adaptability index of existing construction land

The distribution changes of existing construction land (ECL) in different CLS classes in temporal dimension reflect the response process of ECL expansion to suitable space of construction land. Within a county-level administrative unit, the adaptability index is defined as the proportions of ECL areas in the highly suitable, suitable, and moderately suitable CLS classes to the total area. The higher the ECL adaptability, the higher the spatial distribution consistency between ECL and CLS, and the lower the spatial conflict between them. The formula is as follows:
$ C A_{i}=\frac{C B_{i}}{C_{i}} \times 100 \%$
where CAi is ECL adaptability index, which is distributed into five classes by equal intervals: high, relatively high, medium, relatively low, and low. CBi is the ECL area distributed in highly suitable, suitable, and moderately suitable CLS classes in county-level unit of i. Ci is the total ECL area in county-level unit of i.

3.2.2 Vertical gradient index of existing construction land

The ECL in the marginally suitable and unsuitable CLS classes is considered maladaptive to the CLS evaluation results. Referring to the climbing index of construction land (Zhou et al., 2021; Peng et al., 2022), we proposed a vertical gradient index of maladaptive ECL comprising elevation and slope to analyze its internal mechanism quantitatively. Within a county-level administrative unit, the elevation gradient index is defined as the proportions of the total elevation value of the ECL raster grids in the marginally suitable and unsuitable CLS classes to all ECL raster grids of the QTP in these two CLS classes. Similarly, the slope gradient index is defined as the proportions of the total slope value of the ECL raster grids in the marginally suitable and unsuitable CLS classes to all ECL raster grids of the QTP in these two CLS classes. The formula is as follows:
$ R E_{i}=\frac{E_{i}}{\sum_{i}^{n} E_{i}} \times 100 \%$
$ R S_{i}=\frac{S_{i}}{\sum_{i}^{n} S_{i}} \times 100 \%$
where REi is the elevation gradient index of maladaptive ECL in county-level unit of i. RSi is the slope gradient index of maladaptive ECL in county-level unit of i. Ei is the total elevation value of the ECL raster grids in county-level unit of i. Si is the total slope value of the ECL raster grids in county-level unit of i. n is the number of county-level units. REi and RSi are classified into five classes: low, relatively low, medium, relatively high, and high at the threshold of 0.1%, 0.2%, 0.5%, and 1%.

3.3 Potential assessment of reserve suitable construction land

The potential of reserve suitable construction land is defined as the available land resources that can be used for the development of new urban and rural construction land, and its quantity, quality, and spatial distribution reflect the carrying capacity and development direction of future population and urbanization on the QTP (Dang et al., 2015; Xu and Xu, 2016; Xu et al., 2021). Therefore, based on the land use data in 2020 and considering the ecological protection of the QTP, we used the highly suitable, suitable, and moderately suitable CLS classes as the evaluation base, deducted the current construction land, arable land, forest, grassland, water body, and nature reserves, and retained the bare land (marshland and tundra excluded) as potential areas for reserve suitable construction land. The formula is as follows:
$ A=A_{S}-A_{c l}-A_{a l}-A_{f}-A_{g}-A_{w b}-A_{n r}$
$ A_{p}=A / P$
where A is the potential area for reserve suitable construction land. AS is the area sum of highly suitable, suitable, and moderately suitable CLS classes, Acl is current construction land, Aal is arable land, Af is forest, Ag is grassland, Awb is water body, Anr is nature reserve. Ap is per capita area of reserve suitable construction land, P is the population number of long-term residents at county level.

3.4 Data sources

We also used a digital elevation model (DEM), land use data, and administrative divisions. We used NASADEM, a 30-m spatial resolution DEM, and resampled it to 100-m spatial resolution to meet the suitable raster accuracy for CLS evaluation (Xu et al., 2022). The topographic slope was derived from NASADEM. Air density was calculated using the International Standard Atmosphere (ISO2533:1975) (Technical Committee ISO/TC 20, 1975). Land use data was obtained from land use data of the remote sensing monitoring data of land use in China (CNLUCC) for the years 1990, 2000, 2010, and 2020, mapped by the Data Registration and Publishing System of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/DOI) (Liu et al., 2014a). This dataset has a spatial resolution of 30 m and includes 6 first-level land types: arable land, forest, grassland, water body, construction land, unused land, and 25 second-level land types. The construction land includes 3 second-level land types of urban, rural settlement, and other construction land. The other construction land includes 3 third-level types: industrial, mining, transport, and special land. The unused land consists of sand, Gobi, salt-affected land, marshland, bare land, bare rocky land, alpine desert, tundra, and sparse desert grassland. With more than 90% accuracy, CNLUCC is the most extended time series and widely used dataset of large-scale land use/land cover remote sensing monitoring in China (Xu et al., 2018). The population of long-term residents for 2020 was derived from county-level statistical data of the seventh national population census (Office of the Leading Group of the State Council for the Seventh National Population Census, 2022). We collected vector boundary data of national, provincial, municipal, and county-level nature reserves on the QTP from the Ecosystem Assessment and Ecological Security Database website (https://ecosystem.csdb.cn). The administrative division data was derived from 1:1 million basic geographic information data 2019 from the National Catalogue Service for Geographic Information website (https://www.webmap.cn). The boundary of the QTP was downloaded from the Global Change Research Data Publishing and Repository website (http://www.geodoi.ac.cn) (Zhang et al., 2014).

