Bull Mar Sci. 96(1):71–93. 2020 research paper https://doi.org/10.5343/bms.2019.0063

The use of unmanned aircraft systems and high- resolution satellite imagery to monitor tilapia fish- cage aquaculture expansion in , Kenya

1 Geography and Geosciences, Stuart E Hamilton 1 * Salisbury University, Salisbury, 1 Maryland 21801. Silviya M Gallo 1 2 Kenya Marine and Fisheries Noah Krach Research Institute (KMFRI) – Chrisphine S Nyamweya 2 Kisumu Center, Kisumu, Kisumu John K Okechi 2 County, Kenya. Christopher M Aura 2 3 Biology, Boston University, 2 Boston, Massachusetts 02215. Zachary Ogari 1 * Corresponding author email: Paige M Roberts 3 . Les Kaufman

ABSTRACT.—Lake Victoria, the largest lake in the tropics, has a storied history that includes recent shifts in ecology due to a variety of point and nonpoint source anthropogenic impacts. Among the expanding industries contributing to environmental impacts (if not properly managed) is the recent and rapid expansion of cage aquaculture of ( niloticus). As part of an effort to assess the ecological consequences of this new industry, unmanned aerial systems (UAS), very high-resolution satellite imagery, and geographic information systems (GIS) were used to map the tilapia fish cages in the Kenya portion of Lake Victoria, Africa. Understanding the impacts of the growth of commercial finfish cage culture within Lake Victoria requires a systems view which, through the use of UAS and satellite technologies, can provide spatial context and change detection. This synthesis of UAS, very high-resolution satellite imagery, and GIS has allowed for accurate and rapid mapping of inshore tilapia fish cages with high positional accuracy. The significance of these observations lies in the speed and detection accuracy in the methodology, allowing for rapid visualization and assessment of cage culture in the Kenyan portion of Lake Victoria. As of 2012, there were very few floating aquaculture finfish cages in the Kenyan portion of Lake Victoria. Using UAS, satellite, and GIS technologies, Date Submitted: 4 June, 2019. in 2018 the same portion of the lake was found to contain Date Accepted: 15 October, 2019. 4357 fish cages covering 62,132 m2. Available Online: 18 October, 2019.

Bulletin of Marine Science 71 © 2020 Rosenstiel School of Marine & Atmospheric Science of the University of Miami 72 Bulletin of Marine Science. Vol 96, No 1. 2020

Lake Victoria

Lake Victoria and the wider basin was formed approximately 750,000 yrs ago due to crustal uplifting along the western side of the lake which altered the path of both the Kagera and Katonga , diverting them into the newly formed basin (Kendall 1969). The scientific consensus is that Lake Victoria virtually ceased to exist as re- cently as 12,500 yrs ago (Scholz and Rosendahl 1988, Johnson et al. 1996). Conversely, approximately 5000 yrs after this almost complete disappearance, the lake was 12– 18 m higher than the present-day level (Stager et al. 1997, Nicholson 1998), marking a rise of approximately 100 m in only 5000 yrs. Rapid fluctuations of several meters likely still occur over short geologic periods, (Nicholson 1998, Singh 2006), continu- ously changing the size and shape of Lake Victoria (Scholz and Rosendahl 1988). The most recent analysis of Lake Victoria at the lake level estimates a 1:25,000 shoreline length of 7142 km (Hamilton 2016), an average depth of approxi- mately 40 m with a maximum depth of just over 80 m (Hamilton et al. 2016, ILEC 2019), a surface area of 59,947 km2 (Hamilton 2018), a volume of 2424 km3 (Hamilton et al. 2016), and a total catchment area of 169,858 km2 (Hamilton 2018), which is less than three times the lake area. The lake has approximately 985 islands (Hamilton 2016) and is divided between Tanzania, Uganda, and Kenya [with approximately 49% of the lake surface in Tanzania, 45% in Uganda, and 6% in Kenya (Kayombo and Jorgensen 2006)]. Lake Victoria is considered the second largest freshwater lake glob- ally by surface area (ILEC 2019) and the largest lake within Africa (Kayombo and Jorgensen 2006, ILEC 2019). It is the largest lake when measured by surface area in the tropics, and likely contains the world’s largest freshwater fishery (Kayombo and Jorgensen 2006). Lake Victoria drives the economy and livelihoods of the residents within the wider catchment area, acting as a waste repository and providing, food, energy, irrigation, drinking , and transportation (Kayombo and Jorgensen 2006). The total popu- lation in 2015 within 10 km of the lakeshore of Lake Victoria was estimated to be 10.5 million people, and within 50 km of the lakeshore was estimated to be 30.5 mil- lion people [of which 17% and 31%, respectively, are within Kenya (Hamilton 2016, CIESIN 2018)]. The total population in the entire Lake Victoria catchment area is estimated to be almost 52 million people, with 31.5% in the Kenyan catchment area (Hamilton 2016, CIESIN 2018). With the current population and expected growth in future years, the ecological viability of Lake Victoria is paramount to this region. The dynamic population growth in the Lake Victoria basin is listed as a driving factor behind the declines in water quality (Verschuren et al. 2002). Lake Victoria has suffered from severe eutrophication for many decades (Hecky 1993, Verschuren et al. 2002), with regular and massive algal blooms occurring (Kayombo and Jorgensen 2006) for at least the last 30 yrs (Ochumba and Kibaara 1989). The lake has seen a five-fold increase in turbidity since the early 1930s (Kayombo and Jorgensen 2006) with secchi measurements below 1 m typical in the Kenyan portion of Lake Victoria (KLV; Lung’ayia et al. 2001). In addition to problems within the water column, inva- sive species such as water hyacinth have periodically choked entire portions of the Kenyan area of Lake Victoria over many decades (Opande et al. 2004 Güereña et al. 2015). With the health of the lake declining and human population increasing, the historic Lake Victoria fishery is in peril. Hamilton et al.: UAS mapping of fish cages in Kenya 73

