Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology
2.1.1. Image Extraction
2.1.2. Cloud Filter
2.1.3. Band Mapping
2.1.4. Water Classification
2.1.5. HAND and NDVI Masking
2.2. Datasets
2.3. Study Areas
2.3.1. Australia, Queensland Flood Event
2.3.2. Bangladesh, Dhaka Flood Event
2.3.3. Cambodia, Phnom Penh Flood Event
2.3.4. Canada, Red River Flood Event
2.3.5. India, Bihar Flood Event
2.3.6. Mozambique, Malawi Flood Event
2.3.7. Sri Lanka, Colombo flood Event
2.3.8. Thailand, Pathumthani, and Bangkok Flood Event
2.4. Validation Approach
3. Results
3.1. Data Cube Density
3.2. MNDWI, NDVI, and HAND
3.3. Validation
4. Discussion
4.1. Land Use
4.2. Emergency Services
4.3. Insurance
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Perera, D.; Seidou, O.; Agnihotri, J.; Rasmy, M.; Smakhtin, V.; Coulibaly, P.; Mehmood, H. Flood Early Warning Systems: A Review of Benefits, Challenges and Prospects; United Nations University, Institute for Water, Environment and Health: Hamilton, ON, Canada, 2019. [Google Scholar]
- Podlaha, A.; Lörinc, M.; Bhattacharya, A.; Bowen, S. Weather, climate & catastrophe insight. Annu. Rep. 2018, 1, 4–10. [Google Scholar]
- IFRC. 17.5 Million Affected by Floods and Threatened by Disease in South Asia. Available online: https://media.ifrc.org/ifrc/press-release/17-5-million-affected-floods-threatened-disease-south-asia/ (accessed on 20 January 2021).
- Coumou, D.; Rahmstorf, S. A decade of weather extremes. Nat. Clim. Chang. 2012, 2, 491–496. [Google Scholar] [CrossRef]
- Werner, M.G.F. A comparison of flood extent modelling approaches through constraining uncertainties on gauge data. Hydrol. Earth Syst. Sci. 2004, 8, 1141–1152. [Google Scholar] [CrossRef] [Green Version]
- Di Baldassarre, G.; Schumann, G.; Bates, P.D. A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent. J. Hydrol. 2009, 367, 276–282. [Google Scholar] [CrossRef]
- Priestnall, G.; Jaafar, J.; Duncan, A. Extracting urban features from LiDAR digital surface models. Comput. Environ. Urban Syst. 2000, 24, 65–78. [Google Scholar] [CrossRef]
- Galland, J.-C.; Goutal, N.; Hervouet, J.-M. TELEMAC: A new numerical model for solving shallow water equations. Adv. Water Resour. 1991, 14, 138–148. [Google Scholar] [CrossRef]
- Sinha, S.K.; Sotiropoulos, F.; Odgaard, A.J. Three-dimensional numerical model for flow through natural rivers. J. Hydraul. Eng. 1998, 124, 13–24. [Google Scholar] [CrossRef]
- Wing, O.E.J.; Bates, P.D.; Smith, A.M.; Sampson, C.C.; Johnson, K.A.; Fargione, J.; Morefield, P. Estimates of present and future flood risk in the conterminous United States. Environ. Res. Lett. 2018, 13, 034023. [Google Scholar] [CrossRef]
- The Globe and Mail Poor Flood Risk Maps or None at All Keeping are Keeping Communities in Flood Prone Areas. Available online: https://www.theglobeandmail.com/canada/article-poor-flood-risk-maps-or-none-at-all-are-keeping-canadian-communities/ (accessed on 20 January 2021).
