A Study on Spatiotemporal Changes of Ecological Vulnerability in Yunnan Province Based on Interpretation of Remote Sensing Images
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. RS Data Acquisition and Interpretation
2.3. Methods of EVE
2.3.1. Basic Ideas and Indicator System for EVE
- (1)
- Basic Ideas for EVE
- (2)
- Evaluation Indicator System
- (3)
- Assessment Criteria for EVE Indicators
2.3.2. Comprehensive Method for EVE
- (1)
- DINEV.
- (2)
- DEVLU.
- (3)
- DEVLC.
- (4)
- DOEV.
- (5)
- Method for determining indicator weights and resulting values
2.3.3. Ecological Vulnerability Grading System and Associated Standards
3. Results and Analysis
3.1. The Spatiotemporal Evolution Characteristics of INEV and Its Spatial Difference
3.2. The Spatiotemporal Evolution Characteristics of “Acquired” LUEV and LCEV
3.2.1. The Spatiotemporal Evolution Characteristics of LUEV
3.2.2. The Spatiotemporal Evolution Characteristics of LCEV
3.3. The Spatiotemporal Evolution Characteristics of OEV
3.3.1. Characteristics of OEV Change in the Past 40 Years
3.3.2. Spatial Differences in OEV of Yunnan Province
3.3.3. Analysis of the Reasons for the Spatiotemporal Evolution of OEV
4. Discussion
5. Conclusions
6. Shortcomings and Prospects
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Year | RS Image Date | RS Image Data | Spatial Resolution |
---|---|---|---|
1980 | Dec. 1979–Feb. 1982 | Landsat TM | 30 m × 30 m |
1990 | Dec. 1989–Feb. 1992 | Landsat TM | |
2000 | Dec. 1999–Feb. 2000 | Landsat TM/ETM | |
2010 | Dec. 2009–Feb. 2010 | Landsat TM | |
2020 | Jan. 2020–Feb. 2020 | Landsat-8 |
First-Level Land Use Types | Second-Level Land Use Types | Land Use Area of Various Types (Unit: 10,000 Hectares) | ||||||
---|---|---|---|---|---|---|---|---|
Number | Name | Number | Name | In 1980 | In 1990 | In 2000 | In 2010 | In 2020 |
1 | Cultivated Land | 554.02 | 552.45 | 551.08 | 545.96 | 539.56 | ||
11 | Paddy Field | 137.71 | 136.77 | 135.91 | 134.53 | 131.39 | ||
12 | Dryland | 416.31 | 415.68 | 415.17 | 411.43 | 408.17 | ||
2 | Woodland | 1736.23 | 1868.92 | 1998.19 | 2224.10 | 2418.67 | ||
21 | Closed Forest Land | 961.27 | 1112.91 | 1414.58 | 1724.81 | 1884.72 | ||
22 | Other Forest Land | 774.96 | 756.02 | 583.61 | 499.29 | 533.95 | ||
3 | Grassland | 578.38 | 532.64 | 481.25 | 325.65 | 181.12 | ||
31 | Pasture with High Coverage | 370.00 | 340.02 | 307.02 | 195.20 | 105.36 | ||
32 | Pasture with Medium and Low Coverage | 208.38 | 192.62 | 174.23 | 130.46 | 75.76 | ||
4 | Waters | 47.60 | 48.14 | 49.34 | 53.28 | 56.09 | ||
41 | Rivers and Lakes | 32.02 | 31.96 | 31.78 | 31.47 | 31.18 | ||
42 | Reservoir and Pond | 15.58 | 16.18 | 17.56 | 21.81 | 24.91 | ||
5 | Construction Land | 57.87 | 61.78 | 66.72 | 86.73 | 129.69 | ||
51 | Urban Construction Land, Rural Settlement Area and Land for Mining and Industry | 47.64 | 50.82 | 54.84 | 74.86 | 109.17 | ||
52 | Other Building Land | 10.23 | 10.96 | 11.88 | 11.87 | 20.52 | ||
6 | Unused Land | 868.33 | 778.50 | 695.85 | 606.71 | 517.30 | ||
61 | Bare Land | 117.36 | 105.56 | 96.13 | 92.95 | 80.62 | ||
62 | Other Land Types | 750.96 | 672.94 | 599.72 | 513.76 | 436.68 |
Indicators Category | Evaluation Indicators | Element Indicators | Computing Methods and Explanations | Primary Data Acquisition Methods | Optimal Relative Value |
---|---|---|---|---|---|
Degrees of Natural Ecological Vulnerability (DNEV) | 1.1 IMA | Mountain Area (MLA) | IMA = (MAR − Minimum MAR)/Minimum MAR × 100 | The Second National Land Survey in Yunnan Province Dam Area Special Survey | It depends on the background of the region. It takes the minimum MAR in Yunnan province as the relative optimal value. |
Total Land Area (TLA) | MAR (Mountain Area Rate) = MLA/TLA × 100% | ||||
1.2 ISSA | ≥25° Steep Slope Area (SSA) | ISSA = SSAR/Maximum SSAR × 100 | Special Survey of Land Area in Different Climatic Zones and Slopes in Yunnan Province | Considering the regional background and rural development needs, the SSAR in the province’s relatively largest county is taken as the relative minimum value. The closer the SSAR is to 0, the better the ISSA. | |
