remote sensing Article Individual Identification of Cheetah (Acinonyx jubatus) Based on Close-Range Remote Sensing: First Steps of a New Monitoring Technique Guillaume Baralle 1 , Antoine F. J. Marchal 2,*, Philippe Lejeune 1,2 and Adrien Michez 1,3 1 Gembloux Agro-Bio Tech, TERRA Teaching and Research Centre (Forest is Life), University of Liège, 2 Passage des Déportés, 5030 Gembloux, Belgium; [email protected] (G.B.); [email protected] (P.L.); [email protected] (A.M.) 2 Wildlife 3D Tracking (W3DT), rue des Ponts 98, 1480 Tubize, Belgium 3 University Rennes 2 LETG (CNRS UMR 6554), Place du Recteur Henri Le Moal, CEDEX, 35043 Rennes, France * Correspondence: [email protected] Abstract: Wildlife monitoring is an important part of the conservation strategies for certain endan- gered species. Non-invasive methods are of significant interest because they preserve the studied animal. The study of signs, especially tracks, seems to be a valuable compromise between reliability, simplicity and feasibility. The main objective of this study is to develop and test an algorithm that can identify individual cheetahs based on 3D track modelling using proximal sensing with an off-the- shelf camera. More specifically, we propose a methodological approach allowing the identification of individuals, their sex and their foot position (i.e., left/right and hind/front). In addition, we aim to compare different track recording media: 2D photo and 3D photo models. We sampled 669 tracks from eight semi-captive cheetahs, corresponding to about 20 tracks per foot. We manually placed on Citation: Baralle, G.; Marchal, A.F.J.; each track 25 landmarks: fixed points representing the geometry of an object. We also automatically Lejeune, P.; Michez, A. Individual placed 130 semi-landmarks, landmark allowed to move on the surface, per track on only the 3D mod- Identification of Cheetah els. Geometric morphometrics allowed the measurement of shape variation between tracks, while (Acinonyx jubatus) Based on linear discriminant analysis (LDA) with jack-knife prediction enabled track discrimination using Close-Range Remote Sensing: First the information from their size and shape. We tested a total of 82 combinations of features in terms Steps of a New Monitoring Technique. of recording medium, landmarks configuration, extracted information and template used. For foot Remote Sens. 2021, 13, 1090. https:// doi.org/10.3390/rs13061090 position identification, the best combination correctly identified 98.2% of the tracks. Regarding those results, we also ran an identification algorithm on a dataset containing only one kind of foot position Academic Editor: Qinghua Guo to highlight the differences and mimic an algorithm identifying the foot position first and then an individual factor (here, sex and identity). This led to accuracy of 94.8 and 93.7%, respectively, for sex Received: 29 January 2021 and individual identification. These tools appear to be effective in discriminating foot position, sex Accepted: 11 March 2021 and individual identity from tracks. Future works should focus on automating track segmentation Published: 12 March 2021 and landmark positioning for ease of use in conservation strategies. Publisher’s Note: MDPI stays neutral Keywords: Acinonyx jubatus; cheetah; non-invasive monitoring; close-range remote sensing; with regard to jurisdictional claims in photogrammetry; track; wildlife monitoring published maps and institutional affil- iations. 1. Introduction Since the 1950s, the intensive use of ecosystems by humans has led to major changes Copyright: © 2021 by the authors. in the biosphere [1]. Most of the world’s biological diversity cannot adapt to such sudden Licensee MDPI, Basel, Switzerland. transformations. In addition, mammal populations tend to decline drastically, partic- This article is an open access article ularly carnivores [2–4]. Our study case, the cheetah (Acinonyx jubatus), is classified as distributed under the terms and vulnerable with a total estimated population of 6674 mature individuals according to the conditions of the Creative Commons International Union for Conservation of Nature’s Red List assessment [5]. This number Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ keeps declining [6]. Nowadays, the majority of wild cheetahs live in southern and eastern 4.0/). Africa, mainly outside protected areas [5]. Their current range is estimated at 10% of the Remote Sens. 2021, 13, 1090. https://doi.org/10.3390/rs13061090 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 1090 2 of 14 historical range [7]. The need for data to understand and monitor cheetah populations is therefore crucial. Wildlife monitoring enables a better understanding of animal populations. However, it often requires