- Institute of Catastrophic Loss Reduction. Focus on Flood Mapping in Canada; Institute of Catastrophic Loss Reduction: Toronto, ON, Canada, 2019. [Google Scholar]
- Perera, D.; Seidou, O.; Agnihotri, J.; Mehmood, H.; Rasmy, M. Challenges and technical advances in flood early warning systems (FEWSs). In Flood Impact Mitigation and Resilience Enhancement; InTech: London, UK, 2020. [Google Scholar]
- Wulder, M.A.; Masek, J.G.; Cohen, W.B.; Loveland, T.R.; Woodcock, C.E. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sens. Environ. 2012, 122, 2–10. [Google Scholar] [CrossRef]
- Lewis, A.; Lymburner, L.; Purss, M.B.J.; Brooke, B.; Evans, B.; Ip, A.; Dekker, A.; Irons, J.R.; Minchin, S.; Mueller, N.; et al. Rapid, high-resolution detection of environmental change over continental scales from satellite data—the Earth Observation Data Cube. Int. J. Digit. Earth 2016, 9, 106–111. [Google Scholar] [CrossRef]
- Gorelick, N.; Hancher, M.; Dixon, M.; Ilyushchenko, S.; Thau, D.; Moore, R. Google Earth engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 2017, 202, 18–27. [Google Scholar] [CrossRef]
- Saah, D.; Tenneson, K.; Matin, M.; Uddin, K.; Cutter, P.; Poortinga, A.; Nguyen, Q.H.; Patterson, M.; Johnson, G.; Markert, K.; et al. Land cover mapping in data scarce environments: Challenges and opportunities. Front. Environ. Sci. 2019, 7. [Google Scholar] [CrossRef] [Green Version]
- Phongsapan, K.; Chishtie, F.; Poortinga, A.; Bhandari, B.; Meechaiya, C.; Kunlamai, T.; Aung, K.S.; Saah, D.; Anderson, E.; Markert, K.; et al. Operational flood risk index mapping for disaster risk reduction using earth observations and cloud computing technologies: A case study on Myanmar. Front. Environ. Sci. 2019, 7. [Google Scholar] [CrossRef] [Green Version]
- Poortinga, A.; Nguyen, Q.; Tenneson, K.; Troy, A.; Saah, D.; Bhandari, B.; Ellenburg, W.L.; Aekakkararungroj, A.; Ha, L.; Pham, H.; et al. Linking earth observations for assessing the food security situation in Vietnam: A landscape approach. Front. Environ. Sci. 2019, 7. [Google Scholar] [CrossRef] [Green Version]
- Patel, N.N.; Angiuli, E.; Gamba, P.; Gaughan, A.; Lisini, G.; Stevens, F.R.; Tatem, A.J.; Trianni, G. Multitemporal settlement and population mapping from Landsat using Google Earth engine. Int. J. Appl. Earth Obs. Geoinf. 2015, 35, 199–208. [Google Scholar] [CrossRef] [Green Version]
- Mutanga, O.; Kumar, L. Google Earth engine applications. Remote Sens. 2019, 11, 591. [Google Scholar] [CrossRef] [Green Version]
- IWM. Flood Risk Mapping: South Asia. Available online: http://waterdata.iwmi.org/Applications/Catastrophic_Flood_Risk_Mapping/ (accessed on 20 January 2021).
- Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A.S. High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418–422. [Google Scholar] [CrossRef] [PubMed]
- Vanama, V.S.K.; Mandal, D.; Rao, Y.S. GEE4FLOOD: Rapid mapping of flood areas using temporal Sentinel-1 SAR images with Google Earth engine cloud platform. J. Appl. Remote Sens. 2020, 14, 1. [Google Scholar] [CrossRef]
- DeVries, B.; Huang, C.; Armston, J.; Huang, W.; Jones, J.W.; Lang, M.W. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth engine. Remote Sens. Environ. 2020, 240, 111664. [Google Scholar] [CrossRef]
- Manfreda, S.; Samela, C. A digital elevation model based method for a rapid estimation of flood inundation depth. J. Flood Risk Manag. 2019, 12. [Google Scholar] [CrossRef] [Green Version]
- Mosavi, A.; Ozturk, P.; Chau, K.-W. Flood prediction using machine learning models: Literature review. Water 2018, 10, 1536. [Google Scholar] [CrossRef] [Green Version]
- Housman, I.A.; Stam, C. Mask Clouds and Cloud Shadows in Landsat Data and Export Composite. 2020. Available online: https://ee-api.appspot.com/a432bf20510f37ce13e847e4394aee77 (accessed on 10 May 2021).
