A fast and simple method to assess land use statistics using very high resolution imagery from mini-drone

peer reviewedLe suivi de l’utilisation des terres par télédétection a récemment connu un essor important. Cela s’explique par une accessibilité accrue et souvent gratuite des images à (très) haute résolution ainsi que par le développement d’applications web destinées au suivi de l’utilisation des terres. L’accès à ces applications reste cependant soumis à l’existence d’une connexion Internet fiable faisant encore défaut dans certaines régions du globe. Dans ce contexte, la présente étude décrit une méthode permettant de produire des statistiques sur l’évolution de l’occupation du sol en réalisant une photo-interprétation par point sur des images en couleurs vraies à très haute résolution produites par mini-drone. La méthode utilise une application (PINT pour Photo-INTerprétation) intégrée dans le logiciel open source QGIS. Les surfaces de différentes occupations du sol sont dérivées des estimations des proportions de points affectées à chaque classe à partir d’une grille systématique. Pour illustrer l’intérêt de l’outil, l’étude considère les statistiques d’occupation du sol au sein de deux terroirs villageois du Complexe d’aires protégées de la Garamba, en République démocratique du Congo. Les résultats obtenus sont comparés avec ceux d’une cartographie de référence basée sur une photo-interprétation exhaustive après segmentation des images. Les écarts entre surfaces estimées par échantillonnage et surfaces de référence varient entre 0,2 % et 6,1 % pour les principales occupations du sol (forêts et savanes, défriches, jachères, implantations humaines et cultures). Des différences plus importantes (17,4 % et 13,4 %) sont enregistrées pour la classe « arbres isolés ». Le temps global de mise en œuvre de la méthode est de l’ordre de 60 ha par heure d’opérateur. L’utilisation du plugin PINT avec des images « drone » constitue une solution pertinente pour estimer des statistiques d’occupation du sol dans des régions web-isolées et pour des sites d’étendues de quelques (dizaines de) km².Land use monitoring by remote sensing techniques has been developing rapidly, thanks to much easier access, often free of charge, to (very) high-resolution images, and to the development of specific Web applications for land use monitoring.However, access to these applications depends on the existence of a reliable internet connection, which is still lacking in some regions of the world. This study describes a land-use monitoring method based on point-by-point photo-interpretation of very high-resolution images acquired by small drones. The method requires the integration of an application (PINT, for Photo-INTerpretation) into QGIS Open source software. The areas occupied by different land uses are derived from the estimated proportions of the points allocated to each land-use class, based on a systematic grid. To illustrate the advantages of the tool, this study investigated the land-use statistics for two villages in the Greater Garamba Complex of protected areas, in the Democratic Republic of Congo. The results obtained were compared with those of a reference map, on the basis of exhaustive photo-interpretation after segmentation of the images. The differences between the areas estimated by sampling and the reference areas vary from 0.2% to 6.1% for the main land uses (forests and savannas, clearings, fallows, human settlements and crops). Larger differences (17.4% and 13.4%) were recorded for the “isolated trees” class. Implementing the method takes about 1 hour per operator for 60 ha. Using the PINT plugin with drone images appears to be a relevant solution to estimate land-use statistics in Web-isolated regions, for areas of a few to a few dozen km²

Using drone technology to map village lands in protected areas of the democratic republic of Congo

peer reviewedLes aires protégées de la République démocratique du Congo (RDC) sont menacées par diverses pressions anthropiques nécessitant un suivi fréquent et précis. Le mini-drone Falcon équipé d’un appareil photo numérique Sony NEX-7 a été utilisé pour cartographier et suivre la dynamique d’un terroir villageois dans le Domaine de chasse de Mondo Missa à l’est du Parc national de la Garamba, au nord-est de la RDC. Un total de 3 143 photos acquises en avril et juillet 2015, avec une résolution au sol de 8 cm/pixel, a été orthorectifié. La cartographie a porté sur une zone de 114 ha. Les ortho-images ont d’abord été segmentées, les segments étant ensuite classés manuellement par photo-interprétation. Des changements notables ont été constatés entre les deux dates. Les zones des forêts et savanes ont perdu 6,5 ha (86,6 à 80,1 ha). Les jachères sont passées de 16,9 à 8,2 ha, les défriches de 4,1 à 10,0 ha. Les cultures saisonnières ont connu une variation allant de 3,2 à 11,8 ha. La taille moyenne des parcelles cultivées est de 0,2 ha (s = 0,14 ha ; n = 50). Enfin, la surface occupée par les arbres isolés a peu évolué (de 1,3 à 1,9 ha), celle des implantations humaines étant constante (1,7 ha). Ces résultats traduisent le fait que l’expansion de l’agriculture itinérante sur brûlis induit une conversion des habitats naturels et une modification de la composition végétale. Les aéronefs sans pilote à bord permettent de réaliser une cartographie précise et une surveillance rapide des changements d’affectation des terres à petite échelle dans les aires protégées des forêts et savanes tropicales. Ils offrent donc une solution efficace pour évaluer la déforestation et la dégradation au sein des espaces occupés par les communautés locales. Cette évaluation représente un enjeu important dans le processus REDD+ qui envisage de quantifier avec précision ces évolutions

