Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery
Abstract
:1. Introduction
2. Remote Sensing as a Means of Monitoring Aquatic Plants
2.1. Optical Systems
2.2. Radar Systems
2.3. Combined Use of Systems
- Is water hyacinth within Vembanad Lake visible in SAR imaging?
- Can the use of change detectors be implemented to monitor water hyacinth within Vembanad Lake?
3. Methodology and Materials
3.1. Study Area
3.2. Satellite Data
3.3. SAR Pre-Processing
3.4. Scattering Model for Water Hyacinth
3.5. Water Hyacinth Detectors
- In the first, we use a single image of the lake, and we try to separate the pixels of clean and infested using statistical differences between the VV and VH intensity images. The advantage of this method is the simplicity and computational efficiency.
- In the second, we take advantage of Sentinel-1 multitemporal images to apply a change detector to identify when water hyacinth has started growing in the lake. The advantage of this method is the fact that it benchmarks the pixel value and therefore is expected to be more robust against noise and the eventual variability of water pixels.
3.6. Single Image Detection Using Statistical Analysis
3.7. Change Detection Methods
3.8. Standard Change Detectors
4. Results
4.1. Initial Observations of Lake Vembanad
4.2. Preliminary Analysis of Backscattering
4.3. Validation and Statistical Analysis
4.4. Statistical Analysis of Pixels
4.5. Threshold on Intensities
4.6. Change Detection: January 2020 vs. April 2020: Barrage
4.7. Change Detection: January 2020 vs. 24 April 2020: Paddy
4.8. Change Detection: October 2019 vs. 24 April 2020: Paddy
4.9. Heatmap of Water Hyacinth Infestation: 12 January 2019–1 January 2021
5. Discussion
5.1. Visibility of Water Hyacinth
5.2. Detectors
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Infested Pixels | Clean Pixels | |
---|---|---|
Mean | −14.49377783 | −27.60624561 |
Variance | 30.03353544 | 34.71476606 |
Observations | 5252 | 5252 |
Df | 5251 | 5251 |
F | 0.865151601 | |
P (F ≤ f) one-tail | 7.78587 × 10−8 | |
F Critical one-tail | 0.937802638 |
Infested Pixels | Clean Pixels | |
---|---|---|
Mean | −14.4938 | −27.60624561 |
Variance | 30.03354 | 34.71476606 |
Observations | 5252 | 5252 |
Hypothesised Mean Difference | 0 | |
Df | 10447 | |
t Stat | 118.0953 | |
P (T ≤ t) one-tail | 0 | |
t Critical one-tail | 2.326705 | |
P (T ≤ t) two-tail | 0 | |
t Critical two-tail | 2.5763 |
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Simpson, M.D.; Akbari, V.; Marino, A.; Prabhu, G.N.; Bhowmik, D.; Rupavatharam, S.; Datta, A.; Kleczkowski, A.; Sujeetha, J.A.R.P.; Anantrao, G.G.; et al. Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery. Remote Sens. 2022, 14, 2845. https://doi.org/10.3390/rs14122845
Simpson MD, Akbari V, Marino A, Prabhu GN, Bhowmik D, Rupavatharam S, Datta A, Kleczkowski A, Sujeetha JARP, Anantrao GG, et al. Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery. Remote Sensing. 2022; 14(12):2845. https://doi.org/10.3390/rs14122845
Chicago/Turabian StyleSimpson, Morgan David, Vahid Akbari, Armando Marino, G. Nagendra Prabhu, Deepayan Bhowmik, Srikanth Rupavatharam, Aviraj Datta, Adam Kleczkowski, J. Alice R. P. Sujeetha, Girish Gunjotikar Anantrao, and et al. 2022. "Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery" Remote Sensing 14, no. 12: 2845. https://doi.org/10.3390/rs14122845