- Housman, I.W.; Chastain, R.A.; Finco, M.V. An evaluation of forest health insect and disease survey data and satellite-based remote sensing forest change detection methods: Case studies in the United States. Remote Sens. 2018, 10, 1184. [Google Scholar] [CrossRef] [Green Version]
- Ustin, S.L.; Roberts, D.A.; Pinzon, J.; Jacquemoud, S.; Gardner, M.; Scheer, G.; Castañeda, C.M.; Orueta, A.P. Estimating canopy water content of chaparral shrubs using optical methods. Remote Sens. Environ. 1998, 65, 280–291. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Acharya, T.D.; Subedi, A.; Lee, D.H. Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors 2018, 18, 2580. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Donchyts, G.; Winsemius, H.; Schellekens, J.; Erickson, T.; Gao, H.; Savenije, H.; van de Giesen, N. Global 30 m height above the nearest drainage. In Proceedings of the EGU General Assembly Conference, Vienna, Austria, 17–22 April 2016. [Google Scholar]
- Deng, Y.; Jiang, W.; Tang, Z.; Ling, Z.; Wu, Z. Long-term changes of open-surface water bodies in the Yangtze River basin based on the Google Earth engine cloud platform. Remote Sens. 2019, 11, 2213. [Google Scholar] [CrossRef] [Green Version]
- Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, T.R.; et al. High-resolution global maps of 21st-century forest cover change. Science 2013, 342, 850–853. [Google Scholar] [CrossRef] [Green Version]
- Martz, L.; Garbrecht, J. Numerical definition of drainage network and subcatchment areas from Digital Elevation Models. Comput. Geosci. 1992, 18, 747–761. [Google Scholar] [CrossRef]
- Nobre, A.D.; Cuartas, L.A.; Momo, M.R.; Severo, D.L.; Pinheiro, A.; Nobre, C.A. HAND contour: A new proxy predictor of inundation extent. Hydrol. Process. 2016, 30, 320–333. [Google Scholar] [CrossRef]
- Donchyts, G.; Winsemius, H.; Schellekens, J.; Erickson, T.; Gao, H.; Savenije, H.; van de Giesen, N. Global 30 m Height above the Nearest Drainage. 2016. Available online: https://samapriya.github.io/awesome-gee-community-datasets/projects/hand/ (accessed on 10 May 2021).
- Bureau of Meteorology, Australian Government. Report on Queensland Floods; Bureau of Meteorology: Longreach, QLD, Australia, 2008.
- National Climate Change Adaptation Research Facility. Extreme Rainfall and Flood Event in Mackay on 1 February 2008. Snapshot for CoastAdapt; National Climate Change Adaptation Research Facility: Southport, QLD, Australia, 2016. [Google Scholar]
- Trade and Markets Division Food and Agriculture Organisation (FAO). Global Information and Early Warning on Food and Agri-Culture (GIEWS) Update Bangladesh Floods; FAO: Rome, Italy, 2017. [Google Scholar]
- International Federation of Red Cross and Red Crescent (IFRC). DREF Operation Final Report: Cambodia Floods; International Federation of Red Cross and Red Crescent: Geneva, Switzerland, 2012. [Google Scholar]
- The Wire India Floods in Bihar Destroyed 7.54 Lakh Hectares of Agricultural Land This Year. Available online: https://thewire.in/environment/floods-bihar-agricultural-land (accessed on 25 April 2021).
- Floodlist Malawi and Mozambique—Death Toll Rises after Widespread Flooding. Available online: http://floodlist.com/africa/malawi-mozambique-floods-march-2019 (accessed on 25 April 2021).
- ReliefWeb. Sri Lanka: Floods and Landslides Situation Report No. 2; United Nations Office for the Coordination of Humanitarian Affairs: New York, NY, USA, 2016. [Google Scholar]
- Gale, E.L.; Saunders, M.A. The 2011 Thailand flood: Climate causes and return periods. Weather 2013, 68, 233–237. [Google Scholar] [CrossRef] [Green Version]
- Rentschler, J.; Salhab, M. People in Harm’s Way: Flood Exposure and Poverty in 189 Countries; World Bank Group: Washington, DC, USA, 2020; pp. 1–28. [Google Scholar]
- Braden, J.; Simonovic, S.P. A Review of Flood Hazard Mapping Practices across Canada; University of West Ontario, Department of Civil and Environmental Engineering: London, ON, Canada, 2020; ISBN 978-0-7714-3143-2. [Google Scholar]