Utilisation des drones et caméras-traps pour le suivi des activités humaines et de la faune en RDC: Cas des aires protégées

Objectif général: développer et mettre en œuvre des méthodes pour le suivi des activités humaines et de la faune dans les aires protégées de la RDC en utilisant les outils associés à la technologie des drones et des caméra-traps. Objectifs spécifiques: - Évaluer la capacité des mini-drones à réaliser une cartographie précise et une surveillance rapide de la dynamique d’occupation des sols à l’échelle des terroirs villageois. - Évaluer la capacité des mini-drones à détectabilité et au dénombrement de la faune dans des conditions de savanes arbustives. - Caractériser l’état des communauté animales à l’aide des caméras-traps

Seabird species vary in behavioural response to drone census

This is the final version of the article. Available from the publisher via the DOI in this record.Unmanned aerial vehicles (UAVs) provide an opportunity to rapidly census wildlife in remote areas while removing some of the hazards. However, wildlife may respond negatively to the UAVs, thereby skewing counts. We surveyed four species of Arctic cliff-nesting seabirds (glaucous gull Larus hyperboreus, Iceland gull Larus glaucoides, common murre Uria aalge and thick-billed murre Uria lomvia) using a UAV and compared censusing techniques to ground photography. An average of 8.5% of murres flew off in response to the UAV, but >99% of those birds were non-breeders. We were unable to detect any impact of the UAV on breeding success of murres, except at a site where aerial predators were abundant and several birds lost their eggs to predators following UAV flights. Furthermore, we found little evidence for habituation by murres to the UAV. Most gulls flew off in response to the UAV, but returned to the nest within five minutes. Counts of gull nests and adults were similar between UAV and ground photography, however the UAV detected up to 52.4% more chicks because chicks were camouflaged and invisible to ground observers. UAVs provide a less hazardous and potentially more accurate method for surveying wildlife. We provide some simple recommendations for their use.We thank T. Leonard and the Seabird Ecological Reserves Advisory Committee for permission to work at Witless Bay, the Canadian Wildlife Service for permits to work at Newfoundland and Nunavut and the Government of Nunavut for permits to work in Nunavut. Newfoundland and Labrador Murre Fund, Bird Studies Canada and the Molson Foundation directly funded the work. An NSERC Discovery Grant, the Canada Research Chair in Arctic Ecology and Polar Continental Shelf Project also helped fund the project. We thank T. Burke, G. Sorenson, T. Lazarus and M. Guigueno for their help and J. Nakoolak for keeping us safe from bear

Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning

A camera‐based method for estimating absolute density in animals displaying home range behaviour

1. The measurement of animal density may take advantage of recent technological achievements in wildlife video recording. Fostering the theoretical links between the patterns depicted by cameras and absolute density is required to exploit this potential. 2. We explore the applicability of the Hutchinson–Waser’s postulate (i.e. when animal density is stationary at a given temporal and spatial scale, the absolute density is given by the average number of animals counted per frame), which is a counterintuitive statement for most ecologists and managers who are concerned with counting the same individual more than once. We aimed to reconcile such scepticism for animals displaying home range behaviour. 3. The specific objectives of this paper are to generalize the Hutchinson–Waser’s postulate for animals displaying home range behaviour and to propose a Bayesian implementation to estimate density from counts per frame using video cameras. 4. Accuracy and precision of the method was evaluated by means of computer simulation experiments. Specifically, six animal archetypes displaying well-contrasted movement features were considered. The simulation results demonstrate that density could be accurately estimated after an affordable sampling effort (i.e. number of cameras and deployment time) for a great number of animals across taxa. 5. The proposed method may complement other conventional methods for estimating animal density. The major advantages are that identifying an animal at the individual level and precise knowledge on how animals move are not needed, and that density can be estimated in a single survey. The method can accommodate conventional camera trapping data. The major limitations are related to some implicit assumptions of the underlying model: the home range centres should be homogeneously distributed, the detection probability within the area surveyed by the camera should be known, and animals should move independently to one another. Further improvements for circumventing these limitations are discussed.Ministerio de Educación, Cultura y Deporte, Grant/Award Number: FPU13/01440; Ministerio de Economía y Competitividad Juan de la Cierva Postdoctoral Grant, Grant/Award Number: FJCI-2014-21 and CTM2015-69126-C2-1-R

