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Multiple remote sensing products were applied for fl= ood risk mapping and analysis in the Turkana region of Kenya. Using a digit= al terrain model (DTM) and flood extents and surface properties derived fro= m satellite earth observation (EO), it was possible to outline potential ar= eas of flooding that might cause delay and increased cost during seismic su= rvey operations. While providing excellent advance information, field verif= ication was still required to assess and understand the actual ground condi= tions and characteristics.
A seismic survey within the Turkana region of Kenya = needed to be completed with minimal time spent in the field during the loca= l wet season period. It is generally accepted that the rainy season is from= March to May; however, this time period and the amount of precipitation ca= n vary considerably throughout the region. With the survey being carried ou= t in a flood risk area, the likely ground conditions needed to be assessed = to establish whether vibrator and vehicle movements would be adversely affe= cted. Due to security and political difficulties within the region, site vi= sits were not permitted far in advance of the survey date. With these const= raints in place there was a requirement for a flood risk mitigation strateg= y for the seismic survey, which included flood risk mapping and assessment = of likely ground surface conditions, to determine if the operation could go= ahead.
The goal was to provide detailed environmental infor= mation at the planning stage to minimize the amount of time spent in the fi= eld as well as potential downtime due to adverse environmental conditions. = This would avoid additional survey costs due to stand-by (periods in which = the operator cannot proceed) and, more importantly, deliver the final seism= ic acquisition product on time.
To address the project needs, an accurate digital te= rrain model (DTM) on which to plan/develop the flood mapping was required. = Water level data could then be superimposed over the terrain and flooding p= atterns could be assessed. Availability of a DTM was a major concern, with = local data being difficult to obtain but global data perhaps not having the= required detail and accuracy to highlight subtle changes in elevation. Sim= ilar projects in different regions had access to river height gauges and hi= storic governmental flood data, but both of these were unavailable in the s= hort time frame for this survey location.
Following evaluation of the options, satellite EO wa= s selected as the best solution to deliver terrain and flood modelling info= rmation as desktop studies without the need to visit sites. Satellite EO wo= uld also prove a much more cost-effective method than detailed aerial remot= e sensing, which would have cost over $500,000 to acquire.
Three remote sensing products were utilised in the a= ssessment of ground conditions:
Data integration, analysis and visualisation were co= mpleted using ESRI ArcMap software. The overall activities are:
Data processing was completed using ESRI ArcMap soft= ware. SRTM-90 elevation and MODIS derived flood extent data (as identified = by the Dartmouth Flood Observatory) were collected. Figure 1 shows ele= vation ranges scaled to emphasise flat areas, with extracted 5 m contours o= verlain. The cumulative flood extent limit is also shown as derived for the= years 2002, 2005, 2006, 2011, 2012, and 2013. Significant variation in ele= vation is present even in relatively flat areas.
Combining both elevation and historic flood data, it= was possible to identify low lying, low gradient areas that were potential= flood risk zones. The identified flood risk areas were then compared to SP= OT-6 satellite image data, which showed a correlation between the DTM and t= he flood extents and a visual change in ground characteristics within these= extents, as illustrated in Figure 2.
These flood prone areas were cross referenced agains= t the planned seismic survey to identify seismic lines that could potential= ly present difficulties associated with flooding or wet ground when acquisi= tion was carried out. As the flood susceptible area represented a considera= ble part of the acquisition target area, it was difficult to propose a suit= able operation contingency plan avoiding the questionable areas during the = worst months. Ground conditions and soil composition were left as unknowns = in the planning process to be addressed once operations were initiated.
The flood prone areas were identified successfully f= rom analysis of existing satellite EO datasets. The EO datasets are useful = at the start of the planning process to quantify potential issues caused by= flooding and wet ground, but further desirable information would include s= oil composition and stability to establish whether the identified areas are= capable of supporting heavy machinery and vehicles without causing too muc= h damage or delaying operations, and whether this changes depending on the = season.
The seismic survey was concluded with no major time = extensions and with limited standby time caused by flooding, partly due to = lower than average rainfall for the survey acquisition time window. Some lo= gistical problems were associated with wet ground but these could not have = been avoided; however a better understanding of them beforehand would have = been beneficial =E2=80=93 see Figure 3.
Due to time constraints, health and safety and secur= ity risks, and associated costs, it is not always possible to conduct field= verification visits. In this example, due to license agreement commitments= operations had to be carried out in a time frame that overlapped the rainy= season. The object of the pre-survey EO-based study was to identify any is= sues that might arise due to adverse weather conditions in the operation wi= ndow chosen and potentially quantify additional costs and delays.
Future improvements in the products could be possibl= e with access to additional datasets:
Paul Nolan =
RPS Energy
Axminster, EX13 5AX
United Kingdom