Historic Fishery of Lake Victoria.—At the beginning of the 20th century, the of Lake Victoria were described as crystal clear and offering the perfect condi- tions for both feeding and spawning of the abundant fishes (Graham 1929). Ngege, also known as the singada or Graham’s tilapia (), and mbiru () were the mainstays of the small-scale fishery that was gaining vibrancy following the arrival of both the efficient flax gillnets and the Ugandan railway (Graham 1929). Despite such reports, some important livelihood species such as ngege began to diminish in the mid-20th century (Witte et al. 1992). To remedy the perceived decline in the native fishery, colonial officials introduced several new tilapiines including blue spotted tilapia (), redbelly tilapia (Coptodon zillii), redbreast tilapia (Coptodon rendalli), Nile tilapia (Oreochromis niloticus), and (Oreochromis mossambicus) in the early 1950s (Hickling 1961, Mann 1969, Eccles 1986, Lowe-McConnell 1987, Balirwa 1992, Njiru et al. 2006). Nile tilapia thrives to this day in Lake Victoria. Along with the Nile tilapia thriving, the (Lates niloticus), an introduced predator species to many endemic fish including , is leading to cata- strophic fluctuations in the abundance and diversity of native stocks (Kaufman 1992, Goldschmidt et al. 1993, Kaufman and Cohen 1993, Kitchell et al. 1997, Marshall 2018). Additionally, from the 1970s to present, stratification of the lake occurred with deeper waters becoming near anoxic for most of the year (Hecky 1993). The deep-wa- ter anoxic conditions likely restricted most fishes ( including the relatively hypoxia- intolerant Nile perch) to nearer to the surface waters of the lake, and caused massive fish kills near the oxycline. Such conditions likely reduced the deep-water fish bio- mass, including scores of endemic deep-water species. Reduction of the haplochromine stocks plus intensive eutrophication of the lake’s surface waters was coincident with an increase in a single native species, the diminutive endemic silver cyprinid (Rastrineobola argentea) [known locally as dagaa (Tanzania), mukene (Uganda) or omena (Kenya)]. During the 1990s, omena began to flourish in huge , giving rise to a night-lighting fishery that still remains valuable today (Witte et al. 1992, Pringle 2005a,b, Goudswaard et al. 2011). The two introduced species of Nile tilapia and Nile perch (Ogutu-Ohwayo 1990) and the native omena overwhelmingly dominate the current commercial fishery. Nile tilapia and Nile perch have revolutionized the Lake Victoria fishery from a local sub- sistence activity to a globally integrated commercial enterprise. Indeed, during the 1980s, the Nile perch boom made Lake Victoria one of the world’s most productive inland fisheries (Kayanda et al. 2009). Nile perch captures in Kenya alone increased from below 5000 t in 1980 to over 100,000 t by 1993, but by 2017 Nile perch catch had dropped back below 20,000 t (Fig. 1; UN FAO 2017). Since Nile perch landings have dropped off, haplochromine taxa have enjoyed some resurgence in biomass in recent times (Kishe-Machumu et al. 2012). The changing dynamics of Lake Victoria fisheries over the last decades have led to an altered . The potential for even greater alteration is possible in the Kenyan and Ugandan portions of the lakescape, now dominated by relatively new aquaculture infrastructure and culture of Nile tilapia in shallow and inshore waters (Aura et al. 2018, Njiru et al. 2018). The recent expansion of fish cages in the KLV -re flects lake use changes over the last century that have been fueled by the diminishing traditional ecosystem services such as capture fisheries. Deteriorating water quality, 74 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 1. Nile perch catch in Kenya from 1980 to 2017 (UN FAO 2017). The units are tonnes. overfishing, and an increase in human population have occasioned a deficit in fish supply from both the introduced and the native wild fish catches with the proposed solution of fish cage aquaculture.