4 Results

4.1 Results of the construction land suitability assessment

The elevation and slope were overlaid, and the air density parameter (δTE) and coefficient of sloping land suitability for construction (θTS) were input to calculate the CLS index (HAb). Based on the distribution characteristics of human activity and physiological changes of the human body at different altitudes, the CLS classification thresholds of HAb were set as 0.73, 0.58, 0.38 and 0.26. CLS was distributed into five classes, namely: highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable: (1) The highly suitable class has almost no limitations for construction land use and human activity; (2) the suitable class has only minor limitations for construction land use and is appreciably inferior to the highly suitable class; (3) the moderately suitable class has moderately severe limitations for construction land use, in which the physiological suitability of human body is significantly lower and carrying out construction activity is significantly more costly and difficult; (4) The marginally suitable class has severe limitations for sustaining construction land, which are only marginally justified; (5) the unsuitable class has limitations so severe that they preclude the possibility of sustainable construction land at least under current technical and economic conditions. Figure 2 shows the corresponding relationships between CLS and elevation and slope in a rectangular coordinate system consisting of elevation as the Y axis and slope as the X axis. The maximum control elevations of the four suitability classes, namely highly suitable, suitable, moderately suitable, and marginally suitable, were 2500 m, 3000 m, 4000 m, and 4500 m, and the maximum control slopes were 5°, 12°, 15°, and 20°, respectively.
Figure 2 Corresponding relationships between construction land suitability (CLS) and elevation and slope
Figure 3 and Table 2 show the spatial distribution of CLS on the QTP. The highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable CLS classes cover areas of 0.33×104 km2, 10.42×104 km2, 18.06×104 km2, 24.12×104 km2, and 205.29×104 km2, respectively, accounting for 0.13%, 4.04%, 7.00%, 9.34%, and 79.50% of the total land area, respectively. The sum of the highly suitable, suitable, and moderately suitable CLS classes accounts for only 11.16% of the total area that can support large-scale permanent construction land.
Figure 3 Spatial distribution of the construction land suitability (CLS) class on the Qinghai-Tibet Plateau
Table 2 Area of each construction land suitability (CLS) class on the Qinghai-Tibet Plateau
Region Highly suitable Suitable Moderately suitable Marginally suitable Unsuitable
Area
(104 km2)		 Ratio
(%)		 Area
(104 km2)		 Ratio
(%)		 Area
(104 km2)		 Ratio
(%)		 Area
(104 km2)		 Ratio
(%)		 Area
(104 km2)		 Ratio
(%)
Xizang 0.21 0.17 0.43 0.36 .21 1.00 4.80 3.99 113.57 94.47
Qinghai 0.10 0.14 9.36 13.47 9.79 14.09 11.29 16.24 38.95 56.06
Sichuan 0.01 0.04 0.05 0.19 1.43 5.61 2.73 10.70 21.25 83.45
Gansu 0.00 0.04 0.36 3.81 2.71 28.99 1.68 17.95 4.60 49.20
Yunnan 0.01 0.17 0.03 0.75 0.19 5.75 0.30 8.85 2.86 84.48
Xinjiang 0.01 0.02 0.20 0.66 2.73 9.02 3.34 11.00 24.05 79.31
QTP 0.33 0.13 10.42 4.04 18.06 7.00 24.12 9.34 205.29 79.50
(1) The highly suitable class is mainly distributed in the alpine-gorge region in southeast Xizang and the Yellow River-Huangshui River Valley in Qinghai. The highly suitable class in Xizang covers an area of 2047.22 km2, accounting for 0.17% of the total area of Xizang, and is the flat land at the bottom of the valley in the southern regions of Shannan and Nyingchi. The highly suitable class in Qinghai covers an area of 979.21 km2, accounting for 0.14% of Qinghai, and is mainly distributed in the Yellow River Valley and Huangshui River Valley in Xining and Haidong, respectively.
(2) The highly suitable class is distributed in the Qaidam Basin and the Yellow River-Huangshui River Valley in Qinghai and secondary tributary valleys in southeast Xizang. Qinghai has the largest land area of suitable class, accounting for 13.47% of its total land area. Most of the suitable class is distributed in the Qaidam Basin in Haixi, covering an area of 8.85×104 km2. The suitable class in Gonghe Basin, Yellow River Valley, and Huangshui River Valley in Xining, Haidong, and Hainan covers an area of 4498.48 km2. In addition, the suitable class area in Xizang covers 4321.4 km2, accounting for 0.36% of the total area of Xizang, and is concentrated in the Yarlung Tsangpo River and its secondary tributary valleys in the Nyingchi and Shannan sections.
(3) The moderately suitable class in Qinghai is mainly distributed in the Qaidam Basin and its transition areas with the Qilian and Kunlun Mountains, the intermontane valleys of the Qilian Mountains, the Qinghai Lake Basin, the Gonghe Basin, covering an area of 9.79×104 km2, accounting for 14.09% of the total area of Qinghai. The moderately suitable class in Gansu is mainly distributed in the intermontane valleys of the Qilian Mountains and Gannan Mountains, covering an area of 2.71×104 km2, accounting for 28.99% of the total area of Gansu. The moderately suitable class in Xinjiang is mainly distributed in the intermontane valleys of the Kunlun Mountains and Altun Mountains, covering an area of 2.73×104 km2, accounting for 9.02% of the total area of Xinjiang. The moderately suitable class in Sichuan is mainly distributed in the Songpan Plateau and intermontane valleys of the Hengduan Mountains, covering an area of 1.43×104 km2, accounting for 5.61% of the total area of Sichuan. The moderately suitable class in Xizang is mainly distributed in the Xigaze and Lhasa-Shannan Valleys in the Yarlung Tsangpo River Basin and its third tributary valleys, covering an area of 1.21×104 km2, accounting for 1% of the total area of Xizang.
(4) The marginally suitable class is distributed in the southern Qinghai Plateau in Guoluo and Yushu, the intermontane valleys of the Qilian Mountains in Haixi, the Kunlun Mountains in Bayangol, the Gangdis Mountains in Nagri, the Himalayan Mountains in Nagqu and Xigaze, and the Hengduan Mountains in Sichuan. The marginally suitable classes of Qinghai, Xizang, and Xinjiang cover areas of 11.29×104 km2, 4.8×104 km2, and 3.34×104 km2, accounting for 16.24%, 3.99%, and 11% of the total area of these three regions. The unsuitable class is the dominant CLS class distributed throughout the QTP.

