Study on Spatio-Temporal Changes of Land Use Sustainability in Southwestern Border Mountainous Provinces in Recent 20 Years Based on Remote Sensing Interpretation: A Case Study in Yunnan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Data Source and Description
2.2.1. Source and Interpretation of Three Phases RS Image Data
- (1)
- Three increases
- (2)
- Three decreases
2.2.2. Other Data Sources and Descriptions
- (1)
- Socio economic data
- (2)
- Geospatial data
- (3)
- Other basic data and materials
- (4)
- Source of Yunnan county boundary
2.3. Evaluation Method of Land Use Sustainability
2.3.1. Basic Connotation and Criteria of SLU
2.3.2. Basic Ideas and Index System of LUSE
- (1)
- Basic ideas for evaluation
- (2)
- Evaluation index system
- (3)
- Evaluation criteria of LUSE indexes
2.3.3. Comprehensive Method of LUSE
- (1)
- Degrees of Ecological Friendliness (DEF)
- (2)
- Degrees of Economic Viability (DEV)
- (3)
- Degrees of Social Acceptability (DSA)
- (4)
- Degrees of Overall Land Use Sustainability (DOS)
- (5)
- Determination method and result value of index weight
2.3.4. Grading System and Standard of SLU
- (1)
- Classification of EFLU
- (2)
- Classification of EVLU and SALU
- (3)
- Grading of OSLU
3. Results
3.1. Spatio-Temporal Characteristics and Causes Analysis of EFLU
3.1.1. Change Characteristics of EFLU in Recent 20 Years
3.1.2. Characteristics of Spatial Differences of EFLU
3.1.3. Analysis of the Causes of Spatio-Temporal Evolution of EFLU
3.2. Spatio-Temporal Evolution Characteristics and Causes Analysis of EVLU
3.2.1. Change Characteristics of EVLU in Recent 20 Years
3.2.2. Characteristics of Spatial Differences of EVLU
3.2.3. Analysis of the Causes of Spatio-Temporal Evolution of EVLU
3.3. Spatio-Temporal Evolution Characteristics and Causes Analysis of SALU
3.3.1. Change Characteristics of SALU in Recent 20 Years
3.3.2. Characteristics of Spatial Differences of SALU
3.3.3. Analysis of the Causes of Spatio-Temporal Evolution of SALU
3.4. Spatio-Temporal Evolution Characteristics and Causes Analysis of OSLU
3.4.1. Change Characteristics of OSLU in Recent 20 Years
3.4.2. Characteristics of Spatial Differences of the OSLU
3.4.3. Analysis of the Causes of Spatio-Temporal Evolution of OSLU
4. Conclusions and Discussion
4.1. Main Conclusions
4.2. Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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First Class Land Use Type | Second Class Land Use Type | Land Use Classified Area (Unit: 10,000 hectares) | ||||
---|---|---|---|---|---|---|
Number | Name | Number | Name | In 2000 | In 2010 | In 2020 |
1 | Cultivated Land | 551.08 | 545.96 | 539.56 | ||
11 | Paddy Field | 135.91 | 134.53 | 131.39 | ||
12 | Dryland | 415.17 | 411.43 | 408.17 | ||
2 | Woodland | 1998.19 | 2224.10 | 2418.67 | ||
21 | Closed Forest Land | 1414.58 | 1724.81 | 1884.72 | ||
22 | Other Forest Land | 583.61 | 499.29 | 533.95 | ||
3 | Grassland | 481.25 | 325.65 | 181.12 | ||
31 | Pasture with High Coverage | 307.02 | 195.20 | 105.36 | ||
32 | Pasture with Medium and Low Coverage | 174.23 | 130.46 | 75.76 | ||
4 | Waters | 49.34 | 53.28 | 56.09 | ||
41 | Rivers and Lakes | 31.78 | 31.47 | 31.18 | ||
42 | Reservoir and Pond | 17.56 | 21.81 | 24.91 | ||
5 | Construction land | 66.72 | 86.73 | 129.69 | ||
51 | Urban Construction Land, Rural Settlement Area and Land for Mining and Industry | 54.84 | 74.86 | 109.17 | ||
52 | Other Building Land | 11.88 | 11.87 | 20.52 | ||
6 | Unused land | 695.85 | 606.71 | 517.30 | ||
61 | Bare land | 96.13 | 92.95 | 80.62 | ||
62 | Other Land Types | 599.72 | 513.76 | 436.68 |
Indicator Category | Evaluation Indicators | Element Indicator | Calculation Method and Illustrate | The Main Method of Data Acquisition | Control Objective (Relative Optimal Value) |
