Drone-Assisted Aerial Surveys of Large Animals in Siberian Winter Forests

There are two main reasons for monitoring the population of forest animals. First of all, regular surveys reveal the real state of biodiversity. Second, they guarantee a prompt response to any negative environmental factor that affects the animal population and make it possible to eliminate the threat before any permanent damage has been done. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras. Drone planes proved effective in covering large areas, while thermal infrared cameras provided accurate statistics in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, or Kuzbass (Russia). The authors developed an effective methodology for processing the data obtained from drone-mounted thermal infrared cameras. The research provided reliable results concerning the changes in the elk population on the territory in question. The use of drone planes proved an effective means of ungulate animal survey on large areas of Siberian winter forests.


Background
Overpopulation increases the chance of human contacts with nature, which inevitably reduces or changes the natural habitat of wild fauna [1]. Mining and industrial use of pristine lands, as well as urban and rural development, produce a human-induced impact on the environment, thus causing the transformation and degradation of natural biocenoses and reducing biodiversity [2]. In the conditions of limited natural resources, the effectiveness of environmental management decisions depends on an accurate and timely analysis of environmental data. Therefore, improved methods of information collecting and processing can lower the environmental impact of managerial actions in the sphere of rational exploitation of natural resources and natural balance. A consistent approach to environmental management requires new systems of effective environmental monitoring. Surveys of large mammal population and distribution remain an urgent task. Large animals occupy the top of food chains, which makes them a sensitive integral indicator of the environmental state [3].
Animal surveys contribute to the rational conservation of biodiversity. A proper analysis of statistics on migration, fertility, and mortality can reveal cases of poaching and assess its real scale. A competent approach provides basic data for informed managerial decisions on the matters of animal population as an integral part of national wealth [4][5][6][7][8]. Animal survey is an important control factor that helps to balance socio-economic and natural interests. Unfortunately, the existing methods are based on direct counting or indirect evidence, e.g. footprints, feces, etc. Their results often prove unreliable [9]. Based on old databases, these methods are expensive and time-consuming. Moreover, most traditional methods require direct participation of humans, which cannot but affects the life of animals. Aerial survey method is an exception as it presupposes direct observation of animals from an aircraft.
Digital technologies are the most promising way to improve traditional survey methods of land and air accounting. They can reduce the shortcomings of manual methods and simplify their implementation. This approach is especially promising for monitoring the environment in the vast and sparsely populated forests of Siberia. For example, the use of a GPS system makes it easier to determine the length of the daily tracks left in snow by a wild mammal. Winter track count is a typical animal survey method in Russia [10]. GPS collars make it possible to monitor the life of endangered animals, e.g. Amur tigers, cougars, brown bears, etc. [11, 12, 13, 14, 15, 16.17]. Trail cameras are another positive example of digital technology in this sphere [18,19].
Nowadays, aerial accounting often requires the use of drone planes and copters equipped with various sensors and cameras [20][21][22][23][24][25][26][27][28][29][30][31]. Unmanned aerial vehicles (UAV) equipped with radio receivers can track the routes of animals with GPS collars, i.e. as a biotelemetry method [32], for sampling [33], for collecting data from a particular habitat [34], and in anti-poaching operations [20]. The use of UAV eliminates any possible threat to the operator and the researcher team. In addition, UAVs produce less noise pollution, thus increasing the reliability of the survey. They can cover large remote areas in a short time. However, the use of UAVs for animal surveys requires reliable algorithms that would allow naturalists to obtain the necessary information with minimal impact on animal behavior [22,35,36].
In dense forests, camera-based visual counting is almost impossible. Modern thermal imaging systems can solve this problem. It provides high noise immunity even in complex environments [24.25.37.38].
However, UAVs require modern software to process the bulk of high-resolution real time video they record during each ight [39]. The research objective was to develop and test effective methods for monitoring the population of large warm-blooded animals in the winter conditions of Siberian forests. The areas in question are large and sparsely populated, which makes it di cult to control the current state of the environment using traditional methods. Siberian winter lasts 4-6 months, depending on the latitude. The snow cover is total while the temperature drops below -55 °С. To survive in the harsh conditions of low temperatures and limited food resources, forest species seek salvation in long hibernation. European elks (Alces alces), grey wolves (Canis lupus), and Siberian roe deer (Capreolus pygargus) are almost the only large warm-blooded forests of Siberia that do not hibernate. The present research featured the European elk as a test object for animal survey. With its weight reaching ~600 kg, it is the largest deer species on the planet ( Figure 1S). The experimental survey covered the uninhabited territory of the Salair Nature Reserve, located in the northern part of the Salair Ridge (Kemerovo region) near the Tanai ski resort (54°42'46"N, 85°3'42"E).