4.2 Adaptability of existing construction land

Table 3 reports the ECL area in each CLS class during 1990-2020. The ECL covers areas of 1228.84 km², 1340.32 km², 2110.94 km², and 2633.48 km², respectively, accounting for 0.05%, 0.05%, 0.08%, and 0.10% of the total area of QTP, respectively. The ECL area increases from 1990 to 2020, with a significant acceleration after 2000. We spatially overlay the ECL with CLS evaluation results and count the ECL area in each CLS class, and we find that the ECL is highly adaptive to suitable space of construction land for the period 1990-2020. The ECL areas distributed in highly suitable, suitable, and moderately suitable CLS classes account for 85.16%, 85.93%, 85.18%, and 78.01% of the total ECL area, respectively. The ECL adaptability indices are stable at around 85% during 1990-2010 but declined significantly in 2020 compared to 2010, which indicates that the proportion of ECL areas in marginally suitable and unsuitable CLS classes has risen in recent years, and the expansion of construction activity is accelerating in unsuitable space for construction land.
Table 3 Existing construction land (ECL) area in each construction land suitability (CLS) class on the Qinghai-Tibet Plateau, 1990-2020
Year Highly suitable Suitable Moderately suitable Marginally suitable Unsuitable Construction
land area
(km2)		 Total
adaptability
(%)
Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(m2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)
1990 148.48 12.08 434.33 35.34 463.63 37.73 97.23 7.91 85.16 6.93 1228.84 85.16
2000 159.10 11.87 490.22 36.57 502.38 37.48 100.08 7.47 88.54 6.61 1340.32 85.93
2010 200.10 9.48 949.38 44.97 648.73 30.73 170.52 8.08 142.21 6.74 2110.94 85.18
2020 240.30 9.12 826.47 31.38 987.72 37.51 324.09 12.31 254.90 9.68 2633.48 78.01
Figure 4 reveals the spatial distribution of the ECL adaptability index for the period 1990-2020 at the county level. The ECL adaptability shows a significant spatial variation, but its spatial pattern remains stable between 1990 and 2020. The high adaptability counties with an adaptability index of more than 80% account for approximately 1/3 of the total number and are mainly distributed in the Altun Mountains, the Qilian Mountains, the Qaidam Basin, the Yellow River-Huangshui River Valley, the Gannan Mountains, the Songpan Plateau, the Three-Rivers Region, and the Nyingchi Valley. These areas are the main carrying regions of human activity on the QTP, and the construction land is mainly distributed in basins and valleys with better construction conditions. There is a significant spatial variation of ECL adaptability in the alpine-gorge region of southeast Xizang and west Sichuan, with a staggered distribution pattern of relatively high, medium, and relatively low classes related to the great topographic relief. The ECL adaptability index shows a very low level in the northern Tibet-southern Qinghai Plateau, the Gangdis-Nyingchi Tanggula Mountains, and the Himalayan Mountains, and counties lower than 20% account for approximately 1/3 of the total number.
Figure 4 Spatial distribution of existing construction land (ECL) adaptability index on the Qinghai-Tibet Plateau, 1990-2020