Nile Tilapia Fish Cage Culture.—Fish cage culture has grown to be the most successful marine farming system globally [particularly in Asia (Perez et al. 2005)] but is often stated to still be in its infancy in Africa (Asmah et al. 2016, Aura et al. 2018). Fish cages are common systems for use in both freshwater and marine en- vironments and have become popular due to their flexibility in placement, ease of expansion, and high return on investment (Bostock et al. 2010). As of 2016, Nile tilapia aquaculture conducted in inland waters was undertaken in 78 countries with five African countries listed in the top 20 producers, including both Uganda and Kenya (UN FAO 2017). Global Nile tilapia aquaculture production increased by more than 165% between 2010 and 2016 (UN FAO 2018). As of 2016, Nile tilapia constitutes approximately 71% (or just over 3 million t), of the global tilapia harvest. Asia dominates inland Nile tilapia aquaculture, with China, Indonesia, Thailand, and the Philippines producing approximately 86% of all globally farmed Nile tilapia (UN FAO 2017). As of 2016, Africa became the second-highest producing region for farmed Nile tilapia and is responsible for approximately 6% of all global production (UN FAO 2017). Indeed, in sub-Saharan Africa, the production of aquaculture fish has increased more than sixteen-fold since 1995 (UN FAO 2018), driven primarily by the expansion of tilapia-cage aquaculture (Satia 2011). Supplemental Material 1 describes the process of fish cage driven fish culture from broodstock to market as currently practiced for Nile tilapia in the Kenyan portion of the Lake Victoria basin. By 2010, a fish cage industry was emerging at a faster pace in developing countries, such as those in Africa, than in any other region (Bueno et al. 2015). Notable ex- amples of lacustrine fish cage farming in sub-Saharan Africa occur in Lake Victoria in Kenya (Aura et al. 2018), Lake Victoria in Uganda (Blow and Leonard 2007), Lake Volta in Ghana (Asmah et al. 2016), Lake Kariba in Zimbabwe (Berg et al. 1996), in Malawi (Blow and Leonard 2007), and Lake Kariba in Zambia (Blow and Leonard 2007). More sub-Saharan African nations have likely adopted lacustrine fish cage culture over the last few years than those listed above. Indeed, during the re- search for this manuscript, we observed nascent fish cages in the Tanzanian waters of Lake Victoria near Mwanza (Kashindye et al. 2015). Within Kenya and beyond, lacustrine fish cage culture likely plays an increasingly important role in the economic and social welfare of local communities through the Hamilton et al.: UAS mapping of fish cages in Kenya 75 provision of food fish, employment, and as an alternative livelihood. This is not sur- prising as cage culture has low initial investment requirements, provides higher profit than land-based aquaculture systems, and is highly flexible due to the ability to relo- cate within the existing water bodies (El-Sayed 2006, Njiru et al. 2018). Fish culture is often viewed as a food production activity to compensate for needed high-quality protein in areas with declined capture fisheries. If balanced and managed adequately within an ecosystem, aquaculture has the potential to increase fishery production, while also having a negligible effect on recovering wild stocks (Kashindye et al. 2015). While cage aquaculture is appealing and expanding in developing countries, such systems present significant environmental challenges. In cage aquaculture systems, high densities of fish in cages means high production of waste in the form of un- used feed, chemicals, pathogens, feces, and dissolved metabolic waste from the fish (Dauda et al. 2019). The nature of cages that are open to the environment means that this problem is challenging to remedy, and few mitigation measures exist beyond limiting excess feed and placing cages in areas that can better handle the added nu- trient load (Serpa and Duarte 2008, Price et al. 2015). The highly concentrated pres- ence of these waste products in the natural environment can have deleterious effects on water quality resulting in eutrophication, anoxia, and increased levels of turbidity (Neofitou and Klaoudatos 2008, Guo et al. 2009). Freshwater systems are often more vulnerable to these issues as they have less space and contain fewer resources than ocean systems to absorb or dilute the concentrated waste influx (Diana 2009). Not only does poor water quality from aquaculture impact wild organisms, but the cage cultured themselves are also affected since these systems rely on ambient water quality to sustain the farms (Diana 2009). With the existing poor water qual- ity, adding additional biomass to the ecosystem (e.g., new aquaculture operations) could be detrimental to Lake Victoria, particularly in sensitive areas (Nabirye et al. 2016, Aura et al. 2018, Njiru et al. 2018). Escapement of non-native fish from cage culture poses an additional ecological risk, as tilapias are notorious for escapement and are present in the natural environment of every country in which they have been grown for aquaculture (Canonico et al. 2005). Additionally, the spread of diseases among cages too tightly spaced together, or from caged fish to wild fish, poses a seri- ous threat to Lake Victoria. There are also net positive environmental effects from aquaculture operations. Fish cages can add structure to environments in which they reside and can act as species aggregators that can increase both the abundance and diversity of wildlife in the ecosystem immediately surrounding the cages. These concentrations of species can help consume some of the nutrients produced by the cages (Barrett et al. 2018). Regardless of the weight of the environmental advantages and disadvantages of fish cage production, the impacts on natural systems will be felt more acutely as global fish cage aquaculture increases to sustain both local and global protein needs. Despite the rapid growth, fish cage culture development in Africa continues to be hindered by several constraints including a lack of suitable sites, concerns regard- ing impacts on the environment, and multi-use conflicts (Njiru et al. 2018). It has been demonstrated that poorly planned and located aquaculture development may result in overexploitation and unsustainable use of natural resources (Iwama 1991, Aguilar-Manjarrez et al. 2010). When aquaculture expands without monitoring and oversight, the risk of adverse environmental outcomes increases, economic returns are often diminished, and the potential for conflict between aquaculture and other 76 Bulletin of Marine Science. Vol 96, No 1. 2020 resource uses increases (Iwama 1991, Aguilar-Manjarrez et al. 2010). Therefore, au- thoritative tools are needed to help optimize cage culture practice, determine ecosys- tem carrying capacity, monitor existing cage culture installations, assess maximum yield, and carry out site selection for potential fish cage sites. However, the use of such tools and technologies requires an in-depth understanding of the interactions between cage culture practices and their environment in order to evaluate the car- rying capacity of lacustrine areas, maintain fish species production, and reduce both ecological impacts and lake-use conflicts.

Summary.—Cage culture in the KLV is currently expanding (Njiru et al. 2018), but the location, number, density, and other characteristics of the fish cages are difficult to determine without the use of one of the following: (1) costly and time-consuming field data collection by boat and Global Positioning Systems (GPS), (2) traditional aerial photography taken from a manned aircraft, or (3) purchasing large swaths of expensive sub-meter very-high-resolution (VHR) commercial satellite imagery. Indeed, fish cages in the KLV may be expanding so rapidly (Njiru et al. 2018) that traditional censuses that may take many weeks or months become obsolete, as the fish cage data would be out of date before the survey was completed. To overcome the cost and time-limiting factors of mapping fish cages by tradi- tional methods, we utilized unmanned aerial sytems (UAS), commonly referred to as drones, in conjunction with VHR satellite imagery and GIS to locate, map, and moni- tor fish cages in the KLV. Although the fish cages within the KLV have been counted and mapped to regional points in prior research (Aura et al. 2018, Njiru et al. 2018), this is the first attempt to spatially locate each individual fish cage and represent it in a format that allows for spatial analysis of the fish cages. Such a representation is possible using UASs to map the fish cages to within a few centimeters of their ac- tual location. Once obtained, the UAS-derived images are converted into traditional single-scene georeferenced air photographs for use in GIS. GIS is then used for digi- tizing each fish cage, allowing us to count the number of unique fish cages, measure the size of each fish cage, and estimate the fish cage density in differing portions of the KLV. Once the fish cage locations are recorded and the size and density maps are created, the fish cage data are combined with other spatial data (i.e., shorelines and bathymetry) to provide a complete picture of lacustrine aquaculture in the KLV. The goals of this paper are twofold: (1) to count, locate, and attribute the fish cages in the KLV for use in fisheries management, and (2) to develop a framework for oth- ers to use UAS mapping systems in their marine, fisheries, and aquaculture research or mapping projects. Without knowledge of the location and number of fish cages in the KLV, it is challenging to develop management plans that improve water qual- ity, support biodiversity, avoid lake-use conflict, and protect the breeding grounds and juvenile habitats of the remaining critically endangered haplochromine . Even basic aquaculture management tasks such as monitoring water-quality, esti- mating aquaculture output, enforcing aquaculture regulations, and monitoring the spread of disease are difficult without knowledge of the location and number of fish cages present. Hamilton et al.: UAS mapping of fish cages in Kenya 77

Figure 2. The study area runs from Uganda in the north to Tanzania in the south along the shore- line of the Kenyan portion of Lake Victoria as delineated by Hamilton et al. (2016).