direct contact with the studied animal. From the most intimate contact, such as fitting GPS collars, to the most distant, such as camera trapping, monitoring can affect the animals, potentially modifying their fitness [8,9]. The possibility of indirectly monitoring wild animals without disturbing them is of significant interest. Accomplishing this is to record indirect signs of presence. These signs include feces, territorial marks and tracks [10]. Tracks are particularly interesting, because they have specific and differentiable characteristics that are related to the species, sex, age and even individual. This is high- lighted by the ability of some traditional trackers to identify the species, age and sex of animals and/or individual wild animals through their tracks with a high degree of cer- tainty [11,12]. For several years, study programs have been trying to use presence signs to monitor certain animal species. For tracks, successful attempts have been made, first using direct measurement [13] and, more recently using digitized measurements, in particular the FIT (Footprint Identification Technique) program [13–15]. Based on photographs of tracks, researchers were able to create individual identification algorithms for a wide range of mammal species [13–15], with some emphasis on cheetah [16]. The use of a three-dimensional (3D) recording technique is a recent advance in the field, which has been used on lions (Panthera leo)[17–19]. This new method allows the extraction of more information from the track (including depth), to decrease the manipulator bias and allow greater automation of both the recording and feature extraction processes. In order to extract information on the geometry of tracks, the 3D method uses geometric morphometrics analysis. Morphometrics is used to study the geometry of living organisms, their variations and covariations (Cucchi et al., 2015). These techniques are based on the placement of landmarks, which are discrete points corresponding to anatomical loci that are homologous among the studied entities [20]. As landmarks sometimes do not allow the characterization of all geometric information, partic- ularly variations in curves and surfaces, morphometricians developed semilandmarks [21]. These are discrete points that do not correspond to anatomical loci and are therefore able to slide along curves and surfaces [22], hence their designation as curve or surface-sliders. The positions of the landmarks and semilandmarks are then subjected to a generalized procrustes analysis (GPA). This superimposition projects all the landmark coordinates into a non-linear space. It uses a least-squares superposition method to minimize the distance between all homologous landmarks in the sample and thus, to isolate the shape of the studied objects [23]. The new coordinates are known as Procrustes coordinates [21]. This process eliminates the effects of size, orientation and position using an iterative algorithm in order to keep only the true differences between tracks shapes [21]. GPA creates a con- sensus object based on the mean position of the superimposed landmarks made out of the entire dataset. Shape variations between the objects are studied in relation to this consensus. In addition to shape, size is also analyzed by calculating the centroid size of each track. It is mathematically traduced by the square root of the sum of the squared distances to the centroid of the object and corresponds to all the information available on the size [20]. The information added from the shape and the size refers to the form [24]. The method of Marchal et al. [17,18] uses morphometrics to extract geometric information from tracks that can be used for individual identification. However, this method remains under development. Even if it successfully distinguished foot position, age and sex of lions, individual identification remains inaccurate [18]. The objective of this study is to compare different combinations of features, such as recording medium, landmark configuration, type of extracted information and template, to implement an identification algorithm for cheetah tracks. The aim is to find the most suitable set of features to identify the foot position, sex and individual with the highest accuracy. In the end, this could become a reliable, user-friendly and cost-effective tool to Remote Sens. 2021, 13, 1090 3 of 14 identify and sex animals in a non-invasive way that would improve wildlife monitoring, and therefore conservation strategies. 2. Materials and Methods 2.1. Ethics Statement The cheetahs used for this experiment are legally held in captivity and registered by N/a’ankusê Wildlife Sanctuary (Khomas Region, Namibia). The animals were not restrained or harmed in any ways;