4.3 limitations of maladaptive existing construction land

Table 4 reports the ECL area in marginally suitable and unsuitable CLS classes during 1990-2020. The ECL distributed in marginally suitable and unsuitable CLS classes covers areas of 182.39 km2, 188.62 km2, 312.74 km2, and 578.99 km2, respectively, accounting for 14.84%, 14.07%, 14.82%, and 21.99% of the total construction land, respectively. The area of maladaptive ECL increased from 1990 to 2020, significantly accelerating after 2010. In terms of land use type, the maladaptive ECL is dominated by rural settlement land, followed by other construction land, and urban construction land accounts for the smallest share. From a temporal dimension, the proportion of rural settlement land decreases continuously from 78% in 1990 to 41.46% in 2020, while the proportions of urban construction land and other construction land rise constantly from 8.31% and 13.17% in 1990 to 19.41% and 39.13% in 2020, respectively. These data reflect a trend in which rapid urbanization and industrialization have driven more urban construction land, industrial and mining land, and transport land to expand to regions of higher and steeper with increasing speed.
Table 4 Existing construction land (ECL) area in construction land suitability (CLS) classes of marginally suitable and unsuitable on the Qinghai-Tibet Plateau, 1990-2020
Year Marginally suitable Unsuitable Construction
land area
(km2)
Urban
construction
land		 Rural
settlement
land		 Other
construction
land		 Urban
construction
land		 Rural
settlement
land		 Other
construction
land
Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(m2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)
1990 8.16 4.48 76.58 41.99 12.48 6.84 6.99 3.83 66.64 36.54 11.54 6.32 182.39
2000 9.59 5.08 78.01 41.36 12.49 6.62 7.96 4.22 68.52 36.32 12.06 6.39 188.62
2010 42.51 13.59 97.05 31.03 30.96 9.90 23.00 7.35 96.06 30.72 23.15 7.40 312.74
2020 75.10 12.97 123.25 21.29 125.75 21.72 37.26 6.44 116.83 20.18 100.81 17.41 578.99
Figure 5 shows the spatial distribution changes of elevation and slope gradient index from 1990 to 2020. The county number of medium, relatively high, and high elevation gradient increased from 85 in 1990 to 105 in 2020, and its proportion increased from 41.87% to 51.72%. The high class of elevation gradient index is mainly distributed in the southern Qinghai Plateau in Yushu and Golog, and a small amount of it is distributed in the Hengduan Mountains and Qilian Mountains. The relatively high and medium classes of elevation gradient index expand from the Hengduan Mountains, the Qilian Mountains, and the Three-Rivers Region to the high-altitude areas of the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains. The county number of medium, relatively high, and high slope gradient increased from 83 to 105 during 1990-2020, and its proportion increased from 40.89% to 49.75%. The high-value areas of slope gradient index expand from the southern Qinghai Plateau, the Hengduan Mountains, the Qilian Mountains, the Yellow River-Huangshui River Valley, and Gannan Mountains to the northern Tibet Plateau, the Nyingchi Tanggula Mountains, and the Himalayan Mountains, while this expansion trend is significantly weaker than that of the elevation gradient. The maladaptive ECL is generally constrained by both elevation and slope in the southern Qinghai Plateau, the Hengduan Mountains, and the Qilian Mountains. In contrast, elevation is significantly more limiting than slope in the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains.
Figure 5 Spatial distribution of the elevation and slope gradient index of existing construction land (ECL) on the Qinghai-Tibet Plateau, 1990-2020

4.4 Potential assessment results of reserve suitable construction land

Figure 6 and Table 5 report the spatial distribution of potential area and the per capita area of reserve suitable construction land. The potential area of reserve suitable construction land is 124,143.86 km2, accounting for 4.81% of the total area of the QTP, in which the highly suitable, suitable, and moderately classes cover areas of 38.77 km2, 60,376.56 km², and 63,728.53 km2, respectively, accounting for 0.03%, 48.63%, 51.33% of the total area of reserve suitable construction land, respectively, and the per capita area is 9928 m2. The reserve suitable construction land is mainly distributed in Qinghai, covering an area of 95,947 km2, accounting for 77.29% of the total area, and its per capita area is up to 17,143 m2. The reserve suitable construction land resource is vibrant and concentrated in the Qaidam Basin, with a per capita area of more than 50,000 m2, which can carry large-scale solar photovoltaic and wind power plants. The potential area in Gonghe county is 2226.69 km², with a per capita area of 16,691 m2, which could become a vital relocation site for ecological migrants in the Sanjiangyuan National Park after land consolidation and construction of the Yellow River diversion project. The land resources of reserve suitable construction land in Xizang are very scarce, with an area of 464.50 km2 and per capita area of only 130 m2, and mainly distributed piecemeal in intermontane valleys in the southeast Xizang and the Yarlung Tsangpo River Valley. The broad valley of the Yarlung Tsangpo River in the Shannan section has better development and utilization conditions and can carry ecological migrants from the northern Tibet Plateau after land consolidation and construction of agricultural water diversion projects.
Figure 6 Spatial distribution of reserve suitable construction land and per capita area on the Qinghai-Tibet Plateau in 2020
Table 5 Class area of reserve suitable construction land and its per capita area on the Qinghai-Tibet Plateau in 2020
Region Highly suitable Suitable Moderately suitable Potential area of
reserve suitable
construction land (km2)		 Per capita
potential area
(m2)
Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)		 Area
(km2)		 Ratio
(%)
Xizang 21.92 4.72 10.07 2.17 432.52 93.11 464.50 130
Qinghai 1.60 0.00 57430.37 59.86 38515.03 40.14 95947.00 17143
Sichuan 0.17 0.01 1.77 0.09 1863.58 99.90 1865.52 925
Gansu 2.25 0.02 2290.03 19.57 9412.40 80.42 11704.69 18593
Yunnan 0.15 0.79 0.61 3.25 17.94 95.96 18.69 39
Xinjiang 12.68 0.09 643.72 4.55 13487.06 95.36 14143.46 69146
QTP 38.77 0.03 60376.56 48.63 63728.53 51.33 124143.86 9928

