Journal of Resources and Ecology >
Spatial and Temporal Pattern Changes and Driving Forces: Analysis of Salinization in the Yellow River Delta from 2015 to 2020
HONG Mengmeng, E-mail: hongmm@lreis.ac.cn |
Received date: 2021-10-10
Accepted date: 2022-04-11
Online published: 2022-07-15
Supported by
The Strategic Priority Research Program of Chinese Academy of Sciences(XDA19040501)
The Construction Project of the China Knowledge Center for Engineering Sciences and Technology(CKCEST-2021-2-18)
China’s Yellow River Delta represents a typical area with moist semi-humid soil salinization, and its salinization has seriously affected the sustainable use of local resources. The use of remote sensing technology to understand changes in the spatial and temporal patterns of salinization is key to combating regional land degradation. In this study, a feature space model was constructed for remote sensing and monitoring land salinization using Landsat 8 OIL multi-spectral images. The feature parameters were paired to construct a feature space model; a total of eight feature space models were obtained. An accuracy analysis was conducted by combining salt-loving vegetation data with measured data, and the model demonstrating the highest accuracy was selected to develop salinization inversion maps for 2015 and 2020. The results showed that: (1) The total salinization area of the Yellow River Delta displayed a slight upward trend, increasing from 4244 km2 in 2015 to 4629 km2 in 2020. However, the area’s salting degree reduced substantially, and the areas of saline soil and severe salinization were reduced in size; (2) The areas with reduced salinization severity were mainly concentrated in areas surrounding cities, and primarily comprised wetlands and some regions around the Bohai Sea; (3) Numerous factors such as the implementation of the “Bohai Granary” cultivation engagement plan, increase in human activities to greening local residential living environments, and seawater intrusion caused by the reduction of sediment contents have impacted the distribution of salinization areas in the Yellow River Delta; (4) The characteristic space method of salinization monitoring has better applicability and can be promoted in humid-sub humid regions.
HONG Mengmeng , WANG Juanle , HAN Baomin . Spatial and Temporal Pattern Changes and Driving Forces: Analysis of Salinization in the Yellow River Delta from 2015 to 2020[J]. Journal of Resources and Ecology, 2022 , 13(5) : 786 -796 . DOI: 10.5814/j.issn.1674-764x.2022.05.004
Fig. 1 Study area |
Fig. 2 Principle of linear feature space (a) and nonlinear feature space (b) |
Fig. 3 Linear space AS (a), AS1 (b), AS2 (c) and MN (d). |
Fig. 4 Non-linear space NANM (a), MANM (b), NSNM (c) and MSNM (d). |
Fig. 5 Distribution of salinization in 2015 (a) and 2020 (b) |
Fig. 6 Distribution map of salt-loving vegetation in 2015 (a) and 2020 (b) |
Table 1 Classification of salinization grades |
Degree of salinization | Non salinization | Mild salinization | Moderate salinization | Severe salinization | Saline soil |
---|---|---|---|---|---|
AS | ≤0.56 | 0.56<AS≤0.65 | 0.65<AS≤0.74 | 0.74<AS≤0.83 | >0.83 |
AS1 | ≤0.59 | 0.59<AS1≤0.67 | 0.67<AS1≤0.74 | 0.74<AS1≤0.80 | >0.80 |
AS2 | ≤0.59 | 0.59<AS2≤0.66 | 0.66<AS2≤0.73 | 0.73<AS2≤0.80 | >0.80 |
MN | ≤0.1 | 0.1<MN≤0.17 | 0.17<MN≤0.24 | 0.24<MN≤0.31 | >0.31 |
NANM | ≤0.85 | 0.85<NANM≤0.89 | 0.89<NANM≤0.94 | 0.94<NANM≤0.97 | >0.97 |
MANM | ≤0.60 | 0.60<MANM≤0.68 | 0.68<MANM≤0.74 | 0.74<MANM≤0.78 | >0.78 |
NSNM | ≤0.84 | 0.84<NSNM≤0.88 | 0.88<NSNM≤0.89 | 0.89<NSNM≤0.91 | >0.91 |
MSNM | ≤0.79 | 0.79<MSNM≤0.83 | 0.83<MSNM≤0.86 | 0.86<MSNM≤0.89 | >0.89 |
Table 2 Accuracy analysis of each model |
Model | Correct classification | Misclassification | Accuracy (%) |
---|---|---|---|
AS | 17 | 23 | 42.5 |
AS1 | 15 | 25 | 37.5 |
AS2 | 17 | 23 | 42.5 |
MN | 23 | 17 | 57.5 |
NANM | 16 | 24 | 40 |
MANM | 20 | 20 | 50 |
NSNM | 27 | 13 | 67.5 |
MSNM | 19 | 21 | 47.5 |
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