Methods
In 2019 and 2020, rangers measured the elk population in the park using the traditional daily track count method from January 1 to February 28-29. Parallel to the track count, they used drone-mounted thermal infrared cameras: on February 26, 2019 and February 29, 2020. The drone planes were Supercam S250 (Unmanned Systems LLC, Izhevsk, Russia). Table 1 demonstrates the characteristics of this model. Its take-off weight is 7.5-9.5 kg, which allows for 1.5 kg of payload, e.g. a camera and a thermal imager. The drone plane can operate at wind velocity of up to 15 m/s and air temperature from -50°С to +45 °С.
In addition, it can withstand moderate rain or snowfall. These advantageous characteristics make it possible to monitor the territory in almost any weather conditions. The drone plane carries a receiver of the global satellite navigation system (GNSS) for precise coordinate control and positioning of photography points. It has the capabilities of bungee launch and parachute recovery. Tables 2 and 3 show the main characteristics of the camera and the thermal imager, which is important for understanding the capabilities and limitations of this study. Sony RX1R II is a full-frame camera with no crop factor, which makes it possible to cover a wide area without additional maneuvering. The camera is compact: in fact, it is one of the smallest full-size cameras and weighs less than 500 g. The lens does not have to be changed. In addition, it is one of the cheapest cameras in its class. The high resolution allows for visual identi cation of various animal species in the photos and video.
We chose a compact, light, and low power ATOM500 (weight 32 g) thermal imaging camera. Its allowable range of working conditions is quite impressive: this camera can be used in extreme temperature and humidity conditions. As assumed, the sensitivity level allows it to identify thermal signatures of animals against the underlying surface even at temperatures below-10°C, i.e. for much of the year. In winter, the European elk (Alces alces) is covered by thick fur and, respectively, the fur surface temperature is drastically lower the temperature of the body (35.8 -37°С). On this reason, the temperature difference between fur and snow surfaces is unclear, and the detection of thermal anomaly of the European elk on the snow background is not a trivial task.
The research covered the territory of the Salair Nature Reserve (Kemerovo region, Russia). The reserve is an environmentally sensitive territory of regional signi cance. Figure 1 speci es its geographical location.
The Salair national park is mostly black taiga of rs and aspens with patches of birch and aspen undergrowth. The elk is one of the main protected species in the park. Therefore, its population survey is an important tool of its protection and reproduction [40,41]. The Salair taiga borders on agricultural steppe areas in the east, north, and west. In the south, it joins the taiga massif of Gornaya Shoriya and Altai.
The drone planes delivered a large volume of photo and thermal imaging. We processed the obtained data using the Thermal Infrared Object Finder software (TIOF) developed at Kemerovo State University. The application was designed in Python and can be installed on any PC. It is capable of processing a large amount of infrared image data to identify speci c animals. The analysis xes the so-called thermal anomalies, which are warmer than the ambient temperature and indicate the presence of an animal [42].
To determine the effectiveness of the developed algorithm, we compared the UAV survey results with those obtained by the traditional daily track count in 2019-2020.