Total Land Area (TLA) | SSAR (Steep Slope Area Rate) = SSA/TLA × 100% | ||||
1.3 IHAA | High-Altitude Area (HA) | IHAA = HAR/Maximum HAR × 100 | Special Survey of Land Area in Different Climatic Zones and Slopes in Yunnan Province | Considering the background and the needs of rural development, the HAR in the province’s relatively largest county is the relative minimum. The closer the HAR is to 0, the better the IHAA is. | |
Total Land Area (TLA) | HAR (High-Altitude Area Rate) = HA/TLA × 100% | ||||
1.4 IAAR | Average Annual Rainfall (AAR) | IAAR =100 − AAR/Relatively Optimal Value of AAR × 100% | Yunnan Agricultural Climate Dataset | Considering the regional background, after removing extreme values, the average annual rainfall of relatively large counties in the province is taken as the optimal value. | |
Degrees of Ecological Vulnerability of Land Use (DEVLU) | 2.1 IOR | Land Suitable Reclamation Rate (LSRR) | IOR = ORR/Maximum ORR × 100 | Land Suitability Evaluation | Actual reclamation rate ≤ suitable reclamation rate (i.e., ORR = 0). |
Actual Land Reclamation Rate (ALRR) | ORR (Over-Reclaimed Rate) = (ALRR − LSRR)/LSRR × 100% | RS Image Interpretation | |||
2.2 IBLA | Bare Land Area (BLA) | IBLA = BLAR/Maximum BLAR × 100 | RS Image Interpretation | 0 | |
Total Land Area (TLA) | BLAR (Bare Land Area Rate) = BLA/TLA × 100% | ||||
2.3 IEI | Paddy Field Area (PEA) | IEI = 100 − EIR/Maximum EIR × 100 | RS Image Interpretation | The higher the EIR, the lower the vulnerability. | |
Cultivated Area (CA) | EIR (Effective Irrigated Rate of Cultivated Land) = PEA/CA × 100% | ||||
2.4 IGY | Total Yield of Grain Crops (TYGC) | IGY = [ln(Maximum IGY) − ln(IGY)]/[ln(Maximum IGY) − ln(Minimum IGY)] × 100 | Socioeconomic Statistical Yearbook | Considering the regional background, this paper takes the GYPUA of the county with the highest grain yield in the province as the relative optimal value. | |
Sowing Area of Grain Crops (SAGC) | GYPUA (Grain Yield Per Unit Area) = TYGC/SAGC | ||||
Degrees of Ecological Vulnerability of Land Cover (DEVLC) | 3.1 IFC | Closed Forest Area (CFA) | IFC = FCR/Maximum FCR × 100 | RS Image Interpretation | ≥67% (Planning for FCR in Yunnan province by 2035). |
Total Land Area (TLA) | FCR (Forest Coverage Rate) = CFA/TLA × 100% | ||||
3.2 ISEA | Soil Erosion Area (SEA) | ISEA = PSEA/Maximum PSEA × 100 | Existing Thematic Surveys | The larger the proportion of soil erosion area, the higher the vulnerability. This paper takes the proportion of soil erosion area in the county with the largest relative area in the province as the relative extreme value. | |
Total Land Area (TLA) | PSEA (Proportion of Soil Erosion Area) = SEA/TLA × 100% | ||||
3.3 IBRC | Index of Biological Richness (IBR) | IBRC = (Maximum IBR − IBR)/(Maximum IBR − Minimum IBR) × 100 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 Abio = 511.2642 [58,59,60] | Calculate According to the Interpretation Results of RS Images | It depends on the background of the region. According to the counties in Yunnan province with the best ecological protection, the relative optimal value was determined. | |
3.4 IESV | Value of Ecological Service per Unit Land Area (VES) | IESV = (Maximum VES − VES)/(Maximum VES − Minimum VES) × 100 Where, VES is calculated according to Xie Gaodi et al. [61,62] | Calculate According to the Interpretation Results of RS Images | Considering the regional background, the counties with the best VES were used to determine the relative optimal value. |
First-Level Indicators | Weight | Second-Level Indicators | Weight |
---|---|---|---|
1. DINEV | 0.35 | 1.1 IMA | 0.38 |
1.2 ISSA | 0.24 | ||
1.3 IHAA | 0.18 | ||
1.4 IAAR | 0.20 | ||
2. DEVLU | 0.33 | 2.1 IOR | 0.35 |
2.2 IBLA | 0.18 | ||
2.3 IEI | 0.22 | ||
2.4 IGY | 0.25 | ||
3. DEVLC | 0.32 | 3.1 IFC | 0.27 |
3.2 ISEA | 0.29 | ||
3.3 IBRC | 0.23 | ||
3.4 IESV | 0.21 |
Grades of Ecological Vulnerability | DOEV | Meaning |
---|---|---|
1. Very Slightly Vulnerable | <35 | The DEV of regional development is very low; regional resource development and utilization activities have not caused significant impact or damage to the ecological environment; it can ensure the ecological sustainability of regional development. |
