Strengthening Flood Response:Investing in RADAR Satellite Imagery for Effective Flood Response

1.0 BACKGROUND

1.1 Floods in KenyaFloods usually affect Kenya during the March-April-May (MAM) and October-November-December (OND) rainfalls1,2. This often occurs along wetland agro-ecological production systems such as Athi and Tana3, leading to loss of lives, disruption of people’s livelihoods, infrastructure destruction and interruption of economic activities. The most recent major flood in Kenya occurred during the 2018 MAM rainfall4. These led to displacement of over 150,000 people, 72 deaths and 33 injuries in 12 counties (Wajir, Turkana, Garissa, Isiolo, Mandera, Marsabit, Narok, West Pokot, Samburu, Tana River, Kisumu and Taita)5.

The floods led to livelihood disruptions with over 6,000 livestock killed, 8,450 acres of farmland submerged in water, houses and infrastructure such as roads destroyed6. The 291,171 people that were displaced by floods in the 2018 long rains were at risk of disease outbreaks. The increase in stagnant water provided a conducive environment for Rift Valley Fever (RVF)7, a mosquito borne viral zoonosis that mostly affected animals and human lives.

1.2 Kenya Red Cross Society (KRCS) Flood Response ProgrammeThe mandate of KRCS is to provide assistance to communities affected by disasters to save lives and alleviate human suffering. To strengthen its preparedness and response capacity especially

to natural disaster, KRCS has invested heavily on data preparedness through a technology referred to as remote sensing. Remote sensing is a cost-effective technology in earth observation where one can accurately extract information about a disaster e.g. a flood, from an area using satellites without having direct contact to this location8. Practical examples where remote sensing for flood mapping has been used include; catastrophic flood mapping using

synthetic aperture radar9, spatial distribution of flood extent10, determination of flood extend and intensity and assessing damage as a result of flood11.

In the past, KRCS has used ground surveys to map out households affected by floods. These ground surveys would require staff and volunteers to actually be on site with an aim of manually counting flood-affected households. This approach proved to be time consuming, costly and also delayed the response. This is particularly important in the case of floods, a fast onset disaster because to save lives and livelihoods, time is of essence.

With timely acquisition of satellite images of a flooded area, information can be extracted ‘remotely’ and within a short timeframe without sending teams on the ground to find out the extent of a flooding event. In so doing, the affected communities and infrastructure destroyed by floods can be accurately detected within a short-timeframe and identified from ‘space’. This vital information would help KRCS to ensure cost effective, timely preparation and

1Nicholson, Sharon E (2017). Climate and climate variability of rainfall over Eastern Africa, Reviews of Geophysics 55 (3): 590-6352Gamoyo M., et al., (2015). Rainfall variability over the East African Coast, Theoretical and Applied Climatology 120 (1-2): 311-3223Leauthaud C. et al., (2013). Floods and flood-prone areas: An overview, Journal of Coastal Research 31 (4): 1005-10134UNOCHA (2018). Flash Update #6: Floods in Kenya. 7 June 2018

5IFRC (2018). Emergency Appeal, Kenya: Floods. IFRC https://reliefwed.int/sites/reliefweb.int/files/resources/MDRKE043eapdf6KFSSG (2018). The 2018 Long rains season assessment report. http://reliefwe.int/sites/reliefweb.int/files/resources7UNICEF (2018). Kenya Floods Response, Update 15 June 20188Campbell, James B. et al., (2011). Introduction to remote sensing, Guilford Press.9Brivio et al., (2002). Integration of remote sensing data and GIS for accurate mapping of flooded areas, International

effective response to those affected by floods. An ideal KRCS practical scenario for the use of satellite imageries was the Solai Dam burst in 2018. Increased inflow of surface run-off resulted in dam spillage in Solai Dam in Nakuru causing 47 fatalities and displacement of 283 households12. Timely optical satellite images were acquired to estimate the number of households affected by the dam burst. The information extracted from satellites proved vital because within three days, an accurate number of 223 households affected by floods were extracted thereby improving the response, especially the search and rescue. As a result, KRCS disbursed unconditional cash to the 223 affected households in Solai to access safe and dignified alternative housing.

1.3 Mapping Historical Flood Occurrence Using Satellite ImagesHistoric data on flood events are useful for floodplain management, flood insurance rating, emergency planning and flood risk management13. A good collection, auditing, analysis and understanding of satellite data from previous flood events may result in a powerful tool for flood disaster prevention and land use planning14. Satellite data has been successfully used in most phases of flood disaster management including preparedness, warning, response and monitoring15. Such data can be used in the phase of disaster prevention, by mapping geomorphologic elements, historical events and sequential inundation phases, including duration, depth of inundation and direction of current.