5 Discussion

5.1 Comparison with other studies

Major Function Zoning and Territory Spatial Planning are the national strategies and systems being implemented in China. The land resource evaluation methods oriented to construction function are an important basic tool for territorial resource assessment, which is directly related to the layout and optimization of major productive forces (General Office of the State Council of the People’s Republic of China, 2011; Xinhua News Agency, 2019). From the scientific mechanism and logic dimension, Xu et al. (2022) compared the differences, advantages and disadvantages between the CLS evaluation method used in this study and available land resource evaluation in the Major Function Zoning, and land resource evaluation oriented to urban construction function in the Territory Spatial Planning. Therefore, it is necessary to compare further and verify the evaluation results of the QTP based on these three methods.
We measured the available land resource types on the QTP according to the Technical Regulation for Major Function Zoning (Fan, 2019b) and the land resource classes oriented to urban construction function based on the Assessment Guidelines for Resource and Environmental Carrying Capacity and Territorial Development Suitability (Fan, 2019a) (Figure 7). We found that the available and unavailable types calculated by the Major Function Zoning method cover areas of 70.65×104 km2 and 188.18×104 km2, the available area is excessively large, and the high-altitude regions in the northern Tibet Plateau and southern Qinghai Plateau are classified as available land where are unsuitable for carrying large-scale permanent construction land. By comparison, the assessment result of the Territory Spatial Planning is better than Major Function Zoning, with the high, relatively high, medium, relatively low, and low classes covering areas of 9.55×104 km2, 11.69×104 km2, 46.85×104 km2, 33.57×104 km2, and 157.16×104 km2. The spatial distribution of high and relatively high classes same to the evaluation results of this study. However, the medium class area is excessive, accounting for 18.12% of the total area, and high-altitude regions in the northern Tibet Plateau and southern Qinghai Plateau are also classified as medium and relatively low classes. The available land type of the Major Function Zoning and relatively high and medium classes of urban construction land evaluated by the Territory Spatial Planning method are overestimated. This error is because these evaluation methods and analysis framework are mainly targeted at lower-altitude regions of central and eastern China, which take topographic slope as the main limiting factor in judging CLS and focus less on the effect of elevation differentiation on the physiological suitability of human activity. We concluded that these CLS evaluation methods used in the Major Function Zoning and Territory Spatial Planning are mechanistically flawed in highlands (Xu et al., 2022), and the method used in this study is more effective on the QTP. Therefore, it is important to focus on the errors of land resource evaluation caused by elevation when carrying out the spatial planning on the QTP.
Figure 7 Assessment results of construction land suitability (CLS) of Major Function Zoning and Territory Spatial Planning on the Qinghai-Tibet Plateau

5.2 Supplementary explanation of the method application

In this study, the derived CLS constitutes a fundamental evaluation from a land resource perspective, aiming to measure the land use suitability for construction land in terms of two basic aspects: the physiological suitability of the human body and the convenience/difficulty of carrying out construction. This evaluation method can assess the CLS for cities, towns, villages, and industries and the site selection for large-scale solar photovoltaic and wind power plants. However, other factors in practical application should correct the CLS evaluation results, such as the available water resources, water use conditions, engineering geological conditions, and geological hazards. In addition, the analysis accuracy can be further improved for applications in small- and medium-scale areas, such as using higher resolution DEM and finer slope classification in the alpine-gorge region. It should also be noted that the inconsistency of classification and accuracy of land use data affects the potential measurement results of reserve suitable construction land, and the results measured in this study can provide a reference value for subsequent studies. Besides, it is necessary to select appropriate basic data in practical applications according to the scale and accuracy requirement, such as using land resource survey data from the natural resources management department in small- and medium-scale regions.
The special characteristics of religious and transport land must be considered when relocating the maladaptive construction land based on the CLS evaluation result. The Tibetan Buddhist architecture has a typical style of Zongshan Village, commonly constructed on the top or slopping land of mountains with a large topographic slope and a higher altitude compared with the urban and rural settlements. In addition, transport land is a special construction land with different constraints from urban and rural construction land. In practical application, the relocated region should be evaluated comprehensively with detailed land use data and remote sensing images, and special land such as religious land should not be relocated.

