Machine Learning Techniques in Agricultural Flood Assessment and Monitoring Using Earth Observation and Hydromorphological Analysis †
Abstract
:1. Introduction
1.1. Pilot Areas
1.2. Data
- Evia: 29 July 2020, 3 August 2020, 13 August 2020, 28 August 2020;
- Kefalonia: 05 September 2020, 20 September 2020;
- Thessalia: 31 August 2020, 20 September 2020.
2. Methodology
2.1. Processing Techniques
2.2. Visualization and Mapping
3. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Jonkman, S.N. Global perspectives on loss of human life caused by floods. Nat. Hazards Dordr. 2005, 34, 151–175. [Google Scholar] [CrossRef]
- Kharin, V.V.; Zwiers, F.W.; Zhang, X.; Hegerl, G.C. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Clim. 2007, 20, 1419–1444. [Google Scholar] [CrossRef] [Green Version]
- Bresciani, M.; Stroppiana, D.; Odermatt, D.; Morabito, G.; Giardino, C. Assessing remotely sensed chlorophyll-a for the implementation of the water framework directive in European perialpine lakes. Sci. Total Environ. 2011, 409, 3083–3091. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Available online: https://earth.esa.int/web/sentinel/home (accessed on 1 July 2021).
- Reuter, H.I.; Hengl, T.; Gessler, P.; Soille, P. Preparation of DEMs for geomorphometric analysis. Dev. Soil Sci. 2009, 33, 87–120. [Google Scholar]
- Available online: https://geodata.gov.gr/ (accessed on 1 July 2021).
- Kohonen, T. Self-Organization and Associative Memory, 3rd ed.; Springer: Berlin/Heidelberg, Germany; New York, NY, USA, 1989. [Google Scholar]
- Vassilas, N.; Charou, E. A New Methodology for Efficient Classification of Multispectral Satellite Images Using Neural Network Techniques. Neural Process. Lett. 1999, 9, 35–43. [Google Scholar] [CrossRef]
- Elkhrachy, I. Assessment and management flash flood in Najran Wady using GIS and remote sensing. J. Indian Soc. Remote Sens. 2018, 46, 297–308. [Google Scholar] [CrossRef]
Region | Location | Surface Water Conditions | Extent (km2) |
---|---|---|---|
Evia | Politika | Flooded areas due to severe rainfall event—3 fatalities | 84 |
Kefalonia | Municipality Pilareon | Flooding event—natural hazards | 59 |
Thessalia | Enipeas Pinios rivers | Flooded cotton fields | 76 |
Land Use (LU) | Sum Area of LU (m2) | LU Flooded (m2) | LU Remain (m2) | LU Remain (%) | LU Affected (LC) (%) | Total Percentage (%) |
---|---|---|---|---|---|---|
Forest | 3,043,611 | 210,799 | 2,832,812 | 93% | 7% | 100% |
Vineyard | 229,634 | 18,240 | 211,394 | 92% | 8% | 100% |
Vineyard Mix | 181,007 | 27,853 | 153,154 | 85% | 15% | 100% |
Arable | 905,322 | 144,450 | 760,872 | 84% | 16% | 100% |
Arable Mix | 3,397,804 | 475,221 | 2,922,583 | 86% | 14% | 100% |
Olive Growing | 9,266,342 | 412,194 | 8,854,148 | 96% | 4% | 100% |
Olive Growing Mix | 3,354,840 | 384,223 | 2,970,617 | 89% | 11% | 100% |
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Tasiopoulos, L.; Stefouli, M.; Voutos, Y.; Mylonas, P.; Charou, E. Machine Learning Techniques in Agricultural Flood Assessment and Monitoring Using Earth Observation and Hydromorphological Analysis. Eng. Proc. 2021, 9, 40. https://doi.org/10.3390/engproc2021009040
Tasiopoulos L, Stefouli M, Voutos Y, Mylonas P, Charou E. Machine Learning Techniques in Agricultural Flood Assessment and Monitoring Using Earth Observation and Hydromorphological Analysis. Engineering Proceedings. 2021; 9(1):40. https://doi.org/10.3390/engproc2021009040
Chicago/Turabian StyleTasiopoulos, Lampros, Marianthi Stefouli, Yorghos Voutos, Phivos Mylonas, and Eleni Charou. 2021. "Machine Learning Techniques in Agricultural Flood Assessment and Monitoring Using Earth Observation and Hydromorphological Analysis" Engineering Proceedings 9, no. 1: 40. https://doi.org/10.3390/engproc2021009040