Results And Discussion
Thermal imaging is able to detect animals by their thermal signature according to the contrast between the body temperature and the environment, which might reach 30-40 °С. Therefore, winter surveys are more e cient. Unfortunately, the method cannot tell the difference between species of similar mass and shape, e.g. a wolf and a wild boar. Visual analysis of conventional photo and video made it possible to identify animals with the same thermal signature. The simultaneous use of photo and thermal imaging improves the accuracy and reliability of aerial surveys. Figure 2S provides an example of such an analysis. The left image shows three types of objects: the white of the snow background, the numerous translucent round crowns of naked trees and shrubs, and the dark round crowns of coniferous trees. Under this resolution, the patchy background of winter taiga makes it hard to detect heat signatures in the photo image: the silhouettes of trees and shrubs obscure the contours of the animals. However, the thermal image on the right clearly shows the signatures of two elks as their body temperature differs signi cantly from the fairly uniform temperature background of the winter taiga.
In 2019, it took two ights to survey the territory. Figure 3S (a) shows the ight routes. The dots indicate the centers where RGB images were taken. In 2020, we launched one ight, its route shown in Figure 3S (b). To facilitate the comparison, we placed a fan-shape of glades into the bottom left corner of both Figure 3S (a) and Figure 3S (b). The glades are the system of ski slopes of the Tanay ski resort. Table 4 demonstrates the basic technical information on the ights.
We developed the following algorithm to process the images obtained from the drone planes: 1) We sequenced the infrared video with an interval of ~ 0.6 s.
2) After that, the infrared images were processed using software according to the degree of color intensity and pixel clusters. As a result, we obtained numerous infrared images with thermal extremes, which indicated an object with a higher temperature than that of the snow, e.g. an animal, a human, or a car.
3) We uploaded the RGB photos and telemetry into the Agisoft Metashape Professional software for alignment.
4) The infrared images underwent a visual inspection for the initial screening of "junk" data.
5) The coordinates of the infrared images with extremes were compared in-camera with the aligned RGB photographs, and the presence of large game was determined visually. 6) Finally, we compared the research results at different stages. The analysis employed software developed by the Kemerovo State University which allows jpeg and png image processing. The processing time depended on the number of images: it took the program 25-50 s to process materials of one standard UAV ight that lasted 100-150 min. The software allows for a thermal sensitivity that exceeds the capabilities of a human observer. Taking into consideration the limited ight time, this made it possible to detect even weak thermal anomalies. Figure 4S gives a comparative analysis of the processed results for infrared images taken from a height of 200 m and 400 m. Figure 4S shows a thermal signature that is clearly visible to the human eye. The shot was made from a height of 200 m. When the same area was shot from 400 m, the same thermal signature was almost indistinguishable to the human eye, while the software application was able to detect it.
The survey of 2019 detected 34 objects (numbers 1-34). Figure 3 shows their spatial distribution. Out of 34 objects, numbers 1-25 are elks. Table 1S shows the coordinates of the animals detected by the drone planes in 2019. The detected objects (34) also included untargeted objects not related to wild animals, e.g. a human person and a group of animals contained in the rehabilitation center of the Tanay ski resort.
The Tanai resort caused too many "false positives". As a result, the contour of the scanning section had to be changed in 2020 to exclude the Tanai resort premises. Figure 4 demonstrates the ratio of the scanned areas in 2019 and 2020. The survey of 2020 revealed 63 objects, of which 55 were elks. Figure 5 shows their spatial distribution. Table 2S speci es the coordinates of the animals detected by the UAV survey in 2020. We failed to calculate the coordinates of numbers 20, 21, and 22 on the RGB images as these objects were too close to the frame. The remaining objects (8) were people. Figure 5S demonstrates a test snapshot of untargeted search objects -some random shermen that happened to be in the area.
The map of elk distribution ( Figure 5) shows two clusters, the largest one being Group 2, which included 15 elks. Figure 6 demonstrates the maximum number of animals recorded in one RGB image -11 elks.
The maximum number of animals xed in one infrared image was 5 elks (Figure 7). This difference resulted from the different technical characteristics of the thermal infrared camera and the visible spectrum equipment. The bandwidth of the infrared image was 1/3 in the center of the width of the visible spectrum. In Figure 6, the outline of the infrared image is blue. Thus, the shooting area of the infrared image was approximately nine times smaller than the shooting area of the RGB image. All the routes were planned speci cally to achieve a transverse overlap of 10%-15% for infrared imaging, in which case the overlap of RGB images was 70%. Figure 7 compares RGB and infrared images of the same surface areas in Group 2. It becomes clear that the distance between the elks was about 50 m A comparative analysis of the data obtained in 2019 and 2020 revealed that 25 and 55 elks were identi ed in 2019 and 2020, respectively, where the two studied areas overlapped (15.9 km 2 ). Therefore, the number of animals within the same habitat almost doubled. Figure 8 shows their spatial distribution. The survey of 2020 revealed two clusters of elks. Such uneven distribution could be explained by some behavioral characteristics of the animals. We detected two wolves during the visual analysis of the images obtained in 2020 and the corresponding infrared images with thermal anomalies over an area of 7 km 2 ( Figure 6S). It was the rst time wolves had been detected in the Salair Nature Reserve. No traditional track counts had ever revealed wolves on this territory, and naturalists had always considered the park a wolf-free zone. According to daily track counts, the wolf population had almost disappeared in the Kemerovo region by 2015-2017 as a result of man-induced factors: rangers reported only accidental visits from the neighboring regions [41,42]. Thus, the developed method of digital survey provided a more complete identi cation of large animals in the given habitat. Accuracy is especially important for monitoring the population of such large predators as wolves. Mistakes can have an extremely negative effect on the managing populations of herbivores.
During the aerial surveys of 2019-2020, the population, distribution, and habitat of the European elk proved to correspond to the data obtained by the traditional method of winter track counts submitted by the Department of Animal Object Protection of the Kemerovo Region. We registered a signi cant increase in the elk population in the forests of the Salair Ridge. In addition, we detected wolves in the surveyed area. The research justi ed the combined use of various digital technologies for game animal survey, i.e. photo and thermal imaging. The equipment performance was good even in the harsh winter conditions, which means great prospects for research on larger areas.