6 Conclusions and suggestions

6.1 Conclusions

This study measured the construction land suitability (CLS) on the Qinghai-Tibet Plateau (QTP), analyzed the adaptability of existing construction land (ECL) to CLS using a construction land layer of land use data for the period 1990-2020, analyzed the spatiotemporal differences and its vertical gradient characteristics of the maladaptive ECL, and calculated the potential area of reserve suitable construction land. The main findings are as follows:
First, the highly suitable, suitable, moderately suitable, marginally suitable, and unsuitable CLS classes cover areas of 0.33×104 km2, 10.42×104 km2, 18.06×104 km2, 24.12×104 km2, and 205.29×104 km2, respectively, accounting for 0.13%, 4.04%, 7.00%, 9.34%, and 79.50% of the total land area, respectively. The sum of the highly suitable, suitable, and moderately suitable CLS classes accounts for only 11.16% of the total area that can support large-scale permanent construction land, mainly distributed in the Qaidam Basin, the Yellow River-Huangshui River Valley, the Gonghe Basin, the Songpan Plateau, the Xigaze, Lhasa-Shannan, and Nyingchi Valleys.
Second, the ECL adaptability index is generally high and has a significant spatial variation. The ECL on the QTP is highly adaptive to the suitable space of construction land with adaptability indices of 85.16%, 85.93%, 85.18%, and 78.01% during 1990-2020, respectively, and the average index is over 80%. The high adaptability counties with an adaptability index of more than 80% account for approximately 1/3 of the total number and are mainly distributed in the Altun Mountains, the Qilian Mountains, the Qaidam Basin, the Yellow River-Huangshui River Valley, the Gannan Mountains, the Songpan Plateau, the Three-Rivers Region, and the Nyingchi Valley. The ECL adaptability has a significant spatial variation in the alpine-gorge region with a staggered distribution pattern of relatively high, medium, and relatively low classes. The ECL adaptability index shows a very low level in the northern Tibet-southern Qinghai Plateau, the Gangdis-Nyingchi Tanggula Mountains, and the Himalayan Mountains.
Third, the land use type differs from the maladaptive ECL, and its vertical gradient effect has significant spatial and temporal variation. The maladaptive ECL is dominated by rural settlement land, with a rapidly increasing proportion of urban and other construction land. The elevation and slope gradient indices gradually increase, and the spatial distribution expands. The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau, the Hengduan Mountains, and the Qilian Mountains. In contrast, elevation is significantly more limiting than slope in the northern Tibet Plateau, the Gangdis Mountains, and the Himalayan Mountains.
Fourth, the potential area of reserve suitable construction land is 12.41×104 km2, accounting for 4.81% of the total area of the QTP, dominated by suitable and moderately suitable classes, and the per capita area is 9928 m2. The reserve suitable construction land resource is very rich and concentrated in the Qaidam Basin, which can carry large-scale construction of industry, mining, and clean energy infrastructure. The Gonghe Basin and Yarlung Tsangpo River Valley in the Shannan section can be selected as priority relocation areas for ecological migration from the Sanjiangyuan Region and the northern Tibet Plateau.

6.2 Policy suggestions on the ecological migration

The construction land expansion in high altitude and steep areas increases the exposure risk of vulnerable populations in hypoxic environments, leads to a significant cost increase of transport and public service infrastructure, promotes destruction and degradation of the ecological environment, and exacerbates the conflict between socio-economic development and ecological protection. Currently, the local government of Xizang is implementing a new project for ecological migration in high-altitude regions, which plans to migrate herds from high to low-altitude regions (Xinhua News Agency, 2020). However, some questions have not yet been addressed clearly and scientifically, such as where relocation is necessary, how large the migration scale is, and where large-scale permanent human settlements can be relocated.
Based on the results of this study, we propose a migration model from the northern Tibet Plateau to the Lhasa-Shannan Valley in the Yarlung Tsangpo River Basin and a migration model from the Sanjiangyuan Region to the Gonghe Basin. The migration regions on the northern Tibet Plateau include the Gerze, Coqen, Nyima, Xainza, Bangoin, Shuanghu, and Amdo counties, with a total population of about 187,000 and herds population of 143,000. The Xigaze and Lhasa-Shannan Valleys in the Yarlung Tsangpo River Basin are rich in water and land resources but poorly utilized. After the comprehensive land development, river channel consolidation, and irrigation facility building, these should have a migration potential of 80,000-100,000 people. The migration region’s Sanjiangyuan Region includes the Zadoi, Zhidoi, and Qumarleb counties, with a total population of 136,400 and a herdersman population of 58,600. After constructing a water transfer project from the upper Yellow River watershed, the Gonge Basin should be able to host 50,000-100,000 settlers. Moreover, a local migration model of “top-to-bottom” should be adopted in the alpine-gorge region, whereby the population living at high altitudes and on steep land is gradually relocated to the bottom of the valley. The government should incorporate these migration models into a unified framework and conduct feasibility studies.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
Akpoti K, Kabo-bah A T, Zwart S J, 2019. Agricultural land suitability analysis: State-of-the-art and outlooks for integration of climate change analysis. Agricultural Systems, 173: 172-208.

DOI

[2]
Amirshenava S, Osanloo M, 2021. Mined land suitability assessment: A semi-quantitative approach based on a new classification of post-mining land uses. International Journal of Mining Reclamation and Environment, 35(10): 743-763.

DOI

[3]
Ayodele T R, Ogunjuyigbe A S O, Odigie O et al., 2018. A multi-criteria GIS based model for wind farm site selection using interval type-2 fuzzy analytic hierarchy process: The case study of Nigeria. Applied Energy, 228: 1853-1869.

DOI

[4]
Bamrungkhul S, Tanaka T, 2022. The assessment of land suitability for urban development in the anticipated rapid urbanization area from the Belt and Road Initiative: A case study of Nong Khai City, Thailand. Sustainable Cities and Society, 83: 103988.

DOI

[5]
Barry P W, Pollard A J, 2003. Altitude illness. BMJ-British Medical Journal, 326(7395): 915-919.

PMID

[6]
Bärtsch P, Saltin B, 2008. General introduction to altitude adaptation and mountain sickness. Scandinavian Journal of Medicine & Science in Sports, 18(Suppl.1): 1-10.

[7]
Bhatnagar A, 2017. Environmental determinants of Cardiovascular disease. Circulation Research, 121(2): 162-180.

DOI PMID

[8]
Chen F H, Dong G H, Zhang D J et al., 2015. Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. Science, 347(6219): 248-250.

DOI PMID

[9]
Collins M G, Steiner F R, Rushman M J, 2001. Land-use suitability analysis in the United States: Historical development and promising technological achievements. Environmental Management, 28(5): 611-621.

