Map** Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning
Abstract
:1. Introduction
2. Data
2.1. Study Area
2.2. Satellite Data
2.3. Ground Survey Data
3. Methods and Approaches
3.1. Wetlands and Land Use Map**
- “NIR” is the reflectance in the near-infrared band.
- “Red“ is the reflectance in the red band.
- “Green“ is the reflectance in the green band.
- “Blue“ is the reflectance in the blue band.
- “SWIR“ is the reflectance in the shortwave infrared band.
3.2. Separation of Streams Using LULC Vectorization
3.3. Supervised Classification of Ponds to Separate Shrimp Ponds
3.4. Accuracy Assessment
4. Results and Discussion
4.1. Spatial Distribution of LULC
4.2. Streams Vectorization
4.3. Spatial Distribution of Shrimp Ponds/Fishponds
4.4. Accuracy Assessment
4.5. Change Detection in Shrimp Ponds
4.6. Discussion on Monitoring Shrimp Ponds Using RS Technology
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Satellite | Bands—Spatial Resolution | Bands | Utilization |
---|---|---|---|
DMC3/TripleSat | Pan:0.8 m, Multispectral: 3.2 m | Pan, Blue, Green, Red, NIR | 2019 Shrimp ponds Map |
Jilin-1KF01A | Pan: 0.75 m, Multispectral: 3 m | Pan, Blue, Green, Red, NIR | 2022 Shrimp Ponds Map |
Sentinel-2 | B2, B3, B8: 10 m, B11, B12: 20 m | B1-B8, B8a, B9-B12 | Water bodies Mask (through different water Indices) |
LULC | 2022 (ha) | 2019 (ha) | Change (ha) | % Change |
---|---|---|---|---|
Shrimp Ponds | 148.11 | 174.71 | −26.60 | −15.20 |
Other Ponds | 682.51 | 1020.01 | −337.50 | −33.08 |
Streams | 67.48 | 109.56 | −42.08 | −38.40 |
Vegetation | 1210.77 | 560.67 | 650.10 | 115.95 |
Other LULC | 55.09 | 299.08 | −243.99 | −81.58 |
LULC Class | Reference Totals | Classified Totals | Number Correct | Producer’s Accuracy | User’s Accuracy | Kappa Coefficient |
---|---|---|---|---|---|---|
a. 2019 | ||||||
01. Shrimp Ponds | 11 | 11 | 10 | 90.91% | 90.91% | 0.8255 |
02. Ponds (Other) | 22 | 26 | 22 | 100.00% | 84.62% | |
03. Streams | 7 | 6 | 6 | 85.71% | 100.00% | |
04. Vegetation | 14 | 11 | 11 | 78.57% | 100.00% | |
05. Other LULC | 2 | 0 | 0 | |||
Totals | 56 | 56 | 49 | |||
Overall Classification Accuracy | 87.50% | |||||
b. 2022 | ||||||
01. Shrimp Pond | 10 | 9 | 9 | 90.00% | 100.00% | 0.8531 |
02. Ponds (Other) | 21 | 20 | 18 | 85.71% | 90.00% | |
03. Streams | 6 | 5 | 5 | 83.33% | 100.00% | |
04. Vegetation | 16 | 19 | 16 | 100.00% | 84.21% | |
05. Other LULC | 2 | 1 | 1 | 50.00% | 100.00% | |
Totals | 56 | 56 | 50 | |||
Overall Classification Accuracy | 89.29% |
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Bellam, P.K.; Gumma, M.K.; Panjala, P.; Mohammed, I.; Suzuki, A. Map** Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning. AgriEngineering 2023, 5, 1432-1447. https://doi.org/10.3390/agriengineering5030089
Bellam PK, Gumma MK, Panjala P, Mohammed I, Suzuki A. Map** Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning. AgriEngineering. 2023; 5(3):1432-1447. https://doi.org/10.3390/agriengineering5030089
Chicago/Turabian StyleBellam, Pavan Kumar, Murali Krishna Gumma, Pranay Panjala, Ismail Mohammed, and Aya Suzuki. 2023. "Map** Shrimp Pond Dynamics: A Spatiotemporal Study Using Remote Sensing Data and Machine Learning" AgriEngineering 5, no. 3: 1432-1447. https://doi.org/10.3390/agriengineering5030089