Materials and Methods

Study Area.—The study area is the entire coastline of the KLV, as depicted in Figure 2. It includes all of the bays and gulfs along the coastline as delineated by Hamilton (2016), who digitized high-resolution satellite imagery to obtain a 1:25,000 shoreline of all of Lake Victoria. It includes the entire Lake Victoria coastline of Busia County, Siaya County, Kisumu County, Homa Bay County, and Migori County in Kenya. The study area includes all islands within the KLV in addition to the mainland and ends at the Ugandan border in the north and the Tanzanian border in the south.

UAS Mission Planning.—Initial UAS mission planning took place many weeks before flight. In conjunction with the Kenya Marine Fisheries Research Institute Kisumu Center, all currently known fish cage areas were obtained in GIS format as a point file with one point at each fish cage region. In addition to this spatial data, we obtained likely fish cage site locations from the existing fisheries literature, examin- ing commercial aerial imagery, processing Sentinel-2 satellite imagery for the entire study area, and consulting with fish cage owners and lakeshore residents. These -an cillary data were all combined to ascertain where to fly the unmanned aerial vehicle within the study area (Fig. 2). In addition to locations with known fish cages, we flew substantial portions of the coastline where there was no indication of fish cages from any input source. These 78 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 3. The 2 km shoreline buffer of potential cage-areas runs from Uganda in the north to Tanzania in the south along the shoreline of the Kenyan portion of Lake Victoria as delineated by Hamilton et al. (2016). Areas in yellow and green were not considered suitable UAS flight loca- tions out of an abundance-of-caution approach. were called unknown missions. These unknown missions were conducted to esti- mate the number of fish cages that may exist in locations that would not have been counted by only flying the known fish cage regions. Flying over areas with unknown cage presence increases the confidence in the fish cage totals presented. The- un known missions not only allowed us to capture new or unknown fish cage sites, but these missions additionally allowed us to estimate the number of fish cages that may be omitted from our survey. These coastal missions designed to discover unknown fish farm regions were flown in what is known as corridor-mode. The flight corridor followed the coastline on the outward-bound flight and would with the coastline for about 20 min and then return parallel to the outward flight-line but about 1 km offshore. This type of mission images all nearshore waters up to 2 km offshore and along approximately 20 km linear sections of coastline. This inshore region, within 2 km of the lake shore- line, is the location where the known fish cages exist (Njiru et al. 2018). The total area within this potential fish cage region is 1403 km2 and is mapped in Figure 3. The UAS flights occurred within a subset of this region (Fig. 3). Of the 1403 km2 that could potentially have fish cages, approximately 143 km2 was in controlled airspace and hence excluded for UAS flights (Fig. 3). Although our agreements with the Kenyan authorities allowed for flights in this airspace during approved timeslots, we avoided controlled airspace due to there being no known fish Hamilton et al.: UAS mapping of fish cages in Kenya 79

Figure 4. A Phantom 4 quad-copter during landing is depicted in the left panel, and an EBee is depicted mid-flight in the right panel. cage sites in these locations, as as their location in mostly urban areas with high population densities, and out of an abundance-of-caution approach. Additionally, we excluded areas close to international borders totaling approximately 160 km2 (Fig. 3). Although our agreement allowed for flights in all Kenyan airspace controlled by the tower at Kisumu Airport, convention and safety dictated that we did not conduct border flights without informing the neighboring nations’ civil aviation authorities, defense agencies, and border patrol agencies. Therefore, these border regions were again excluded out of an abundance-of-caution approach. Satellite imagery (color and color-IR) was donated by Planet Explorer (Planet Team 2017) for the controlled airspace and border regions, and this imagery was used instead to identify and map any fish cages that existed in these regions. The approximately 50 fish cages found in these regions are not delineated as accurately as those derived from UAS imagery. After excluding the border and airport regions, the maximum potential flight area was reduced to approximately 1100 km2 (Fig. 3).

UAS and Camera Specifications.—Two UASs were utilized for the fish cage flights. The primary UAS utilized was the Parrot senseFly EBee X system (senseFly 2018a; Fig. 4). This UAS was used to map large geographic areas and areas far off- shore. The secondary UAS was a DJI Phantom 4 Advanced system (DJI 2017; Fig. 4) which was used to map smaller geographic areas close to shore and to scout for unknown fish cages before sending the EBee X to map these regions. UAS flight plans were created prior to each flight but were ultimately adjusted and finalized in the field based on hourly weather conditions, the local built environment, and finding suitable takeoff and landing sites. Once suitable landing and takeoff locations were located and permission to launch and land was given by the relevant party, the UAS was updated with the latest weather, takeoff location, landing location, and home location. Once updated, the flight commenced. Flights were either flown in a grid system over known cage locations or a corridor system in unknown mode (Fig. 5), with photographs taken at 50% overlap on both the x-axis and y-axis of the flight. The overlap is often referred to as a longitudi- nal overlap and lateral overlap in UAS mission planning. This grid method of data capture ensured that each cage location was captured in no fewer than nine photo- graphs (Fig. 5). The overlapping flight lines allowed for more accurate textures to be obtained and improved geolocating of each image, hence improving cage mapping. The flight missions generally ran parallel to the shoreline to capture as much of the 80 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 5. A typical representation of a grid mission. terrestrial shoreline as possible in each image (Fig. 5), again allowing for more accu- rate texture processing and geolocating of the fish cages. Due to airspace restrictions agreed upon with the Kenya Civil Aviation Authority, each drone was limited to 500 m above the ground. Most flights were conducted at 10 cm/px which resulted in a flight altitude of approximately 425 m above the lake level. Some flights were flown at 5 cm/px which result in a flight altitude of 212.5 m above the lake level. Supplemental Material 1 provides a full review of the UAS systems used and the post-processing software.

