Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preprocessing
2.3. Methods
2.3.1. Construction of the Remote Sensing Ecological Index Evaluation Model
2.3.2. Pearson Correlation Analysis
2.3.3. Random Forest Regression Model
2.3.4. Accuracy Verification of Random Forest Models
3. Results
3.1. Analysis of Spatiotemporal Variation of the RSEI Long Time Series in Mining Cities
3.1.1. Changes in the Spatial Pattern of RSEI in Mining Cities
3.1.2. The RSEI Pixel Distribution Characteristics and Mean Value Changes over the Years
3.2. Analysis of Changes in the Spatial and Temporal Difference Characteristics of RSEI in Mining Areas and Mining Cities
3.2.1. Differences in Spatial Patterns of RSEI
3.2.2. Differences in Temporal Changes in RSEI
3.3. Analysis of the Gradient Change in RSEI along the Distance from the Mine Site
3.4. Analysis of the Relationship between RSEI and Mine Area
3.5. Analysis of Driving Factors of RSEI in Mining Cities
3.5.1. Driving Factors and Processing
3.5.2. Correlation of RSEI with Factors
3.5.3. Results of Random Forest Regression Model
3.5.4. The Importance of the Driving Factors
4. Discussion
4.1. Applicability of the RSEI Model Applied to the Assessment of Ecological Quality in Mining Cities
4.2. Advantages of Cloud Computing Platforms and Comparison with GEE Platforms
4.3. Impact of Mining Activities on the Quality of the Ecological Environment in Mining Cities
4.4. Key Driving Factors for RSEI in Mining Cities
4.5. Limitations and Prospects
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Mining City | Index | 2001 | 2004 | 2007 | 2010 | 2013 | 2016 | 2019 |
---|---|---|---|---|---|---|---|---|
Anshan | NDVI | 0.510 | 0.566 | 0.561 | 0.595 | 0.641 | 0.543 | 0.530 |
WET | 0.272 | 0.183 | 0.131 | 0.114 | 0.023 | 0.231 | 0.140 | |
NDBSI | −0.619 | −0.624 | −0.610 | −0.623 | −0.601 | −0.620 | −0.619 | |
LST | −0.532 | −0.507 | −0.544 | −0.495 | −0.476 | −0.517 | −0.562 | |
Contribution (%) | 62.444 | 65.938 | 66.907 | 70.017 | 73.715 | 72.669 | 71.917 | |
Handan | NDVI | 0.589 | 0.603 | 0.592 | 0.560 | 0.596 | 0.512 | 0.502 |
WET | 0.071 | 0.012 | 0.034 | 0.063 | 0.101 | 0.229 | 0.226 | |
NDBSI | −0.498 | −0.571 | −0.568 | −0.617 | −0.472 | −0.468 | −0.496 | |
LST | −0.633 | −0.557 | −0.571 | −0.549 | −0.641 | −0.683 | −0.671 | |
Contribution (%) | 56.012 | 55.623 | 56.489 | 55.674 | 60.350 | 61.235 | 61.852 | |
Huangshi | NDVI | 0.451 | 0.553 | 0.411 | 0.570 | 0.495 | 0.499 | 0.522 |
WET | 0.220 | 0.126 | 0.214 | −0.015 | 0.122 | 0.075 | 0.122 | |
NDBSI | −0.587 | −0.614 | −0.605 | −0.665 | −0.654 | −0.654 | −0.620 | |
LST | −0.636 | −0.549 | −0.648 | −0.482 | −0.559 | −0.564 | −0.572 | |
Contribution (%) | 56.012 | 55.623 | 56.489 | 55.674 | 60.350 | 61.235 | 61.852 | |
Ma’anshan | NDVI | 0.079 | 0.212 | −0.170 | 0.695 | 0.650 | 0.611 | 0.656 |
WET | −0.735 | −0.812 | 0.777 | −0.682 | −0.241 | −0.175 | −0.349 | |
NDBSI | 0.481 | 0.372 | −0.430 | −0.212 | −0.521 | −0.619 | −0.590 | |
LST | 0.471 | 0.396 | −0.428 | −0.086 | −0.498 | −0.462 | −0.316 | |
Contribution (%) | 62.295 | 56.179 | 48.250 | 51.023 | 47.683 | 52.300 | 50.155 | |
Panzhihua | NDVI | 0.636 | 0.486 | 0.327 | 0.366 | 0.569 | 0.370 | 0.357 |
WET | 0.104 | −0.175 | −0.244 | −0.166 | −0.063 | −0.175 | −0.266 | |
NDBSI | −0.030 | 0.290 | 0.491 | 0.359 | 0.271 | 0.506 | 0.485 | |
LST | 0.764 | 0.805 | 0.769 | 0.842 | 0.774 | 0.760 | 0.753 | |
Contribution (%) | 50.872 | 51.301 | 57.600 | 53.516 | 50.098 | 58.150 | 57.755 | |
Tangshan | NDVI | 0.265 | 0.379 | 0.391 | 0.450 | 0.460 | 0.436 | 0.399 |