---|---|---|---|---|---|
1. Ecological friendliness evaluation index | 1.1 IOR | Land suitable reclamation rate | IOR = 100 − [(Actual land reclamation rate − Land suitable reclamation rate)/Land suitable reclamation rate × 100] | Land Survey and Evaluation [1] | Actual reclamation rate ≤ suitable reclamation rate (Over–reclaimed Rate = 0) |
Actual land reclamation rate | Land survey, remote sensing monitoring | ||||
1.2 IBLA | Bare land area | IBLA = 100 −(Bare land area/Total land area × 100) | Land survey, remote sensing monitoring | 0 | |
Total land area | Land survey | ||||
1.3 IEI | Effective irrigated area of cultivated land | IEI = Effective irrigated area of cultivated land/Cultivated area × 100 | Land survey, remote sensing monitoring | 100% | |
Cultivated area | Land survey, remote sensing monitoring | ||||
1.4 IFC | Closed Forest area | IFC = RFC/Highest RFC in the region × 100 Where, RFC (Forest-coverage Rate) = Closed Forest area/Total land area × 100% | Land Remote Sensing Survey (or Forest Census) | ≥67% (Yunnan province’s 2035 Forest Coverage Planning Target) | |
Total land area | Land survey | ||||
1.5 IBRC | Index of Biological Richness (IBR) | IBRC = IBR/Highest IBR in the region × 100 Where, IBR = Abio × (Woodland area × 0.35 + Grassland area × 0.21 + Waters area × 0.28 + Cultivated land area × 0.11 + Construction land area × 0.04 + Unused land area × 0.01)/Total land area; reference value of Abio is 511.2642 [59,60] | Thematic investigations and calculations | Consider the regional context. The relative optimum value was taken as the relative optimum value of the counties with the best ecological protection in the province. | |
1.6 IESV | Ecological service value per unit land area (VES) | IESV = VES/Highest VES in the region × 100 Where VES is calculated according to Xie Gaodi et al. [61] (2003) and the corresponding estimation method [62] | Thematic investigations and calculations | Consider the regional context. The relative best value of ecological service per unit land area in the county is taken as the relative optimal value. | |
2. Economic viability evaluation index | 2.1 ICLP | Regional GDP per unit land area | ICLP = ln (CLP)/ ln (Highest CLP in the region) Where CLP (Comprehensive land productivity) = GDP/Total land area. Because the index data of some years and counties are not stable, the calculation formula is treated as a natural logarithm. IALP, IBLP, IFRUA, and IPGDP are also the same, aiming to make each index more stable [63]. When the calculated ICLP value is greater than 100, it takes 100 | Socioeconomic Statistics | Regional unit land GDP ≥ national average unit land GDP |
National average GDP per unit land area | Socioeconomic Statistics | ||||
2.2 IALP | Output value of primary industry per unit of agricultural land in the region | IALP = ln (ALP)/ln (Highest ALP in the region) Where ALP (Agricultural Land Productivity) = Output value of primary industry/Agricultural land area | Socioeconomic Statistics, Land Remote Sensing Survey | Regional unit agricultural real estate value ≥ national average unit agricultural real estate value | |
National average output value of primary industry per unit of agricultural land | Socioeconomic Statistics, Land Remote Sensing Survey | ||||
2.3 IBLP | Output value of secondary and tertiary industries of building land in regional units | IBLP = ln (BLP)/ln (Highest BLP in the region) Where BLP (Building Land Productivity) = Output value of secondary and tertiary industries/Building land area | Socioeconomic Statistics, Land Remote Sensing Survey | Regional unit construction real estate value ≥ national average unit construction real estate value | |
