As with other great ape species, chimpanzee numbers have declined over the past decades and the species is currently threatened by extinction [1
]. Several studies have documented the impact of habitat loss [2
], poaching [5
] and infectious disease [8
] on wild populations. In Tanzania, 75% of wild chimpanzees are found within a 20,000 km2
area of national parks [10
]. Monitoring these chimpanzees is therefore crucial for their conservation in Tanzania. For conservation management, it is important to establish where and how many individuals remain and to understand the potential connectivity between populations. These data represent key information that is used towards creating baseline estimates for assessing the effectiveness of conservation efforts over time [16
There are several established methods for studying and monitoring wild animal populations. Line transect surveys are widely used to estimate population density for a variety of mammal species, including great apes [18
]. Data from direct observations of animals or indirect evidence such as dung [10
], nests [22
] and calls [24
] can be converted into density and subsequently population estimates across larger landscapes [25
]. Indirect evidence is especially important in great ape surveys given the elusive nature of the species and their extensive range and distribution [26
Traditional land-based transects are time-consuming and expensive, and for these reasons geographically wide surveys are not repeated frequently [26
]. Aerial surveys with light aircraft can be effective across broad areas for counting large mammals [27
], but have limitations. While such surveys may provide an unbiased population size estimate for large mammals found in open areas (e.g., elephants, buffalos, zebras), they are unlikely to provide accurate estimates for smaller species (e.g., black-backed jackal, bushbuck, vervet monkey) [29
] or those that live in habitats with greater canopy cover. Furthermore, aircraft surveys are logistically difficult to implement due to their very high cost and the risk they pose to operators (i.e., aircraft crashes) [30
]. Due to their increasing availability, high resolution satellite images have also been used to detect animals or their signs [31
]. Although promising, this method is also unlikely to provide accurate estimates for small species and is hampered by cost and atmospheric interference from clouds, especially problematic in the tropical regions where great apes are distributed [32
]. Camera-traps and acoustic sensors are other promising remote technologies that enable broad spatiotemporal and precise information on animals that are elusive and otherwise difficult to study [33
]. Nevertheless, these methods have high initial costs and still require intensive manual labor for deployment, memory card collection and substantial expertise in subsequent data analyses.
Recently, scientists have started to deploy drones—remotely operated aircraft with autonomous flight capabilities—for wildlife monitoring [35
]. This application allows for rapid and frequent monitoring across moderate to broad spatial extents while providing high-resolution spatial data. Several studies have now reported successful animal detection using drone-derived aerial imagery, ranging from birds [36
] to large terrestrial [39
] and marine [41
] mammals. Recent studies on using drones to detect indirect signs of animals have also reported promising results in detecting orangutan [45
] as well as chimpanzee [46
Given the extent of the area in need of monitoring, exploring drone applications for chimpanzee population surveys in Tanzania may reduce cost and time investments. Visibility bias (i.e., failure to detect all animals within a sampled area) is a primary source of error in aerial surveys [27
]. Prior to the widespread deployment of drones for a census, it is important to first evaluate bias in the method (i.e., calculate a correction factor) by comparing the resulting detections with traditional ground survey results. Numerous factors can impact the detectability of a direct or indirect sign of wildlife [25
]. Thus, it is critical to determine what affects chimpanzee nest detectability in drone-acquired images. In the current study, we assessed several factors known to affect target detectability in aerial images: image resolution [39
]; canopy cover and vegetation type [29
]; and target size and color [29
In summary, our objectives were to (1) evaluate drone performance for chimpanzee nest surveys by comparing ground and aerial surveys; and (2) assess the factors that influence detectability from drone data. Based on the results of previous studies, we hypothesized that using a higher resolution camera as well as flying at a lower altitude would increase the nest detection probability. We also expected a higher detection probability during the leaf-off season and in the more open miombo woodland vegetation than the closed riverine forest. Finally, we predicted that nests higher in the canopy and with a color that contrasts with their surroundings will be easier to detect.