- Sivapalan, M.; Takeuchi, K.; Franks, S.; Gupta, V.K.; Karambiri, H.; Lakshmi, V.; Liang, X.; Mcdonnell, J.; Mendiondo, E.M.; O’Connell, P.E.; et al. IAHS decade on predictions in ungauged basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences. Hydrol. Sci. J. 2003, 48, 857–880. [Google Scholar] [CrossRef] [Green Version]
- Grimes, D.; Pardo-Iguzquiza, E.; Bonifacio, R. Optimal areal rainfall estimation using raingauges and satellite data. J. Hydrol. 1999, 222, 93–108. [Google Scholar] [CrossRef]
- Rumson, A.G.; Hallett, S. Innovations in the use of data facilitating insurance as a resilience mechanism for coastal flood risk. Sci. Total. Environ. 2019, 661, 598–612. [Google Scholar] [CrossRef]
- De Leeuw, J.; Vrieling, A.; Shee, A.; Atzberger, C.; Hadgu, K.M.; Biradar, C.M.; Keah, H.; Turvey, C. The potential and uptake of remote sensing in insurance: A review. Remote Sens. 2014, 6, 10888–10912. [Google Scholar] [CrossRef] [Green Version]
Satellite | Sensor | Launch Year | Resolution |
---|---|---|---|
Landsat 5 | MSS/TM | 1984 | 30 m |
Landsat 7 | ETM+ | 1999 | 30 m |
Landsat 8 | OLI/TIRS | 2013 | 30 m |
Country, City | Year | Data Source |
---|---|---|
Australia, Queensland | 2008 | Dartmouth Flood Observatory, University of Colorado |
Bangladesh, Dhaka | 2014 | Copernicus, Emergency Management Service |
Canada, Red River | 2019 | Natural Resources Canada |
Cambodia, Phnom Penh | 2011 | Governmental Information Technology Development Agency, Thailand |
India, Bihar | 2017 | Dartmouth Flood Observatory, University of Colorado |
Mozambique, Malawi | 2015 | The Humanitarian Data Exchange |
Sri Lanka, Colombo | 2016 | Irrigation Department, Sri Lanka |
Thailand, Pathumthani | 2011 | Governmental Information Technology Development Agency, Thailand |
Thailand, Bangkok | 2011 | Governmental Information Technology Development Agency, Thailand |
Index | Description |
---|---|
True positive () | Number of flood pixels in the ground truth data detected as flood by FMA |
True negative () | Number of non-flood pixels in the ground truth data detected as non-flood by FMA |
False positive () | Number of non-flood pixels in the ground truth data detected as flood by FMA |
False negative () | Number of flood pixels in the ground truth data detected as non-flood by FMA |
Country, City | Tp | Tn | Fp | Fn |
---|---|---|---|---|
Australia, Queensland | 8,703,425 | 1,921,535 | 339,094 | 1,017,283 |
Bangladesh, Dhaka | 3,480,371 | 902,318 | 171,870 | 1,331,994 |
Canada, Red River | 137,645 | 22,941 | 1835 | 20,647 |
Cambodia, Phnom Penh | 31,846,960 | 8,685,534 | 804,216 | 10,615,653 |
India, Bihar | 1,904,012 | 469,821 | 86,546 | 370,911 |
Mozambique, Malawi | 983,934 | 226,039 | 26,593 | 146,261 |
Sri Lanka, Colombo | 767,032 | 108,171 | 19,667 | 245,844 |
Thailand, Pathumthani | 640,674 | 186,863 | 26,695 | 266,948 |
Thailand, Bangkok | 1,125,724 | 354,680 | 61,683 | 447,205 |
Country, City | TPR | FPR | Accuracy | Error Rate |
---|---|---|---|---|
Australia, Queensland | 0.90 | 0.15 | 0.89 | 0.11 |
Bangladesh, Dhaka | 0.72 | 0.16 | 0.74 | 0.26 |
Canada, Red River | 0.87 | 0.07 | 0.88 | 0.12 |
Cambodia, Phnom Penh | 0.75 | 0.08 | 0.78 | 0.22 |
India, Bihar | 0.84 | 0.16 | 0.84 | 0.16 |
Mozambique, Malawi | 0.87 | 0.11 | 0.88 | 0.14 |
Sri Lanka, Colombo | 0.76 | 0.15 | 0.77 | 0.23 |
Thailand, Pathumthani | 0.71 | 0.13 | 0.74 | 0.26 |
Thailand, Bangkok | 0.72 | 0.15 | 0.74 | 0.26 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Mehmood, H.; Conway, C.; Perera, D. Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform. Atmosphere 2021, 12, 866. https://doi.org/10.3390/atmos12070866
Mehmood H, Conway C, Perera D. Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform. Atmosphere. 2021; 12(7):866. https://doi.org/10.3390/atmos12070866
Chicago/Turabian StyleMehmood, Hamid, Crystal Conway, and Duminda Perera. 2021. "Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform" Atmosphere 12, no. 7: 866. https://doi.org/10.3390/atmos12070866
APA StyleMehmood, H., Conway, C., & Perera, D. (2021). Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform. Atmosphere, 12(7), 866. https://doi.org/10.3390/atmos12070866