Demands for risk monitoring and risk-informed decision-making are expressed by the Sendai Framework (United Nations 2015). Similarly, the needs for integrative assessments, including basic infrastructure, urban features and disaster risk, are expressed in the New Urban Agenda (UN/HABITAT 2017). However, while disaster risk studies have covered major cities and national capitals, studies on smaller cities or peri-urban areas are lacking widely (Birkmann et al. 2016).
Peri-urban areas are often among the fastest-growing urban areas when close to a major city or capital like Nairobi. Urban growth also expands the ratio of built-up areas potentially exposed to natural hazards such as floods or droughts. Monitoring of disaster risk needs to capture static pictures of risk and include the dynamics to better understand risk and resilience development and identify pathways to govern urban areas towards better resilience in coping with shocks and disasters (Simonovic and Peck 2013). This notion of the importance of dynamic risk assessments is expressed by recent research on aspects of the transformation of cities (Pelling et al. 2015), pathways in coping with climate change (Gibson et al. 2016), or transition (Solecki et al. 2017). However, this leads to a demand for data available for long periods to analyse changes.
Limitations of static risk assessments have long been acknowledged (King 2001), but few studies provide change assessments of disaster risk. This is often due to the lack of data for comparison or awareness about existing sources of data. While this is a challenge existing worldwide, availability and access to data are a major constraint for many countries in Africa (Osuteye et al. 2017). It seems paramount to improve data usability for risk assessments, and this paper points to data sources not used widely yet.
There is a wealth of studies utilising remote sensing data and urban area classification approaches (Bhatta 2010; Patino and Duque 2013). Multi-temporal and time-series analyses have increasingly been applied in this area of urban and land-use mapping (Acevedo and Masuoka 1997; Li et al. 2018), or peri-urban growth (Shaw and Das 2018). Mapping transformation using remote sensing increasingly starts applying multi-spatial-resolution change detection (Zhang et al. 2016). However, many approaches in remote sensing studies focus on the technical advancements and are less applied for the operational mapping of urban transformation yet. And while urban growth studies are common (Bagan and Yamagata 2012; Masek et al. 2000; Song et al. 2016), fewer studies combine it with a spatial detection of exposure to natural hazards, especially over a range over more than 20-30 years in comparison. Even fewer studies have used satellite data before the 1970s, such as the declassified satellite data of the USA (Day et al. 1998). The USA has disclosed declassified former espionage imagery from the 1960s onwards that are hardly used for risk assessments so far (Fekete 2020). Declassified satellite data from the CORONA, GAMBIT or HEXAGON series have been applied rather selectively and not very often yet, to fields such as archaeology (Ur 2003), generation of digital elevation models (Altmaier and Kany 2002), glacier monitoring (Bolch et al. 2008; Narama et al. 2010), land-use (Tappan et al. 2000) or urban growth (Masek, et al. 2000; Stewart et al. 2004; Cecchini et al. 2019; Hepcan et al. 2013).
There is an increasing demand for multi-modal, multi-spectral and multi-temporal remote sensing approaches (Brito and Quintanilha 2012; Qiming 2011; Stiller et al. 2021), and making use of a combination of aerial and satellite images can offer opportunities for that.
Aerial imagery and old declassified former espionage reconnaissance satellite imagery are major data sources to capture previous land cover back to the 1940s. Kenya has been covered by UK Royal Airforce aerial imagery campaigns due to the former colonial history. Access to such data is still restricted; it is on data servers not known much outside remote sensing communities. A lot of this data is open access, but sometimes, fees have to be paid, and although they are relatively low, it can still mean a restriction for researchers or local institutions. Another data access challenge is finding hazard data. This is the case for industrialised countries as well as for many other countries too.
Geospatial assessments are used in Kenya for monitoring land-use change or property rights (Koeva et al. 2017). Aerial imagery is also used for monitoring the sustainable development of agriculture (Ekbom et al. 2001). Challenges in defining urban boundaries and overestimating urban areas by single criteria such as population density are analysed using aerial imagery (Potts 2017). Hazards analysed using aerial images cover many hazards, including erosion and contamination, but also deal with lake basin (Khan et al. 2011; Habib et al. 2009) or riverine floods (Olang and Fürst 2011), flash floods (Hoedjes et al. 2014), rainfall variability (Gamoyo et al. 2015) or vector-borne diseases concerning floods (Pope et al. 1992). The early warning seems to be a topic of special interest for the whole country (Hoedjes, et al. 2014) or the Tana river basin (Otieno et al. 2019). The Tana river is covered by some studies (Leauthaud et al. 2013). But other areas are covered much less (Annex A). Existing land-use change assessments do not use data earlier than the mid-1970s when LANDSAT satellite imagery became used. For example, Nairobi has been analysed regarding land-use and land cover change for 1976-2000 (Mundia and Murayama 2010). However, systematic multi-temporal assessments of exposure of settlements to floods, droughts or other hazards have not been conducted yet, only urban growth or exposure mapping for single static time periods. But there is a great potential applying flood exposure mapping for the assessment of multiple risks related to floods, such as flood risk indices (Hategekimana et al. 2018), health risks (Okaka and Odhiambo 2019) or early warning (Otieno, et al. 2019).
Therefore, the objective of this paper is to expand existing land cover change assessments back to the 1940s using aerial imagery. This enables capturing urban growth outside the national capital, Nairobi, along urban rims and in expanding towns in Kenya. A transferrable mapping approach is demonstrated, and it is indicated which natural hazard data could be used to analyse urban growth into potential hazard zones over time. Natural hazards include river floods in this case, and exposure of settlement area to be flooded is the main focus here. Using heterogeneous remote sensing data from different satellite missions and with different sensors and resolutions, a temporal comparison is made possible that exceeds previous approaches. In addition, this approach can analyse data-poor areas and areas not covered yet by research on natural hazards and exposure, which may inspire similar research in other areas.