GIS Processes.—The fish cages were extracted from the imagery via manual heads-up digitizing of the outlines of the fish cages, and each cage was stored as an individual feature in a vector polygon format. The use of polygon format allowed for the analysis of fish cage areas. A typical 3× 4 m fish cage will be represented by approximately 1200 individual pixels in the UAS-derived georeferenced image, al- lowing for highly accurate digitizing of the fish cage polygon. Supplemental Figure 1 provides a full resolution image of fish cages on the lake. Large fish cages may be constituted by as many as 10,000 pixels (10 cm resolution). Fish farms were also man- ually digitized and consisted of a connected group of fish cages. The fish farm bound- ary was determined either by digitizing the floating boundary markers or by creating a best-fit polygon around discrete groupings of connected fish cages. The fish farms were also stored as vector polygons. Finally, aquaculture regions were identified as discrete clusters of fish farms containing >50 fish cages that occur in a large singular geographic cluster. To allow for accurate comparisons of the aquaculture regions, the regions were created using the minimum bounding box of all fish farms within them. To verify all digitizing outputs, a rules-based topological system was used and all fish cages, fish farms, and aquaculture regions were verified by another individual. Validation rules included not allowing fish cages to overlap, not allowing fish cages to exist outside of farms, and not allowing exceptionally small or large fish cages without additional verification. Hamilton et al.: UAS mapping of fish cages in Kenya 81

Spatial Analyses.—By mapping each unique fish cage in real space, spatial anal- ysis of the fish cages was possible and was used to answer important questions. By using a 100 m resolution bathymetric map of Lake Victoria compiled from over 4 million input hydrographic sample points (Hamilton et al. 2016), we were able to estimate the water depth for each of the mapped fish cages. Using high-resolution shoreline images derived from VHR satellite data (Hamilton 2016), we were able to calculate the distance from fish cages to the nearest shoreline. The water depth and distance to shoreline for each fish cage were stored as attributes of the individual fish cages. We then calculated a fish cage density for each aquaculture region as a percentage of each aquaculture area that is comprised of fish cages. That is, if 10% of an aquaculture region was fish cages then a density value of 10 was given to that aquaculture region. The fish cage density measures were stored as attributes of each aquaculture region. The same density calculation was also conducted for fish farms in aquaculture regions.

Hardware and Software.—The senseFly EBee X (senseFly 2018a) was used as the primary UAS. The senseFly SODA 3D mapping camera (senseFly 2017) was used at the primary sensor. Emotion3 (senseFly 2018b) was used to fly the EBee X and control the SODA camera. The DJI Phantom 4 Advanced (DJI 2017) was used as the secondary UAS. The DJI Phantom 4 Advanced integrated camera (DJI 2017) was used as the secondary sensor. Map Pilot (Drones Made Easy 2019) was used to fly the Phantom 4 Advanced and control the integrated camera. SkyVector Aeronautical Charts (SkyVector 2019) were used for flight mission planning. A 30 m SRTM pro- vided by the USGS and NASA (USGS 2019) was used to account for the varying ter- conditions while flying the senseFly EBee X to and from fish cages sites while over land. ESRI ArcGIS Desktop 10.6 (ESRI 2018) was used for project planning, spa- tial data management, heads-up digitizing, and spatial data analysis. QGIS (QGIS Development Team 2017) was used for third-party imagery integration and some limited fish cage digitization. Maps Made Easy (Maps Made Easy 2019) and Pix4D (Pix4D SA 2017) were used to match image texture and create georeferenced image mosaics from the many tens of thousands of individual photographs taken. Planet Explorer provided proprietary high-resolution satellite data, including RapidEye im- agery (Planet Team 2017). The European Space Agency provided Sentinel-2 (European Space Agency 2019) imagery for identification of potential cage sites.