National average unit building land output value of secondary and tertiary industries | Socioeconomic Statistics, Land Remote Sensing Survey | ||||
2.4 IFRUA | Fiscal revenue per unit land area | IFRUA = ln (FRUA)/ln (Highest FRUA in the region) Where FRUA (Fiscal Revenue per unit Land Area) = Total local financial revenue/Total land area | Socioeconomic Statistics | Regional fiscal revenue per unit land area ≥ national average fiscal revenue per unit land area | |
National average fiscal revenue per unit land area | Socioeconomic Statistics | ||||
2.5 IYGC | Actual unit yield of grain crops | IYGC = Average annual grain yield per unit area/Crop Potential Productivity | Rural Economic Statistics | Actual productivity = Potential productivity | |
Crop Potential Productivity | Special Research [1] | ||||
2.6 ILUR | Proportion of used land area | ILUR = LUR/Highest LUR in the region Where LUR (Land Use Rate) = Total area of used land/Total land area | Land survey, remote sensing monitoring | Consider the regional context. Take the county with the highest degree of land use as the relative optimal value. | |
3. Social acceptability evaluation index | 3.1 IPP | Actual population | IPP = (10-PP)/(10-1) × 100 Where PP (Population Pressure) = Actual population/ PSCL; PSCL = Suitable cultivated area × Proportion of grain sowing × Possible grain yield per unit area/Per capita grain consumption | Socioeconomic Statistics | Actual population ≤ land population carrying capacity |
Population-Supporting Capacity of Land Resources (PSCL) | Special Research [1] | ||||
3.2 IPGDP | Regional GDP per capita | IPGDP = ln (GDP per capita)/ln (Highest GDP per capita in the region) | Socioeconomic Statistics | Regional per capita GDP ≥ national per capita GDP | |
National GDP per capita | Socioeconomic Statistics | ||||
3.3 IDIR | Per capita disposable income of regional rural residents | IDIR = Per Capita Disposable Income of Rural Residents/Highest Per Capita Disposable Income of Rural Residents in the region | Rural Socioeconomic Statistics | Regional farmers’ per capita net income ≥ national farmers’ per capita net income | |
National average per capita disposable income of rural residents | Rural Socioeconomic Statistics | ||||
3.4 IPYG | Per capita food production | IPYG = Per capita grain output (kg/person)/500 kg/person × 100 | Socioeconomic Statistics | Per capita grain output ≥ 400 kg | |
Per capita food production target | Economic and Social Planning |
First–Level | Weight | Second–Level Indicators | Weight |
---|---|---|---|
1. DEF | 0.38 | 1.1 IOR | 0.18 |
1.2 IBLA | 0.15 | ||
1.3 IEI | 0.13 | ||
1.4 IFC | 0.20 | ||
1.5 IBRC | 0.18 | ||
1.6 IESV | 0.16 | ||
2. DEV | 0.32 | 2.1 ICLP | 0.22 |
2.2 IALP | 0.18 | ||
2.3 IBLP | 0.16 | ||
2.4 IFRUA | 0.15 | ||
2.5 IYGC | 0.12 | ||
2.6 ILUR | 0.17 | ||
3. DOS | 0.30 | 3.1 IPP | 0.18 |
3.2 IPGDP | 0.32 | ||
3.3 IDIR | 0.34 | ||
3.4 IPYG | 0.16 |
Level of EFLU | DEF | Meaning |
---|---|---|
1. Highly Friendly | ≥90 | The DEF is very high. The land development and utilization activities do not cause obvious influence and destruction of the ecological environment, and they can ensure the EFLU. |
2. Moderately Friendly | 75~90 | The DEF is moderate. Land development and utilization activities have caused a certain degree of influence and destruction of the ecology. By taking general ecological construction and environmental protection measures, the EFLU can be ensured. |
3. Lowly Friendly | 60~75 | The DEF is low. Land development and utilization activities have caused significant influence and destruction of the ecological environment. Effective ecological construction and environmental protection projects should be made to promote the EFLU. |