PMID

[10]
Dang L J, Xu Y, Tang Q, 2015. The pattern of available construction land along the Xijiang River in Guangxi, China. Land Use Policy, 42: 102-112.

DOI

[11]
Fan J, 2009. State Planning for Post-Wenchuan Earthquake Restoration and Reconstruction:Resource Environment Carrying Capacity Evaluation. Beijing: Science Press. (in Chinese)

[12]
Fan J, 2010. Post-Yushu Earthquake Restoration and Reconstruction:Resource Environment Carrying Capacity Evaluation. Beijing: Science Press. (in Chinese)

[13]
Fan J, 2014. Post-Lushan Earthquake Restoration and Reconstruction:Resource Environment Carrying Capacity Evaluation. Beijing: Science Press. (in Chinese)

[14]
Fan J, 2019a. Assessment Guidelines for Resource and Environmental Carrying Capacity and Territorial Development Suitability. Beijing: Science Press. (in Chinese)

[15]
Fan J, 2019b. Technical Regulation for Major Function Zoning. Beijing: Science Press. (in Chinese)

[16]
Fang C L, 2022. Special thinking and green development path of urbanization in Qinghai-Tibet Plateau. Acta Geographica Sinica, 77(8): 1907-1919. (in Chinese)

DOI

[17]
Food and Agriculture Organization of the United Nations, 1976. A framework for land evaluation. Rome: FAO Soils Bulletin No.32.

[18]
Fu B J, 2014. The integrated studies of geography: Coupling of patterns and processes. Acta Geographica Sinica, 69(8): 1052-1059. (in Chinese)

DOI

[19]
General Office of the State Council of the People’s Republic of China, 2011. Major Function Oriented Zoning of China. Retrieved 2011-06-08/2023-03-23 from http://www.gov.cn/zwgk/2011-06/08/content_1879180.htm. (in Chinese)

[20]
Gouareh A, Settou B, Settou N, 2021. A new geographical information system approach based on best worst method and analytic hierarchy process for site suitability and technical potential evaluation for large-scale CSP on-grid plant: An application for Algeria territory. Energy Conversion and Management, 235: 113963.

DOI

[21]
Greene R, Devillers R, Luther J E et al., 2011. GIS-based multiple-criteria decision analysis. Geography Compass, 5(6): 412-432.

DOI

[22]
Jin G, Wang Z Q, Li W S et al., 2014. Suitable evaluation on cultivated land based on Fuzzy weights of evidence method in the Yarlung Zangbo River, Nyangqu River and Lhasa River region, Tibet. Journal of Natural Resources, 29(7): 1246-1256. (in Chinese)

[23]
Kang Z Q, Wang S, Xu L et al., 2021. Suitability assessment of urban land use in Dalian, China using PNN and GIS. Natural Hazards, 106(1): 913-936.

DOI

[24]
León-Velarde F, Maggiorini M, Reeves J T et al., 2005. Consensus statement on chronic and subacute high altitude diseases. High Altitude Medicine & Biology, 6(2): 147-157.

[25]
Li S C, Zhang Y L, Wang Z F et al., 2018. Mapping human influence intensity in the Tibetan Plateau for conservation of ecological service functions. Ecosystem Services, 30: 276-286.

DOI

[26]
Liu J H, Xin Z B, Huang Y Z et al., 2022. Climate suitability assessment on the Qinghai-Tibet Plateau. Science of The Total Environment, 816: 151653.

DOI

[27]
Liu J Y, Kuang W H, Zhang Z X et al., 2014a. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica, 69(1): 3-14. (in Chinese)

[28]
Liu R Z, Zhang K, Zhang Z J et al., 2014b. Land-use suitability analysis for urban development in Beijing. Journal of Environmental Management, 145: 170-179.

DOI

[29]
Luan C X, Liu R Z, Peng S C, 2021. Land-use suitability assessment for urban development using a GIS-based soft computing approach: A case study of Ili Valley, China. Ecological Indicators, 123: 107333.

DOI

[30]
Malczewski J, 2004. GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1): 3-65.

DOI

[31]
Malczewski J, 2006a. GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7): 703-726.

DOI

[32]
Malczewski J, 2006b. Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4): 270-277.

DOI

[33]
Mallet R T, Burtscher J, Richalet J P et al., 2021. Impact of high altitude on Cardiovascular health: Current perspectives. Vascular Health and Risk Management, 17: 317-335.

DOI PMID

[34]
Nguyen T T, Verdoodt A, Tran V Y et al., 2015. Design of a GIS and multi-criteria based land evaluation procedure for sustainable land-use planning at the regional level. Agriculture Ecosystems & Environment, 200: 1-11.

DOI

[35]
Office of the Leading Group of the State Council for the Seventh National Population Census, 2022. Tabulation on 2020 China Population Census by County. Beijing: China Statistics Press. (in Chinese)

[36]
Peng Q Z, Ma S H, Deng Q H, Ma J W, 2022. Relationship between construction land and slope in rapidly expanding mountain cities: A case study in Guiyang, China. Journal of Natural Resources, 37(7): 1865-1875. (in Chinese)

DOI

[37]
Romano G, Dal Sasso P, Liuzzi G T et al., 2015. Multi-criteria decision analysis for land suitability mapping in a rural area of southern Italy. Land Use Policy, 48: 131-143.