Réinventer les inventaires fauniques à l'aide des petits drones: nouvelles méthodes de suivi pour les ongulés des écosystèmes semi-ouverts africains

As the Earth has entered the new Anthropocene Era due to the major human- driven environmental changes, human impact on biodiversity has triggered the planet’s sixth mass extinction. Most terrestrial vertebrate populations have shown a sharp decline in abundance and range. The vast (semi-)open ecosystems of Sub- Saharan Africa have among the most incredible mammal richness in the world, including many large ungulates species. Unfortunately, many are threatened by increasing human pressures. Natural ecosystem conservation requires adaptive management, supported by key elements such as regular wildlife abundance surveys. In large areas, aerial surveys with light aircraft generally remain the best alternative for counting large mammals. However, it presents a lot of challenges inherent to plane logistics and heavy costs. In this context, technological progress such as the availability of small Unmanned Aerial Systems (UASs) can be a powerful tool to help preserve and monitor ungulate populations. UASs exhibit high spatial and temporal resolution, and are safer and often less intrusive than manned airplanes. However, the range of small affordable UASs is generally too short for large-scale application of the traditional aerial surveys. The large amount of images collected is also difficult to manage and has impacted developments. In order to enable this emerging technology so that it can become fully operational for large game counts, the aforementioned issues must be addressed. Therefore, this thesis aims to develop and implement new sampling and counting methods with small UASs in order to facilitate regular censuses of large terrestrial African ungulates. The specific objectives are: (i) to design an innovative flight plan and sampling protocol for large ungulates counts (which will be adapted to small UAS constraints, and have relevant statistical performances in regard to the current standard transect sampling method); and (ii) to investigate the potential of UAS imagery to count precisely the often disregarded, partially submerged, hippopotamus populations. First, we identified the opportunities and limits of UASs use in wildlife monitoring based on a review of the available literature (Chapter 2). We describe the range of available models and sensors used by researchers and provided evidence that most studies focused on optical imagery and used small affordable UASs for a wide range of tasks. We focused on detection possibilities and the types of survey plans performed, and the contributions towards anti-poaching surveillance. Our findings indicate that the main drawback preventing UASs from becoming an effective alternative for large-scale censuses is the generally low flight endurance ultimately limiting the area covered. We identified research gaps in terms of innovative sampling methods and the availability of appropriate statistical approaches. To address the issue of managing the large datasets produced by drone flights, and help reviewing thousands of aerial images manually, we developed a user-friendly interface called WiMUAS (chapter 3). An image viewer allows multiple observers to annotate various observations as well as compare counts. The software can generate maps of the projected observations and sampling strips based on telemetry data and payload parameters for any type of flight plan. We tested it on more than 5000 images from flights performed in Garamba National Parc v savannahs. We assessed that flying at 100 m is the best compromise between resolution and surface covered to detect accurately the main medium- to large-sized antelopes. Then, we evaluated the relevance of a new sampling method adapted to small UAS limitations to census large African ungulates (chapter 4). We identified that a multiple rosettes flight plan based on the UAS operational range could be more efficient in terms of logistics. We showed by numerical simulations that four aggregated repetitions of the rosette flight plan can give accurate density estimates with a coefficient of variation under 15 % for antelope populations. However, the precision remains low compared to classic transect sampling with manned aircraft. We further identified the impact of gregariousness on density estimates by modeling population distribution for both buffalo herds and the more randomly dispersed antelopes, and concluded the lower sampling rate of the rosette design is unsuited for highly aggregative species. Second, we focused on optimizing the detection of hippos for total counts. We assessed the environmental and flight parameters influencing counts accuracy based on 252 RGB photos taken over two large well-known hippo schools (chapter 5). Eight observers reviewed the images, and the observer's experience had a significant impact on detection probability. Of environmental parameters, sun reflection on water had the worst effect on detection, with cloud cover showing a slight impact and wind speed no influence. Altitude up to 250 m did not have a significant impact on the counts, however it affected observers' confidence in their observations. We calculated correction factors that account for hippos' regular diving behavior and found it similar to previous studies. As counting individuals in dense pods proved tedious and highly impacted by the observers' personal capabilities, we used hippos as a case study to develop an algorithm for an automatic count (chapter 6). TIR imagery provided very clear and contrasting images of hippo schools at several flight heights, ranging from 38 to 155 meters above ground level. The algorithm was based on pixel value thresholding and generation of isolines and polygons, and required images to be cut to show only the group of hippos, as surrounding objects interfered with the detection. Estimated automatic counts showed very similar results to visual counts. However, hippos sometimes appeared cut in multiple polygons as they are partially submerged, which is not always addressed adequately by the algorithm. Finally, we summarized our conclusions of the main results achieved regarding new wildlife census methods with UASs (chapter 7). Following our findings, we discussed the practical implications for using UASs in the field of wildlife monitoring in general, and shared some relevant experiences and points of awareness regarding operations, especially under the challenging context of remote protected areas in tropical environments. We drew attention to the social implications of drones and underlined the importance of stringent legislation. We addressed the remaining crucial endurance limitation of the technology for large- scale wildlife aerial censuses and discussed a potential alternative with sensors mounted on ultralight aircraft to combine low costs and efficiency. Lastly, we developed perspectives to handle the large amount of data produced by drone surveys and concluded that automatic detection with machine learning will likely be one of the most important developments required for the future