We investigated the feasibility of using drones to detect chimpanzee nests in the Issa Valley, western Tanzania, and evaluated the influence of image resolution, seasonality, vegetation type, nest height and color on nest detectability. An average of 10% of the nests observed from the ground were detected from the air, with improved nest detection in imagery with higher spatial resolution. Our overall detection rate was lower than that previously reported for chimpanzee nests in Gabon (39.9%) [46
] and orangutan nests in Indonesia (17.4%) [45
]. This discrepancy is likely due to methodological differences and our systematic approach. In their study, van Andel et al. [46
] used two approaches that biased the probability of detection. In the first, they collected nest data first via ground surveys and then used the location of the recorded nests to confirm their presence in drone images. In the second, nests were first detected on drone images and then confirmed on the ground using the location of the aerial observations. These methods effectively demonstrated that it was indeed possible to detect chimpanzee nests from drones, although these specific approaches resulted in an increased probability of detecting a nest in the drone images for the first approach and on the ground for the second approach. Wich et al. [45
] used a buffer of 25 m around nests recorded on the ground to select which nest detected from the air would be included in the analyses, comparing the relative density of nests from the aerial and ground-based surveys. The smaller 15 m buffer used in our study could be associated with our smaller detection rate, i.e., we were more conservative regarding what constituted a match. Moreover, aerial nest surveys may be more efficient for orangutan nests as they tend to build nests higher in the tree canopy and visual contrasts of nest materials and canopy color are seemingly more apparent in these habitats [58
Of the factors hypothesized to influence the probability of chimpanzee nest detection in drone-derived aerial imagery, only image resolution was identified as having a significant influence on the recall rate, with a higher probability of nest detection associated with the higher-resolution camera at a lower flight altitude AGL. This finding is consistent with that of Mulero-Pázmány et al. [39
], who also found that the targets (i.e., rhinoceros, people acting as poachers) were better detected with a lower-flying drone. Our results are also consistent with those of Dulava et al. [49
], who reported a significant negative relation between ground sampling distance (GSD) and correct waterbird identification with a minimum of 5 mm GSD. In our study, we favored flight altitude AGL above GSD as a measure of resolution because of identical camera parameters, however, the two are conceptually interchangeable. We obtained the highest probability of nest detection at the lowest possible flight altitude (AGL: 65 m), corresponding to 1.4 cm GSD. Flying at lower altitude would have threatened drone safety. These findings reflect the inherent trade-offs between monitoring at a high spatial resolution (grain) versus across broad spatial extents, such as ground sampling distance (GSD) and ground sampling area (GSA), scale inversely with one another. This highlights the importance of the a priori identification of the minimum GSD required to detect ground targets from the air during the survey design period, particularly if planning for extensive area surveys where the balance between GSD and GSA should be optimized.
Contrary to expectations, we did not find a significant influence of nest height on aerial nest detection. Nests constructed higher in trees are expected to be more visible from the air, however, the visibility also depends on the height of the tree (i.e., a nest at 15 m will be more visible in a tree of 15 m height than in a tree of 20 m). The inclusion of tree height into models will be important in subsequent analyses.
Another surprising result of our study was the lack of influence of canopy cover and vegetation type, with no significant differences between the probability of nest detection in the leaf-off season and the “greener season” as well as between the more open miombo woodland vegetation and the closed, riverine forest. Even more surprising, the probability of nest detection tended to be higher in closed rather than in open vegetation. This finding contradicts numerous other studies that demonstrated a significant improvement of target detection from drone imagery in more open habitats (e.g., [29
]). A possible explanation for this might be the difficulty of detecting brown nests against a similarly colored background, in this case the less continuous and more earth-toned colors of the Miombo woodland and the grassland mosaic. Light body color has been demonstrated to negatively influence animal detection during aerial surveys in a conservation area of northern Tanzania (e.g., dark Ostrich (Struthio camelus
) better detected than light Grant’s gazelle (Nanger granti
]. The results from Chabot and Bird [61
] further support the importance of contrast in target detection. In their investigation into the use drones to survey flocks of geese they reported a poor detection of low-contrast Canada Geese (Branta canadensis
) but good aerial survey performance for the high-contrast Snow Geese (Chen caerulescens
) resulting in more efficient aerial count compared to ground count (60% higher). We were unable to test the role of contrast in our study due to an insufficient sample of recent (green) nests.
Findings from the analysis of the factors influencing false alarm rates support this hypothesis. Different vegetation types significantly affected the false alarm rate depending on the season. The false alarm rate was higher in miombo woodland at the beginning of the dry season. The canopy cover in miombo woodland is much higher during this period than at the end of the dry season. At the beginning of the dry season, the miombo woodland reflects a mosaic of green leaves and a brown understory, leading to potential misinterpretation of aerial data. At the end of the dry season, however, reflection is mostly from the brown understory, making nest detection more difficult but more accurate. As only Paring A was flown in both seasons, we acknowledge that technological factors may play a role in these seasonal effects, however we strongly believe future studies will benefit from considering and further exploring the effects of seasonal canopy differences on nest detection.