DOI

[38]
Saxena A, Jat M K, 2020. Land suitability and urban growth modeling: Development of SLEUTH-Suitability. Computers Environment and Urban Systems, 81: 101475.

DOI

[39]
Steiner F, McSherry L, Cohen J, 2000. Land suitability analysis for the upper Gila River watershed. Landscape and Urban Planning, 50(4): 199-214.

DOI

[40]
Technical Committee ISO/TC 20, 1975. Standard Atmosphere, ISO2533: 1975. Geneva: International Organization for Standardization.

[41]
Ustaoglu E, Aydinoglu A C, 2020. Suitability evaluation of urban construction land in Pendik district of Istanbul, Turkey. Land Use Policy, 99: 104783.

DOI

[42]
Villafuerte F C, Corante N, 2016. Chronic mountain sickness: Clinical aspects, etiology, management, and treatment. High Altitude Medicine & Biology, 17(2): 61-69.

[43]
West J B, 2002. Highest permanent human habitation. High Altitude Medicine & Biology, 3(4): 401-407.

[44]
Wu Z Q, Li D H, 2010. Principles of Urban Planning. 4th ed. Beijing: China Architecture & Building Press. (in Chinese)

[45]
Xinhua News Agency, 2019. Several Opinions on Establishing a National Territorial Spatial Planning System and Supervising Its Implementation of the General Office of the Communist Party of China Central Committee and General Office of the State Council of the People’s Republic of China. Retrieved 2019-05-23/2023-03-23 from https://www.gov.cn/zhengce/2019-05/23/content_5394187.htm. (in Chinese)

[46]
Xinhua News Agency, 2020. The migration of life across half a century: Tibet’s ecological migration in extremely high-altitude to solve the problem of coexistence between human and nature. Retrieved 2020-03-17/2023-03-23 from http://www.xinhuanet.com/politics/2020-03/17/c_1125726595.htm. (in Chinese)

[47]
Xu X L, Liu J Y, Zhang S W et al., 2018. Remote sensing monitoring data of land use in China. Data Registration and Publishing System of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/DOI). doi: 10.12078/2018070201. (in Chinese)

[48]
Xu X R, Wang L, Xu Y, Duan J, 2021. Evaluation of reserve available land resources based on three types of territorial space: A case study of Jiexiu city in Shanxi province. Progress in Geography, 40(2): 272-282. (in Chinese)

DOI

[49]
Xu X R, Xu Y, 2016. Potential of available construction land in the Yangtze River Economic Belt. Resources and Environment in the Yangtze Basin, 25(12): 1789-1796. (in Chinese)

[50]
Xu Y, Tang Q, Fan J et al., 2011. Assessing construction land potential and its spatial pattern in China. Landscape and Urban Planning, 103(2): 207-216.

DOI

[51]
Xu Y, Wang L J, Yang H, 2022. Evaluation method and empirical application of human activity suitability of land resources in Qinghai-Tibet Plateau. Acta Geographica Sinica, 77(7): 1615-1633. (in Chinese)

DOI

[52]
Xu Y, Zhao S, Fan J, 2020. Urban planning construction land standard and its revision of climate and topography in China. Acta Geographica Sinica, 75(1): 194-208. (in Chinese)

DOI

[53]
Yang C, Liu H Z, Li Q Q et al., 2022. Human expansion into Asian highlands in the 21st century and its effects. Nature Communications, 13(1): 4955.

DOI PMID

[54]
Yang H, Xu Y, Wang L J, 2023. Evaluation method and empirical application of construction land suitability and arable land suitability in alpine-gorge region of Qinghai-Tibet Plateau: A case study of Nyingchi city. Journal of Natural Resources, 38(5): 1283-1299. (in Chinese)

DOI

[55]
Yao M L, Shao D G, Lv C H et al., 2021. Evaluation of arable land suitability based on the suitability function: A case study of the Qinghai-Tibet Plateau. Science of The Total Environment, 787: 147414.

DOI

[56]
Zhang H P, He R W, Liu Y W et al., 2020. Land suitability evaluation and reconstruction of settlements in the pastoral area of Tibetan Plateau: A case study of Nagqu county in northern Tibet. Journal of Natural Resources, 35(3): 698-712. (in Chinese)

DOI

[57]
Zhang X R, Fang C L, Wang Z B et al., 2013. Urban construction land suitability evaluation based on improved multi-criteria evaluation based on GIS (MCE-GIS): Case of New Hefei City, China. Chinese Geographical Science, 23(6): 740-753.

DOI

[58]
Zhang Y L, Li B Y, Zheng D, 2014. Datasets of the boundary and area of the Tibetan Plateau. Acta Geographica Sinica, 69(Suppl. 1): 65-68. (in Chinese)

[59]
Zhou K, Wang C S, 2016. Spatial-temporal pattern of poverty-stricken areas and its differential policies for poverty alleviation in China. Bulletin of Chinese Academy of Sciences, 31(1): 101-111. (in Chinese)

[60]
Zhou L, Dang X W, Zhou C H et al., 2021. Evolution characteristics of slope spectrum and slope-climbing effects of built-up land in China. Acta Geographica Sinica, 76(7): 1747-1762. (in Chinese)

DOI

[61]
Zhou M G, Wang H D, Zeng X Y et al., 2019. Mortality, morbidity, and risk factors in China and its provinces, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet, 394(10204): 1145-1158.

DOI PMID

Options
Outlines

/

〈 〉