Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources and Preprocessing
2.3. Methodology
2.3.1. Constructing the IRSEI and RSEI
- (1)
- RSEI model
- (2)
- IRSEI model
2.3.2. Geographically Weighted Regression
3. Results
3.1. Changes in the Indices
3.2. Comparative Analysis of IRSEI and RSEI
3.3. Spatiotempral Distribution of IRSEI
3.4. Analysis of Ecological Environmental Quality Driver
3.4.1. Global Spatial Autocorrelations
3.4.2. Results of GWR-Based Regression Coefficients of Driving Factors
4. Discussion
4.1. Calculation of IRSEI
4.2. Driving Forces of Ecological Environment Quality
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Type | Resolution | Processing and Methodogy | Source | |
---|---|---|---|---|
2000–2020 remote sensing images | 30 m | Geometric correction | Nearest | USGS (https://www.usgs.gov/) (accessed on 2 March 2022) |
Mosaicking | Nearest Neighbor | |||
Cropping | - | |||
JRC Global Surface Water Mapping Layers | 30 m | Cropping | - | JRC JEODPP Data Browser (https://jeodpp.jrc.ec.europa.eu/) (accessed on 5 May 2022) |
Vectorization | - | |||
GDP (2000\2010\2019) | 1 km | Formula calculation | - | Resource and Environment Science Data Center (http://www.resdc.cn/) (accessed on 12 July 2022) |
Cropping | - | |||
Resampling | - | |||
Population density | 100 m | Cropping | - | WorldPop (https://hub.worldpop.org/) (accessed on 13 July 2022) |
Resampling | Bilinear | |||
Nighttime lighting data (2000\2010\2020) | 1 km | Cropping | - | National Tibetan Plateau Data Center (http://data.tpdc.ac.cn) (accessed on 15 July 2022) |
Resampling | Bilinear | |||
Road network data (2020) | 30 m | Formula calculation | - | OpenStreetMap (https://www.openstreetmap.org/) (accessed on 26 August 2022) |
Cropping | - | |||
Resampling | Bilinear |
Indicators | Calculation Formula | Explanation |
---|---|---|
Greenness | for the near-infrared band and the red band, respectively [7]. | |
Humidity | correspond to the reflectance of TM and OLI remote sensing images, respectively [7,12]. | |
Heat | Calculated with reference to [20]. | |
Dryness | SI represents soil index, IBI represents building index other bands are interpreted as above [7]. |
NDVI | WET | NDSI | LST | |
---|---|---|---|---|
Entropy weight/% | 54.60 | 5.60 | 15.57 | 24.26 |
Grade Change | 2000–2010 | 2010–2020 | 2000–2020 | |||
---|---|---|---|---|---|---|
Area (km2) | Percentage | Area (km2) | Percentage | Area (km2) | Percentage | |
Better | 3074.21 | 30.90% | 793.67 | 7.99% | 2980.93 | 30.12% |
Unchanged | 6657.92 | 66.91% | 7733.95 | 77.88% | 6147.59 | 62.12% |
Worse | 218.11 | 2.19% | 1402.58 | 14.12% | 767.39 | 7.75% |
Total | 9950.24 | 100.00% | 9930.20 | 100.00% | 9895.91 | 100.00% |
2000 | 2010 | 2020 | |||||||
---|---|---|---|---|---|---|---|---|---|
Moran’s I | z | p | Moran’s I | z | p | Moran’s I | z | p | |
1500 m | 0.573 | 94.653 | 0.000 | 0.542 | 89.591 | 0.000 | 0.567 | 78.566 | 0.000 |
2000 m | 0.630 | 47.733 | 0.000 | 0.607 | 46.113 | 0.000 | 0.608 | 46.041 | 0.000 |
2500 m | 0.625 | 38.189 | 0.000 | 0.602 | 36.794 | 0.000 | 0.607 | 37.051 | 0.000 |
3000 m | 0.609 | 31.247 | 0.000 | 0.585 | 30.012 | 0.000 | 0.588 | 30.132 | 0.000 |
Model | Scale/km2 | 2000 | 2010 | 2020 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
AICc | R2 | R2 Adjusted | AICc | R2 | R2 Adjusted | AICc | R2 | R2 Adjusted | ||
Geo-weighted regression (GWR) | 1.52 | −8064.885 | 0.636 | 0.619 | −7124.161 | 0.611 | 0.590 | −5102.653 | 0.391 | 0.381 |
22 | −3128.020 | 0.430 | 0.420 | −2914.127 | 0.465 | 0.450 | −2571.443 | 0.387 | 0.374 | |
2.52 | −2939.980 | 0.695 | 0.664 | −2595.370 | 0.688 | 0.649 | −2216.013 | 0.598 | 0.562 | |
32 | −1914.689 | 0.684 | 0.645 | −1724.518 | 0.702 | 0.649 | −1425.883 | 0.589 | 0.544 | |
Least squares regression (OLS) | 1.52 | −3453.493 | 0.056 | 0.055 | −2753.340 | 0.030 | 0.029 | −2843.504 | 0.038 | 0.038 |
22 | −1727.017 | 0.071 | 0.070 | −1271.876 | 0.041 | 0.040 | −1332.177 | 0.049 | 0.048 | |
2.52 | −1074.246 | 0.095 | 0.093 | −753.246 | 0.059 | 0.057 | −796.346 | 0.068 | 0.066 | |
32 | −691.520 | 0.101 | 0.098 | −465.933 | 0.066 | 0.064 | −498.346 | 0.072 | 0.069 |
Driving Factor | Minimum | Median | Maximum | Average | Percentage of Positive | Percentage of Negative | |
---|---|---|---|---|---|---|---|
2000 | GDP | −1.807 | 0.480 | 16.824 | 1.873 | 82.45% | 17.55% |
POP | −11.506 | −0.568 | 7.721 | 1.530 | 18.56% | 81.44% | |
NTL | −0.207 | −0.083 | 2.365 | 0.492 | 37.93% | 62.07% | |
Road | −0.761 | 0.150 | 3.225 | 0.321 | 78.90% | 21.10% | |
2010 | GDP | −11.581 | 0.191 | 18.657 | 1.667 | 69.16% | 20.84% |
POP | −15.002 | −0.328 | 10.985 | 1.391 | 27.44% | 72.56% | |
NTL | −7.075 | −0.071 | 2.878 | 0.535 | 36.51% | 63.49% | |
Road | −1.084 | 0.113 | 3.418 | 0.379 | 70.53% | 29.47% | |
2020 | GDP | 0.912 | 0.028 | 0.807 | 0.161 | 63.73% | 36.27% |
POP | −1.295 | −0.144 | 3.717 | 0.320 | 18.81% | 81.19% | |
NTL | −0.328 | −0.061 | 0.274 | 0.086 | 25.86% | 74.14% | |
Road | −0.273 | −0.066 | 0.420 | 0.079 | 18.86% | 81.14% |
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Chen, N.; Cheng, G.; Yang, J.; Ding, H.; He, S. Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis. Sustainability 2023, 15, 8464. https://doi.org/10.3390/su15118464
Chen N, Cheng G, Yang J, Ding H, He S. Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis. Sustainability. 2023; 15(11):8464. https://doi.org/10.3390/su15118464
Chicago/Turabian StyleChen, Na, Gang Cheng, Jie Yang, Huan Ding, and Shi He. 2023. "Evaluation of Urban Ecological Environment Quality Based on Improved RSEI and Driving Factors Analysis" Sustainability 15, no. 11: 8464. https://doi.org/10.3390/su15118464