The limitations on the use of drones to survey chimpanzees are threefold. Firstly, only a small proportion of chimpanzee nests are detectable from the air. Most chimpanzee nests are built in the middle of the tree crown [62
] making them undetectable from above the tree canopy [46
]. Chimpanzees also exhibit ground night nesting [63
], which would also be difficult to detect from aerial surveys. Secondly, the high proportion of false alarm rate highlighted in this study is problematic. The false alarm rate is an important parameter that must be taken into consideration when assessing new wildlife survey methods, as it may lead to an overestimation of the population density [29
]. However, the false alarm rate has not been described in previous studies investigating the use of drones to detect great ape nests. In this study, we reported a 42.8% false alarm rate. These aerial observations, for which the location did not align with any of the nests spotted from the ground, can be explained in two ways: (1) These could be nests visible from the air, but not the ground, as would be the case of nests high in the canopy that might be obscured from ground teams by the mid-canopy. Van Schaik et al. [64
] noted that nests can go undetected during ground surveys, resulting in an underestimation of ape densities; (2) alternatively, false positives could represent dead leaves or canopy gaps revealing the brown understory that was mistaken for nests. This uncertainty represents an important problem in the deployment of drones to assess chimpanzee presence/density, especially in a new area where little information is available. We argue here that whilst aerial imagery offers an improvement in spatial coverage and data collection time and frequency, this approach still requires complimentary validation from ground surveys. Finally, the time associated with analyzing thousands of images to identify nests represents an additional key limitation to using drones in this context.
The limitations we discussed above are significant but not prohibitive, and the findings from our study provide guidance for future investigation on the use of drones for ape population surveys. Firstly, it is important to generate high spatial resolution images and lower GSD, providing greater details and significantly increasing the probability of nest detection. For our survey, we decided to use fixed-wing drone models allowing longer flights that can cover larger areas. Because of the mountainous terrain, flying at lower altitude was not possible. Most chimpanzees do not live across mountainous terrain, therefore this problem would not affect large parts of their range. Multirotor drones have smaller flight time capacities but can fly at lower altitudes [65
]. This technology is improving rapidly (e.g., drone design optimization allowing longer flight time [66
]), which could make multirotors a viable option in the future. Meanwhile, camera resolution is improving, which will allow future studies to obtain higher resolution images from fixed wing surveys. Reliable detection also requires a high contrast background. During both our survey seasons, the brown understory made nest identification difficult. We therefore recommend conducting future surveys during seasons with green vegetation on the ground to contrast otherwise brown nests. We acknowledge that this context might reduce the probability of detecting fresh green nests, however, given their low abundance, their non-detectability is less likely to impact chimpanzee density estimations. Multispectral sensors may help address this problem. Widely used for landcover classification and vegetation monitoring [68
] this technology uses green, red, red-edge and near infrared wavebands to capture detail not available to standard RGB cameras. Green vegetation materials are characterized by high reflectance in the near infra-red (NIR) domain (outside of the spectral range of human vision); a multispectral camera can provide useful contrast to discriminate between live and dead vegetation. Furthermore, it would be interesting to assess the potential of oblique aerial images. This perspective may offer better glimpses through foliage and more intuitively interpretable representations of the targets. Another step would be to assess the potential of 3D mapping of the canopy surface for nest detection. 3D models can now be created using point clouds from drone imagery [74
] providing better perspectives for visual interpretation of the data. Another complimentary approach would be to use light detection and ranging (LiDAR) technology. Recently developed at sizes suitable for drone payloads [75
], this remote sensing technique offers new insights beyond simple top of canopy structure that may help nest detectability algorithms. For example, these technologies could be used to better establish the habitat characteristics of trees holding nests. These data could be used in computer vision algorithms [76
] to refine automatic nest detection, possibly reducing the false alarm rate. A recent study on using a drone to detect eagle nests reported 75% nest detection using a semi-automated method [80
]. Similar to the difficulties encountered with chimpanzee nest detection, eagle nests are found in highly heterogeneous environments with many features that resemble nests, at small scale (~1–2 m), and with variable nest size, shape and context. This result is promising for broader nest detection applications, including those of great apes.
Given the shy and elusive nature of great apes, direct surveys are rarely feasible. Researchers thus must rely on indirect signs to estimate population density. However, to convert nest counts into ape density, the nest decay rate and nest production rate are required. These factors are highly dependent on apes species and environmental characteristics, and therefore require extensive study [26
]. Recent studies have now shown the potential of thermal cameras mounted on drones for animal detection [39
]. However, this approach would require extensive spatial coverage and further research is required to assess whether apes could be detected using a thermal camera mounted on a drone.