WET | 0.380 | 0.353 | 0.412 | 0.312 | 0.264 | 0.370 | 0.298 | |
NDBSI | −0.545 | −0.545 | −0.337 | −0.404 | −0.494 | −0.450 | −0.374 | |
LST | −0.699 | −0.659 | −0.751 | −0.733 | −0.690 | −0.686 | −0.783 | |
Contribution (%) | 70.957 | 67.091 | 66.187 | 70.512 | 69.402 | 70.161 | 73.519 | |
Cangjiang | NDVI | 0.358 | 0.357 | 0.268 | 0.262 | 0.179 | 0.244 | 0.273 |
WET | 0.371 | 0.358 | 0.322 | 0.392 | 0.272 | 0.392 | 0.390 | |
NDBSI | −0.555 | −0.648 | −0.657 | −0.655 | −0.640 | −0.644 | −0.639 | |
LST | −0.653 | −0.570 | −0.627 | −0.590 | −0.696 | −0.611 | −0.604 | |
Contribution (%) | 77.137 | 86.831 | 84.226 | 86.643 | 81.287 | 87.124 | 85.311 |
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Data | Resolution | Indicator | Data Sources |
---|---|---|---|
Terra vegetation indices MOD13A1.006 | 0.5 km × 0.5 km, 16 days | NDVI | NASA (https://www.earthdata.nasa.gov/, accessed on 22 November 2023) |
Terra surface reflectance MOD09A1.006 | 0.5 km × 0.5 km, 8 days | Wetness NDBSI | NASA (https://www.earthdata.nasa.gov/, accessed on 22 November 2023) |
Terra land surface temperature and emissivity MOD11A2.006 | 1 km × 1 km, 8 days | LST | NASA (https://www.earthdata.nasa.gov/, accessed on 22 November 2023) |
Administrative boundaries of municipal divisions in China | Vector data | Administrative boundaries of the study area | National Catalogue Service for Geographic Information (https://www.webmap.cn/, accessed on 15 November 2023) |
Database of global-scale mining sites | Vector data | Mine area polygons Distance to the mine Area of the mine Percentage of the mine area | Tang et al. [73] |
Elevation—global version 1 | 1 km × 1 km | DEM, slope | Geospatial Information Authority of Japan (https://globalmaps.github.io/el.html, accessed on 4 December 2023) |
1-km monthly precipitation dataset for China [74] | 1 km × 1 km | Annual precipitation | National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/home, accessed on 4 December 2023) |
China’s population in km grid dataset [75] | 1 km × 1 km | GDP | Resource and Environment Science and Data Center (https://www.resdc.cn/DOI/DOI.aspx?DOIID=33, accessed on 4 December 2023) |
VIIRS stray light corrected nighttime day: Night band composites version 1 | 750 m × 750 m | Nighttime light intensity | Earth Observation Group (https://eogdata.mines.edu/products/vnl/, accessed on 4 December 2023) |
Global gridded electricity consumption data | 1 km × 1 km | Electricity consumption | Chen et al. [76] |
ODIAC fossil fuel CO2 emissions dataset [77] | 1 km × 1 km | CO2 emissions | Center for Global Environmental Research, National Institute for Environmental Studies (https://db.cger.nies.go.jp/dataset/ODIAC/, accessed on 4 December 2023) |
1 km ground-level PM2.5 dataset for China [78,79] | 1 km × 1 km | PM2.5 content | National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/home, accessed on 4 December 2023) |
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Zeng, J.; Dai, X.; Li, W.; Xu, J.; Li, W.; Liu, D. Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China. Sustainability 2024, 16, 1447. https://doi.org/10.3390/su16041447
Zeng J, Dai X, Li W, Xu J, Li W, Liu D. Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China. Sustainability. 2024; 16(4):1447. https://doi.org/10.3390/su16041447
Chicago/Turabian StyleZeng, Jianwen, Xiaoai Dai, Wenyu Li, Jipeng Xu, Weile Li, and Dongsheng Liu. 2024. "Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China" Sustainability 16, no. 4: 1447. https://doi.org/10.3390/su16041447