Mapping historical flood events would help KRCS to understand the impacts of the previous floods

to help in flood management including effective flood response. Historical flood maps would also act as inputs to climate forecast models with a view to predicting flood extent with credible probabilistic weather forecasts. A synthesis of flood exposure maps and climate forecast models forms the basis for early forecast-based action16 thereby reducing the impacts of potential floods in communities living in flood prone areas through early warning, early action (EWEA) protocols.

With high acquisition rates, wide spatial coverage and systematic acquisition strategies, satellites can map current phenomena e.g. floods and archive these phenomena for historical referencing and post-disaster assessment.

The essence of accessing archived satellites is to better understand historical events and to explore linkages of such events with other explanatory variables e.g. changes in weather events, population, land use and infrastructure.

Journal of Remote Sensing 23 (3): 429-441.10Bates, Paul D. (2004). Remote sensing and flood inundation modeling, Hydrological Processes 18 (13): 2593-259711Klemas, V. (2014). Remote sensing of floods and flood-prone areas: An overview, Journal of Coastal Research 31(4): 1005-1013.12https://cropnuts.com/solai-dam-tradegy/13Bathrellos et al., (2018). Temporal and spatial analysis of flood occurrences in the drainage basins of Pinios River, Land 7:166.

14Rijal et al., (2018). Flood hazard mapping of a rapidly urbanizing in the foothills (Birendranagar, Surkhet) of Nepal, Land 7:60.15CEOS (2003). The use of earth observing satellite for hazard support assessment and scenarios: Final report of the CEOS Disaster Management Support Group (DMSG): CEOS Rome16Coughlan de Perez et al., (2016). Action-based flood forecasting for triggering humanitarian action, Hydrology and Earth System Sciences 20 (9): 3549-3560.17Chakraborti, A.K. (1999). Satellite remote sensing for near-

1.4 Radar Images For Humanitarian ResponseThe extensive cloud that is mostly present during a flood event seriously limits the use of optical sensors for flood mapping. More often than not, optical satellite images acquired during floods would have clouds due to the effect of heavy rainfall. The presence of clouds in optical satellites hinders extraction of information on flood extent, buildings, road networks and other layers affected by floods. Synthetic Aperture Radar (SAR) from ERS and RADARSTAT (Radio Detection and Ranging) has proven useful for mapping flood inundation areas, due to their bad weather capability17.

Radar images are vital in humanitarian response specifically in all phases of disaster cycle, more precisely in pre-disaster assessment, response and post-disaster assessment. Humanitarian organisations such as the United Nations Institute of Training and Research- Operational Satellite Applications Program (UNOSAT) and United Nations Office for Outer Space Affairs (UN-SPIDER) continue to advocate the use of

radar satellites for disaster preparedness. In May 2018, UNOSAT used radar satellite images to detect flood-water extent over Lamu West, Malindi, Garsen and Magarini sub-counties. The analysis was conducted using Sentinel-1 images acquired on 4th of May 2018. Over 25,000 hectares of land had flood-waters with around 20,000 people affected18. The UN-SPIDER has also recommended the use of radar for flood mapping using Sentinel-1 image. A derived methodology implemented in google earth engine by UN-SPIDER could be applied globally for flood extent mapping19.

Radar images have active sensors hence emit their own energy (not dependent on sun energy) that do penetrate through clouds, haze, atmospheric aerosols and particulate matter. Hence they can capture information such as flood extent underneath clouds and any other atmospheric interference as illustrated in Figure 1. This information will help evaluate the impact and determine the needs to size the necessary response appropriately. The images provide a synoptic view of extended remote areas in a relatively quick and cost effective manner.

real-time flood and drought impact assessment – Indian Experience. Workshop on Natural Disasters and their Mitigation - A Remote Sensing and GIS Perspective 11-15 October 1999, Dehradur, India.18UNOSAT (2018). Satellite detected water over Garsen

sub-County, Tana River County. United Nations Institute of Training and Research – Operational Satellite Applications Program. 19UN-SPIDER (2014). Recommended Practice: Radar-Based Flood Mapping. http://www.un-spider.org.

Figure 1: (Left) An optical image of the flood area that doesn’t clearly show the flood extent due to clouds and shadows, (Right) A radar image of the same area that clearly shows the flood extent

Figure 2: (Left) A radar image that clearly shows the flood extent, (Right) An archive RADAR image of the area that clearly shows water bodies in non-flood conditions

2.0 RADAR SATELLITE IMAGES FOR KRCS FLOOD RESPONSE PROGRAMMING

2.1 Mapping of Tana River Floods using RADARTana River is usually affected by floods during MAM and OND rainfalls20. A historical flood map for Tana River was derived from two sentinel-1A (S1A) images. These images were acquired during flooding and before a flood event. The dates of acquisition were: During flooding – 16th May 2018 and before flooding – 15th March 2018. The flooded areas appear as obvious dark patches of low backscatter due to specular reflection over the smooth water surface while the surrounding land is much brighter (Figure 2).