4. Unfriendly | 45~60 | The DEF is low. Land development and utilization activities have caused great influence and destruction of the ecological environment. Strong ecological construction and environmental protection measures are needed to ensure the EFLU. |
5. Very Unfriendly | <45 | The DEF is very low, and the unfriendliness is particularly prominent. Land development and utilization activities have caused significant influence and destruction of the ecology. It is essential to fundamentally reverse the land use mode and take major ecological construction and environmental protection measures in order to ensure the EFLU. |
Economic Viability Grading System and Criteria | Social Acceptability Grading System and Criteria | ||
---|---|---|---|
Level of EVLU | DEV | Level of SALU | DSA |
1. Highly Viable | ≥90 | 1. Highly Acceptable | ≥90 |
2. Moderately Viable | 75~90 | 2. Moderately Acceptable | 75~90 |
3. Lowly Viable | 60~75 | 3. Lowly Acceptable | 60~75 |
4. Nonviable | 45~60 | 4. Unacceptable | 45~60 |
5. Very Nonviable | <45 | 5. Very Unacceptable | <45 |
Level OSLU | DOS | Meaning |
---|---|---|
1. Highly sustainable | ≥90 | The EFLU, EVLU and SALU are all high, so the overall sustainability is high. The land development and utilization activities have not caused obvious influence and destruction of the ecology, and the economic and social benefits are great. |
2. Moderately sustainable | 75~90 | The OSLU is medium, and the EFLU, EVLU and SALU have different degrees of deficiencies or defects. Land development and utilization activities have resulted in a certain grade of influence and destruction of the ecology, or economic benefits and social benefits are not high. By taking general measures of ecology and environment, economy or comprehensiveness, it is generally possible to ensure the land use system sustainability. |
3. Lowly sustainable | 60~75 | The OSLU is low, and there are significant deficiencies or defects in EFLU, EVLU and SALU. Land development and utilization activities have caused significant influence and destruction of the ecology or economy and are of low social benefit. Measures of effective ecology and environment, economy or comprehensiveness are needed. |
4. Conditionally sustainable | 45~60 | The OSLU is low, and the EFLU, EVLU and SALU are largely inadequate or flawed, or one or two aspects of the 3 aspects are largely flawed. Strong measures of ecology and environment, economy or comprehensiveness can improve the OSLU. |
5. Unsustainable | <45 | Low OSLU, major deficiencies in EFLU, EVLU and SALU, or major deficiencies in 1–2 of the 3. It is essential to fundamentally reverse the way of land use and take major measures of ecology and environment, economy or comprehensiveness to greatly improve the OSLU. |
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Yang, R.; Wu, Q.; Yang, Z.; Yang, S. Study on Spatio-Temporal Changes of Land Use Sustainability in Southwestern Border Mountainous Provinces in Recent 20 Years Based on Remote Sensing Interpretation: A Case Study in Yunnan Province, China. Land 2022, 11, 1957. https://0-doi-org.brum.beds.ac.uk/10.3390/land11111957
Yang R, Wu Q, Yang Z, Yang S. Study on Spatio-Temporal Changes of Land Use Sustainability in Southwestern Border Mountainous Provinces in Recent 20 Years Based on Remote Sensing Interpretation: A Case Study in Yunnan Province, China. Land. 2022; 11(11):1957. https://0-doi-org.brum.beds.ac.uk/10.3390/land11111957
Chicago/Turabian StyleYang, Renyi, Qiuju Wu, Zisheng Yang, and Shiqin Yang. 2022. "Study on Spatio-Temporal Changes of Land Use Sustainability in Southwestern Border Mountainous Provinces in Recent 20 Years Based on Remote Sensing Interpretation: A Case Study in Yunnan Province, China" Land 11, no. 11: 1957. https://0-doi-org.brum.beds.ac.uk/10.3390/land11111957