Results

We conducted a total of 19 missions covering approximately 156 km2. Of the total flight area, 70% was over known fish cages areas, and 30% was in the unknown -ex ploratory mode. Only two fish cages were found in unknown areas despite flying 47 km2 in this mode. This gives high confidence that we have captured the vast major- ity of fish cages in the KLV. The total number of photographs taken across all mis- sions was 13,982. The total amount of raw UAS-derived imagery data generated was greater than 129 GB. The fish cages depicted in the UAS imagery allowed for highly granular analysis when compared to other potential data sources. Figure 6 compares multiple images of the same fish cage location across three conventional remotely-sensed systems 82 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 6. The four panels represent different remotely sensed images of the same portion of Kadimo Bay at approximately the same time. In Panel 1 are fish cages as captured by the UAS. Panels 2, 3, and 4 are differing VHR or high-resolution satellite images of the same area. The red arrows point to the fish cage represented in Panel 1 in Panels 2, 3, and 4. A full resolution image (without compression or print reduction of quality) is available in Online Supplemental Material 1. used in this study and the UAS system utilized. Figure 6 Panel 1 is the UAS im- agery captured from this mission between 5 and 10 cm resolution. Figure 6 Panel 2 is a VHR satellite image provided at 3 to 4 m resolution and obtained from the PlanetScope constellation (Planet Team 2017). Figure 6 Panel 3 is a high-resolu- tion Sentinel-2 image provided by the European Space Agency at 10 m resolution (European Space Agency 2019). Figure 6 Panel 4 is a high-to-medium resolution Landsat 8 image (USGS 2018a,b) provided by the USGS and NASA at 30 m resolu- tion. All images are displayed in standard RGB format with the same cages present around the same time, and each image is displayed at close to the highest viewable resolution. In Figure 6 Panel 4, there is little to no indication that fish cages exist in what is the most densely packed and largest aquaculture region in the KLV. At the traditional remote sensing mapping resolution of 30 m utilized by Landsat, the fish cages, fish farms, and aquaculture regions could not be mapped or observed. In Figure 6 Panel 3, the fish farms are somewhat visible, but it is not possible to map the fish farms, and individual fish cages are difficult to observe. The 10 m resolution data allows for- not ing that a potential fish cage region exists in this area and is hence useful for mission planning. It may be possible to count fish farms in a point format at this resolution, Hamilton et al.: UAS mapping of fish cages in Kenya 83 but it is not possible to count or map fish cages. In Figure 6 Panel 2, the VHR imag- ery begins to depict individual fish farms well, but individual fish cages are not as well represented. Imagery at 4 m resolution allows for fish farms to be delineated, but individual fish cages could not be accurately mapped as the boundaries cannot be determined. Finally, the data collected in this mission (Fig. 6 Panel 1) allows for full two-dimensional mapping of each fish cage, fish farm, and aquaculture region. Indeed, the individual netted divisions within fish cages are visible as are the floats supporting the fish cages. Each fish cage has approximately 1600 (at 10 cm resolution) or 6400 (at 5 cm resolution) photographic pixels within its footprint. From the UAS imagery data, a full delineation of fish cages was undertaken to facilitate the subse- quent spatial analysis. The UAS data captured as part of this study revealed that tilapia fish cage deploy- ment in the KVL could be divided into three distinct hierarchical levels, and it is useful to expand upon these findings first and equate the structure to a traditional terrestrial farm before providing the remainder of the results.

Fish Cages.—At the lowest level of the hierarchy, the UAS imagery identified in- dividual and discrete fish cages. These fish cages are in many ways equivalent to a delineated and single-crop field on a traditional terrestrial farm. Each fish cage is distinct just as each field on a farm is distinct, but the individual fish cages rarely -ex ist in isolation, just as farms rarely only have one field.

Fish Farms.—Moving up the hierarchy, the UAS imagery revealed that fish cag- es are often connected in groups. These connected fish cages are delineated as fish farms. These fish farm areas are in many ways equivalent to terrestrial farms which are often made up of many nearby or adjacent fields, or in this case, many nearby or connected fish cages. The perimeter of a fish farm is often marked by small white buoys or flotation devices in the same manner that walls, hedgerows, or fences may border a terrestrial farm, and these buoys can be seen in the UAS imagery. When such buoys were not present, the fish farms were merely delineated by adjacency or connectivity of fish cages.

Aquaculture Regions.—Finally, at the top level of the fish cage hierarchy are distinct aquaculture regions that exist where fish farms dominate large portions of the KLV. These aquaculture regions are equivalent to terrestrial farming regions or terrestrial agricultural areas. Just as with terrestrial farming regions, the region is fish farm dominated, but the landcover is unlikely to be 100% farms. These aquacul- ture regions are best thought of as the portion of the KLV dedicated exclusively to fish farming.

Fish Cage Statistics.—Of all the cages delineated in the survey, 74% (n = 3236) were mapped and digitized from UAS data, and 37.5% were delineated and measured using the supplemental VHR data. This number of fish cages is a substantial increase from the prior comprehensive attempts to count fish cages in the KLV during 2016 and 2017. The two prior fish cage counts in Figure 7 were conducted over 3 mo in late 2016, and over 3 mo during mid-2017 (Njiru et al. 2018). These previous surveys used field research, farmer interviews, and GPS to map the cage areas using a single GPS 84 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 7. The growth in fish cages from 2008 to 2018. point for each region, and then tied the interview and field-obtained fish cage data for each region to that GPS point. The 2016 and 2017 field surveys counted 1663 (Aura et al. 2018) and 3398 (Njiru et al. 2018) fish cages in the KLV, respectively (Fig. 7). Therefore, in the exact 2 yrs since the 2016 estimate was compiled, the number of fish cages in the KLV has increased by 2373, or 262% based on the 2016 fish cage count. That is, in the KLV fish cages have increased by 1347 yr−1, or 81% annually, since December 2016. Indeed, as recently as 2008, few if any fish cages existed in the KLV with the first experimental fish cages only appearing in 2005 (Aura et al. 2018). Approximately 62% more fish cages have been constructed in the KLV from December 2016 to December 2018 than were con- structed in the entire decade preceding 2016 (Fig. 7). Although this manuscript reports that fish cages have increased by 262% in the two years since January 2017, part of this apparent increase is likely due to the en- hanced methodology presented in this paper accomplished by using UAS. UAS map- ping of fish cages, as opposed to attempting a site visit and conducting a GPS data collection process for each cage, likely results in a more accurate count of fish cages. Additionally, the UAS approach likely locates more fish cages in remote locations that may be missed when each fish cage must be visited in person. The exact number of fish cages omitted in 2017 or in this survey remains unknown, although in this survey, the UAS missions flown over areas of KLV not known to have fish cages likely results in higher confidence in the accuracy of the fish cage count. The total area covered by all aquaculture fish cages in the KLV is 62,132 m2. The average size of a fish cage in the KLV is 14.26 m2 (SD 14.63) with a maximum cage size of 105 m2 and a minimum fish cage size of 3.13 m2. The average fish cage perimeter is 14.11 m (SD 5.97). Most of the observed fish cages are square or rectangular, and a few sizeable circular fish cages are also present. These few circular fish cages are the largest in the KLV, and these relatively few fish cages cause both the mean cage size and the mean cage perimeter to be larger or longer than reported in earlier stud- ies that report on fish cage size (Njiru et al. 2018). The largest cage in the KLV was delineated at 105 m2 and is a circular cage. A cluster of these larger and circular fish cages, including the largest fish cage, can be found at 0.767°S, 34.068°E. These larger circular fish cages appear to be a new occurrence in the KLV as they are not reported in earlier surveys that note both fish cage shape and area (Njiru et al. 2018). Hamilton et al.: UAS mapping of fish cages in Kenya 85

Figure 8. The 14 identified aquaculture regions and their respective cage counts.

