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- Liao, Yiping, et al.
(författare)
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Study of the relationship between urbanization and environment in the Jiulong river basin based on coupling coordination degree model
- 2023
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Ingår i: Frontiers in Environmental Science. - 2296-665X. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- Rapid urbanization has placed the sustainable development of some watershed ecosystems in jeopardy. In order to achieve sustainable urban development, it is vital to identify the coupling mechanisms between urbanization and the ecological environment quality. This study establishes indicators to evaluate the Jiulong River Basin’s urbanization and ecological environment systems. These are utilized to analyze spatial and temporal changes and build a coupling coordination degree model. This research investigates the level of development coordination between urbanization and the ecological environment quality in the basin. The data sources include nighttime lighting and Landsat data from 2000 to 2020. The findings indicated the following: 1) Urbanization levels in the basin rise annually, and the years 2010–2020 represent a stage of high urbanization growth. In addition, the development levels are spatially heterogeneous, with high levels in the south and low levels in the north. 2) The ecological environment quality category for the basin is generally Excellent; however, many facets of the climate and human activity drastically lowered this grade in 2005. 3) In the basin, there is a basic coordination relationship between urbanization and environmental quality, but the number of cities falling into the moderate imbalance category has grown. 4) Increasing the urbanization level raises the coordination between urbanization and the ecological environment more than improving environmental quality, but antagonistic effects make it necessary to strengthen the protection of the ecological environment alongside economic development. From the viewpoint of counties, this study examines the long-term interactions between the ecological environment and urbanization in Fujian Province, China. Recommendations for balancing urban development and environmental concerns in coastal watersheds are presented, providing references to the fact that the future of this area of China can be sustainable.
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| 2. |
- Zheng, Meiduan, et al.
(författare)
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Ground-Based Hyperspectral Retrieval of Soil Arsenic Concentration in Pingtan Island, China
- 2023
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Ingår i: Remote Sensing. - 2072-4292. ; 15:17
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Tidskriftsartikel (refereegranskat)abstract
- The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide.
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