Analysis and Dynamic Evaluation of Eco-Environmental Quality in the Yellow River Delta from 2000 to 2020
<p>Distribution of the Yellow River Delta area in Shandong Province.</p> "> Figure 2
<p>The flow chart.</p> "> Figure 3
<p>Percentage of RSEI grades in each year of the Yellow River Delta.</p> "> Figure 4
<p>Distribution of RSEI grades in the Yellow River Delta.</p> "> Figure 5
<p>Analysis of RSEI trend in the Yellow River Delta.</p> "> Figure 6
<p>Spatial and temporal distribution of NPP in the Yellow River Delta.</p> "> Figure 7
<p>Land transfer matrix in the Yellow River Delta.</p> ">
Abstract
:1. Introduction
2. Study Area and Data Sources
2.1. Study Area
2.2. Data Source and Processing
- Landsat5 (TM) and Landsat8 (OLI) image data from 2000 to 2020 were used as remote sensing images in this study, and LUCC and NPP data were primarily employed as impact factor data.
- The LUCC data were published by Xin Huang in Wuhan University. Based on 335,709 scenes of Landsat data on Google Earth Engine, Xin Huang constructed spatio-temporal features of land use, combined with a random forest classifier to obtain classification results of land use, and proposed a post-processing method that includes spatio-temporal filtering and logical inference to further improve the spatio-temporal consistency of the annual China Land Cover Dataset (CLCD). The CLCD contained year-by-year land cover information for China from 1985 + 1990–2020. The overall accuracy of the CLCD was 79.31% based on 5463 visually interpreted samples.
- The NPP data were obtained using the CASA model inversion. The relevant data (MOD13Q1 image, MOD11A2 image, monthly solar radiation data, monthly mean temperature data, and monthly precipitation data) used for calculations in this study were provided by NASA and the China Meteorological Science Data Network Shared Services Network.
3. Research Indicators and Methods
3.1. RSEI Model
3.1.1. Greenness Index
3.1.2. Wetness Index
3.1.3. Heat Index
3.1.4. Dryness Index
3.1.5. Normalization of Ecological Indicators
3.1.6. Determination of the Weight of Each Ecological Index Factor
3.2. Inversion Estimation of NPP in the Yellow River Delta by CASA Model
3.3. Land-Use Dynamic Degree
4. Results and Analysis
4.1. Indicator Analysis
4.2. Variation Characteristics of RSEI in the Yellow River Delta
4.2.1. Analysis of Spatio-Temporal Variation of RSEI in the Yellow River Delta Region
4.2.2. Analysis of the Interannual Variation Trend of RSEI in the Yellow River Delta Region
4.3. Characteristics of NPP and LUCC Change in the Yellow River Delta
4.3.1. Interannual Spatio-Temporal Variation of NPP in the Yellow River Delta
4.3.2. Spatio-Temporal Dynamic Changes of LUCC in the Yellow River Delta
4.4. The Relationship between NPP, LUCC and RSEI in the Yellow River Delta
4.4.1. Relationship between NPP and RSEI in the Yellow River Delta
4.4.2. The Relationship between LUCC and RSEI in the Yellow River Delta
5. Discussion
- (1)
- Land transformation and human disturbance in coastal mudflats in the Yellow River Delta region should be reduced to maintain the balance between the eco-environmental quality and the coastal mudflats.
- (2)
- When building near the Yellow River basin, attention should be paid to improving the ecological conditions of the coastline area. Take measures to reduce large-scale industrial expansion and construction, return farmland to forests, lakes, and grasses, and enhance the ecological environment.
- (3)
- Restore some abandoned land in the delta to lakes, forests, and grasslands to stop the spread of arable land. At the same time, a sound urban development plan should be formulated, and ecological construction subsidies should be implemented in urban development areas to reduce its negative impact.
6. Conclusions
- (1)
- In the Yellow River Delta region, the areas with good eco-environmental quality were mainly located near the Yellow River basin, and the distribution resembles a tilted “Y”, and the areas with poor environmental quality were located in the areas near the seaside edge of the Yellow River Delta. The eco-environmental quality gradually deteriorated from the middle to the edge of the Yellow River Delta, and the eco-environmental quality in urban areas was poor.
- (2)
- The eco-environmental quality of the Yellow River Delta showed a “V”-shaped fluctuation within 20 years, and the overall quality of the ecological environment was improving. From 2000 to 2010, the eco-environmental quality gradually became better. The eco-environmental quality of Dongying District gradually deteriorated, while Hekou District, Kenli District, and Lijin County gradually improved. From 2010 to 2020, the eco-environmental quality gradually deteriorated, with a large area of the eastern, central, and western parts of the delta experiencing the deterioration of the ecological environment, and the rest of the region showing a staggered deterioration and improvement.
