Landscape Ecological Risk Assessment and Planning Enlightenment of Songhua River Basin Based on Multi-Source Heterogeneous Data Fusion
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset and Landscape Ecological Risk Assessment Method
2.2.1. Data Sources
- (1)
- Natural dimension indicator data.
- (2)
- Social dimension indicator data.
- (3)
- Landscape pattern dimension indicator data.
2.2.2. Extraction of Land Use Types
2.2.3. Accuracy Verification
2.3. Landscape Ecological Risk Assessment Method
2.3.1. Index Selection of Landscape Ecological Risk Assessment
- (1)
- Natural dimension indicators.
- (2)
- Social dimension indicators.
- (3)
- Landscape pattern dimension indicators.
2.3.2. Landscape Ecological Risk Assessment Method Based on SPCA
3. Results and Discussion
3.1. Extraction of Land Use Types
3.2. Analysis of Land Use Evolution in River Basin
3.2.1. Analysis of Land Use Area Change
3.2.2. Analysis on Spatial Transfer of Land Use Types
3.2.3. Analysis on Driving Factors of Land Use Change
- (1)
- Natural driving factors
- (2)
- Socio-economic driving factors
3.3. Risk Assessment Results and Analysis
3.3.1. Results of the SPCA
3.3.2. Ecological Risk Assessment of The River Basin Landscape
- (1)
- Regional assessment of low ecological risk level
- (2)
- Regional assessment of medium-low ecological risk level
- (3)
- Regional assessment of medium ecological risk level
- (4)
- Regional assessment of medium-high ecological risk level
- (5)
- Regional assessment of high ecological risk level
3.4. Ecological Protection and Planning Enlightenment
4. Conclusions
- (1)
- The application of study on driving factors of land use change complements the deficiency of the application of unstructured image data in the ecological environment. At the same time, it can better promote the application of big data technology in the field of the ecological environment and make up for the shortcomings of existing research.
- (2)
- The evaluation system of multi-source data fusion can improve the accuracy and comprehensiveness of landscape ecological risk assessment results and provide an effective basis for watershed ecological protection and planning. At the same time, the integration of multi-source data can better promote the application of big data technology in the field of ecological environment and make up for the deficiencies of existing research. In the landscape ecological risk assessment, multi-source data were fused, and the landscape pattern index data extracted from remote sensing images were used. The influences of natural and social factors on the landscape ecological risk were comprehensively considered, a three-dimensional comprehensive index system of nine influencing factors of the natural, social and landscape pattern was constructed, and the SPCA method was used to evaluate the landscape ecological risk in the study area comprehensively.
- (3)
- The landscape ecological risk assessment method based on spatial principal component analysis (SPCA) is effective and has good replicability of applications in other regions. The comprehensive landscape ecological risk assessment results were obtained by a weighted superposition of five principal component scores. The results showed that the overall risk index value of the buffer zone was between 0.166 and 0.838. The results were divided into five levels by the Natural Breaks method: low ecological risk, medium-low ecological risk, medium ecological risk, medium-high ecological risk, and high ecological risk. In this study, the spatial distribution characteristics of each ecological risk area and the characteristics of each index were analyzed and evaluated, providing basic information for mining the influencing factors of environmental ecological risk.