Conclusions
The current state of Siberian forestry requires new methods of poaching control and competent resource management. Therefore, traditional methods for animal survey have to be perfected. Digital technologies proved to be the most promising method that can improve the shortcomings of traditional accounting methods. These technologies eliminate the problem of inaccessibility of research sites and reduce error probability caused by the human factor. According to the new methodology, survey is done automatically using drone-mounted thermal infrared cameras, and the data processing is performed by specialized software.
This research featured the population of the European elk (Alces alces) on the territory of the Salair Nature Reserve using drone planes with two types of payload. The obtained data on the elk population con rmed the data obtained by the traditional winter track counts. This indicates that: 1) Aerial surveys are a promising practical method for determining the population of large ungulate animals, e.g. elks, roe deer, wild boars, deer, as well as wolves.
2) Drone-mounted thermal infrared cameras provide accurate data on the animal presence in the winter period. The combined use of video and thermal imaging cameras allows for reliable identi cation of the thermal signature of the detected object.
3) The method can be used to check the data obtained by traditional survey methods, i.e. as a part of a complex survey. 4) Unmanned aerial vehicles make it possible to monitor vast forest areas in a short period of time. This advantage allows scientists to observe animal behavior in winter.

Declarations
Ethics approval and consent to participate Not applicable.

Consent for publication
Not applicable Availability of data and material All data generated or analyzed in this study are included in this published article and its supplementary information le.

Competing interests
There are no competing interests Funding There are no funding for this study.
Authors' contributions AP designed this study and wrote the paper. AV studied scienti c sources, processed the information and compiled the manuscript. VA provided the conceptualization and wrote the paper. AK carried out the measurements and analysis of the results. All authors read and approved the nal manuscript. Alexander Kuznetsov (author information is not available at the moment of paper submission).