Aerial surveys using an Unmanned Aerial System (UAS): comparison of different methods for estimating the surface area of sampling strips

La protection et la gestion raisonnée des écosystèmes naturels passent par la nécessité de quantifier l'importance des populations animales. Les inventaires de la grande faune, traditionnellement des inventaires aériens par échantillonnage, pourraient être avantageusement remplacés par des inventaires utilisant de petits avions sans pilote (avions ou hélicoptères téléguidés avec appareil photo embarqué). Bien que l'utilisation de drones comme outils d'inventaire des grands mammifères ait déjà été mis en œuvre par le passé, certaines questions persistent, notamment la manière la plus adéquate de déterminer la surface de la bande inventoriée. La surface inventoriée est en effet une information capitale pour le calcul de la densité d'animaux ainsi que de leurs effectif total. La surface de la bande photographiée peut se calculer selon différentes approches en fonction des informations et outils utilisés pour le géoréférencement du bloc d'images. Deux méthodes utilisant les données de navigation du drone (GPS et attitude) sont comparées dans cet article, l'une utilisant les équations de colinéarité afin de projeter individuellement l'emprise (fauchée) de chaque image sur le sol et l'autre utilisant des outils de vision par ordinateur et de photogrammétrie afin de déterminer l'orientation du bloc d'images. Les surfaces estimées selon ces méthodes furent comparées à une surface de référence, calculée au moyen du géoréférencement du bloc d'image avec des points d'appuis. La méthode de projection des fauchées, bien que plus simple à mettre en œuvre, s'avère à la fois la plus rapide et la plus précise en terrain à faible dénivelé et répond correctement aux attentes relatives aux inventaires fauniqueConservation of natural ecosystems requires regular monitoring of biodiversity, including the estimation of wildlife density. Recently, unmanned aerial systems (UAS) have become more available for numerous civilian applications. The use of small drones for wildlife surveys as a surrogate for manned aerial surveys is becoming increasingly attractive and has already been implemented with some success. This raises the question of how to process UAS imagery in order to determine the surface area of sampling strips within an acceptable confidence level. For the purpose of wildlife surveys, the estimation of sampling strip surface area needs to be both accurate and quick, and easy to implement. As GPS and an inertial measurement units are commonly integrated within unmanned aircraft platforms, two methods of direct georeferencing were compared here. On the one hand, we used the image footprint projection (IFP) method, which utilizes collinearity equations on each image individually. On the other hand, the Structure from Motion (SfM) technique was used for block orientation and georeferencing. These two methods were compared on eight sampling strips. An absolute orientation of the strip was determined by indirect georeferencing using ground control points. This absolute orientation was considered as the reference and was used for validating the other two methods. The IFP method was demonstrated to be the most accurate and the easiest to implement. It was also found to be less demanding in terms of image quality and overlap. However, even though a flat landscape is the type most widely encountered in wildlife surveys in Africa, we recommend estimating IFP sensitivity at an accentuation of the relief