Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data and Pre-Processing
2.3. Construction of the SDI
2.3.1. SI
2.3.2. Albedo
2.3.3. NDVI
2.3.4. LSM
2.3.5. Constructing SDI Based on PCA
2.4. Spatial Autocorrelation Analysis
3. Results
3.1. Integration of the Remote Sensing Indexes Based on PCA
3.2. Spatiotemporal Changes in the Land Degradation
3.3. Spatial Autocorrelation Analysis of the SDI
3.4. Spatial and Temporal Changes in Land Use and Salinization
4. Discussion
4.1. Effectiveness of the Proposed SDI
4.2. Factors Influencing the Land Degradation in the ADD
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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1990 | 2000 | 2010 | 2019 | |
---|---|---|---|---|
Loading of the SI | 0.58 | 0.64 | 0.57 | 0.61 |
Loading of albedo | 0.23 | 0.43 | 0.23 | 0.26 |
Loading of the NDVI | –0.32 | –0.16 | –0.46 | –0.24 |
Loading of LSM | –0.71 | –0.62 | –0.64 | –0.71 |
Eigenvalue contribution percentage (%) | 78.61 | 83.24 | 86.35 | 87.67 |
Minimum | Maximum | Mean | Median | Skewness | Kurtosis | Standard Deviation | |
---|---|---|---|---|---|---|---|
1990 | 0.04 | 1.15 | 0.42 | 0.38 | 0.48 | −0.33 | 0.19 |
2000 | 0.04 | 1.21 | 0.43 | 0.40 | 0.31 | 0.14 | 0.16 |
2010 | 0.03 | 1.12 | 0.41 | 0.38 | 0.49 | −0.71 | 0.18 |
2019 | 0.04 | 1.12 | 0.30 | 0.28 | 0.42 | −0.80 | 0.18 |
Type | 1990–2000 | 2000–2010 | 2010–2019 | 1990–2019 | ||||
---|---|---|---|---|---|---|---|---|
km2 | % | km2 | % | km2 | % | km2 | % | |
Seriously developed | 130.0 | 1.1 | 110.1 | 0.9 | 40.3 | 0.3 | 217.8 | 1.8 |
Developed | 2687.5 | 22.5 | 3728.8 | 31.3 | 2438.0 | 20.5 | 2901.7 | 24.3 |
Stable | 5291.8 | 44.4 | 5094.0 | 42.7 | 7061.7 | 59.2 | 5385.8 | 45.2 |
Improvement | 3653.8 | 30.6 | 2886.0 | 24.2 | 2354.3 | 19.7 | 3314.7 | 27.8 |
Significant improvement | 160.5 | 1.4 | 104.8 | 0.8 | 29.8 | 0.3 | 103.4 | 0.9 |
1990 | 2000 | 2010 | 2019 | |||||
---|---|---|---|---|---|---|---|---|
Type | km2 | % | km2 | % | km2 | % | km2 | % |
Bare soil | 2165.71 | 18.42 | 1968.62 | 16.74 | 1946.80 | 16.56 | 1989.45 | 16.92 |
built-up land | 205.52 | 1.75 | 528.64 | 4.50 | 570.81 | 4.85 | 588.47 | 5.00 |
Cropland | 6593.86 | 56.08 | 6624.91 | 56.34 | 6952.42 | 59.13 | 6350.16 | 54.01 |
Grassland | 2412.67 | 20.52 | 2253.93 | 19.17 | 1915.46 | 16.29 | 2407.83 | 20.48 |
Forest | 380.66 | 3.24 | 382.32 | 3.25 | 372.93 | 3.17 | 422.51 | 3.59 |
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Yu, T.; Jiapaer, G.; Bao, A.; Zheng, G.; Jiang, L.; Yuan, Y.; Huang, X. Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area. Remote Sens. 2021, 13, 2851. https://doi.org/10.3390/rs13152851
Yu T, Jiapaer G, Bao A, Zheng G, Jiang L, Yuan Y, Huang X. Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area. Remote Sensing. 2021; 13(15):2851. https://doi.org/10.3390/rs13152851
Chicago/Turabian StyleYu, Tao, Guli Jiapaer, Anming Bao, Guoxiong Zheng, Liangliang Jiang, Ye Yuan, and Xiaoran Huang. 2021. "Using Synthetic Remote Sensing Indicators to Monitor the Land Degradation in a Salinized Area" Remote Sensing 13, no. 15: 2851. https://doi.org/10.3390/rs13152851