Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Methodological Scheme
2.3. SAR Dataset and Training Points
2.4. SAR Image Processing
2.5. Calculation of the Geometric Attributes
2.6. Regression Analysis
2.7. Field Data and Validation
3. Results
3.1. Distribution and Availability of SAR Data
3.2. Obtaining the Geometric Attributes
3.3. Data Analysis and Prediction
3.3.1. Data Normalization
3.3.2. Regression Methods
3.3.3. Validation
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Lake | SAR Images Available | Water Masks Analysed | |||||||
---|---|---|---|---|---|---|---|---|---|
DVV 2014/10/15–2021/01/29 | AVV 2014/10/06–2021/01/20 | DVH 2016/02/07–2021/01/29 | AVH 2017/05/17–2021/01/20 | Total | CART | RF | SVM | Total | |
Burlan | 153 | 137 | 123 | 104 | 517 | 517 | 517 | 517 | 1551 |
Pomacochas | 153 | 137 | 123 | 104 | 517 | 517 | 517 | 517 | 1551 |
Total | 1034 | 3102 |
Classifier | Geometric Attribute | Burlan Lake | Pomacochas Lake | |||||||
---|---|---|---|---|---|---|---|---|---|---|
AVH | AVV | DVH | DVV | AVH | AVV | DVH | DVV | |||
Classification and regression tree(CART) | Area (ha) | Minimum | 38.6 | 39 | 39.2 | 40.9 | 414 | 417.8 | 408.3 | 415.6 |
Maximum | 45 | 48 | 48.1 | 50.2 | 441.4 | 455.8 | 430.1 | 452.2 | ||
Average | 42.1 | 43.3 | 43 | 44.9 | 426.8 | 434.8 | 419.3 | 431.4 | ||
Perimeter (km) | Minimum | 3.31 | 3.34 | 3.36 | 3.42 | 11.06 | 10.94 | 10.9 | 10.89 | |
Maximum | 3.72 | 4.8 | 4.46 | 4.72 | 17.59 | 20.06 | 13.54 | 17.26 | ||
Average | 3.46 | 3.67 | 3.55 | 3.93 | 14.16 | 16.52 | 11.36 | 13.03 | ||
Random Forest(RF) | Area (ha) | Minimum | 39.3 | 40 | 40.3 | 41.1 | 416 | 416.2 | 414.8 | 415.6 |
Maximum | 45.6 | 48 | 47.6 | 49.3 | 441.4 | 455.8 | 426.5 | 456.5 | ||
Average | 42.2 | 43.3 | 43 | 44.8 | 427.2 | 435.1 | 419.7 | 431.3 | ||
Perimeter (km) | Minimum | 3.33 | 3.37 | 3.36 | 3.43 | 11.06 | 10.92 | 10.92 | 10.89 | |
Maximum | 3.67 | 4.8 | 4.12 | 4.72 | 17.79 | 19.79 | 13.2 | 18.52 | ||
Average | 3.46 | 3.68 | 3.54 | 3.91 | 14.2 | 16.59 | 11.38 | 13.02 | ||
Support Vector Machine(SVM) | Area (ha) | Minimum | 38.9 | 39 | 39.2 | 39.8 | 409.2 | 405.4 | 405.5 | 405.4 |
Maximum | 47.7 | 49.2 | 48.3 | 53 | 466.8 | 470.8 | 450.6 | 458 | ||
Average | 42.1 | 42.8 | 43 | 44.9 | 430.5 | 433.5 | 420.1 | 434.3 | ||
Perimeter (km) | Minimum | 3.33 | 3.34 | 3.36 | 3.37 | 11.14 | 10.87 | 10.88 | 10.8 | |
Maximum | 4.81 | 5.73 | 4.72 | 5.05 | 20.52 | 20.58 | 19.17 | 19.92 | ||
Average | 3.55 | 3.66 | 3.57 | 3.95 | 14.72 | 16.43 | 11.65 | 13.86 |
SLR | PR | SVR | DTR | RFR | ||
---|---|---|---|---|---|---|
Burlan Lake | Area | 42.46 | 42.3 | 42.43 | 45.2 | 44.47 |
R2 | 0.12 | 0.15 | 0.22 | 0.37 | 0.46 | |
Combination | DVH | DVH | DVH | AVV | AVV | |
Perimeter | 3.43 | 3.41 | 3.41 | 3.43 | 3.82 | |
R2 | 0.15 | 0.2 | 0.29 | 0.23 | 0.43 | |
Combination | AVH | AVH | DVH | DVV | DVV | |
Pomacochas Lake | Area | 417.8 | 408 | 411.42 | 414 | 413.1 |
R2 | -0.004 | 0.38 | 0.41 | 0.13 | 0.15 | |
Combination | DVH | DVH | DVH | DVH | DVH | |
Perimeter | 13.28 | 16.5 | 15.14 | 17.1 | 17.46 | |
R2 | 0.095 | 0.24 | 0.42 | 0.16 | 0.26 | |
Combination | DVV | AVV | AVH | AVV | AVV |
SAR Image | Best Regressionmethod | ∆% | RPAS | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
DVV | DVH | |||||||||||||||
CART | ∆% | RF | ∆% | SVM | ∆% | CART | ∆% | RF | ∆% | SVM | ∆% | |||||
Burlan lake | A | 43.53 | −3.27 | 42.89 | −4.69 | 43.42 | −3.51 | 42.46 | −5.64 | 42.48 | −5.60 | 42.48 | −5.60 | 44.47 | −1.18 | 45.63 |
P | 3.4 | −17.68 | 3.3 | −20.10 | 3.38 | −18.16 | 2.87 | −30.51 | 2.87 | −30.51 | 2.87 | −30.51 | 3.82 | −7.51 | 4.13 | |
Pomacochas lake | A | 434.89 | 1.35 | 430.77 | 0.39 | 437.18 | 1.89 | 420.57 | −1.99 | 420.57 | −1.99 | 414.23 | −3.46 | 411.89 | −4.01 | 429.09 |
P | 12.21 | 23.46 | 11.13 | 12.54 | 13.03 | 31.75 | 9.51 | −3.84 | 9.49 | −4.04 | 9.14 | −7.58 | 17.46 | 76.54 | 9.89 |
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Gómez Fernández, D.; Salas López, R.; Rojas Briceño, N.B.; Silva López, J.O.; Oliva, M. Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods. ISPRS Int. J. Geo-Inf. 2022, 11, 534. https://doi.org/10.3390/ijgi11110534
Gómez Fernández D, Salas López R, Rojas Briceño NB, Silva López JO, Oliva M. Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods. ISPRS International Journal of Geo-Information. 2022; 11(11):534. https://doi.org/10.3390/ijgi11110534
Chicago/Turabian StyleGómez Fernández, Darwin, Rolando Salas López, Nilton B. Rojas Briceño, Jhonsy O. Silva López, and Manuel Oliva. 2022. "Dynamics of the Burlan and Pomacochas Lakes Using SAR Data in GEE, Machine Learning Classifiers, and Regression Methods" ISPRS International Journal of Geo-Information 11, no. 11: 534. https://doi.org/10.3390/ijgi11110534