- (3)
- There was no stable correlation between the eco-environmental quality and NPP in the Yellow River Delta region, indicating that there was no stable correlation between carbon fixation capacity and eco-environmental quality in the Yellow River Delta region. There was a positive correlation between urban construction land and eco-environmental quality among land use types in the Yellow River Delta, with the largest absolute value of land-use dynamic degree of 8.78%, significant land expansion, and obvious changes in land transfer. Arable land and eco-environmental quality showed a weak correlation, and the absolute value of land-use dynamic degree was the smallest, which was −1.01%. Arable land showed a slow decreasing trend, and land transfer changes were more obvious. Forest land and eco-environmental quality did not show correlation, and coastal mudflats showed negative correlation with eco-environmental quality, indicating that coastal mudflats in the Yellow River Delta had an important role in improving eco-environmental quality.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Set | Data Source | Resolution | Data Description |
---|---|---|---|
Landsat5 (TM) | Google Earth Engine | 30 | |
Landsat8 (OLI) | Google Earth Engine | 30 | |
China Land Cover Dataset (CLCD) | Xin Huang of Wuhan University | 30 | The overall accuracy rate for CLCD was 79.31% |
MOD13Q1 | Google Earth Engine | 500 | |
MOD11A2 | Google Earth Engine | 500 | |
Monthly solar radiation data, | NASA | 500 | |
monthly mean temperature data | China Meteorological Science Data Network Shared Services Network | 500 | |
monthly precipitation data | China Meteorological Science Data Network Shared Services Network | 500 |
Symbol | Bands | |
---|---|---|
Landsat5 (TM) | Landsat8 (OLI) | |
Band1 | Band2 | |
Band2 | Band3 | |
Band3 | Band4 | |
Band4 | Band5 | |
Band5 | Band6 | |
Band7 | Band7 |
Year | Mean RSEI | WET | NDVI | NDBSI | LST | PC1 (%) |
---|---|---|---|---|---|---|
2000 | 0.63 | 0.61 | 0.47 | −0.67 | −0.61 | 70.32 |
2005 | 0.47 | 0.65 | 0.45 | −0.60 | −0.53 | 68.79 |
2010 | 0.42 | 0.73 | 0.56 | −0.49 | −0.64 | 66.69 |
2015 | 0.52 | 0.60 | 0.48 | −0.55 | −0.49 | 74.64 |
2020 | 0.51 | 0.71 | 0.55 | −0.53 | −0.54 | 80.85 |
mean | 0.51 | 0.66 | 0.50 | −0.57 | −0.56 | 70.32 |
Year | RSEI Grade | Study Area | |||
---|---|---|---|---|---|
Dong Ying | He Kou | Ken Li | Li Jin | ||
2000 | Excellent | 13.55% | 2.75% | 4.84% | 0.81% |
Good | 18.42% | 7.28% | 10.14% | 3.34% | |
Moderate | 36.24% | 19.13% | 24.99% | 17.79% | |
Fair | 29.07% | 50.06% | 41.76% | 55.08% | |
Poor | 2.63% | 20.77% | 18.24% | 22.97% | |
2005 | Excellent | 2.80% | 5.92% | 15.08% | 9.89% |
Good | 17.62% | 26.63% | 32.54% | 43.96% | |
Moderate | 30.50% | 34.20% | 27.20% | 28.78% | |
Fair | 42.31% | 25.02% | 20.27% | 13.26% | |
Poor | 6.77% | 8.23% | 4.86% | 4.11% | |
2010 | Excellent | 1.99% | 7.09% | 11.13% | 16.17% |
Good | 14.78% | 45.34% | 45.57% | 55.47% | |
Moderate | 28.55% | 29.91% | 24.34% | 18.24% | |
Fair | 37.58% | 12.83% | 14.04% | 8.11% | |
Poor | 17.10% | 4.83% | 4.86% | 2.01% | |
2015 | Excellent | 1.02% | 3.83% | 12.00% | 2.61% |
Good | 14.45% | 17.57% | 23.55% | 26.47% | |
Moderate | 28.13% | 42.09% | 32.64% | 43.11% | |
Fair | 40.50% | 30.83% | 24.20% | 21.09% | |
Poor | 15.89% | 5.64% | 7.41% | 6.71% | |
2020 | Excellent | 1.42% | 3.56% | 15.95% | 5.82% |
Good | 13.44% | 22.29% | 26.01% | 23.18% | |
Moderate | 32.17% | 33.85% | 28.68% | 33.30% | |
Fair | 37.84% | 27.63% | 23.13% | 26.74% | |
Poor | 15.14% | 12.67% | 6.10% | 10.95% |
Year | Mean NPP (g C/m2) |
---|---|
2000 | 136.97 |
2005 | 182.43 |
2010 | 170.15 |
2015 | 186.77 |
2020 | 199.19 |
Year | Correlation |
---|---|
2000 | 0.122 |
2005 | 0 |
2010 | 0 |
2015 | −0.395 |
2020 | −0.397 |
Land Use/Cover Types | Spearman Correlation Coefficient |
---|---|
arable land | 0.34 |
forest land | 0.08 |
coastal mudflats | −0.61 |
urban construction land | 0.79 |
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Ma, D.; Huang, Q.; Liu, B.; Zhang, Q. Analysis and Dynamic Evaluation of Eco-Environmental Quality in the Yellow River Delta from 2000 to 2020. Sustainability 2023, 15, 7835. https://doi.org/10.3390/su15107835
Ma D, Huang Q, Liu B, Zhang Q. Analysis and Dynamic Evaluation of Eco-Environmental Quality in the Yellow River Delta from 2000 to 2020. Sustainability. 2023; 15(10):7835. https://doi.org/10.3390/su15107835
Chicago/Turabian StyleMa, Dongling, Qingji Huang, Baoze Liu, and Qian Zhang. 2023. "Analysis and Dynamic Evaluation of Eco-Environmental Quality in the Yellow River Delta from 2000 to 2020" Sustainability 15, no. 10: 7835. https://doi.org/10.3390/su15107835