Fish Cage Location.—The UAS survey revealed that the fish cages are clustered in limited fish farms and a few aquaculture regions. For example, the 4357 fish cages delineated are located within 534 discrete fish farms, consuming 190,248 m2 of the KLV. The majority of the 4357 fish cages within the 534 fish farms also exist in just 14 larger aquaculture regions (Fig. 8). We defined an aquaculture region as a cluster of 50 or more fish cages that exist discrete from any other fish cage region. Conversely, only 57 of the 534 fish farms appear to contain less than 50 fish cages and exist out- side of a defined aquaculture region, and these may be artisanal or noncommercial fish cages. Across the KLV, the data presented reveal 14 distinct regions of fish cages. These 14 delineated aquaculture regions account for 92% of all the fish cages pres- ent in the KLV (Fig. 8). A single aquaculture region (centered on 0.096°S, 34.079°E) contains 1932 fish cages or 44% of all the fish cages present in the KLVFig. ( 8, inset map). These fourteen aquaculture regions consume 1 km2 (SD 0.13) of lake surface area when combined. Of the 4357 fish cages mapped, 3502 are outside of Winam Gulf to the north of the gulf, 819 are outside of Winam Gulf to the south of the gulf, and only 36 are inside of Winam Gulf (Fig. 9). In the northeast corner of Siaya County, the four cells overlap- ping portions of Kadimo Bay contain 2891 of the 4357 fish cages present in the KLV (Fig. 9), and 307 of the 534 total fish farms delineated in the KLV. It also contains seven of the 14 aquaculture regions delineated in the KLV. Kadimo Bay is the epicen- ter of fish aquaculture in the KLV. 86 Bulletin of Marine Science. Vol 96, No 1. 2020

Figure 9. The colored squares represent a 5 × 5 km square grid, known as a fishnet, draped over the entire Kenyan portion of Lake Victoria. The total number of fish cages is then summed for each 5 × 5 km cell within the entire fishnet to present a visual representation of cage locations at the scale of the Kenyan portion of Lake Victoria. When no fish cages are present in any individ- ual 5 × 5 km cell, that cell is made transparent; otherwise, the total number of cages in each cell are represented. The background image is a Sentinel-2 (European Space Agency 2019) mosaic of three scenes from early 2019.

In addition to clustering at the northern end of the KLV, the preferred location for fish cages appears to be in sheltered locations such as Kadimo, Usenge, and Kaksingiri bays. Indeed, almost all fish cages appear to be located in bays with a narrow opening to the exposed lake waters (e.g., Kadimo Bay), sheltered coastal concave bays (e.g., Kaksingiri Bay), or on the protected mainland side of peninsulas and islands (e.g., Mtara Bay or east of Mfangano Island).

Fish Cage Spatial Analysis.—By integrating the UAS-derived fish cage loca- tions with other high-resolution ancillary data, we were able to compute fish cage metrics such as distance to shoreline, fish cage depth, and fish cage density. As -ex pected, the fish cages are in shallow waters with an average depth of 8.10 m (SD 5.14), which aligns with earlier estimates (Njiru et al. 2018). The average distance of a fish cage to the shoreline is 232 m (SD 156). It appears that the preferred fish cage loca- tions are near the shoreline, as the average distance from shore was only 232 m and Hamilton et al.: UAS mapping of fish cages in Kenya 87 the maximum distance was less than 1 km. This finding gives increased confidence in our fish cage capture rate as we generally flew up to 2 km offshore. The fish cage density can be described across all the KLV, across the 2 km shoreline buffer where fish cages exist (Fig. 3), or across the delineated aquaculture regions (Fig. 8). Across the KLV and the 2 km shoreline buffer, the total fish cage or fish farm density is close to zero as most of the KLV does not yet contain fish cages (Fig. 9). Across the delineated aquaculture regions, fish cages constitute 5% of the lake surface, with a maximum of 20.82% and a minimum of 2.60%. The fish farms cover 15.48% of the 14 aquaculture regions. No comparable earlier estimates of fish cage density exist as a UAS-style survey is required to make this estimate.

Data Availability.—All data compiled for this project, aside from the commer- cial satellite imagery, are available for download. All fish cages, aquaculture farms, aquaculture regions, terrestrial , terrestrial farms, shorelines, bathymetry, and UAS imagery are available for download in open source formats.

Discussion

Based on 2016 data, Njiru et al. (2018) predicted that fish cage aquaculture was likely to expand rapidly and in an unregulated manner in the coming years across the KLV. The results presented in this study indicate that such unregulated expan- sion is occurring and likely exceeds the initial prediction. Indeed, the expansion of fish cages in the KLV is so rapid that, using the four data points available and a simple linear forecast, it can be estimated that the number of fish cages will have increased to approximately 6000 by the end of 2019 (r2 = 0.99). Uganda is already experiencing increases in cage culture and, should Tanzania adopt fish cages at the same rate as Kenya, it is likely that the lakescape of Lake Victoria will undergo another substan- tial transition in the next few years. These data reveal the transition may already be underway. Such a transition has the potential to impact the ecology and fisheries of Lake Victoria. Lake Victoria has been subject to human impacts of increasing diversity and sever- ity since the most recent epoch of massive deforestation, eutrophication, and rap- idly increasing fishing pressure. The introduction of cage aquaculture constitutes the most recent human intervention with potentially large-scale effects, along with anthropogenic climate change. Our surveys and analysis show a substantial increase in the number of fish cages deployed in the KLV over only two years. If reported in- crease continues at a similar rate, it would be reasonable to expect declines in water quality, biodiversity, and ultimately, cage fish yields and fish quality. Since people also depend upon Lake Victoria for ecosystem services other than fish production, the potential for runaway expansion of the cage aquaculture sector is of economic, ecological, and human health concern. It is not only the modern fishery but also the historic fishery that will likely be impacted by increasing fish cage culture in Lake Victoria. Historically, the haplo- chromines of Lake Victoria prefer the sub-littoral zone of the lake from about 6 to 30 m, with maximum catch yields reported at a depth of about 10 m, with an additional but more rarified deep-water assemblage (Witte and van Oijen 1990, Marshall 2018). Therefore, the habitats of maximum haplochromine biomass and the areas in which new fish cages are appearing overlap, as the average fish cage depth is approximately 88 Bulletin of Marine Science. Vol 96, No 1. 2020