2. Lowly Vulnerable | 35~45 | The DEV in regional development is not high; regional resource development and utilization activities have caused a certain degree of impact and damage; by adopting general measures, the ecological sustainability of regional development can be ensured. |
3. Moderately Vulnerable | 45~55 | The DEV in regional development is relatively high; regional resource development and utilization activities have caused significant impact and damage; effective measures need to be taken to ensure the ecological sustainability of regional development. |
4. Highly Vulnerable | 55~65 | The DEV in regional development is high; regional resource development and utilization activities have caused great impact and damage; strong measures need to be taken to ensure the ecological sustainability of regional development. |
5. Very Highly Vulnerable | ≥65 | The DEV in regional development is very high, and the degradation and deterioration of the ecological environment are particularly prominent; regional resource development and utilization activities have caused tremendous impact and damage. It is necessary to fundamentally reverse the ways of resource utilization and regional development and take significant measures to ensure the ecological sustainability of regional development. |
Grades of Ecological Vulnerability | 1. Very Slightly Vulnerable | 2. Lowly Vulnerable | 3. Moderately Vulnerable | 4. Highly Vulnerable | 5. Very Highly Vulnerable |
---|---|---|---|---|---|
County number | 10 | 17 | 34 | 47 | 21 |
Year | 1. Very Slightly Vulnerable | 2. Lowly Vulnerable | 3. Moderately Vulnerable | 4. Highly Vulnerable | 5. Very Highly Vulnerable |
---|---|---|---|---|---|
1980 | 3 | 18 | 53 | 41 | 14 |
1990 | 6 | 33 | 51 | 34 | 5 |
2000 | 8 | 43 | 47 | 27 | 4 |
2010 | 14 | 51 | 45 | 17 | 2 |
2020 | 27 | 49 | 41 | 11 | 1 |
Year | 1. Very Slightly Vulnerable | 2. Lowly Vulnerable | 3. Moderately Vulnerable | 4. Highly Vulnerable | 5. Very Highly Vulnerable |
---|---|---|---|---|---|
1980 | 4 | 15 | 29 | 33 | 48 |
1990 | 12 | 21 | 24 | 36 | 36 |
2000 | 26 | 22 | 30 | 25 | 26 |
2010 | 42 | 29 | 30 | 19 | 9 |
2020 | 66 | 29 | 24 | 8 | 2 |
Year | 1. Very Slightly Vulnerable | 2. Lowly Vulnerable | 3. Moderately Vulnerable | 4. Highly Vulnerable | 5. Very Highly Vulnerable |
---|---|---|---|---|---|
1980 | 0 | 10 | 44 | 61 | 14 |
1990 | 0 | 17 | 57 | 42 | 13 |
2000 | 1 | 28 | 60 | 35 | 5 |
2010 | 4 | 45 | 62 | 15 | 3 |
2020 | 15 | 55 | 50 | 8 | 1 |
Years | ORR (%) | BLAR (%) | EIR (%) | FCR (%) | IBR | VES (CNY/Hectare) |
---|---|---|---|---|---|---|
1980 | 17.69 | 15.27 | 24.86 | 25.02 | 108.36 | 96.09 |
1990 | 17.35 | 13.74 | 24.76 | 28.96 | 113.16 | 102.48 |
2000 | 17.06 | 12.51 | 24.66 | 36.81 | 117.69 | 111.29 |
2010 | 15.98 | 12.09 | 24.64 | 44.89 | 123.92 | 122.18 |
2020 | 14.64 | 10.49 | 24.35 | 49.05 | 129.06 | 129.63 |
Increase or decrease over 40 years (%) | −17.24 | −31.30 | −2.05 | 96.04 | 19.10 | 34.90 |
Average annual increase or decrease (%) | −0.43 | −0.78 | −0.05 | 2.40 | 0.48 | 0.87 |
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Yang, Z.; Yang, S.; Yang, R.; Wu, Q. A Study on Spatiotemporal Changes of Ecological Vulnerability in Yunnan Province Based on Interpretation of Remote Sensing Images. Diversity 2023, 15, 963. https://doi.org/10.3390/d15090963
Yang Z, Yang S, Yang R, Wu Q. A Study on Spatiotemporal Changes of Ecological Vulnerability in Yunnan Province Based on Interpretation of Remote Sensing Images. Diversity. 2023; 15(9):963. https://doi.org/10.3390/d15090963
Chicago/Turabian StyleYang, Zisheng, Shiqin Yang, Renyi Yang, and Qiuju Wu. 2023. "A Study on Spatiotemporal Changes of Ecological Vulnerability in Yunnan Province Based on Interpretation of Remote Sensing Images" Diversity 15, no. 9: 963. https://doi.org/10.3390/d15090963
APA StyleYang, Z., Yang, S., Yang, R., & Wu, Q. (2023). A Study on Spatiotemporal Changes of Ecological Vulnerability in Yunnan Province Based on Interpretation of Remote Sensing Images. Diversity, 15(9), 963. https://doi.org/10.3390/d15090963