- (4)
- In future research, based on the results of landscape ecological risk assessment and the characteristics of risk sources in each risk level area, the ecological protection and planning enlightenment suitable for each risk level area can be obtained effectively. Thus, future research can provide ideas and evidence for environmental managers to formulate ecological risk protection countermeasures and reduce the impact of ecological risk threat factors.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Number | Land Use Type | Interpretation Sign | Image Display |
---|---|---|---|
1 | Water | Its geometric boundary is clear and distinct, smooth. The color is dark blue. | |
2 | Forest Land | It is mostly found in mountainous areas and has a clear trend. The color is dark green. | |
3 | Dry Land | It has a regular and continuous distribution with different spectral characteristics and is smooth. The color is orange. | |
4 | Bare Land | It is distributed at the top or bottom of the mountain, and its texture is rough. The color is brownish. | |
5 | Built-up Land | It is planar in distribution, with a rough texture and distinct borders. The color is red. | |
6 | Water Field | It is distributed near the river, lumpy. The color is light green. | |
7 | Saline Land | It shows a planar distribution, distributed on the edge of the bare ground of the pond with obvious borders. The color is grayish-white. |
Time | Overall Classification Accuracy (%) | Kappa Coefficient |
---|---|---|
2005 | 85.73 | 0.8113 |
2006 | 80.65 | 0.7642 |
2007 | 79.81 | 0.7218 |
2008 | 82.94 | 0.8073 |
2009 | 76.18 | 0.7186 |
2010 | 82.25 | 0.7752 |
2011 | 82.47 | 0.7762 |
2012 | 84.33 | 0.8013 |
2013 | 85.40 | 0.8065 |
2014 | 83.80 | 0.7916 |
2015 | 80.33 | 0.7592 |
2016 | 75.28 | 0.7122 |
2017 | 83.21 | 0.7863 |
2018 | 79.84 | 0.7546 |
Principal Component | Eigenvalues | Contribution Rates | Cumulative Contribution Rates |
---|---|---|---|
pc1 | 3.451 | 41.7% | 41.7% |
pc2 | 2.846 | 26.7% | 68.4% |
pc3 | 2.152 | 10.1% | 78.5% |
pc4 | 1.617 | 8.1% | 86.6% |
pc5 | 1.359 | 5.8% | 92.4% |
Comprehensive Evaluation Factors | Original Index | pc1 | pc2 | pc3 | pc4 | pc5 |
---|---|---|---|---|---|---|
Landscape pattern factors | Land use types | 0.792 | 0.567 | 0.111 | 0.185 | 0.024 |
SHDI | 0.496 | 0.526 | 0.099 | 0.555 | 0.305 | |
CONTAG | 0.297 | 0.627 | 0.263 | 0.588 | 0.191 | |
NDVI | 0.126 | 0.075 | 0.718 | 0.100 | 0.604 | |
Social factors | Population density | 0.031 | 0.020 | 0.136 | 0.368 | 0.003 |
GDP per unit area | 0.107 | 0.089 | 0.112 | 0.097 | 0.045 | |
Natural factors | Soil types | 0.125 | 0.042 | 0.068 | 0.416 | 0.316 |
Soil texture | 0.009 | 0.006 | 0.600 | 0.313 | 0.635 | |
Elevation | 0.081 | 0.021 | 0.098 | 0.144 | 0.046 |
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Zhao, Y.; Tao, Z.; Wang, M.; Chen, Y.; Wu, R.; Guo, L. Landscape Ecological Risk Assessment and Planning Enlightenment of Songhua River Basin Based on Multi-Source Heterogeneous Data Fusion. Water 2022, 14, 4060. https://doi.org/10.3390/w14244060
Zhao Y, Tao Z, Wang M, Chen Y, Wu R, Guo L. Landscape Ecological Risk Assessment and Planning Enlightenment of Songhua River Basin Based on Multi-Source Heterogeneous Data Fusion. Water. 2022; 14(24):4060. https://doi.org/10.3390/w14244060
Chicago/Turabian StyleZhao, Ying, Zhe Tao, Mengnan Wang, Yuanhua Chen, Rui Wu, and Liang Guo. 2022. "Landscape Ecological Risk Assessment and Planning Enlightenment of Songhua River Basin Based on Multi-Source Heterogeneous Data Fusion" Water 14, no. 24: 4060. https://doi.org/10.3390/w14244060
APA StyleZhao, Y., Tao, Z., Wang, M., Chen, Y., Wu, R., & Guo, L. (2022). Landscape Ecological Risk Assessment and Planning Enlightenment of Songhua River Basin Based on Multi-Source Heterogeneous Data Fusion. Water, 14(24), 4060. https://doi.org/10.3390/w14244060