8 m. For this reason, any management decisions to conserve these endemic haplo- chromines will have to take these new fish cages into account. There are numerous other potential pathways for fish cages to interfere with wild stocks, such as anoxia resulting in localized fish kills that have been reported in and around the fish cages in Lake Victoria (KMFRI 2016, Njiru et al. 2018). Other concerns include increasing levels of ammonia in and around fish cages and the introduction and spread of dis- ease from cage populations to wild fish populations (KMFRI 2016). Fish cages appear to be placed preferentially close to the shoreline, which is likely the result of having to transport goods to and from the cages via artisanal vessels and to allow for increased storm protection. The UAS imagery additionally reveals that the primary means of getting to the fish cages are small wooden vessels, often reliant on oar power. These have limited range and cannot venture too far from the shore into the deeper, rougher portions of the KLV. While the average lake depth is 40 m, the fish cages are rarely in water more than 10 m deep. The use of UAS overcame many of the obstacles of traditional field or even aircraft surveys. The initial cost of a new or used mapping UAS is typically a small frac- tion of that of an aircraft suitable for mapping and even less than that of a truck or a boat required to visit and GPS each of the fish cage sites in a traditional manner. For mapping fish cages in the KLV, the UAS approach costs only a small fraction of the cost of purchasing VHR satellite imagery at or below 1 m resolution from com- mercial satellite imagery vendors. The non-labor costs of operating the UAS in the field were low: merely the cost of electricity to charge the UAS batteries (which was often accomplished from solar power in remote locations), and routine maintenance of the UAS to ensure flight safety. Additional costs for this mission included liability insurance at the cost of approximately 100 USD and replacement UAS parts totaling approximately 400 USD. Future aquatic UAS missions will likely become more af- fordable and less time-consuming as UAS costs decrease and performance increases. For example, the mapping UAS released in 2019 have flight times 50% to 100% longer than the 1-hr flight times used by the UAS released in 2017 and used in this analysis. Additionally, the costs of these new UAS have dropped since 2017 by almost 50%. That is, UAS is currently decreasing in price but increasing in performance. The primary advantage and reason for undertaking this mission with a UAS was the desired increase in spatial resolution to allow for exact polygonal delineation of fish cages as small as 3 m2 in the KLV. UAS-derived imagery allows for fish cage metrics such as fish cage count, size, condition, shape, cover, associated fish cage infrastructure, and even some limited fish cage lifecycle information. Additionally, The UAS flight allowed for a suite of other spatial products to be created such as a 10 cm resolution shoreline map and 10 cm delineation of terrestrial ponds; the flights also resulted in improved mapping of wetlands, a count of all the boats present at a landing site, and the geolocation of small rocky outcrops and other impediments to navigation. One unexpected capture by the UAS was of fish cage nets, which are usually only used on fish cages during the formative period of activity. Therefore, the UAS data may be able to ascertain if cages are active or not at the time of flight as well as the growout phase of the fish within the cages. Planned future missions include expanding the current project to all of Lake Victoria including mapping the fish cages in both the Ugandan and Tanzanian por- tions of Lake Victoria. In addition to KLV, portions of Tanzania were flown as part of this project, but fish cages are still at a nascent stage, and only a handful of fish Hamilton et al.: UAS mapping of fish cages in Kenya 89 cages were detected. Expansion into Uganda has been on hold as the required flight permissions have yet to be granted, but progress has been made in this area. In addi- tion to expanding geographically, future KLV missions will be conducted to assess if the expansion of fish cages is still occurring and at what rate. UASs are becoming an established technology for water quality monitoring used in many analyses (Koparan et al. 2018) and water quality issues are known to occur around the fish cages in the KLV (Aura et al. 2018). Future UAS plans include using the multispectral capabilities of UASs for water quality monitoring at the fish cage sites. Expanding the research to include these parameters will be useful to inform comprehensive management plans for the future expansion of cage aquaculture. Understanding not only the loca- tion and density of cages but also their impact on the local environment and water quality will enable responsible, more sustainable expansion of cage aquaculture in the future. Bodies of water shared across international boundaries can only be effectively managed if a systems approach is adopted. For the sustainable development of caged finfish aquaculture in Lake Victoria (East Africa), this need is obvious. Some fac- tors entailed in the design of aquaculture farms seem local, such as the number and disposition of cages, stocking densities, and site selection criteria (e.g., ambient wa- ter quality). However, all are related to the spatial dynamics of water quality on a scale of the entire lake. Therefore, the development of Lake Victoria’s cage aquacul- ture industry must be guided by spatial planning based upon an intimate grasp of lake ecology, , and change over time, built from remote and ground-based observational methods and interpreted in a spatially and temporally explicit man- ner. Also, aquaculture development must take into account the need to maintain wild capture fisheries and indigenous biodiversity as well. Based on this knowledge, a system of protected, wild-capture, and aquaculture development areas can be de- lineated. Aquaculture build-out can also be planned to take maximal advantage of favorable conditions while remaining sustainable at both a local and a lake-wide scale. Guided by an explicit understanding of lake dynamics, the overall flow of eco- system services from Lake Victoria can be rationalized, managed, and sustained.

Acknowledgments

The National Science Foundation funded portions of this research under Award Number 1518532. We want to thank S Agwata and C Ongore for assistance in the field and trip planning.

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