The flood mapping process involved taking a subset of where flooding has taken place. A multi-looking was applied to reduce speckle and speed up processing time. The RADAR digital numbers were calibrated to physical quantities (decibels) with a view to converting pixels from a linear scale to a non-linear (logarithmic) scale to

achieve a clear distinction of land and water features. Range Doppler terrain correction was later applied to project the images to a map system and correct distortion due to terrain. The last process involved combining the two images and creating a red-green-blue (RGB) spectral band to distinguish permanent water areas and flooded areas. The red channel (low backscatter) would have the ‘during-flood’ image while the green and blue channels (high backscatter) would have the ‘before flood’ image. These processes were implemented in the SNAP open source software.

20Leauthaud et al., (2013). Floods and livelihoods: The impact of changing water resources on wetland agro-ecological production systems in the Tana River Delta, Kenya, Global Environmental Change 23 (3): 590-635.

Figure 3: (Left) Flood extent derived from RADAR satellite imagery. Flooded areas are depicted in cyan colour. (Right) Image shows optical image of flooded area with clouds and corresponding shadows.

Flood extent was derived from two RADAR satellite imageries; one acquired before the flooding event and the other acquired during the flooding event along the Tana River Basin. From the derived flood extent, flooded areas are depicted as colour cyan. In comparison to the optical satellite image acquired during the same flood event - where clouds and their corresponding shadows seem to obscure flood extent information - the RADAR images fully captured the flooding extent due to its capability of penetrating through clouds.

3.0 RECOMMENDATIONS AND CONCLUSIONMore often than not, optical satellite images along the Kenyan coastal strip, where flooding normally occurs, are prone to clouds and atmospheric interferences. In such instances, KRCS is recommended to use alternate and openly available RADAR images, from the European Space Agency (ESA) because RADAR penetrates through clouds and other atmospheric interferences, have wide spatial coverages of flood (> 80 kilometers), have better detection of flooded areas as compared to optical satellite imageries, have high revisit time of 12 days, can capture flooding events both during the day and at night; and can be implemented easily in open source software

such as SNAP and Google Earth engine. This is as recommended by UNOSAT and UN-SPIDER. On the down side, RADAR images have high computation requirements, have high data storage requirements and more often than not, geometric errors due to terrain can lead to false flood detection. However, the advantages outweigh the disadvantages.

RADAR satellite images will therefore strengthen humanitarian data preparedness through extraction of accurate information on flood extent and accurate detection of flood-affected households for effective and timely disaster response including development of flood maps. These flood maps would act as inputs to climate forecast models with a view of predicting flood extent with credible probabilistic weather forecasts21 further strengthening effective disaster preparedness. A synthesis of flood exposure maps and climate forecast models forms basis for KRCS early forecast-based action22 (Erin Coughlan de Perez et al. 2016) thereby reducing the impacts of

21Oludhe C. (2002). Deterministic and probabilistic prediction approaches in seasonal to inter-annual climate forecasting. In expert meeting on the Application of Climate Forecasts for Agriculture. Banjul, Gambia, 9A13 December.22Coughlan de Perez et al., (2016). Action-based flood forecasting for triggering humanitarian action, Hydrology and Earth System Sciences 20 (9): 3549-3560.

potential floods in communities living in flood prone areas through early warning, early action (EWEA) protocols. A good example of an EWEA protocol initiated through flood mapping of households is; triggering cash transfer programming to communities living in flood risk areas for timely evacuation to safer grounds (E. Coughlan de Perez et al. 2015).

Due to radar’s high repetition rate of 12 days, wide coverage of above 80 kilometers and a systematic acquisition strategy, it is highly recommended for KRCS and other humanitarian organisations to use openly available radar satellite imageries that are provided by the European space agency (ESA) and recommended by (UNOSAT 2018) and (UN-SPIDER 2014) to effectively monitor floods in numerous areas of interest along the coast where available optical satellite images have clouds and other atmospheric interferences. This would strengthen KRCS’ data preparedness for effective and timely disaster response mainly through forecast-based action and early warning and early action protocols.

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Fax: (+254-20) 3950444 | Mobile: (+254) 703 037000/722 206958/733 333040Email: info@redcross.or.ke | Website: www.redcross.or.ke